From 9230d067de15b8faa92ef7fdcb88327ec3d69884 Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Thu, 28 Aug 2025 10:59:19 +0200
Subject: [PATCH 01/12] plot rmse instead of mse because more interpretable

---
 notebooks/project_starter.ipynb | 975 +++++++++++++-------------------
 scripts/rf.py                   |  45 +-
 2 files changed, 408 insertions(+), 612 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index d7cf0fc..08d026a 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -23,16 +23,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
+    "from pathlib import Path\n",
     "import sys\n",
+    "import os\n",
     "\n",
-    "os.chdir(r'c:\\\\Users\\\\jessi\\\\Documents\\\\OFFProject\\\\scripts')\n",
-    "sys.path.append(os.path.abspath(os.path.join(os.getcwd(), '..'))) \n",
-    "sys.path.append(\"../../\")\n",
+    "# Go up one level (from notebooks/ to OFFProject/)\n",
+    "project_root = Path().resolve().parent  \n",
+    "sys.path.append(str(project_root))\n",
     "\n",
     "import pandas as pd\n",
     "import numpy as np\n",
@@ -40,36 +41,31 @@
     "import seaborn as sns\n",
     "\n",
     "from scripts import Data_filter_Jess as dfj\n",
-    "from scripts import encoding_func \n",
-    "from scripts import Imputing\n",
-    "from scripts import Scaling\n",
-    "from encoding_func import *\n",
-    "from Scaling import *\n",
-    "from Imputing import *\n"
+    "from scripts import encoding_func, Imputing, Scaling, rf\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_23760\\1388454853.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n"
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_3936\\3952692224.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n"
      ]
     }
    ],
    "source": [
     "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n",
-    "df = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)"
+    "df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1152,7 +1148,7 @@
          "type": "float"
         }
        ],
-       "ref": "c91cbbb4-7de4-4f19-b68f-80b1ffb03bdb",
+       "ref": "13247bfe-82aa-4e60-b200-7db725d979ec",
        "rows": [
         [
          "0",
@@ -2459,7 +2455,7 @@
        "[5 rows x 214 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2484,7 +2480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -2518,7 +2514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -2530,7 +2526,7 @@
        "       'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2541,27 +2537,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                pnns_groups_1      PNNS_pro\n",
-      "6                     unknown            NA\n",
-      "9                     unknown            NA\n",
-      "11      Fruits and vegetables   Plant_based\n",
-      "12                    unknown            NA\n",
-      "14            Composite foods     Processed\n",
-      "...                       ...           ...\n",
-      "9946          Composite foods     Processed\n",
-      "9955  Milk and dairy products  Animal_based\n",
-      "9961            Sugary snacks        Snacks\n",
-      "9970            Sugary snacks        Snacks\n",
-      "9990           Fat and sauces     Processed\n",
+      "              pnns_groups_1      PNNS_pro\n",
+      "6                   unknown            NA\n",
+      "9                   unknown            NA\n",
+      "11    Fruits and vegetables   Plant_based\n",
+      "12                  unknown            NA\n",
+      "14          Composite foods     Processed\n",
+      "...                     ...           ...\n",
+      "4929         Fish Meat Eggs  Animal_based\n",
+      "4930                unknown            NA\n",
+      "4937        Composite foods     Processed\n",
+      "4980          Sugary snacks        Snacks\n",
+      "4993   Cereals and potatoes   Plant_based\n",
       "\n",
-      "[1208 rows x 2 columns]\n"
+      "[439 rows x 2 columns]\n"
      ]
     }
    ],
@@ -2586,7 +2582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2598,16 +2594,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "### maybe we should reduce pnns_groups by : plant_based; animal; processed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2649,16 +2636,6 @@
          "rawType": "float64",
          "type": "float"
         },
-        {
-         "name": "trans-fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "cholesterol_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
         {
          "name": "carbohydrates_100g",
          "rawType": "float64",
@@ -2689,26 +2666,6 @@
          "rawType": "float64",
          "type": "float"
         },
-        {
-         "name": "vitamin-a_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "vitamin-c_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "calcium_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "iron_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
         {
          "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
          "rawType": "float64",
@@ -2745,7 +2702,7 @@
          "type": "float"
         }
        ],
-       "ref": "ca6b955b-dab1-4e55-bb09-51fec154a3ab",
+       "ref": "1728316d-0f43-4982-8224-6ac423611514",
        "rows": [
         [
          "6",
@@ -2755,18 +2712,12 @@
          "2401.0",
          "12.0",
          "10.5",
-         "0.0",
-         "0.0",
          "13.0",
          "9.0",
          "36.0",
          "23.0",
          "0.3",
          "0.12",
-         null,
-         null,
-         null,
-         null,
          "0.0",
          "0.0",
          "0.0",
@@ -2783,8 +2734,6 @@
          "1520.0",
          "11.0",
          "2.0",
-         "0.0",
-         "0.01",
          "25.0",
          "0.98",
          "9.0",
@@ -2792,10 +2741,6 @@
          "0.95",
          "0.38",
          null,
-         null,
-         null,
-         null,
-         null,
          "0.0",
          "0.0",
          "0.0",
@@ -2811,18 +2756,12 @@
          "4.0",
          "1.0",
          "1.0",
-         null,
-         null,
          "1.0",
          "1.0",
          "1.0",
          "1.0",
          "1.0",
          "0.4",
-         null,
-         null,
-         null,
-         null,
          "0.0",
          "0.0",
          "0.0",
@@ -2839,18 +2778,12 @@
          "1510.0",
          "2.0",
          "0.5",
-         null,
-         null,
          "6.7",
          "1.7",
          "10.714286",
          "76.0",
          "1.5",
          "0.6",
-         "0.00023214286",
-         "0.07142857",
-         "0.17857143",
-         "0.008928571",
          "0.0",
          "0.0",
          "0.0",
@@ -2867,8 +2800,6 @@
          "293.0",
          "0.5",
          "0.06",
-         null,
-         null,
          "2.0",
          "0.24",
          "88.0",
@@ -2876,10 +2807,6 @@
          "0.275",
          "0.11",
          null,
-         "0.09",
-         null,
-         null,
-         null,
          "0.0",
          "0.0",
          "1.0",
@@ -2889,7 +2816,7 @@
         ]
        ],
        "shape": {
-        "columns": 25,
+        "columns": 19,
         "rows": 5
        }
       },
@@ -2918,14 +2845,12 @@
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
        "      <th>saturated-fat_100g</th>\n",
-       "      <th>trans-fat_100g</th>\n",
-       "      <th>cholesterol_100g</th>\n",
        "      <th>carbohydrates_100g</th>\n",
        "      <th>sugars_100g</th>\n",
-       "      <th>...</th>\n",
-       "      <th>vitamin-c_100g</th>\n",
-       "      <th>calcium_100g</th>\n",
-       "      <th>iron_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
        "      <th>PNNS_pro_Animal_based</th>\n",
        "      <th>PNNS_pro_Drinks</th>\n",
@@ -2944,14 +2869,12 @@
        "      <td>2401.0</td>\n",
        "      <td>12.0</td>\n",
        "      <td>10.50</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.00</td>\n",
        "      <td>13.0</td>\n",
        "      <td>9.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.12</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2968,14 +2891,12 @@
        "      <td>1520.0</td>\n",
        "      <td>11.0</td>\n",
        "      <td>2.00</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.01</td>\n",
        "      <td>25.0</td>\n",
        "      <td>0.98</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.38</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2992,14 +2913,12 @@
        "      <td>4.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.40</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3016,14 +2935,12 @@
        "      <td>1510.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.50</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>6.7</td>\n",
        "      <td>1.70</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.071429</td>\n",
-       "      <td>0.178571</td>\n",
-       "      <td>0.008929</td>\n",
+       "      <td>10.714286</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>0.60</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3040,14 +2957,12 @@
        "      <td>293.0</td>\n",
        "      <td>0.5</td>\n",
        "      <td>0.06</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.24</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.090000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.275</td>\n",
+       "      <td>0.11</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3058,7 +2973,6 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -3069,19 +2983,19 @@
        "12   8.0          1.0               6.0       1510.0       2.0   \n",
        "14   9.0          NaN             -11.0        293.0       0.5   \n",
        "\n",
-       "    saturated-fat_100g  trans-fat_100g  cholesterol_100g  carbohydrates_100g  \\\n",
-       "6                10.50             0.0              0.00                13.0   \n",
-       "9                 2.00             0.0              0.01                25.0   \n",
-       "11                1.00             NaN               NaN                 1.0   \n",
-       "12                0.50             NaN               NaN                 6.7   \n",
-       "14                0.06             NaN               NaN                 2.0   \n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6                10.50                13.0         9.00   36.000000   \n",
+       "9                 2.00                25.0         0.98    9.000000   \n",
+       "11                1.00                 1.0         1.00    1.000000   \n",
+       "12                0.50                 6.7         1.70   10.714286   \n",
+       "14                0.06                 2.0         0.24   88.000000   \n",
        "\n",
-       "    sugars_100g  ...  vitamin-c_100g  calcium_100g  iron_100g  \\\n",
-       "6          9.00  ...             NaN           NaN        NaN   \n",
-       "9          0.98  ...             NaN           NaN        NaN   \n",
-       "11         1.00  ...             NaN           NaN        NaN   \n",
-       "12         1.70  ...        0.071429      0.178571   0.008929   \n",
-       "14         0.24  ...        0.090000           NaN        NaN   \n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6            23.0      0.300         0.12   \n",
+       "9            22.0      0.950         0.38   \n",
+       "11            1.0      1.000         0.40   \n",
+       "12           76.0      1.500         0.60   \n",
+       "14           18.0      0.275         0.11   \n",
        "\n",
        "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
        "6                                                 0.0       \n",
@@ -3102,12 +3016,10 @@
        "9                  1.0              0.0  \n",
        "11                 0.0              0.0  \n",
        "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  \n",
-       "\n",
-       "[5 rows x 25 columns]"
+       "14                 0.0              0.0  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3125,14 +3037,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(2298, 25)\n"
+      ": shape of df with only numeric features=(811, 19)\n"
      ]
     },
     {
@@ -3174,16 +3086,6 @@
          "rawType": "float64",
          "type": "float"
         },
-        {
-         "name": "trans-fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "cholesterol_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
         {
          "name": "carbohydrates_100g",
          "rawType": "float64",
@@ -3214,26 +3116,6 @@
          "rawType": "float64",
          "type": "float"
         },
-        {
-         "name": "vitamin-a_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "vitamin-c_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "calcium_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "iron_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
         {
          "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
          "rawType": "float64",
@@ -3270,7 +3152,7 @@
          "type": "float"
         }
        ],
-       "ref": "f13568b6-b0da-4dca-bee9-18c1152773e3",
+       "ref": "a657fe56-a896-456d-a3ee-ee7e5e0e21e7",
        "rows": [
         [
          "6",
@@ -3280,18 +3162,12 @@
          "2401.0",
          "12.0",
          "10.5",
-         "0.0",
-         "0.0",
          "13.0",
          "9.0",
          "36.0",
          "23.0",
          "0.3",
          "0.12",
-         "0.030258428792000004",
-         "0.058185714",
-         "0.176374286",
-         "0.010164708552",
          "0.0",
          "0.0",
          "0.0",
@@ -3308,18 +3184,12 @@
          "1520.0",
          "11.0",
          "2.0",
-         "0.0",
-         "0.01",
          "25.0",
          "0.98",
          "9.0",
          "22.0",
          "0.95",
          "0.38",
-         "0.030258428792000004",
-         "0.058185713999999986",
-         "0.171798016",
-         "0.008664708552",
          "20.400223270165018",
          "0.0",
          "0.0",
@@ -3336,18 +3206,12 @@
          "4.0",
          "1.0",
          "1.0",
-         "0.129619454",
-         "0.011300083200000002",
          "1.0",
          "1.0",
          "1.0",
          "1.0",
          "1.0",
          "0.4",
-         "0.030258428792000004",
-         "0.058185714",
-         "0.196138016",
-         "0.015798708552000003",
          "0.0",
          "0.0",
          "0.0",
@@ -3364,18 +3228,12 @@
          "1510.0",
          "2.0",
          "0.5",
-         "0.11299435",
-         "0.0167446326",
          "6.7",
          "1.7",
          "10.714286",
          "76.0",
          "1.5",
          "0.6",
-         "0.00023214286",
-         "0.07142857",
-         "0.17857143",
-         "0.008928571",
          "0.0",
          "0.0",
          "0.0",
@@ -3392,18 +3250,12 @@
          "293.0",
          "0.5",
          "0.06",
-         "0.129619454",
-         "0.0097602602",
          "2.0",
          "0.24",
          "88.0",
          "18.0",
          "0.275",
          "0.11",
-         "0.030258428792000004",
-         "0.09",
-         "0.171798016",
-         "0.008664708552",
          "20.00029130415483",
          "0.0",
          "0.0",
@@ -3414,7 +3266,7 @@
         ]
        ],
        "shape": {
-        "columns": 25,
+        "columns": 19,
         "rows": 5
        }
       },
@@ -3443,14 +3295,12 @@
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
        "      <th>saturated-fat_100g</th>\n",
-       "      <th>trans-fat_100g</th>\n",
-       "      <th>cholesterol_100g</th>\n",
        "      <th>carbohydrates_100g</th>\n",
        "      <th>sugars_100g</th>\n",
-       "      <th>...</th>\n",
-       "      <th>vitamin-c_100g</th>\n",
-       "      <th>calcium_100g</th>\n",
-       "      <th>iron_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
        "      <th>PNNS_pro_Animal_based</th>\n",
        "      <th>PNNS_pro_Drinks</th>\n",
@@ -3469,14 +3319,12 @@
        "      <td>2401.0</td>\n",
        "      <td>12.0</td>\n",
        "      <td>10.50</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.000000</td>\n",
        "      <td>13.0</td>\n",
        "      <td>9.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.058186</td>\n",
-       "      <td>0.176374</td>\n",
-       "      <td>0.010165</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.12</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3493,14 +3341,12 @@
        "      <td>1520.0</td>\n",
        "      <td>11.0</td>\n",
        "      <td>2.00</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.010000</td>\n",
        "      <td>25.0</td>\n",
        "      <td>0.98</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.058186</td>\n",
-       "      <td>0.171798</td>\n",
-       "      <td>0.008665</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.38</td>\n",
        "      <td>20.400223</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3517,14 +3363,12 @@
        "      <td>4.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>0.129619</td>\n",
-       "      <td>0.011300</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.058186</td>\n",
-       "      <td>0.196138</td>\n",
-       "      <td>0.015799</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.40</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3541,14 +3385,12 @@
        "      <td>1510.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.50</td>\n",
-       "      <td>0.112994</td>\n",
-       "      <td>0.016745</td>\n",
        "      <td>6.7</td>\n",
        "      <td>1.70</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.071429</td>\n",
-       "      <td>0.178571</td>\n",
-       "      <td>0.008929</td>\n",
+       "      <td>10.714286</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>0.60</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3565,14 +3407,12 @@
        "      <td>293.0</td>\n",
        "      <td>0.5</td>\n",
        "      <td>0.06</td>\n",
-       "      <td>0.129619</td>\n",
-       "      <td>0.009760</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.24</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.090000</td>\n",
-       "      <td>0.171798</td>\n",
-       "      <td>0.008665</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.275</td>\n",
+       "      <td>0.11</td>\n",
        "      <td>20.000291</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3583,7 +3423,6 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -3594,19 +3433,19 @@
        "12   8.0          1.0               6.0       1510.0       2.0   \n",
        "14   9.0          0.6             -11.0        293.0       0.5   \n",
        "\n",
-       "    saturated-fat_100g  trans-fat_100g  cholesterol_100g  carbohydrates_100g  \\\n",
-       "6                10.50        0.000000          0.000000                13.0   \n",
-       "9                 2.00        0.000000          0.010000                25.0   \n",
-       "11                1.00        0.129619          0.011300                 1.0   \n",
-       "12                0.50        0.112994          0.016745                 6.7   \n",
-       "14                0.06        0.129619          0.009760                 2.0   \n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6                10.50                13.0         9.00   36.000000   \n",
+       "9                 2.00                25.0         0.98    9.000000   \n",
+       "11                1.00                 1.0         1.00    1.000000   \n",
+       "12                0.50                 6.7         1.70   10.714286   \n",
+       "14                0.06                 2.0         0.24   88.000000   \n",
        "\n",
-       "    sugars_100g  ...  vitamin-c_100g  calcium_100g  iron_100g  \\\n",
-       "6          9.00  ...        0.058186      0.176374   0.010165   \n",
-       "9          0.98  ...        0.058186      0.171798   0.008665   \n",
-       "11         1.00  ...        0.058186      0.196138   0.015799   \n",
-       "12         1.70  ...        0.071429      0.178571   0.008929   \n",
-       "14         0.24  ...        0.090000      0.171798   0.008665   \n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6            23.0      0.300         0.12   \n",
+       "9            22.0      0.950         0.38   \n",
+       "11            1.0      1.000         0.40   \n",
+       "12           76.0      1.500         0.60   \n",
+       "14           18.0      0.275         0.11   \n",
        "\n",
        "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
        "6                                            0.000000       \n",
@@ -3627,12 +3466,10 @@
        "9                  1.0              0.0  \n",
        "11                 0.0              0.0  \n",
        "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  \n",
-       "\n",
-       "[5 rows x 25 columns]"
+       "14                 0.0              0.0  "
       ]
      },
-     "execution_count": 9,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3652,14 +3489,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(2298, 24)\n"
+      ": shape of df with only numeric features=(811, 18)\n"
      ]
     }
    ],
@@ -3670,7 +3507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -3712,16 +3549,6 @@
          "rawType": "float64",
          "type": "float"
         },
-        {
-         "name": "trans-fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "cholesterol_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
         {
          "name": "carbohydrates_100g",
          "rawType": "float64",
@@ -3752,26 +3579,6 @@
          "rawType": "float64",
          "type": "float"
         },
-        {
-         "name": "vitamin-a_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "vitamin-c_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "calcium_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "iron_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
         {
          "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
          "rawType": "float64",
@@ -3808,151 +3615,121 @@
          "type": "float"
         }
        ],
-       "ref": "10f8f3ab-25d3-467f-be48-16f8d3033a97",
+       "ref": "aa46019b-3848-42bd-b830-57af01874ea4",
        "rows": [
         [
          "6",
-         "-1.1471821469838115e-05",
-         "-0.5",
+         "-1.4239992335937918e-06",
+         "-0.37499999999999994",
          "15.0",
-         "1.6109967656571595",
-         "-0.04447695683979516",
-         "1.0905011219147345",
-         "0.0",
-         "-1.0",
-         "-0.29071770334928226",
-         "0.10163996939651848",
-         "3.7230002693239967",
-         "0.3394462423588637",
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-         "-0.09067944815078699",
-         "0.0",
-         "-0.10051365369360706",
-         "0.5786159715663349",
-         "0.0",
-         "-0.3869010792154957",
+         "1.4541042112776588",
+         "0.021972656250000073",
+         "1.157948717948718",
+         "-0.3322965266977708",
+         "-0.09684323167469229",
+         "2.0840311151787483",
+         "0.09493032908390153",
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+         "-0.2424609789362025",
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          "0.0",
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          "-0.5",
+         "-0.33333333333333337",
          "0.0",
-         "-1.0",
-         "4.0"
+         "5.0"
         ],
         [
          "9",
-         "-1.146929017648643e-05",
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+         "-1.404357864854567e-06",
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          "4.0",
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-         "0.021101009133734375",
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-         "1.4999999999999998",
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+         "0.0"
         ],
         [
          "11",
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          "4.0",
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+         "-0.15215814998852972",
          "0.0",
          "0.0",
          "-0.5",
-         "5.0",
-         "-1.0",
-         "-1.0"
+         "1.3333333333333335",
+         "0.0",
+         "0.0"
         ],
         [
          "12",
-         "-1.1466758883134744e-05",
-         "0.12499999999999997",
+         "-1.3847164961153424e-06",
+         "0.25",
          "6.0",
-         "0.30108791531902374",
-         "-0.6297000731528896",
-         "-0.7793567688855647",
-         "0.11299435",
-         "0.7813438936170212",
-         "-0.4112918660287081",
-         "-0.24637681137017242",
-         "0.31797549151629384",
-         "2.2452355267889246",
-         "0.06477103439341927",
-         "0.06477103439341926",
-         "-0.9864357615942851",
-         "0.09913097560507718",
-         "0.596629145260994",
-         "-0.156182321039179",
-         "-0.3869010792154957",
+         "0.18215560314061371",
+         "-0.5883789062499999",
+         "-0.5514529914529915",
+         "-0.4955935717988595",
+         "-0.4223292313179953",
+         "0.14361289321797227",
+         "1.666231841091017",
+         "0.1212304894681012",
+         "0.12123048946810122",
+         "-0.15215814998852972",
          "0.0",
          "0.0",
          "2.0",
+         "-0.33333333333333337",
          "0.0",
-         "-1.0",
-         "-1.0"
+         "0.0"
         ],
         [
          "14",
-         "-1.1465493236458901e-05",
-         "-0.12500000000000003",
-         "-11.0",
-         "-1.4880917377241987",
-         "-0.7174835405998538",
-         "-0.861630516080778",
-         "0.129619454",
-         "0.0383255531914894",
-         "-0.5012440191387559",
-         "-0.3159801675235106",
-         "10.72542418529491",
-         "0.1596548004314995",
-         "-0.09391799987045794",
-         "-0.09391799987045796",
+         "-1.37489581174573e-06",
          "0.0",
-         "0.3791073083460255",
-         "0.5410976539760193",
-         "-0.18952056037754653",
-         "0.013102416413996421",
+         "-11.0",
+         "-1.555174875089222",
+         "-0.6799316406249999",
+         "-0.6266666666666667",
+         "-0.6174183514774494",
+         "-0.4874264312466559",
+         "6.074495752323674",
+         "-0.053305662614882954",
+         "-0.2500378845279588",
+         "-0.25003788452795883",
+         "0.2872800031729008",
          "0.0",
          "0.0",
          "2.0",
+         "-0.33333333333333337",
          "0.0",
-         "-1.0",
-         "-1.0"
+         "0.0"
         ]
        ],
        "shape": {
-        "columns": 25,
+        "columns": 19,
         "rows": 5
        }
       },
@@ -3981,14 +3758,12 @@
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
        "      <th>saturated-fat_100g</th>\n",
-       "      <th>trans-fat_100g</th>\n",
-       "      <th>cholesterol_100g</th>\n",
        "      <th>carbohydrates_100g</th>\n",
        "      <th>sugars_100g</th>\n",
-       "      <th>...</th>\n",
-       "      <th>vitamin-c_100g</th>\n",
-       "      <th>calcium_100g</th>\n",
-       "      <th>iron_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
        "      <th>PNNS_pro_Animal_based</th>\n",
        "      <th>PNNS_pro_Drinks</th>\n",
@@ -4001,176 +3776,163 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>-0.500</td>\n",
+       "      <td>-0.000001</td>\n",
+       "      <td>-0.375</td>\n",
        "      <td>15.0</td>\n",
-       "      <td>1.610997</td>\n",
-       "      <td>-0.044477</td>\n",
-       "      <td>1.090501</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-1.000000</td>\n",
-       "      <td>-0.290718</td>\n",
-       "      <td>0.101640</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.100514</td>\n",
-       "      <td>0.578616</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-0.386901</td>\n",
+       "      <td>1.454104</td>\n",
+       "      <td>0.021973</td>\n",
+       "      <td>1.157949</td>\n",
+       "      <td>-0.332297</td>\n",
+       "      <td>-0.096843</td>\n",
+       "      <td>2.084031</td>\n",
+       "      <td>0.094930</td>\n",
+       "      <td>-0.242461</td>\n",
+       "      <td>-0.242461</td>\n",
+       "      <td>-0.152158</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>-0.5</td>\n",
+       "      <td>-0.333333</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>0.625</td>\n",
+       "      <td>-0.000001</td>\n",
+       "      <td>0.750</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>0.315789</td>\n",
-       "      <td>-0.102999</td>\n",
-       "      <td>-0.498878</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.063830</td>\n",
-       "      <td>-0.061053</td>\n",
-       "      <td>-0.280702</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.100514</td>\n",
-       "      <td>0.541098</td>\n",
-       "      <td>-0.189521</td>\n",
-       "      <td>0.021101</td>\n",
+       "      <td>0.196431</td>\n",
+       "      <td>-0.039062</td>\n",
+       "      <td>-0.295043</td>\n",
+       "      <td>-0.021255</td>\n",
+       "      <td>-0.454432</td>\n",
+       "      <td>0.012059</td>\n",
+       "      <td>0.065283</td>\n",
+       "      <td>-0.045461</td>\n",
+       "      <td>-0.045461</td>\n",
+       "      <td>0.296067</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>-0.5</td>\n",
+       "      <td>-0.333333</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>-0.500</td>\n",
+       "      <td>-0.000001</td>\n",
+       "      <td>-0.375</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>-1.912967</td>\n",
-       "      <td>-0.688222</td>\n",
-       "      <td>-0.685864</td>\n",
-       "      <td>0.129619</td>\n",
-       "      <td>0.202137</td>\n",
-       "      <td>-0.520383</td>\n",
-       "      <td>-0.279748</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.100514</td>\n",
-       "      <td>0.740648</td>\n",
-       "      <td>0.711839</td>\n",
-       "      <td>-0.386901</td>\n",
+       "      <td>-1.967737</td>\n",
+       "      <td>-0.649414</td>\n",
+       "      <td>-0.465983</td>\n",
+       "      <td>-0.643339</td>\n",
+       "      <td>-0.453540</td>\n",
+       "      <td>-0.601859</td>\n",
+       "      <td>-0.557308</td>\n",
+       "      <td>-0.030308</td>\n",
+       "      <td>-0.030308</td>\n",
+       "      <td>-0.152158</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>-0.5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
+       "      <td>1.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>0.125</td>\n",
+       "      <td>-0.000001</td>\n",
+       "      <td>0.250</td>\n",
        "      <td>6.0</td>\n",
-       "      <td>0.301088</td>\n",
-       "      <td>-0.629700</td>\n",
-       "      <td>-0.779357</td>\n",
-       "      <td>0.112994</td>\n",
-       "      <td>0.781344</td>\n",
-       "      <td>-0.411292</td>\n",
-       "      <td>-0.246377</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.099131</td>\n",
-       "      <td>0.596629</td>\n",
-       "      <td>-0.156182</td>\n",
-       "      <td>-0.386901</td>\n",
+       "      <td>0.182156</td>\n",
+       "      <td>-0.588379</td>\n",
+       "      <td>-0.551453</td>\n",
+       "      <td>-0.495594</td>\n",
+       "      <td>-0.422329</td>\n",
+       "      <td>0.143613</td>\n",
+       "      <td>1.666232</td>\n",
+       "      <td>0.121230</td>\n",
+       "      <td>0.121230</td>\n",
+       "      <td>-0.152158</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
+       "      <td>-0.333333</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>-0.000011</td>\n",
-       "      <td>-0.125</td>\n",
+       "      <td>-0.000001</td>\n",
+       "      <td>0.000</td>\n",
        "      <td>-11.0</td>\n",
-       "      <td>-1.488092</td>\n",
-       "      <td>-0.717484</td>\n",
-       "      <td>-0.861631</td>\n",
-       "      <td>0.129619</td>\n",
-       "      <td>0.038326</td>\n",
-       "      <td>-0.501244</td>\n",
-       "      <td>-0.315980</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.379107</td>\n",
-       "      <td>0.541098</td>\n",
-       "      <td>-0.189521</td>\n",
-       "      <td>0.013102</td>\n",
+       "      <td>-1.555175</td>\n",
+       "      <td>-0.679932</td>\n",
+       "      <td>-0.626667</td>\n",
+       "      <td>-0.617418</td>\n",
+       "      <td>-0.487426</td>\n",
+       "      <td>6.074496</td>\n",
+       "      <td>-0.053306</td>\n",
+       "      <td>-0.250038</td>\n",
+       "      <td>-0.250038</td>\n",
+       "      <td>0.287280</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
+       "      <td>-0.333333</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "        code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6  -0.000011       -0.500              15.0     1.610997 -0.044477   \n",
-       "9  -0.000011        0.625               4.0     0.315789 -0.102999   \n",
-       "11 -0.000011       -0.500               4.0    -1.912967 -0.688222   \n",
-       "12 -0.000011        0.125               6.0     0.301088 -0.629700   \n",
-       "14 -0.000011       -0.125             -11.0    -1.488092 -0.717484   \n",
+       "6  -0.000001       -0.375              15.0     1.454104  0.021973   \n",
+       "9  -0.000001        0.750               4.0     0.196431 -0.039062   \n",
+       "11 -0.000001       -0.375               4.0    -1.967737 -0.649414   \n",
+       "12 -0.000001        0.250               6.0     0.182156 -0.588379   \n",
+       "14 -0.000001        0.000             -11.0    -1.555175 -0.679932   \n",
        "\n",
-       "    saturated-fat_100g  trans-fat_100g  cholesterol_100g  carbohydrates_100g  \\\n",
-       "6             1.090501        0.000000         -1.000000           -0.290718   \n",
-       "9            -0.498878        0.000000          0.063830           -0.061053   \n",
-       "11           -0.685864        0.129619          0.202137           -0.520383   \n",
-       "12           -0.779357        0.112994          0.781344           -0.411292   \n",
-       "14           -0.861631        0.129619          0.038326           -0.501244   \n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6             1.157949           -0.332297    -0.096843    2.084031   \n",
+       "9            -0.295043           -0.021255    -0.454432    0.012059   \n",
+       "11           -0.465983           -0.643339    -0.453540   -0.601859   \n",
+       "12           -0.551453           -0.495594    -0.422329    0.143613   \n",
+       "14           -0.626667           -0.617418    -0.487426    6.074496   \n",
        "\n",
-       "    sugars_100g  ...  vitamin-c_100g  calcium_100g  iron_100g  \\\n",
-       "6      0.101640  ...       -0.100514      0.578616   0.000000   \n",
-       "9     -0.280702  ...       -0.100514      0.541098  -0.189521   \n",
-       "11    -0.279748  ...       -0.100514      0.740648   0.711839   \n",
-       "12    -0.246377  ...        0.099131      0.596629  -0.156182   \n",
-       "14    -0.315980  ...        0.379107      0.541098  -0.189521   \n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6        0.094930  -0.242461    -0.242461   \n",
+       "9        0.065283  -0.045461    -0.045461   \n",
+       "11      -0.557308  -0.030308    -0.030308   \n",
+       "12       1.666232   0.121230     0.121230   \n",
+       "14      -0.053306  -0.250038    -0.250038   \n",
        "\n",
        "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                           -0.386901       \n",
-       "9                                            0.021101       \n",
-       "11                                          -0.386901       \n",
-       "12                                          -0.386901       \n",
-       "14                                           0.013102       \n",
+       "6                                           -0.152158       \n",
+       "9                                            0.296067       \n",
+       "11                                          -0.152158       \n",
+       "12                                          -0.152158       \n",
+       "14                                           0.287280       \n",
        "\n",
        "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0         -0.5                   0.0   \n",
-       "9                     0.0              0.0         -0.5                   0.0   \n",
-       "11                    0.0              0.0         -0.5                   5.0   \n",
-       "12                    0.0              0.0          2.0                   0.0   \n",
-       "14                    0.0              0.0          2.0                   0.0   \n",
+       "6                     0.0              0.0         -0.5             -0.333333   \n",
+       "9                     0.0              0.0         -0.5             -0.333333   \n",
+       "11                    0.0              0.0         -0.5              1.333333   \n",
+       "12                    0.0              0.0          2.0             -0.333333   \n",
+       "14                    0.0              0.0          2.0             -0.333333   \n",
        "\n",
        "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                 -1.0              4.0  \n",
-       "9                  1.5             -1.0  \n",
-       "11                -1.0             -1.0  \n",
-       "12                -1.0             -1.0  \n",
-       "14                -1.0             -1.0  \n",
-       "\n",
-       "[5 rows x 25 columns]"
+       "6                  0.0              5.0  \n",
+       "9                  1.0              0.0  \n",
+       "11                 0.0              0.0  \n",
+       "12                 0.0              0.0  \n",
+       "14                 0.0              0.0  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4211,7 +3973,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-16.0\n",
+      "50.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(min(y))\n",
+    "print(max(y))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -4232,7 +4013,7 @@
       "\n",
       "Below are more details about the failures:\n",
       "--------------------------------------------------------------------------------\n",
-      "268 fits failed with the following error:\n",
+      "498 fits failed with the following error:\n",
       "Traceback (most recent call last):\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
       "    estimator.fit(X_train, y_train, **fit_params)\n",
@@ -4242,10 +4023,10 @@
       "    validate_parameter_constraints(\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
       "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "272 fits failed with the following error:\n",
+      "42 fits failed with the following error:\n",
       "Traceback (most recent call last):\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
       "    estimator.fit(X_train, y_train, **fit_params)\n",
@@ -4255,7 +4036,7 @@
       "    validate_parameter_constraints(\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
       "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
       "\n",
       "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
       "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [         nan          nan          nan          nan          nan\n",
@@ -4263,66 +4044,66 @@
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
-      "          nan          nan -15.43366587 -11.6164328  -11.27435849\n",
-      " -14.5820768  -11.23554016 -11.36335203 -15.76857583 -12.44221896\n",
-      " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n",
-      " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n",
-      " -17.47154562 -13.5015248  -13.25917603 -17.47154562 -13.5015248\n",
-      " -13.25917603 -14.9627059  -13.17550421 -13.18977974 -15.43366587\n",
-      " -11.6164328  -11.27435849 -14.5820768  -11.23554016 -11.36335203\n",
-      " -15.76857583 -12.44221896 -12.12038863 -14.66678277 -11.81629708\n",
-      " -11.61178998 -15.03447951 -12.10550901 -11.79386245 -15.61337517\n",
-      " -12.82035289 -12.67222122 -17.47154562 -13.5015248  -13.25917603\n",
-      " -17.47154562 -13.5015248  -13.25917603 -14.9627059  -13.17550421\n",
-      " -13.18977974          nan          nan          nan          nan\n",
+      "          nan          nan -24.47451567 -17.51118198 -17.58537547\n",
+      " -28.06035404 -18.22279658 -17.89023802 -27.04168073 -19.63694956\n",
+      " -19.58299176 -25.40079344 -19.1005516  -18.73631141 -25.74814605\n",
+      " -19.73786442 -19.128602   -29.23809438 -20.54209621 -20.13463464\n",
+      " -26.0040655  -20.90029875 -20.57899564 -26.0040655  -20.90029875\n",
+      " -20.57899564 -29.50884242 -22.06421854 -21.63159919 -24.47451567\n",
+      " -17.51118198 -17.58537547 -28.06035404 -18.22279658 -17.89023802\n",
+      " -27.04168073 -19.63694956 -19.58299176 -25.40079344 -19.1005516\n",
+      " -18.73631141 -25.74814605 -19.73786442 -19.128602   -29.23809438\n",
+      " -20.54209621 -20.13463464 -26.0040655  -20.90029875 -20.57899564\n",
+      " -26.0040655  -20.90029875 -20.57899564 -29.50884242 -22.06421854\n",
+      " -21.63159919          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan -15.91594522 -12.16781458\n",
-      " -12.06318706 -16.250399   -11.91238305 -12.01762806 -16.13582335\n",
-      " -12.50536945 -12.63605136 -15.5718352  -12.82225453 -12.11801577\n",
-      " -14.72627438 -12.28481043 -12.25241906 -16.73890135 -12.97095\n",
-      " -13.08337804 -16.24138596 -13.48690631 -13.3459846  -16.24138596\n",
-      " -13.48690631 -13.3459846  -16.35859837 -13.62628501 -13.44813366\n",
-      " -15.91594522 -12.16781458 -12.06318706 -16.250399   -11.91238305\n",
-      " -12.01762806 -16.13582335 -12.50536945 -12.63605136 -15.5718352\n",
-      " -12.82225453 -12.11801577 -14.72627438 -12.28481043 -12.25241906\n",
-      " -16.73890135 -12.97095    -13.08337804 -16.24138596 -13.48690631\n",
-      " -13.3459846  -16.24138596 -13.48690631 -13.3459846  -16.35859837\n",
-      " -13.62628501 -13.44813366          nan          nan          nan\n",
+      "          nan          nan          nan -26.18493585 -18.49932398\n",
+      " -18.03499343 -27.47932759 -19.17392547 -18.56475395 -29.03894481\n",
+      " -20.93822886 -20.42710061 -25.2441473  -19.71794472 -18.88071978\n",
+      " -25.11245421 -19.17820697 -19.14722588 -28.50097379 -20.36063732\n",
+      " -20.47120177 -28.83965768 -21.28475519 -21.07701    -28.83965768\n",
+      " -21.28475519 -21.07701    -28.6957801  -22.88387847 -22.20248943\n",
+      " -26.18493585 -18.49932398 -18.03499343 -27.47932759 -19.17392547\n",
+      " -18.56475395 -29.03894481 -20.93822886 -20.42710061 -25.2441473\n",
+      " -19.71794472 -18.88071978 -25.11245421 -19.17820697 -19.14722588\n",
+      " -28.50097379 -20.36063732 -20.47120177 -28.83965768 -21.28475519\n",
+      " -21.07701    -28.83965768 -21.28475519 -21.07701    -28.6957801\n",
+      " -22.88387847 -22.20248943          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan -15.91293058\n",
-      " -11.5947586  -11.45373592 -14.44348788 -11.32279156 -11.47162727\n",
-      " -15.49815865 -12.46546805 -12.09982189 -14.27964486 -11.78431829\n",
-      " -11.5571342  -14.91128126 -12.11213898 -11.79477435 -15.61337517\n",
-      " -12.82248465 -12.66730308 -17.47154562 -13.5015248  -13.25917603\n",
-      " -17.47154562 -13.5015248  -13.25917603 -14.9627059  -13.17550421\n",
-      " -13.18977974 -15.91293058 -11.5947586  -11.45373592 -14.44348788\n",
-      " -11.32279156 -11.47162727 -15.49815865 -12.46546805 -12.09982189\n",
-      " -14.27964486 -11.78431829 -11.5571342  -14.91128126 -12.11213898\n",
-      " -11.79477435 -15.61337517 -12.82248465 -12.66730308 -17.47154562\n",
-      " -13.5015248  -13.25917603 -17.47154562 -13.5015248  -13.25917603\n",
-      " -14.9627059  -13.17550421 -13.18977974          nan          nan\n",
+      "          nan          nan          nan          nan -24.17555381\n",
+      " -17.53245455 -17.56714795 -28.24716711 -18.22477793 -17.91956499\n",
+      " -27.04168073 -19.6452736  -19.59832376 -25.40079344 -19.1005516\n",
+      " -18.73631141 -25.74814605 -19.73786442 -19.128602   -29.23809438\n",
+      " -20.54209621 -20.13463464 -26.0040655  -20.90029875 -20.57899564\n",
+      " -26.0040655  -20.90029875 -20.57899564 -29.50884242 -22.06421854\n",
+      " -21.63159919 -24.17555381 -17.53245455 -17.56714795 -28.24716711\n",
+      " -18.22477793 -17.91956499 -27.04168073 -19.6452736  -19.59832376\n",
+      " -25.40079344 -19.1005516  -18.73631141 -25.74814605 -19.73786442\n",
+      " -19.128602   -29.23809438 -20.54209621 -20.13463464 -26.0040655\n",
+      " -20.90029875 -20.57899564 -26.0040655  -20.90029875 -20.57899564\n",
+      " -29.50884242 -22.06421854 -21.63159919          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
       "          nan          nan          nan          nan          nan\n",
-      " -15.43366587 -11.6164328  -11.27435849 -14.5820768  -11.23554016\n",
-      " -11.36335203 -15.76857583 -12.44221896 -12.12038863 -14.66678277\n",
-      " -11.81629708 -11.61178998 -15.03447951 -12.10550901 -11.79386245\n",
-      " -15.61337517 -12.82035289 -12.67222122 -17.47154562 -13.5015248\n",
-      " -13.25917603 -17.47154562 -13.5015248  -13.25917603 -14.9627059\n",
-      " -13.17550421 -13.18977974 -15.43366587 -11.6164328  -11.27435849\n",
-      " -14.5820768  -11.23554016 -11.36335203 -15.76857583 -12.44221896\n",
-      " -12.12038863 -14.66678277 -11.81629708 -11.61178998 -15.03447951\n",
-      " -12.10550901 -11.79386245 -15.61337517 -12.82035289 -12.67222122\n",
-      " -17.47154562 -13.5015248  -13.25917603 -17.47154562 -13.5015248\n",
-      " -13.25917603 -14.9627059  -13.17550421 -13.18977974]\n",
+      " -24.47451567 -17.51118198 -17.58537547 -28.06035404 -18.22279658\n",
+      " -17.89023802 -27.04168073 -19.63694956 -19.58299176 -25.40079344\n",
+      " -19.1005516  -18.73631141 -25.74814605 -19.73786442 -19.128602\n",
+      " -29.23809438 -20.54209621 -20.13463464 -26.0040655  -20.90029875\n",
+      " -20.57899564 -26.0040655  -20.90029875 -20.57899564 -29.50884242\n",
+      " -22.06421854 -21.63159919 -24.47451567 -17.51118198 -17.58537547\n",
+      " -28.06035404 -18.22279658 -17.89023802 -27.04168073 -19.63694956\n",
+      " -19.58299176 -25.40079344 -19.1005516  -18.73631141 -25.74814605\n",
+      " -19.73786442 -19.128602   -29.23809438 -20.54209621 -20.13463464\n",
+      " -26.0040655  -20.90029875 -20.57899564 -26.0040655  -20.90029875\n",
+      " -20.57899564 -29.50884242 -22.06421854 -21.63159919]\n",
       "  warnings.warn(\n"
      ]
     },
@@ -4330,9 +4111,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Meilleurs paramètres trouvés :  {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 50}\n",
-      "MSE : 15.536399122952776\n",
-      "R² : 0.8255571826701679\n"
+      "Meilleurs paramètres trouvés :  {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
+      "MSE : 18.487348024539877\n",
+      "R² : 0.7469523387400461\n"
      ]
     }
    ],
@@ -4349,12 +4130,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -4364,7 +4145,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -4377,7 +4158,7 @@
     "\n",
     "from scripts.rf import plot_learning_curve, plot_mse\n",
     "\n",
-    "plot_learning_curve(best_rf, X, y)\n",
+    "plot_learning_curve_rmse(best_rf, X, y)\n",
     "plot_mse(best_rf, X_train, X_test, y_train, y_test)\n"
    ]
   },
diff --git a/scripts/rf.py b/scripts/rf.py
index ba53ac3..cdd38bc 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -48,7 +48,11 @@ def random_forest_GS(X, y):
 
 
 # Courbe d'apprentissage / Learning curve (Lr):
-def plot_learning_curve(model, X, y, cv=5):
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import learning_curve
+
+def plot_learning_curve_rmse(model, X, y, cv=5):
     train_sizes, train_scores, test_scores = learning_curve(
         model,
         X, y,
@@ -60,27 +64,38 @@ def plot_learning_curve(model, X, y, cv=5):
         random_state=42
     )
 
-    # Moyenne et écart-type
-    train_scores_mean = -np.mean(train_scores, axis=1)
-    test_scores_mean = -np.mean(test_scores, axis=1)
+    # Convert negative MSE to positive, then take sqrt to get RMSE
+    train_scores_mean = np.sqrt(-np.mean(train_scores, axis=1))
+    test_scores_mean  = np.sqrt(-np.mean(test_scores, axis=1))
+
+    # Standard deviation in RMSE scale
+    train_scores_std  = np.sqrt(np.std(-train_scores, axis=1))
+    test_scores_std   = np.sqrt(np.std(-test_scores, axis=1))
+
+    plt.figure(figsize=(8, 5))
+    plt.plot(train_sizes, train_scores_mean, "o-", label="RMSE entraînement")
+    plt.plot(train_sizes, test_scores_mean, "o-", label="RMSE validation")
+
+    # Shaded area = mean ± std
+    plt.fill_between(train_sizes,
+                     train_scores_mean - train_scores_std,
+                     train_scores_mean + train_scores_std,
+                     alpha=0.2)
+    plt.fill_between(train_sizes,
+                     test_scores_mean - test_scores_std,
+                     test_scores_mean + test_scores_std,
+                     alpha=0.2)
 
-    plt.figure(figsize=(8,5))
-    plt.plot(train_sizes, train_scores_mean, "o-", label="Erreur entraînement")
-    plt.plot(train_sizes, test_scores_mean, "o-", label="Erreur validation")
-    plt.fill_between(
-        train_sizes,
-        test_scores_mean - np.std(test_scores, axis=1),
-        test_scores_mean + np.std(test_scores, axis=1),
-        alpha=0.2
-    )
     plt.xlabel("Taille de l'échantillon d'entraînement")
-    plt.ylabel("MSE")
-    plt.title("Courbe d'apprentissage - Random Forest")
+    plt.ylabel("RMSE")
+    plt.title(f"Courbe d'apprentissage - {model.__class__.__name__}")
     plt.legend()
     plt.grid(True)
     plt.show()
 
 
+
+
 # Loss en fonction du nombre d'arbres / Loss function // number of threes
 def plot_mse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]):
     errors = []

From d3a89b7ef958888921e64a43278a6362f70ee4fd Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Thu, 28 Aug 2025 11:02:28 +0200
Subject: [PATCH 02/12] restrained the n estimators of rf in grid search to get
 closer to optimum parameters (refinement)

---
 scripts/rf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/rf.py b/scripts/rf.py
index cdd38bc..0d022c6 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -13,7 +13,7 @@ def random_forest_GS(X, y):
 
     # Grille des hyperparamètres à tester
     param_grid = {
-        "n_estimators": [5, 50, 100],              
+        "n_estimators": [5, 10, 60],              
         "max_depth": [None, 10, 20, 50],         
         "min_samples_split": [2, 5, 10],         
         "min_samples_leaf": [1, 2, 4],           

From eae1d2ef0d64a5f21c78c4a7fac6bcd1a012439e Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Thu, 28 Aug 2025 15:18:31 +0200
Subject: [PATCH 03/12] new plot

---
 scripts/rf.py | 69 +++++++++++++++++++++++++++++++--------------------
 1 file changed, 42 insertions(+), 27 deletions(-)

diff --git a/scripts/rf.py b/scripts/rf.py
index 0d022c6..b81fab2 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -1,6 +1,6 @@
 from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve
 from sklearn.ensemble import RandomForestRegressor
-from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.metrics import root_mean_squared_error, mean_squared_error, r2_score
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -26,7 +26,7 @@ def random_forest_GS(X, y):
         param_grid=param_grid,
         cv=5,                 
         n_jobs=-1,            
-        scoring="neg_mean_squared_error",  
+        scoring="root_mean_squared_error",  
         verbose=2
     )
 
@@ -40,7 +40,7 @@ def random_forest_GS(X, y):
     print("Meilleurs paramètres trouvés : ", grid_search.best_params_)
     
     # Évaluation
-    print("MSE :", mean_squared_error(y_test, y_pred))
+    print("MSE :", root_mean_squared_error(y_test, y_pred))
     print("R² :", r2_score(y_test, y_pred))
 
     return grid_search.best_estimator_, X_train, X_test, y_train, y_test, y_pred
@@ -57,40 +57,55 @@ def plot_learning_curve_rmse(model, X, y, cv=5):
         model,
         X, y,
         cv=cv,
-        scoring="neg_mean_squared_error",
+        scoring="neg_root_mean_squared_error",
         n_jobs=-1,
         train_sizes=np.linspace(0.1, 1.0, 10),
         shuffle=True,
         random_state=42
     )
 
-    # Convert negative MSE to positive, then take sqrt to get RMSE
-    train_scores_mean = np.sqrt(-np.mean(train_scores, axis=1))
-    test_scores_mean  = np.sqrt(-np.mean(test_scores, axis=1))
-
-    # Standard deviation in RMSE scale
-    train_scores_std  = np.sqrt(np.std(-train_scores, axis=1))
-    test_scores_std   = np.sqrt(np.std(-test_scores, axis=1))
-
-    plt.figure(figsize=(8, 5))
-    plt.plot(train_sizes, train_scores_mean, "o-", label="RMSE entraînement")
-    plt.plot(train_sizes, test_scores_mean, "o-", label="RMSE validation")
-
-    # Shaded area = mean ± std
-    plt.fill_between(train_sizes,
-                     train_scores_mean - train_scores_std,
-                     train_scores_mean + train_scores_std,
-                     alpha=0.2)
-    plt.fill_between(train_sizes,
-                     test_scores_mean - test_scores_std,
-                     test_scores_mean + test_scores_std,
-                     alpha=0.2)
-
+    # Convertir scores négatifs en positifs
+    train_scores = -train_scores
+    test_scores  = -test_scores
+
+    train_scores_mean = np.mean(train_scores, axis=1)
+    test_scores_mean  = np.mean(test_scores, axis=1)
+    train_scores_std  = np.std(train_scores, axis=1)
+    test_scores_std   = np.std(test_scores, axis=1)
+
+    # Couleurs personnalisées
+    color_train = "#2ca02c"  # vert
+    color_test  = "#9467bd"  # violet
+
+    plt.figure(figsize=(8, 5), facecolor="white")
+    ax = plt.gca()
+    ax.set_facecolor("white")  # fond blanc
+
+    # Tracer les courbes
+    plt.plot(train_sizes, train_scores_mean, "o-", color=color_train, label="RMSE entraînement", linewidth=2)
+    plt.plot(train_sizes, test_scores_mean, "o-", color=color_test, label="RMSE validation", linewidth=2)
+
+    # Zones ombrées
+    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
+                                  train_scores_mean + train_scores_std,
+                                  color=color_train, alpha=0.2)
+    plt.fill_between(train_sizes,test_scores_mean - test_scores_std,
+                                test_scores_mean + test_scores_std,
+                                color=color_test,alpha=0.2)
+
+    # Style épuré : enlever spines gauche et bas
+    ax.spines['top'].set_visible(False)
+    ax.spines['right'].set_visible(False)
+    ax.spines['bottom'].set_visible(False)
+    ax.spines['left'].set_visible(False)
+
+    # Axes et grille
     plt.xlabel("Taille de l'échantillon d'entraînement")
     plt.ylabel("RMSE")
     plt.title(f"Courbe d'apprentissage - {model.__class__.__name__}")
     plt.legend()
-    plt.grid(True)
+    plt.grid(True, linestyle='--', alpha=0.5)
+    plt.tight_layout()
     plt.show()
 
 

From 731bb18af2efd508b1445f061608607ff7e4cecc Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Thu, 28 Aug 2025 15:24:25 +0200
Subject: [PATCH 04/12] update

---
 scripts/rf.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/scripts/rf.py b/scripts/rf.py
index b81fab2..f8f2651 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -109,8 +109,6 @@ def plot_learning_curve_rmse(model, X, y, cv=5):
     plt.show()
 
 
-
-
 # Loss en fonction du nombre d'arbres / Loss function // number of threes
 def plot_mse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]):
     errors = []

From 4c544202828ea0d933fac6ffc080090667a84e46 Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Fri, 29 Aug 2025 18:06:37 +0200
Subject: [PATCH 05/12] update of preprocessing

---
 notebooks/project_starter.ipynb | 704 ++++++++++++++++++++++++++++----
 scripts/Data_filter_Jess.py     |  27 +-
 2 files changed, 639 insertions(+), 92 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index 08d026a..111b3bb 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,26 +46,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_3936\\3952692224.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n"
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_23304\\962525088.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_23304\\962525088.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n"
      ]
     }
    ],
    "source": [
     "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n",
-    "df = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)"
+    "df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
+    "\n",
+    "df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n",
+    "               # read 5000 rows (5001 → 10000)\n",
+    "\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1148,7 +1154,7 @@
          "type": "float"
         }
        ],
-       "ref": "13247bfe-82aa-4e60-b200-7db725d979ec",
+       "ref": "48d506fa-f248-4661-9ba0-e8f156efa833",
        "rows": [
         [
          "0",
@@ -2455,13 +2461,13 @@
        "[5 rows x 214 columns]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head()"
+    "df_train.head()"
    ]
   },
   {
@@ -2475,34 +2481,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### 2.1 Data curation "
+    "##### 2.1 Preliminary data curation (of the train and test df)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:58: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n"
+     "ename": "TypeError",
+     "evalue": "filter_nutriscore_data() takes 1 positional argument but 2 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[33], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m filtered_df, filtered_df_test \u001b[38;5;241m=\u001b[39m \u001b[43mdfj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter_nutriscore_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m cat_df, cat_df_test \u001b[38;5;241m=\u001b[39m dfj\u001b[38;5;241m.\u001b[39mcategorical_filter(filtered_df, filtered_df_test, cat_keep\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      3\u001b[0m num_df, num_df_test \u001b[38;5;241m=\u001b[39m dfj\u001b[38;5;241m.\u001b[39mnumerical_filter(filtered_df,filtered_df_test, num_drop\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n",
+      "\u001b[1;31mTypeError\u001b[0m: filter_nutriscore_data() takes 1 positional argument but 2 were given"
      ]
     }
    ],
    "source": [
-    "filtered_df = dfj.filter_nutriscore_data(df)\n",
-    "cat_df = dfj.categorical_filter(filtered_df, cat_keep= True)\n",
-    "num_df = dfj.numerical_filter(filtered_df, num_drop= True)\n",
-    "final_df = dfj.final_df(cat_df, num_df)\n",
+    "filtered_df, filtered_df_test = dfj.filter_nutriscore_data(df_train, df_test)\n",
+    "cat_df, cat_df_test = dfj.categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n",
+    "num_df, num_df_test = dfj.numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n",
+    "final_df, final_df_test = dfj.final_df(cat_df, num_df, cat_df_test, num_df_test)\n",
+    "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
+    "final_df_test = final_df_test.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
     "final_df.head()\n",
-    "target = df['nutriscore_score']\n",
-    "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])"
+    "final_df_test.head()"
    ]
   },
   {
@@ -2514,30 +2521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['unknown', 'Beverages', nan, 'Fruits and vegetables',\n",
-       "       'Composite foods', 'Sugary snacks', 'Cereals and potatoes',\n",
-       "       'Salty snacks', 'Fat and sauces', 'Milk and dairy products',\n",
-       "       'Fish Meat Eggs', 'Alcoholic beverages'], dtype=object)"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['pnns_groups_1'].unique()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -2551,13 +2535,13 @@
       "12                  unknown            NA\n",
       "14          Composite foods     Processed\n",
       "...                     ...           ...\n",
-      "4929         Fish Meat Eggs  Animal_based\n",
-      "4930                unknown            NA\n",
-      "4937        Composite foods     Processed\n",
-      "4980          Sugary snacks        Snacks\n",
-      "4993   Cereals and potatoes   Plant_based\n",
+      "4926   Cereals and potatoes   Plant_based\n",
+      "4935         Fish Meat Eggs  Animal_based\n",
+      "4936                unknown            NA\n",
+      "4943        Composite foods     Processed\n",
+      "4987          Sugary snacks        Snacks\n",
       "\n",
-      "[439 rows x 2 columns]\n"
+      "[438 rows x 2 columns]\n"
      ]
     }
    ],
@@ -2577,7 +2561,260 @@
     "}\n",
     "\n",
     "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
-    "print(final_df[['pnns_groups_1', 'PNNS_pro']])"
+    "final_df_test['PNNS_pro'] = final_df_test['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
+    "print(final_df[['pnns_groups_1', 'PNNS_pro']])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "PNNS_pro",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "ref": "f5cf055f-f21b-4126-a537-93bd9532e793",
+       "rows": [
+        [
+         "0",
+         "Plant_based"
+        ],
+        [
+         "7",
+         "Animal_based"
+        ],
+        [
+         "42",
+         "Plant_based"
+        ],
+        [
+         "45",
+         "NA"
+        ],
+        [
+         "47",
+         "NA"
+        ],
+        [
+         "51",
+         "Animal_based"
+        ],
+        [
+         "63",
+         "Snacks"
+        ],
+        [
+         "70",
+         "Plant_based"
+        ],
+        [
+         "73",
+         "Snacks"
+        ],
+        [
+         "118",
+         "NA"
+        ],
+        [
+         "119",
+         "Snacks"
+        ],
+        [
+         "125",
+         "NA"
+        ],
+        [
+         "126",
+         "Snacks"
+        ],
+        [
+         "128",
+         "NA"
+        ],
+        [
+         "129",
+         "NA"
+        ],
+        [
+         "131",
+         "Snacks"
+        ],
+        [
+         "132",
+         "Snacks"
+        ],
+        [
+         "134",
+         "Snacks"
+        ],
+        [
+         "137",
+         "Snacks"
+        ],
+        [
+         "138",
+         "Snacks"
+        ],
+        [
+         "139",
+         "NA"
+        ],
+        [
+         "140",
+         "Snacks"
+        ],
+        [
+         "141",
+         "Snacks"
+        ],
+        [
+         "142",
+         "Snacks"
+        ],
+        [
+         "143",
+         "NA"
+        ],
+        [
+         "144",
+         "NA"
+        ],
+        [
+         "145",
+         "Snacks"
+        ],
+        [
+         "146",
+         "Snacks"
+        ],
+        [
+         "147",
+         "Snacks"
+        ],
+        [
+         "148",
+         "Snacks"
+        ],
+        [
+         "149",
+         "Snacks"
+        ],
+        [
+         "150",
+         "Snacks"
+        ],
+        [
+         "218",
+         "NA"
+        ],
+        [
+         "223",
+         "Plant_based"
+        ],
+        [
+         "237",
+         "Snacks"
+        ],
+        [
+         "238",
+         "NA"
+        ],
+        [
+         "267",
+         "NA"
+        ],
+        [
+         "316",
+         "NA"
+        ],
+        [
+         "317",
+         "NA"
+        ],
+        [
+         "318",
+         "NA"
+        ],
+        [
+         "319",
+         "Plant_based"
+        ],
+        [
+         "322",
+         "NA"
+        ],
+        [
+         "323",
+         "NA"
+        ],
+        [
+         "324",
+         "NA"
+        ],
+        [
+         "325",
+         "NA"
+        ],
+        [
+         "326",
+         "NA"
+        ],
+        [
+         "329",
+         "NA"
+        ],
+        [
+         "330",
+         "NA"
+        ],
+        [
+         "335",
+         "NA"
+        ],
+        [
+         "336",
+         "NA"
+        ]
+       ],
+       "shape": {
+        "columns": 1,
+        "rows": 769
+       }
+      },
+      "text/plain": [
+       "0        Plant_based\n",
+       "7       Animal_based\n",
+       "42       Plant_based\n",
+       "45                NA\n",
+       "47                NA\n",
+       "            ...     \n",
+       "4949          Snacks\n",
+       "4963       Processed\n",
+       "4972    Animal_based\n",
+       "4978          Snacks\n",
+       "4987          Snacks\n",
+       "Name: PNNS_pro, Length: 769, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "final_df_test['PNNS_pro']"
    ]
   },
   {
@@ -2586,10 +2823,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from scripts.encoding_func import one_hot_encode_column\n",
     "\n",
     "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n",
+    "filterd_df_test = one_hot_encode_column(final_df_test, 'PNNS_pro')\n",
     "filtered_df.head()\n",
-    "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])"
+    "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n",
+    "filtered_df_test = filtered_df_test.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags'])"
    ]
   },
   {
@@ -3044,7 +3284,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(811, 19)\n"
+      ": shape of df with only numeric features=(809, 19)\n",
+      ": shape of df with only numeric features=(769, 142)\n"
      ]
     },
     {
@@ -3152,7 +3393,7 @@
          "type": "float"
         }
        ],
-       "ref": "a657fe56-a896-456d-a3ee-ee7e5e0e21e7",
+       "ref": "ddb532b2-653b-4e81-ba04-b89522719ae4",
        "rows": [
         [
          "6",
@@ -3475,8 +3716,9 @@
     }
    ],
    "source": [
-    "\n",
+    "from scripts.Imputing import knn_impute_numeric\n",
     "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
+    "imputed_df_test = knn_impute_numeric(filtered_df_test, n_neighbors = 5)\n",
     "imputed_df.head()"
    ]
   },
@@ -3496,13 +3738,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(811, 18)\n"
+      ": shape of df with only numeric features=(809, 18)\n",
+      ": shape of df with only numeric features=(769, 141)\n"
      ]
     }
    ],
    "source": [
-    "\n",
-    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')"
+    "from scripts.Scaling import scaler_numeric\n",
+    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
+    "work_df_test = scaler_numeric(imputed_df_test, 'nutriscore_score')"
    ]
   },
   {
@@ -3941,6 +4185,13 @@
     "work_df.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Outliers?"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -3955,44 +4206,209 @@
     "##### 3.1 : Decision tree"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 3.2 Random forest"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### code for that"
+    "from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.metrics import mean_squared_error, r2_score\n",
+    "\n",
+    "def random_forest_GS(X_train, y_train, X_test, y_test): \n",
+    "    # Séparer en train/test\n",
+    "    #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
+    "    \n",
+    "    # Définir le modèle\n",
+    "    rf = RandomForestRegressor(random_state=42)\n",
+    "\n",
+    "    # Grille des hyperparamètres à tester\n",
+    "    param_grid = {\n",
+    "        \"n_estimators\": [5, 10, 60],              \n",
+    "        \"max_depth\": [None, 10, 20, 50],         \n",
+    "        \"min_samples_split\": [2, 5, 10],         \n",
+    "        \"min_samples_leaf\": [1, 2, 4],           \n",
+    "        \"max_features\": [\"auto\", \"sqrt\", \"log2\"] \n",
+    "    }\n",
+    "\n",
+    "    # Grid Search avec validation croisée\n",
+    "    grid_search = GridSearchCV(\n",
+    "        estimator=rf,\n",
+    "        param_grid=param_grid,\n",
+    "        cv=5,                 \n",
+    "        n_jobs=-1,            \n",
+    "        scoring=\"neg_root_mean_squared_error\",  \n",
+    "        verbose=2\n",
+    "    )\n",
+    "\n",
+    "    # Entraînement\n",
+    "    grid_search.fit(X_train, y_train)\n",
+    "    \n",
+    "    # Prédictions\n",
+    "    y_pred = grid_search.best_estimator_.predict(X_test)\n",
+    "\n",
+    "    # Meilleurs paramètres\n",
+    "    print(\"Meilleurs paramètres trouvés : \", grid_search.best_params_)\n",
+    "    \n",
+    "    # Évaluation\n",
+    "    print(\"MSE :\", mean_squared_error(y_test, y_pred))\n",
+    "    print(\"R² :\", r2_score(y_test, y_pred))\n",
+    "\n",
+    "    return grid_search.best_estimator_, X_train, X_test, y_train, y_test, y_pred"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 31,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "##### 3.2 Random forest"
+    "X = work_df.drop(\"nutriscore_score\", axis=1)  \n",
+    "y = work_df[\"nutriscore_score\"]\n",
+    "X_test = work_df_test.drop(\"nutriscore_score\", axis=1) \n",
+    "y_test = work_df_test[\"nutriscore_score\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "-16.0\n",
-      "50.0\n"
+      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n",
+      "540 fits failed out of a total of 1620.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "342 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
+      "    estimator._validate_params()\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
+      "    validate_parameter_constraints(\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
+      "    raise InvalidParameterError(\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "198 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
+      "    estimator._validate_params()\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
+      "    validate_parameter_constraints(\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
+      "    raise InvalidParameterError(\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [        nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan -4.17441534 -3.9850685  -3.869491\n",
+      " -4.43024577 -4.14775395 -3.76619056 -4.46170689 -4.46606186 -3.9911324\n",
+      " -4.36181718 -4.13390446 -3.94874007 -4.59103923 -4.30176094 -3.96973599\n",
+      " -4.30587871 -4.21863185 -3.99385929 -4.30387585 -4.14318382 -4.06357957\n",
+      " -4.30387585 -4.14318382 -4.06357957 -4.3324884  -4.26769304 -4.15057326\n",
+      " -4.17441534 -3.9850685  -3.869491   -4.43024577 -4.14775395 -3.76619056\n",
+      " -4.46170689 -4.46606186 -3.9911324  -4.36181718 -4.13390446 -3.94874007\n",
+      " -4.59103923 -4.30176094 -3.96973599 -4.30587871 -4.21863185 -3.99385929\n",
+      " -4.30387585 -4.14318382 -4.06357957 -4.30387585 -4.14318382 -4.06357957\n",
+      " -4.3324884  -4.26769304 -4.15057326         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      " -4.21952674 -4.18778716 -3.86417073 -4.34736341 -4.17625035 -3.91895388\n",
+      " -4.70056037 -4.46621254 -4.00376246 -4.23921756 -4.20411889 -3.90976299\n",
+      " -4.489981   -4.11515935 -3.96809022 -4.33314957 -4.31300635 -3.99178119\n",
+      " -4.48692777 -4.26130711 -4.09123967 -4.48692777 -4.26130711 -4.09123967\n",
+      " -4.45914192 -4.34581783 -4.16687368 -4.21952674 -4.18778716 -3.86417073\n",
+      " -4.34736341 -4.17625035 -3.91895388 -4.70056037 -4.46621254 -4.00376246\n",
+      " -4.23921756 -4.20411889 -3.90976299 -4.489981   -4.11515935 -3.96809022\n",
+      " -4.33314957 -4.31300635 -3.99178119 -4.48692777 -4.26130711 -4.09123967\n",
+      " -4.48692777 -4.26130711 -4.09123967 -4.45914192 -4.34581783 -4.16687368\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan -4.16498725 -3.98663985 -3.87277587\n",
+      " -4.43024577 -4.14775395 -3.76619075 -4.46187139 -4.46607952 -3.99388922\n",
+      " -4.36181718 -4.13390446 -3.94882138 -4.59103923 -4.30176094 -3.96973599\n",
+      " -4.30587871 -4.21863185 -3.99385929 -4.30387585 -4.14318382 -4.06357957\n",
+      " -4.30387585 -4.14318382 -4.06357957 -4.3324884  -4.26769304 -4.15057326\n",
+      " -4.16498725 -3.98663985 -3.87277587 -4.43024577 -4.14775395 -3.76619075\n",
+      " -4.46187139 -4.46607952 -3.99388922 -4.36181718 -4.13390446 -3.94882138\n",
+      " -4.59103923 -4.30176094 -3.96973599 -4.30587871 -4.21863185 -3.99385929\n",
+      " -4.30387585 -4.14318382 -4.06357957 -4.30387585 -4.14318382 -4.06357957\n",
+      " -4.3324884  -4.26769304 -4.15057326         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      "         nan         nan         nan         nan         nan         nan\n",
+      " -4.17441534 -3.9850685  -3.869491   -4.43024577 -4.14775395 -3.76619056\n",
+      " -4.46170689 -4.46606186 -3.9911324  -4.36181718 -4.13390446 -3.94874007\n",
+      " -4.59103923 -4.30176094 -3.96973599 -4.30587871 -4.21863185 -3.99385929\n",
+      " -4.30387585 -4.14318382 -4.06357957 -4.30387585 -4.14318382 -4.06357957\n",
+      " -4.3324884  -4.26769304 -4.15057326 -4.17441534 -3.9850685  -3.869491\n",
+      " -4.43024577 -4.14775395 -3.76619056 -4.46170689 -4.46606186 -3.9911324\n",
+      " -4.36181718 -4.13390446 -3.94874007 -4.59103923 -4.30176094 -3.96973599\n",
+      " -4.30587871 -4.21863185 -3.99385929 -4.30387585 -4.14318382 -4.06357957\n",
+      " -4.30387585 -4.14318382 -4.06357957 -4.3324884  -4.26769304 -4.15057326]\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "The feature names should match those that were passed during fit.\nFeature names unseen at fit time:\n- abbreviated_product_name\n- acidity_100g\n- added-salt_100g\n- added-sugars_100g\n- additives\n- ...\nFeature names seen at fit time, yet now missing:\n- PNNS_pro_Animal_based\n- PNNS_pro_Drinks\n- PNNS_pro_NA\n- PNNS_pro_Plant_based\n- PNNS_pro_Processed\n- ...\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[32], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m best_rf, X_train, X_test, y_train, y_test, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mrandom_forest_GS\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[30], line 37\u001b[0m, in \u001b[0;36mrandom_forest_GS\u001b[1;34m(X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[0;32m     34\u001b[0m grid_search\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m     36\u001b[0m \u001b[38;5;66;03m# Prédictions\u001b[39;00m\n\u001b[1;32m---> 37\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbest_estimator_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;66;03m# Meilleurs paramètres\u001b[39;00m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMeilleurs paramètres trouvés : \u001b[39m\u001b[38;5;124m\"\u001b[39m, grid_search\u001b[38;5;241m.\u001b[39mbest_params_)\n",
+      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:1066\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m   1064\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m   1065\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m-> 1066\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1068\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m   1069\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n",
+      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:638\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    636\u001b[0m     ensure_all_finite \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 638\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mvalidate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    639\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    640\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    641\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    642\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    643\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    644\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    645\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    646\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m    647\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\validation.py:2919\u001b[0m, in \u001b[0;36mvalidate_data\u001b[1;34m(_estimator, X, y, reset, validate_separately, skip_check_array, **check_params)\u001b[0m\n\u001b[0;32m   2835\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalidate_data\u001b[39m(\n\u001b[0;32m   2836\u001b[0m     _estimator,\n\u001b[0;32m   2837\u001b[0m     \u001b[38;5;241m/\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2843\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m   2844\u001b[0m ):\n\u001b[0;32m   2845\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate input data and set or check feature names and counts of the input.\u001b[39;00m\n\u001b[0;32m   2846\u001b[0m \n\u001b[0;32m   2847\u001b[0m \u001b[38;5;124;03m    This helper function should be used in an estimator that requires input\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2917\u001b[0m \u001b[38;5;124;03m        validated.\u001b[39;00m\n\u001b[0;32m   2918\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2919\u001b[0m     \u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_estimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2920\u001b[0m     tags \u001b[38;5;241m=\u001b[39m get_tags(_estimator)\n\u001b[0;32m   2921\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m tags\u001b[38;5;241m.\u001b[39mtarget_tags\u001b[38;5;241m.\u001b[39mrequired:\n",
+      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\validation.py:2777\u001b[0m, in \u001b[0;36m_check_feature_names\u001b[1;34m(estimator, X, reset)\u001b[0m\n\u001b[0;32m   2774\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m missing_names \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unexpected_names:\n\u001b[0;32m   2775\u001b[0m     message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names must be in the same order as they were in fit.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 2777\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(message)\n",
+      "\u001b[1;31mValueError\u001b[0m: The feature names should match those that were passed during fit.\nFeature names unseen at fit time:\n- abbreviated_product_name\n- acidity_100g\n- added-salt_100g\n- added-sugars_100g\n- additives\n- ...\nFeature names seen at fit time, yet now missing:\n- PNNS_pro_Animal_based\n- PNNS_pro_Drinks\n- PNNS_pro_NA\n- PNNS_pro_Plant_based\n- PNNS_pro_Processed\n- ...\n"
      ]
     }
    ],
    "source": [
-    "print(min(y))\n",
-    "print(max(y))"
+    "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y, X_test, y_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4013,7 +4429,7 @@
       "\n",
       "Below are more details about the failures:\n",
       "--------------------------------------------------------------------------------\n",
-      "498 fits failed with the following error:\n",
+      "275 fits failed with the following error:\n",
       "Traceback (most recent call last):\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
       "    estimator.fit(X_train, y_train, **fit_params)\n",
@@ -4023,10 +4439,10 @@
       "    validate_parameter_constraints(\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
       "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
-      "42 fits failed with the following error:\n",
+      "265 fits failed with the following error:\n",
       "Traceback (most recent call last):\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
       "    estimator.fit(X_train, y_train, **fit_params)\n",
@@ -4036,7 +4452,7 @@
       "    validate_parameter_constraints(\n",
       "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
       "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
       "\n",
       "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
       "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [         nan          nan          nan          nan          nan\n",
@@ -4130,12 +4546,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_learning_curve_rmse(model, X, y, cv=5):\n",
+    "    train_sizes, train_scores, test_scores = learning_curve(\n",
+    "        model,\n",
+    "        X, y,\n",
+    "        cv=cv,\n",
+    "        scoring=\"neg_root_mean_squared_error\",\n",
+    "        n_jobs=-1,\n",
+    "        train_sizes=np.linspace(0.1, 1.0, 10),\n",
+    "        shuffle=True,\n",
+    "        random_state=42\n",
+    "    )\n",
+    "\n",
+    "    # Convertir scores négatifs en positifs\n",
+    "    train_scores = -train_scores\n",
+    "    test_scores  = -test_scores\n",
+    "\n",
+    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
+    "    test_scores_mean  = np.mean(test_scores, axis=1)\n",
+    "    train_scores_std  = np.std(train_scores, axis=1)\n",
+    "    test_scores_std   = np.std(test_scores, axis=1)\n",
+    "\n",
+    "    # Couleurs personnalisées\n",
+    "    color_train = \"#2ca02c\"  # vert\n",
+    "    color_test  = \"#9467bd\"  # violet\n",
+    "\n",
+    "    plt.figure(figsize=(8, 5), facecolor=\"white\")\n",
+    "    ax = plt.gca()\n",
+    "    ax.set_facecolor(\"white\")  # fond blanc\n",
+    "\n",
+    "    # Tracer les courbes\n",
+    "    plt.plot(train_sizes, train_scores_mean, \"o-\", color=color_train, label=\"RMSE entraînement\", linewidth=2)\n",
+    "    plt.plot(train_sizes, test_scores_mean, \"o-\", color=color_test, label=\"RMSE validation\", linewidth=2)\n",
+    "\n",
+    "    # Zones ombrées\n",
+    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
+    "                                  train_scores_mean + train_scores_std,\n",
+    "                                  color=color_train, alpha=0.2)\n",
+    "    plt.fill_between(train_sizes,test_scores_mean - test_scores_std,\n",
+    "                                test_scores_mean + test_scores_std,\n",
+    "                                color=color_test,alpha=0.2)\n",
+    "\n",
+    "    # Style épuré : enlever spines gauche et bas\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['bottom'].set_visible(False)\n",
+    "    ax.spines['left'].set_visible(False)\n",
+    "\n",
+    "    # Axes et grille\n",
+    "    plt.xlabel(\"Taille de l'échantillon d'entraînement\")\n",
+    "    plt.ylabel(\"RMSE\")\n",
+    "    plt.title(f\"Courbe d'apprentissage - {model.__class__.__name__}\")\n",
+    "    plt.legend(frameon=False)\n",
+    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "def plot_rmse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]):\n",
+    "    errors = []\n",
+    "\n",
+    "    for n in n_estimators_range:\n",
+    "        rf_tmp = RandomForestRegressor(\n",
+    "            n_estimators=n,\n",
+    "            max_depth=best_rf.max_depth,\n",
+    "            min_samples_split=best_rf.min_samples_split,\n",
+    "            min_samples_leaf=best_rf.min_samples_leaf,\n",
+    "            max_features=best_rf.max_features,\n",
+    "            random_state=42,\n",
+    "            n_jobs=-1\n",
+    "        )\n",
+    "        rf_tmp.fit(X_train, y_train)\n",
+    "        y_pred_tmp = rf_tmp.predict(X_test)\n",
+    "        \n",
+    "        # Compute RMSE explicitly\n",
+    "        mse = mean_squared_error(y_test, y_pred_tmp)\n",
+    "        rmse = np.sqrt(mse)\n",
+    "        errors.append(rmse)\n",
+    "\n",
+    "    # Couleurs personnalisées\n",
+    "    color = \"#9467bd\"  # violet\n",
+    "\n",
+    "    plt.figure(figsize=(8, 5), facecolor=\"white\")\n",
+    "    ax = plt.gca()\n",
+    "    ax.set_facecolor(\"white\")  # fond blanc\n",
+    "\n",
+    "    # Style épuré : enlever spines\n",
+    "    ax.spines['top'].set_visible(False)\n",
+    "    ax.spines['right'].set_visible(False)\n",
+    "    ax.spines['bottom'].set_visible(False)\n",
+    "    ax.spines['left'].set_visible(False)\n",
+    "\n",
+    "    # Courbe RMSE\n",
+    "    plt.plot(n_estimators_range, errors, marker=\"o\", color=color, label=\"RMSE\")\n",
+    "    plt.xlabel(\"Nombre d'arbres (n_estimators)\")\n",
+    "    plt.ylabel(\"RMSE sur test\")\n",
+    "    plt.legend(frameon=False)\n",
+    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
+    "    plt.tight_layout()\n",
+    "    plt.show()\n",
+    "    \n",
+    "    return errors  # also return RMSE values for later use\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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FGcuDFvJgP5hQZ1ofgT4z8XhYpmeffTbgfbBMxn2xhwEtsGhVR2svDhxF4EcrKrYnwufq1avVnhPcjpZj/IhEWMeP0kgtM9kTgytZBq0MaAlYsGCBOpCiNjhSGB+M+FWPDzXzwQf48sXt5hYgjDNDqxMOPKgrPJ75lzoOIsByGAdZ4IseX5CYxmiVDBdaJvBharTUmOFDOxQENIQQ7BI2w3NHSAn0XMyw3Hg+/l9eW7ZsqdFdDA6uCKfPS+PAHwShQC1b9VXXHyxoxcKAA9JwkAkO3EGLlLGrGa1FCA4YsD3RCosDOxAg0MIT7rrF6w4Ha2FdmpcR6zWS68W/rMHckhQulHugFRqtq+eff773aHGULqDkBQc9GdDKXF9YJ/hB6r9O/F/TxnsW4/1bKTHO/J6ONrwXUc4Q6H2Hcgn8aDGCVzDGNsYPo3C2MaZHqysGvDfxgwY/sNBDSX1e808++aRqecVrHgeo4TmhNAg/2EMtT7iv41DPB71uoAU11LJjfWI6DAi6+KGIg9AQZo1lxecPWmcx4PMbP7bw3EaOHBmR7UX2xRpXsgzq7fDhhDCBAOoP3boYXRShxQhwtK2Z8esedXHmD1D8ojdDQA7W4lobo7sdA3oLAOMIanyYooUArQD+LTy4jlAQDO6HWjO0uuBoYgN2t6EVJhTc3/8xUWfm35WQAT0hmEszEM4Q5o3nYkDPBuYuufClgev4ssAR8bXB7Vj///d//6d2YfrDLr76wOvE/8dIINg97L9OjBZOo0se/22CLzkjvBvThLtusf0w7rPPPvOOwxHQOCo6kusFX+bmIVD3caGgxQ8hCa8v1BcazxPMzxXbG61b9YX3Kn784PVlQGug/+5q1N0i5CFkmbtLQgs4ltH8no42rAfsjsZ6MZfO4HMJP3zQihmqbAWvBUyDEBaovtrYxlgX/l3q4bWBkGleD+G+5s3zQJkMempA6QOeE66jtTLQni3zay7c13FtUG6DeQS6D0qijCP50dWcv1DvUfzQRB/EeJ1i3UZie5F9scWVLIMPWnzI4Bc1WlHNZ86aP3++CgrG7tCePXuq3V348jN2by5cuFDt2sVuMrSOGRCEccAPPrRx9iC0AiAIBmqFDAUtTzhgAfWPaBlGawhqrbA8xnN47LHHVCsAPkCxLPgCwv3Q+oEDUtBVVDAIvNgVhlo1tPohNBr9F5rrBoO1WGP3Lg6SQJdi6HoGXQSZD1Yww+5ufKBjenzA40cAWhf9D8BACQdq8PB80IqM3XjYtY51X1vNrhECUb+GMIzngMdCPRy+0NCagi8T7MKvKwQ/1LzhhwqWDy3c/t3dAF4PCF1oUcS2QVDHFyke1/jxg9cHvjzRyofd6ajvwzrHl6fRmh/uukUr5ksvvaQOyEG3PQiUmM442MhoeYrWeqkrvJ+wuxXbF69VPDfsocB7C90PYXmxq7shu9nxesI6wfsZB/NhnWCe/h3U47WE5cC6wPsZ69DoDgst++hHNJbwPjb6F8V7EUEfP9gQpgL1VesP2xBdX+FsbkcddZTqKgo/9vCjFLvEUZ+J9YK9F2hpRNBDGMPj4LMCz918+lm85jE/LBfepwj5wepnDTjbIOq38d5GCywGvL7wXsF2wePhtY9SELyfjBAZ7uu4NnjeeGx89uIx8XzRWIAWUIzHZzB+rOB9hYYF/DBBSy9qX/GexXsR6x4QSlFugXmgDhg/ZLB8uI9xgGlDtxfZmNXdGhD99ttvqquYTp06qe5N0J1Jv379tBdffNGnO57KykptzJgxqvugtLQ0rX379trIkSN9pgF0YzRixAitoKBAdemDLojWrl0btDusQF0nGV01oWuqv/zlL2qZmjRpot16660+XfcYPvroI9XdCrptwoBuZG655RZt9erVIZ//nDlztKOPPlo9d3Qvhe5jjMevDZ73Pffco7q7QbdAWGcLFixQ3dWYu9Exus9577331PpCF0SYfujQoT7dcAHuh25o0H3SCSecoLrqwnp76aWXfKYz5vnBBx8EXLZly5ZpF1xwgerWCl1IYR4XX3yxNmvWrBrr2L9bHP8urowujtDFDZYbtxnb0X/apUuXapdeeqnWoUMH9bh4rmeddZZ6PoYPP/xQGzRokLoN6xzTovunrVu31nndwu+//67WJaZr3ry5uh9eD1iu77//vs7rJRLw2Hj9BTJ69GifLo7QTdHxxx+vlr9Nmzba8OHDta+++ipgN0iBuijCtsDzMMPrCl3J4f2H9yG6IEOXTf7zhP/85z9a79691fpo2rSpdvnll2ubNm2q8Rh4X/kLtkxYHmyTcF+vBrx+8HnRqFEjteynnnqqNn/+fJ9pavvcMB4L80B3U3j/HHzwwdrVV1/tfQ2iaypsG3xG4DlhuuOOO86n+zCjGy08B3z24PGM112o59K/f3/V3du+ffvU9e3bt6vHw+clPjfRfdmAAQO0V199tV6v42Dr3OjibNy4cep2bE98ZuKzDZ/b6AIR8FpHd1d4reH9h0u8Z/E9YHjllVfU+914n2Ad3nfffd55RHJ7kf048I/V4ZmIogd1jGiRRgt2qNNd4ih8dGFT14PmyBdavNBiiC590LJKZEd8HVM8Yo0rEVEDoH7PDLWB2GWJ7sf4ZU92wdcx2QVrXImIGgAH6KEvWtTIot9T1EGjrg81gkR2wdcx2QWDKxFRA+CIbBx4hS94HIyCA2DQ9RYOOiSyC76OyS4srXHFkYXokBtHnqJbHhxZiSNdA8GRithtgX4I0XkzERERESUXS2tc0a8buhXy7yvTHwItznqDbnCIiIiIKDlZWiqAPg39Oz/3h34Ob7vtNtUHXCw7pCYiIiKi+BLXNa44FSM6NUanyuGe4hCdD5vPPoJ5oJPlZs2aNeg890REREQUHahcxUljsHcdJ22xZXDFWVVwNgyc0SVcY8eOVWcjIiIiIiJ72bhxozqTmu2CKw7Ywqn/cGq6urSU4tSbd999t/c6uvVAFx84BadxqrhkgXM649R76Hw+1Kk6KfK4/q3HbWAtrn/rcRtYi+s/fGhtxem8Q2W1uA2uc+fOVecwRug0oIuOe+65R53NA+dRDyQjI0MNgc7TjnNJJ9sbBucHR5kE3zCxx/VvPW4Da3H9W4/bwFpc/+Ez1k+oxsq4Da6obR04cGCNfuYw/pprrrFsuYiIiIjIGpYG1+LiYlm7dq33OnbnL1++XLWOoqUVv1D803irVq2ka9euFiwtERERESVtcF28eLGq+zAYtanDhg2Tt956y8IlIyIiIqJ4Y2lw7d+/v+r+IFzB6lqJiIjsDN+FVVVV6liOSNdYoneesrKyiM+bQuP6r+Z0OtW6aGjXpHFb40pERJQMKioq1GnPS0tLoxKIUWKHLobYl3nscf37woFqrVu3lvT0dKkvBlciIiKL4CQ5OL4DrVHoeB1f6JEMOJg/jidp1KhRrZ26U3Rw/VcHePxA27lzp3q9d+nSpd7rg8GViIjIIvgyR7hp3769ao2KNMwbj5GZmZnUwckqXP/VsrKy1EH2GzZs8K6T+kjutUhERBQHkj3UUHJIicDrnO8UIiIiIrIFBlciIiKiGEKt5+jRo9VBW1Q3rHElIiKyOZdbk4Xr98iO/WXSIjdTju3cVJwpPIo9Xt1yyy1y4MABWbBggXz11VdWL46tsMWViIjIxqb/vFVOHPe1XPra93LHlOXqEtcxPlquvvpq1fsBBhxw07lzZxk+fLjqr9TMmOb777/3GV9eXq7OjonbvvnmG+/4OXPmyGmnnabOoImD1XD0OU5KhIN5ANMa8/Qftm3bFrXnazyXTz75pMHz+eijj9S8Pv/8c+nYsaO8+uqrYnffeLbLvn37ov5YbHElIiKyKYTTm/61VPxP5bOtsEyNn3BZb+nbIfK9FcCQIUNk0qRJqpP9JUuWqICJ8DJu3Dif6dBjAqY7/vjjveOmTp2quojas2ePd9zKlSvVPG+77TZ54YUX1FHoa9asUUHPv/P+1atXS15ens+4Fi1aiNUQsEP1UXrhhReqARIhtMYaW1yJiIjirM/L0oqqkMP+skp5+LNfaoRWNQ/P5SPTVkpxWeh5YajLmSwhIyNDda6PYHreeefJwIEDZebMmTWmQ6CdMmWK2jVuePPNN9V4sxkzZqj5PfXUU9KjRw85+OCDVZB97bXXVIj1D6mY1jzUdsT6zz//LGeccYYKyy1btpQrr7xSdu3a5XMmz9tvv121GqO1F/NDDaqhU6dO6vL8889X4dy4jml69eolr7/+ump1Nrp4mj59upx44olqXgcddJCcffbZsm7dOp8zgWI+y5cv92mxnDVrlvTp00e1Nvft21cFdLNPP/1UjjrqKPU4mO+YMWPUGdcMmMcrr7wiZ511lppH9+7dVTnC2rVr1XPMyclR8zUvS7jzxXPE8zdawj/77DPvczn11FPV302aNFHTokU+WtjiGg37Noqk54hk5oukOK1eGiIispEDlS45bFTD6x4RQ7cVlcuJ438Ia/qVjwyW7PT6xQIEw/nz56td3/6OPvpoFfTQcnrFFVfIn3/+Kd9++61MmDBBHn30Ue90CIs4gxhuO/nkkyVSsPsa5Qd/+9vf5LnnnlMBesSIEXLxxRfL119/7Z3u7bfflrvvvlt++OEHFfYQvvr16yenn366LFq0SIVltBwjTOOEEQaEQjy3jz/+2Du+pKREzQsBfPv27SqMI/QhqNYWsP/xj3/IM888I82bN5cbb7xRrr32Wvnuu+/UbXPnzpWrrrpKtUafdNJJKnzecMMN6raHH37YOw+s02effVYNeJ6XXXaZCqMjR46UDh06qHneeuut8uWXX9ZpvgizeB5PP/20vPjii3L55ZerPlnxwwXPH63IRku4/w+NSGJwjYayfSKl+CXnEMnI1QNsRp5Iav1PcUZERBRPpk2bplow0TKHmlUEspdeeingtAhLaGVFcH3rrbfkzDPPVOHM7KKLLlIHKp1yyikqxKK0YMCAASpU+ZcFtGvXzuc6AvMvv/wS8LGxTL1795YnnnjCOw7LgsD122+/yaGHHqrGHXnkkd6ghhZF3A8toAiuxrI2btxYLZt/ecDkyZN9no9RCoATECDwvvHGG6qlF+UQCLPBPP744+r5w/333y9Dhw5VdcNoCUVwvP/++70t1QijCKloJTYHzGuuuUaFckBwPeGEE+Shhx6SwYMHq3F33HGHmsYQ7nwR5C+99FL1N9Ylgu7ChQtVkEfLMuC5Yh1FE4NrVGki5UX6AGnZ1SE2PTo1R0REZG9ZaU7V+hkKehG4etKikNNNuKi7nHJ4u5Cdv+Nx6wK7hydOnKhaF9GSmZqa6g1s/hBYEY5+//13FVwRevyhtRItmo899phqCUXLJwISamYRkHCOewNaCXNzc73XcYBYMCtWrJDZs2erkO0PrYvm4GqGx9uxY0fI9YDQ7B/CUZs7atQo9RxQkoAAC2htri24mpfBeL5YBrSU4nl89913KtwaUPuLYFtaWuo985p5HgjLcMQRR/iMw32KiorUD4L6zBclB7hvOOsn0hhcY6myVB/2bxVxpleHWLTKRvDc1EREZF+oEQxnl/1JXZpL6/xMdSBWoOpUfKu0ys+U4zs3UfOL9Nm5EF4OOeQQbwtmz549VcviddddV2Na9CCAukvchkCEetP9+/cHnG/btm1VDSoGtPwhWL788suqZdCAetJwW/aKi4tVjan/QWNgDsP+4RfbwQicodaDPzweAi3qTRHwEP4Q/IzeEYIxLwMeH4xlwPMYM2aMXHDBBTXuZz59aqB5RHq+dVk/kcbgahVXhUjJTn1wOEUy86qDLOtiiYgoBPTT+vDZh6neAxBFzOHVaAp5aGj3mPTnilD8wAMPqLpO1FQGqnFEuQBKBLD72lwjWhsc7INwiVbd+sJBR6jBRJ0tWoXrC8HNv3eDQHbv3q1qPXFQGWpk0bL5448/SkPheaxevdr7YyFSIjFfoyeFcNZPQ7FXgXiguUQO7BXZ+4fItp9Edq8TKdklUlX7LzMiIkpuQ3q0lolXHKVaVs1wHeOH9PCtx4wm1KgikOKgq0BQC4kzRj3yyCMBb0fr5E033aR6F8AufNSsIuTiEi2YZthFjX5bzQO65QrW2T+63UJ9Jg6ywrxRS4s6z7oELQRf1Lzisfbu3Vtr2EYLM7q6woFbONjs3nvvlYZC6cHkyZNV6yjWyapVq1RvDQ8++KDl80XrMlpgUfeMbYxW3GhhcI3XutjCjSI7fhHZuVpk/zaRilKrF4yIiOI0vM4bcZq8d/3x8vxfe6lLXMf4WEJrJo5Wx5HngVpIEWwKCgqC9nN67LHHqsCDo+kPP/xwdZASTlyATv+NA5YMXbt2VS2x5gF9yQbSpk0bVcOJkDpo0CBV73nnnXeqUoO6lE/gaH9094WDunCwVzCYJ4IflgflAWiJDlSmUFc4uGratGkq2B9zzDHq4DXUFgfqySHW80V5h3GQF2po8TqIFodW147bbAZN9Pn5+VJYWFjjqMSoQaupu7r/s4ipY10sfn1+8cUXatdMbYXrFB1c/9bjNrAW139oqPdcv369Tx+gkYQaROMgnEjXuFJoXP/hv97DzWuscbV7XSxCLPuLJSIioiTA4Gr3ulgM7C+WiIiIkgCDayL3F5vCvmKJiIgocTC4JnJ/sS5P/2pFW0RymrC/WCIiIrI1BtdkgNPPlu9hXSwRERHZGoNrMmFdLBEREdkYg2vSClIXixCbztpYIiIiij8MruRbF7t/a537iyUiIiKKBQZXqon9xRIREVEc4mkcKLy62H0b9DOC7V4nUrJLpKrC6iUjIiKD2yWyfq7ITx/ql7hOEfPWW2+pU8QaRo8eLb169ar1PldffbWcf/75DX5szOe8885r8HwSBYMr1b0utnCjyI5fRHauFtm/TaSi1OoFIyJKXis/ExnfQ+Tts0Q+uk6/xHWMjxKEKYfDoQaczhen8Bw+fLg6paeZMc3333/vM768vFyaNWumbvvmm2+84+fMmSOnnXaaNG3aVLKzs6VLly4ybNgwqajQG0swrTFP/2Hbtm0SK/fee6/MmjUrovP8448/1PNYvny5z/jnn39eBWfSMbhS/Rk1sbtWi2z/RaRwk0jpHpFK3w8uIiKKEoTT96/S++s2K9qqj1/1edQeesiQIbJ161b5/fff5bnnnpNXXnlFHn744RrTtW/fXiZNmuQzburUqdKoUSOfcStXrlTz7NOnj3z77bfy008/yYsvvijp6enicvm2IK9evVo9tnlo0aKFxAqWHcE7FvLz831ae5MdgytFti4WJQU7V4ls/VFk1xpTmD0gomlWLyURUfzDZ2VFSeihrEjky+H63rCaM1H/OqbfL1K+P7z51fEzOiMjQ1q1aqWCKXZlDxw4UGbOnFljOrSYTpkyRQ4cOOAd9+abb6rxZjNmzFDze+qpp6RHjx5y8MEHqyD72muvSVZWls+0CKmY1jykpNSMNG63W9q1aycTJ070Gb9s2TI1/YYNG9T1Z599Vo444gjJyclRz+fmm2+W4uLioM/dv1QAwfruu+9WAROBFq3Pmt/6nD59upx44oneac466yxZt26d93a0WkPv3r1Vy2v//v0DlgqUl5fL7bffrtZBZmammueiRYu8txut0mgRxo8AtFz37dtXhf1EwOBK0auNrSg2hdlfRbau0MsL9v2p18nig9LtObsXERFV7816ok3o4cn2+l6voDRx7N8ijSf2kJQn24WeHx63nn7++WeZP3++ah31d/TRR0unTp3ko48+Utf//PNP1aJ65ZVX+kyH8ImWU9wWKQinl156qbz77rs+4//9739Lv379pGPHjt7pXnjhBfnll1/k7bfflq+//lqFz3A988wzanc+Avm8efNkz549qlXZrKSkRIXbxYsXq1CJx0QNLMI1LFy4UF3+73//U+vh448/DvhYw4cPV+sSy7l06VI55JBDZPDgweoxzf7xj3+o5cLjpaamyrXXXiuJgL0KUAxp1d1uyW7POIdIaqZIWpbel2wa/s5m7wVERHFu2rRpapd5VVWVagVEEHvppZcCTovQhFB3xRVXqIB35plnSvPmzX2mueiii+Srr76SU045RYXY448/XgYMGCBXXXWV5OXl+UyLVlQzBFCEzkAuv/xyFeAQmDt06KCCIlqAH3zwQe80d955p/dvhOzHHntMbrzxRvnnP/8Z1roYP368jBw5Ui644AJ1/eWXX1bPxezCCy/0uY71gXWAEgm0MBvrA62xeP6BlJSUqNZjrMMzzjhDjUOLNFq633jjDbnvvvu80z7++ONqXcL9998vQ4cOVTXIaKW1MwZXspgmUnVAHw6Yfi06M0xh1nPp5MuViJIAPu8e8KtZDWTDfJF//yXkZMXnvS3Z3QYE3JVe43Hr4NRTT1UhCmEKNa5o1fMPZwYEVoQn1MMidKF105/T6VS1sAiNaPH84Ycf5IknnpBx48ap1sjWrVt7p507d67k5uZWL3paWtDlxC797t27q1ZXLAMOANuxY4cKyga0co4dO1Z+/fVXKSoqUmEcIa+0tFTtaq9NYWGhaiE97rjjvOOwLrCb3mhNhTVr1sioUaPU89q1a5f3NgRqBNdwrFu3TiorK1Vrsfm5H3vssbJq1SqfaY888kjv38a6w/NGeLczlgpQfHKVi5TtE9m/RWTPOpHtP+kHgKE7Lhx0cGAfu+QiosSEk76k54QeDj5NJK+Nvucq8IxEy2srVR1OCm9+dTzZDOpBsZu6Z8+eqvUQgQytfoEYNZ3XXXedCoRGa2Egbdu2VWUEaL1FKyqmRwumGepB8djGYOzyDwatrka5AC5RO2scXIWj+bFsCHrYBb9kyRKZMGGCus3ozSASzj77bLU7Hy2kWFcYIv0YZuYwj5pXMAdpu2JwJXsdAIbuuIq3iexdr3fJZfQtiyNq0d9sVbnVS0lEFBsoqRoyznPFP3Tq17XBY2NSeoXW3AceeEDtfjcfhOVfLoADh7DrH62r4WjSpIlqLUSrbkNcdtllqg4XofTDDz9UQdaAcQh0KCdAecKhhx4qW7aE0eJtOuofy2gEUUCLLeZr2L17tzo4CusH5Q9oAd67d6/PfIz6YP8eFMwOPvhgNd13333nHYcWWBycddhhh0ky4L5Xsjd3lR5mMRhwti9VXmAqNUAdLU9dS0SJ5rBzRC6eLDJ9hG+XWGiJHfKkSLezRIpMn49RhF3vqLFEayX6OfWHVs6dO3fWqFc1oDst9GGKA5YQ0NDSOnnyZNXqim6xzLDL27/PWLSgBisZQN0qjqxHiy+C4TnnnOO9DS22CH94DLSKIhT6t/CGcscdd8iTTz6p+p3t1q2b6qVg3759PgEcy/fqq6+qkIvyAJQtmKGXAPSegN4HUMOLWlSEYv9W7ptuukmtZ/R1i93+6IUBJQ14bsmALa6UHD0abPtRZOdvIvs2ipTsrlfXL0REcRte7/xZZNg0kQvf0C/v/EkfH0Oo67z11ltVkArUQord1QUFBQF7HgDUaaILKhwUdfjhh6sDi3Digk8++cR7kJGha9euKgCaB3MLZyBoZV2xYoUKxubutVDqgKCJWlrUmqLHAdS71sU999yjyhvQxdcJJ5yg6m/NZ81CizQOCMMy4jHuuusuefrpp2usP9T+IsC3adNGzj333ICP9eSTT6paYjzeUUcdJWvXrlUHgiEcJwOH5t/RWIJBkTV+saB4OtivvIjD7mu0BFqs0uWWL5ZukjOPaidpTv5Gqcm/RwPPZagDGMKEX/BffPGFOnq2tgMHKHq4DazF9R8aWg3Xr1+vajajcbQ3doHjexDffyEPzqKI4/oP//Uebl5jqQAlMfZoQEREZCf8NiYK1KOB0auBwZkeIMyyBYmIiCiWGFyJwu3RAENZYfW4lNTqIIuuZDJ9i+iJiIgoshhciSLVo0FqlkhuK5GsxlYvGRERUUKytFIY5yNG1xM4eg5HG+LIQXNR/4gRI+SII45Q3T9gGvT9Vpe+1YhiCrWy6F9252rfllkiohAS/Dhpooi9zi0NruguA91QGGeoMEOfZEuXLpWHHnpIXX788ceq815z32tEcamyVGTP7/qJEYiIamH0toDvPKJEV+p5nTeklxFLSwVwyrdgp31DlwgzZ870GYfTv6GfN3Tca/dz7VISqPT0Y7j7d5EmbUUyGlm9REQUZ3AGqcaNG6sO9SE7O9t7es5IdceEU4qiGyJ2xxR7XP/VLa0IrXid4/Ue7pnTbF/jir698IbGkw6mvLxcDeZ+wYzSAwwxUeUW0dxx0Y+r+ZIsWv9lRSI7ikXSc0UatRJJr+74mqLLeM/H7L1PPrj+w4MzKuFsTtu3b49KYEBoQp+ZkQzEFB6uf1/onxWv90CfCeF+TtgmuGLDo+b10ksvrbVjWpztYsyYMTXGz5gxQ/2STUYzV7AuOH7W/yoLlyR5+e+9odji+g8Pgk1DWqKI4hl+nNVW4xpuuUzcnDkLb9ipU6fKeeedFzCF4/RmmzZtkm+++abW4BqoxbV9+/aya9eu2J05a/tKES0+zpyF0HR6zzY8c1Y8rv+MfL0FNi3DisVLCvjsQGg6/fTTeeYmC3D9W4/bwFpc/+FDXsMpgW1/5ixs9Isvvlg2bNggX3/9dcjwmZGRoQZ/eMHE7EWTmiLijp+giNDE4BqH679qv8i+YpGsJno3WqkMsNES0/c/1cD1bz1uA2tx/YcW7vpJtUNoXbNmjcyePVvVRRAlFk0/3eyBvSLZTfUW2NR0qxeKiIgoLlkaXIuLi2Xt2rXe6+vXr5fly5dL06ZNpXXr1vKXv/xFdYU1bdo0VRuxbds2NR1uT0/nlzslEk2kdLdI6R6RnAKRRi15SlkiIqJ4Cq6LFy+WU0891Xv97rvvVpfDhg2T0aNHy2effaau9+rVy+d+aH3t379/jJeWKBY0kZKdeojNNgJsXO8YISIiihlLvxERPms7NixOjhsjij10p1ayQw+wOc1FGrUQSeHRxkRElNzYlEMUzzSXSPE2vRUW4TUHAZYH2hERUXJicCWyS4Ddv9UTYFvqZQQMsERElGT4zUdkJ+4qkaLNIjtWihTvRD2N1UtEREQUMwyuRHbkrhQp2qQH2JLdDLBERJQUGFyJ7MxVIVL4p8iOVXpXWkRERAmMwZUoEbjKRfZt0AMsTmZARESUgBhciRJJVZnI3j9EdvwqcmCf1UtDREQUUQyuRImo6oDI3vUiO38TKSuyemmIiIgigt1hESWyyhKRPetE0huJ5LYWyWhk9RIRERHVG1tciZJBRbHI7jUiu9eJVJRYvTRERET1whZXomRSXqQPGXl6C2x6ttVLREREFDYGV6JkDrCZjUVyW4mkZVm9RERERCExuBIls7J9+pDVRG+BTc2weomIiIiCYnAlIr3v1wPmAJtu9RIRERHVwOBKRB6ayIE9eojNbqaXEDjTrF4oIiIiLwZXIvKjiZTu0kMsAmwjBFh+VBARkfX4bUREgWlukZKdIqW7RXKai+S0YIAlIiJL8VuIiEIH2OLtIiW79ADbqIVIitPqpSIioiTE4EpE4dFcIsXb9DICtL4ixKbwHCZERBQ7DK5EVDfuKpH9W0RKdog0aimSXcAAS0REMcHgSkT1D7BFm/UBHAivDv1SDaa/1fjabvMf7wgxL8/fDMxEREmFwZWIIlcLqy5dMX5gR/Bw69L0Sfb+IZKW5heGawvXoYK1Ea6JiCiWGFyJyOY0PSx7MqoPlydM4/S2VVFonXWm66fLTc3SL9OyefIGIqIoYnAlIqovV4U+SGH1uJRUU5A1wmwGW2iJiCKAwZWIKNK1vxX79cGA0gJzq2xapn6dNbpERHXC4EpEFIv638oSffByiKRm6iFWhVlPqGUfuUREQTG4EhFZQhOpOqAPB/bWrJs1wixaZlk3S0SkMLgSEcVj3WyZX92sN8gaLbSZVi4lEZElGFyJiOxQN4ueETCY62bVgV/mUoMsHgRGRAmNwZWIyK51sxXF+lCjbtZUaoCBdbNElCAYXImIErJudk/1aGeGqXsuT6h14oQMRET2wuBKRJToXOX6ULavelxKWs0wi/5miYjiGIMrEVEycleKlFf61c06/YKsp+yAdbNEFCcYXImISIdT5waqm/XpzYB1s0RkHQZXIiKqhSZSWaoP5rpZlBoYJ1DAJcoM0Oesk18rRBQ9/IQhIqL6lRpUVPqe2tboc1YF2czqrxhXpUgaDwYjooZjcCUiosj2OWuUG7jc+ridq0RSg7TQ8qxgRFQHDK5ERBSb+tnKEn0ww4kUjBZaI9Cqmlr2cEBENTG4EhGRtSdSMGpo/QOt6n/Wv4U2g70cECUxBlciIorPQGucTMGHwxNi/VtoMxloiZIAgysREdns7GBl+uDDIeJMN7XQmoaUFIuWlYgijcGViIgSgFZ9hjAp9L0JgdYcZI1wy75oiWyHwZWIiBKbq0IfzGcJM5/21qf0IJN90RLFMUv3n3z77bdy9tlnS5s2bcThcMgnn3zic7umaTJq1Chp3bq1ZGVlycCBA2XNmjWWLS8RESXaaW+LREp2ihRuFNm9RmT7TyLbfhLZtVZk30aR4p0i5fv1vmiJKLmDa0lJifTs2VMmTJgQ8PannnpKXnjhBXn55Zflhx9+kJycHBk8eLCUlfnXNhEREUWyL9r9IqW7RIo2iexeK7L9Z5GtP4psXymya43Int9F9v0pUrRFpHiHSOkekbJCkYoSkapyEbenD1siiihL94ecccYZaggEra3jx4+XBx98UM4991w1bvLkydKyZUvVMvvXv/41xktLRESS7H3RulyeOtowoEsvnEnMOzgDXE+rvs4SBaKQ4vZdsn79etm2bZsqDzDk5+fLcccdJwsWLAgaXMvLy9VgKCrSa5oqKyvVEBNVbr0rF4tVes5aY1xSbHH9W4/bwFpc/3jeVXW7i8MccAMF3VS/aWrfcWp878Xs+498cP2HL9x1FLfBFaEV0MJqhuvGbYGMHTtWxowZU2P8jBkzJDs7W5LRzBVbrF6EpMb1bz1uA2tx/Vtv5syZVi9CUuP6D6201O8kJHYLrvU1cuRIufvuu31aXNu3by+DBg2SvLy82CwEaqC0Ov7KjgK0cuAL4/SebSTNyX4MY43r33rcBtbi+o/HbeAQcZhbcv3/Nq6nmVp4eWKHhrQiIrSefvrpkpaWZvXixDVjD7ltg2urVq3U5fbt21WvAgZc79WrV9D7ZWRkqMEfXjAxe9Gkpoi44+dDGh9W/NKwDte/9bgNrMX1H2/bACUMFSJuDGHcGUHXmebpDzdDPxWv6kIMf6cz2IYhphnEpsJdP3EbXDt37qzC66xZs7xBFWkcvQvcdNNNVi8eERFR8hyUVoWhTKTGcWmeM5Z5gyxDLUWXpcG1uLhY1q5d63NA1vLly6Vp06bSoUMHufPOO+Wxxx6TLl26qCD70EMPqT5fzzvvPCsXm4iIiPzPWMZQS4keXBcvXiynnnqq97pRmzps2DB56623ZPjw4aqv1xtuuEH27dsnJ554okyfPl0yMzMtXGoiIiKKfKj1nJqXoZbiNbj2799f9dcaDM6m9cgjj6iBiIiIEgVDLdVP3Na4EhERUTKqR6g1yhAYahMegysRERElVqhVl5kMtQmIwZWIiIgSK9TWwFCbKBhciYiIKMEx1CYKBlciIiJKYgy1dsLgSkRERNSQUMt+amOGwZWIiIgo0geKIcBqTv1q6W6RjOzqYEv1xuBKREREFFGafopcl1u/WrRZxJniG2q9pQd+ZQhUKwZXIiIioliHWgzlRb43OVJMNbUZfjW1DLXA4EpEREQUDzR3daj1p0JthuksYua/0yRZMLgSERER2SLUHtAHKQwSao0h09Rym1ihlsGViIiIKGFCrR+HM3hNrdN+MdB+S0xERERE4dFcIpWl+hAs1NbozgtDfEbE+FwqIiIiIrIu1GbmizQ9SOKN0TcDEREREZFO0yQeMbgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS0wuBIRERGRLTC4EhEREZEtMLgSERERkS3EdXB1uVzy0EMPSefOnSUrK0sOPvhgefTRR0XTNKsXjYiIiIhiLFXi2Lhx42TixIny9ttvy+GHHy6LFy+Wa665RvLz8+X222+3evGIiIiIKIbiOrjOnz9fzj33XBk6dKi63qlTJ3nvvfdk4cKFVi8aEREREcVYXAfXvn37yquvviq//fabHHroobJixQqZN2+ePPvss0HvU15ergZDUVGRuqysrFRDTFS5RTS3WK3S5fa5pNji+rcet4G1uP6tx21gLVuv/yoXwlPMHi7cjObQ4rhg1O12ywMPPCBPPfWUOJ1OVfP6+OOPy8iRI4PeZ/To0TJmzJga4999913Jzs6O8hITERERUV2VlpbKZZddJoWFhZKXl2fP4DplyhS577775Omnn1Y1rsuXL5c777xTtbgOGzYs7BbX9u3by65du2pdERG1faWIViVWwy+8mSu2yOk920iaM66Pw0tIXP/W4zawFte/9bgNrGXr9Z+eK9K0c8weDnmtoKAgZHCN61IBhNb7779f/vrXv6rrRxxxhGzYsEHGjh0bNLhmZGSowV9aWpoaYiI1RcQdPy9QvFls94ZJIFz/1uM2sBbXv/W4Daxly/Wf6kR4itnDhZvRUuK92TglxXcRUTKAEgIiIiIiSi5x3eJ69tlnq5rWDh06qFKBZcuWqTKBa6+91upFIyIiIqIYi+vg+uKLL6oTENx8882yY8cOadOmjfz973+XUaNGWb1oRERERBRjcR1cc3NzZfz48WogIiIiouQW1zWuREREREQGBlciIiIisgUGVyIiIiKyBQZXIiIiIrIFBlciIiIisgUGVyIiIiKyBQZXIiIiIkq84IqTANSmqqpKFi5c2NBlIiIiIiJqWHBt3bq1T3g94ogjZOPGjd7ru3fvlhNOOKEusyQiIiIiinxw1TTN5/off/whlZWVtU5DRERERBSXNa4OhyPSsyQiIiIi4sFZRERERGQPqXVtTd2/f79kZmaqkgBcLy4ulqKiInW7cUlEREREZGlwRVg99NBDfa737t3b5zpLBYiIiIjI8uA6e/bsqCxEwnC7RDbMF9m0SCSrsUirI0VSnFYvFREREVHyBddTTjklektidys/E5k+QqRoS/W4nOYifW8T6XyylUtGRERElHwHZ+EEA+Xl5T7jtm/fLmPGjJHhw4fLvHnzJGlD6/tX+YZWKNkpMnOUyPpvrVoyIiIiouQMrtdff73cfvvt3us4UOuYY46RCRMmyFdffSWnnnqqfPHFF5J05QFoaZVa+q+d/5I+HRERERHFJrh+9913cuGFF3qvT548WVwul6xZs0ZWrFghd999tzz99NOSVFDT6t/S6q9kh8icp0TWzxUp2oqj2GK1dERERETJWeO6efNm6dKli/f6rFmzVJDNz89X14cNGyaTJk2SpFK8Pbzp1nylD5CWI1JwiEjTg0WaHaL/3aSTiDM9qotKRERElDTBFf23HjhwwHv9+++/92lhxe3o1zWpNGoZ3nTtjhUp2yeyZ71IZYnI1hX6YHA4RZp01MMsgiwCbbODRTIbR23RiYiIiBI2uPbq1UveeecdGTt2rMydO1cdmHXaaad5b1+3bp20adNGkkrHviJ5bfQSgGB1rjktRIaM1bvGcleJ7PtTZNdakd1rRfas1f8uLxLZ87s+rJ1pum9zPcCqIOsZ8HgOnvSMiIiIkkudguuoUaPkjDPOkPfff1+2bt0qV199tbRu3dp7+9SpU6Vfv36SVBBGh4zTexUQR+Dw2vfW6v5cU1JFmh6kDzJIH4eaV/RAgCDrHdaJFG3Wx2P48/vq+aVlVZcZGEPTziKpGTF60kREREQ26Md1yZIlMmPGDGnVqpVcdNFFNVpkjz32WEk6h50jcvHkAP24ttBDa6h+XHG2sUYt9AEtuIaKUpE963zDLFpkKw+IbP9ZH7zzSBHJb18dZFFu0LhzFJ4sERERkQ2CK3Tv3l0Ngdxwww2StBBeuw2N7Jmz0rNFWh2hDwaUGhRu0oMsSgwQbHet0etn923Qh3Wz1KRpIjI4NV+cO7ua6mZRatCWZ/QiIiKixA6u334bXkf6J5+cpGeKQhjsfJIeWhEwo/IYqXoPBBgOGVhdanBgT3XdrGfQCjdJZlWhyKaF+mBIzdRLC3xKDQ7SSxDqC/3UbvtRpHSPSHZTnu6WiIiIrA2u/fv3Fwd2a6usFPhAJNyOvl0phrBNspuJdMBwnHd0VVmJLPj+B+lXUCTOveuqSw2qykR2rNKH6pmI5LfzDbM4KAzz9WzzoHBmsPkv6rW4Bp7uloiIiKwMrk2aNJHc3Fx1UNaVV14pBQUFkV4eiqS0LNmbc4i4u7cTpzOlumUUB32Z62ZxWbpbpHCjPvw+u3oe6I7LXDeLg8Iat9dbfo3QitPa+jNOd3v6IwyvREREFPvgip4E0HPAm2++KU899ZSceeaZct1118mQIUO8LbEU57D7vnEHfTi4uisztYtf1csa3XSt07vtQu3s5sX6YMCJElBqgBCL4FobnO62Yz+WDRAREVFsg2t6erpccsklavjzzz/lrbfekltvvVXKy8vVWbPGjBkjqal1Pt6L4gHqUjG0O6Z6XFW5fsIEczddCLTo1WDnan0IBae7/W2GyEEn6WcM4w8cIiIiqqd6p8wOHTqofl1RMoBW1yeffFLuueceadq0aX1nSfEG/cK26KYPBs2td/mFELtmpsiG70LP59tx+oCDv1D76jMUVP/dqLlIRj7DLREREUUuuKKF9aOPPlIlAwsWLJChQ4fKf//7X4bWZKD6i22nD5n54QVXBFa00mJA+QGGYJxpAcKtJ9Qaf2c14ZnDiIiIklCdguvChQtl0qRJMmXKFOnUqZNcc8016ixaDKxJCl1eIUiaexPwh5MwXPqeiLtSpGSXPm2x52xg/sOBvSKuSr1F13wiB38Op29LbaBwi7IH4wCyaGNXYERERDFRp2/2448/XpUI3H777XL00UercfPmzasx3TnnnBO5JaT4hXCGLq8C9Srgf7pbDEZLbTCuCpGS3YFDrTEgHGoukeLt+hAMWmSzmgYOtd7QW6AfaNYQtXUF1uHEhs2biIiIfNS5SQoHZT366KNBb2c/rkkGXV2hy6sa4S3M092aIUTmtdaHYHBiB4RX/0Dr04q7Sw+3pbv0Yae5v1o/6O4rYLg1hdxgJ2YI0RWYY8BoETko/OdPREREkQuubrc75DSlpaV1mSUlAoRTdHkVi93l2P3fqIU+BIMDyFB2YJQmBAy3O/UWXnT3hWH3muDzy8gVyS7wK0VoJrLo9VoX1fn9BJEuTzfgyRIREZFZxIoAccDWhAkTVP+u27Zti9RsyS4QUtv0lriAMgEESwzNuwaeBmd+Ky8KHmqNAQeUle/Xh73r67YYJTulWTG6DOsQmedFRESU5FLrGk5Hjx4tM2fOVH26Dh8+XM477zzVu8CDDz4oTqdT7rrrrugtLVGkoMst9IqAAWcFC6aiJHC4xely0adtCG32LRQpPVokl2eZIyIiimlwRb+tr7zyigwcOFDmz58vF110kepZ4Pvvv5dnn31WXUd4JUoY6Tn60KST7/gty0Smhf6RdtCuWSLvzdLPMtaujz6gjAJ95BIREVH0gusHH3wgkydPVr0G/Pzzz3LkkUdKVVWVrFixgqd8peQSRldgWlq2FDqbS+OyDXrrLIYf/6P3VYv74yxlbY8WaXYw+6UlIiKKdHDdtGmTtxusHj16SEZGhioNYGilpBNGV2Cuk4fLnD0HyZndG0natmUimxaLbF6sh93NS/QBcEIFBFi0xrbto/dkQERERA0LrujmCrWt3junpkqjRo3qMguipOkKTEM/rns2iWQ1FjlkgD7goDCcOQwBdtMikS3L9R4Q1v5PHwBlCao1to9I6yODd8dFRESUZOoUXDVNk6uvvlq1tEJZWZnceOONkpOT4zPdxx9/HNmlJLJjV2CuAN3HYe9Ek4760ONC/Uxh23+pbo3duVpk7x/68NMHIikoK+hR3Rpb0IVlBURElLTqFFyHDRvmc/2KK66QaNu8ebOMGDFCvvzyS9VH7CGHHKJOO9unT5+oPzZR1LsCQ71rm176IH8TKSvUD/xCayzCLM4OhusY5DW9F4Q2R+ktsgiztfVnS0RElMzBFYExlvbu3Sv9+vWTU089VQXX5s2by5o1a6RJkyYxXQ6imEEwPai/PqCsoHCTp6xgsR5eEWx/n60P0LiD3hKLEIvwm5Zt9TMgIiKK/xMQRMO4ceOkffv2PoG5c+fOli4TUcygrKBxe304/Hz9dLc7VuohFsPOX/V6WQy/fKyfVazl4dVBtuDQ6Jy9jIiIyCJxHVw/++wzGTx4sOofds6cOdK2bVu5+eab5frrr6/1JAkYDEVFReqysrJSDTFR5dZPO2qxSk+NpXFJdl//KSLNe+hD76vV2bwcW5eJY/MSSdm8WBz7t4psXaEPi98QLT1XtDa9xd22j2gIs7mtJNnwPWAtrn/rcRtYy9brv8qF8BSzhws3ozk0HHEVpzIzM9Xl3XffrcLrokWL5I477pCXX365Rr2tAWf2GjNmTI3x7777rmRnczcqJa7s8u3SYv8v0rzoJ2levErSXKU+txdntJSduT1kR24P2ZV7mFQ52VsBERHFBxzHdNlll0lhYaHk5eXZM7ii6y0chIWzdBluv/12FWAXLFgQdosryg127dpV64qIqO0rRbQqsRp+4c1csUVO79lG0pw8Ej2p1r/bJY6dv4pjyxJxoDV2x0pxmPYCaI4U0VocJlqbo1VrrNa8W0KWFfA9YC2uf+txG1jL1us/PVekaezKM5HXCgoKQgbXuC4VaN26tRx22GE+47p37y4fffRR0Pugqy6juy6ztLQ0NcREaoqIO35eoHiz2O4Nk0AsWf94vDZH6EOfq0UqivU+Yz3dbjkKN4lj+88iGJa9rZ/WVvVW4KmPzWsb+jHcrsDdgMUhvgesxfVvPW4Da9ly/ac6EZ5i9nDhZrS4Dq7oUWD16tU+43777Tfp2LGjZctEZEvpjUQ6nagPgHrYTUs8J0FYqupl5Y+5+gC5bUTa4Wxex+hdfWXk+s5v/bcBTrzQXD+bGPq2TXQ2Cu1ERIkkroMrTifbt29feeKJJ+Tiiy+WhQsXyquvvqoGImqA3NYi3c/SB4SwXb9VnwRh288i+7eIrMLwuX7CA5QSGK2xJbtFZtWsI1chFqfAxdnEEjm8JntoJyKyUFwH12OOOUamTp0qI0eOlEceeUR1hTV+/Hi5/PLLrV40osSBlsIW3fXhqCtFKkpFti4X2expkUV3W+iGC8PSySiNr31+81/SzyaWiC2QCK0I58ka2omILBbXwRXOOussNRBRjKRni3Tsqw9QvMPTd+wikY0/iFT69lZQQ8kOkX9fLJKWqbfWoj9a4zS1xnUxxpv/Nm5zBLjudz//adT8TeM890F07rW7VJwluSIpftPUdblwHCtOw1sbtMQmamgnIooDcR9cichiOK1stzP1Yc1MkdmPh77Pgd0iB8RyiKWqIn5PjB4QLa//ukAkr41IdnORnGYiOQWevwuqB57hjIioXhhciSh8CF3h6HeHSLND9FZK0fQTcuBvo0su47oY481/G7dpAa4Hup///Kvv53K75dfN+6Rbmzxxiv88alkuv/mov/dt1A9kCwWn5cUgq4JPk5bjG2QDhdysJmy5JSLyw+BKROHD0fM4EMl8YJK/nBYi3c+Ji9DldrllbdUmObRnO3E2tCuaLcvCC6797hLJaSpSsqt6KN1Z/TdKLSpLRPZh2BB8PihRQI8F/q213uvN9L9R2hEr7E2BiCzG4EpE4UNIwdHzgQ5QMvS9NTHDTNih/azanz8OfisNEmqN6wiGaOk1xtXykKrsoEaoLYh86y17UyCiOMDgSkR1g5CCo+drhJgWemhN1BATqdCOFtL0DiKNO9Tesnlgr1+w3V0z5KLlFi246PkBQ22tt1lNTYEWlwFCbrDWW/amQERxgsGViOoOIQVHzyfbbuNYhXasRyNQSrfg0yG0BitJMFp1S3frrbe4Xhpm660Ktp4BgVd1g5akXaARUVxhcCWi+kFIwVm1kk08hXYEzcZhtN6W7dODdrCQW5fW22BdoH39qEizLiKZeSIZefplWq5kYr6uFiLOzAY/XSIiBlciokQO7VjWbBzI1UykuYRovUU5AoKsKdRuXymy69fQj/P7N/pggjOPD8YfP+PbJlMkM7861KpLXM/1XM/XL823pedU97VrNR6YRhQXGFyJiMjTeouhfc3eFKbdFfr+B/XXw2lZkUh5keoSTFOXReJAd2JVZSLFGLaHv0wIrQi23pDrCbZG0DVCr38gTs2QiOKBaURxg8GViIga3pvCaQ/VaIGscrnliyV/ypk9mkpa1X491KpgW2gKuHrI9f7tCb0q6KI21+gXt3Bj+MuMAO0fcs0lDDVaffF3o8CtuzwwjSiuMLgSEVH0elNQraaNRLLzRPLahv+4rgqRcoRdv5BrhF5zyDWHXoRdhF4MqL0NF5YzvZEeZI1QixbdP+bWfr/vnhdp3VMva0hJwK9UlkhQnEnAdxkREdm+CzRnenVtbrhwpjMcDFaXll1cVh7QAy/+xoATn4ULvTZMPtezzGkiqVkiaZ4BLb+BLtXfftOkZXrGZda8HfO1AkskKA4xuBIRkb16UwjG4dBbTTHktQn/fq5Kv0DrCb2bl4qs+7pu8zHmFUloyQ0afv0Dr2/wdaSkS8H+EnHs2C+Ske0bjPHjAOssEJZIUJxicCUiosTrTaEu0KIZqHUXpQ3hBNcznxYpOFQvT0DrbSVKFXDpGdTfntuq/C+NafzvWybirtTn764SqSjWh3p8yffDH2uDlEcEahXGgB8otZk3XqSgq0h2Ez0AE8UIgysREVFDDkxrc5Sn5Tk/so+PwFqX8GsOvZ7b3ZWlUrx/v+SmVonDmB71w4DyCHSDhqGuDuwRee8S/W8EXlUbbPTukB98MA6Ms0PYZX1vXGJwJSIiiuZpfuv9+Kme7sBy6z0Ll8sts5dukjOPaidpzpTqQFZb+N28RGTVZ2HMHWUGqCv23Hf/trp1v2YcCKcCrRFu84IH3ljW+rK+N24xuBIREcXTgWnRhqCNXhAwBILwGE5wHfqMSEGX6i7L/AfjADjvYOr5wWjp3b81/OVOy6kZbGu08Ppdr09PD6zvjWsMrkRERHY/MM2KEgl0A4Z1gBbh/HbhzVv13lAcJNgGCb3oFk2FXZyWuKQeYTdIqA0UenFgH36k1Gb+S/rrIVG3f5xjcCUiIkrWA9NiXSKBA8KME0OEyxx2g7bm+gff/Z4yBiPsbpGIQf/Aq6aJdDhOJKuxfjAbxQyDKxEREcVviYRP2PU7JXEwqONFLwzmMoVQwdcIu+H47jmR7zx/I7hmNRHJbKwHWeMyq7E40vOleVGVyK5SkZym+ng7HJgWxxhciYiIKLFKJLCMRglAuBB2/5wvMuOh0NNivhWlepdlOMAN5QsBShgQsvrij3V+5QtZ+X5ht0mAcZ5LK87I5naJbFossmWZSKOWIh37xs12Z3AlIiKiwJKtRKJD3/Dqey99T28JRhnCgX36UGa+3Ktacd2le2X/nh2Sl1IqDtymuarLF4rCLF9A3S0CrTnMZjb2hFxP2DXGoVW6oUE3UI8KOKHHkHEih50jVmNwJSIiIqpPfa9xprYgB6ehO7JvjO7IUhx6+YIKtZ6Q6wm46tI//GI86nuNk08UbgzjCTj0g+WCBV2/UgZ1gBoCeKgeFYq2irx/lcjFky0PrwyuRERERNGu78Xpdb398nYIs053f80W3QOmsGseZ9Toon4Xw74NYdYPG70qNBbZsSrIhKj9dYhMv1+k21BLywYYXImIiIjirb5X1el6WkibhHmmtTIcdBYo6AYoZzC6GVOtvXvDeABNpGizyIb5Ip1PEqswuBIRERHZvb43JVUP2BjC4ar09KjgCbN/zBNZ+Uno+xVvFysxuBIRERElG2eaSE6BPhhBPZzgil4GLGSqyCUiIiKipNTKc8a0oBwieW31rrEsxOBKRERElOxSPD0qBOTQL4Y8aXl/rgyuRERERCTeHhX8W17Rj2scdIUFrHElIiIiIt8eFXav01tXeeYsIiIiIopbKU6Rdn1Emh0s8YalAkRERERkCwyuRERERGQLDK5EREREZAsMrkRERERkCwyuRERERGQLDK5EREREZAsMrkRERERkCwyuRERERGQLDK5EREREZAsMrkRERERkCwyuRERERGQLtgquTz75pDgcDrnzzjutXhQiIiIiijHbBNdFixbJK6+8IkceeaTVi0JEREREFrBFcC0uLpbLL79cXnvtNWnSpInVi0NEREREFkgVG7jllltk6NChMnDgQHnsscdqnba8vFwNhqKiInVZWVmphpiocotobrFapcvtc0mxxfVvPW4Da3H9W4/bwFq2Xv9VLoSnmD1cuBkt7oPrlClTZOnSpapUIBxjx46VMWPG1Bg/Y8YMyc7OlmQ0c8UWqxchqXH9W4/bwFpc/9bjNrCWfdf/qpg9UmlpaVjTOTRN0yRObdy4Ufr06SMzZ8701rb2799fevXqJePHjw+7xbV9+/aya9cuycvLi82Cb18polWJ1fALD2+W03u2kTSnLapCEgrXv/W4DazF9W89bgNr2Xr9p+eKNO0cs4dDXisoKJDCwsJa81pct7guWbJEduzYIUcddZR3nMvlkm+//VZeeuklFVCdTqfPfTIyMtTgLy0tTQ0xkZoi4o6fFyjeLLZ7wyQQrn/rcRtYi+vfetwG1rLl+k91IjzF7OHCzWhxHVwHDBggP/30k8+4a665Rrp16yYjRoyoEVqJiIiIKHHFdXDNzc2VHj16+IzLycmRZs2a1RhPRERERInNZu3WRERERJSs4rrFNZBvvvlG4t2WfQckRVyS7qlpSXU6JN3pFLuVtxARERHFE9sFVzs4UOkWt6tmrwIpDlEBNi3VoRdqpyDYOiQ1FX87LFlWIiIiIrtgcI0htyZSVuWSsgA9ZSG2piPAOs2DJ+A6GWqJiIiIGFzjBDrTLa9yq8EfYmtaqqmF1pki6aZWWwdzLRERESUBBlebhNqKKrdUSOBQm+rXQqvX1KbodyQiIiJKEAyuNqd5zswR6DzILtQmeA4Wy0xzmlpt9YEHixEREZGdMLgmycFiFa6aza9Oh6fcIFVvoTVaa9UlDxYjIiKiOMPgmsRcmiauIAeL6T0gIMj6HSzGHhCIiIjIIgyuVEsPCG6RIAeLGT0gqBZalB94DhZTtbVEREREUcDgShHtAQGNsZlpqZKZliIZaSmSmepk2QERERFFBIMrRbyltrSiSkorqsehVRYBFgeIZaamSEaqU1LYMEtERER1xOBKUVfl0qTYVSXF5XoxLdpfMxBgVZB1qpZZXCciIiKqDYMrWVJqgPpZDIVSqcaxxICIiIhCYXCluMASAyIiIgqFwZXiFksMiIiIyIzBlWxfYpDhaZVVJQY4QxhLDIiIiBISgyvZvsTgQKVLDQaWGBARESUmBldKOCwxICIiSkwMrpTwWGJARESUGBhcKSmxxICIiMh+GFyJaikxSFcBNqW6j1mWGBAREVmGwZWolhKD8iq3GorK9DDLEgMiIiLrMLgSRajEwJniUGf7SnE4xOl0iGiIviJut4g4LVxoIiKiBMHgShShEgN/LqRcEVm/u0QFWoTZVEeKCrjOFATeFBVy1W2mgae6JSIiCozBlSgGNE/ArZLqltpgEFurgyyCrn49FX+r1lwRJwKwCsIOcTDnEhFRkmBwJYrHkOvW1CCCOoPaoYEWrbcq1HpabI3Qi14RjOto7WUvCUREZGcMrkQ2h3xbURU64AIaZ42Qi9pcI/SyZIGIiOyAwZUoiaANt9Ll1k/DULMsN2DJgjrgLETJglMwTYyeBBERJS0GVyKKSMmCuTbX4WnBRf2tMS5FPJcIwJ6D1FQPDAy8REQUJgZXIopC0A0fAq/RhZhvuPULwaZxRmkDERElFwZXIrIUYq5L08RVVbfAC8iuqucFh3jLGVJM4VbzhOiyCpe40zANe2IgIrIzBlcisi3kUrdRsxugnMHoS3dzYZkKsz5lDajNdRjlCvqBakZPDChlMLf6qmkwLcsaiIgsxeBKREncr27dyxr0UobqcgVVxxuorEEdsKYPbOUlIooMBlciojrW8aqm3jryP3jNaME1h141zukbetljAxFRNQZXIqI4PngtUEuvOfT6jGPoJaIEx+BKRJQELb0qzPr12mAOvShvQEmDfzi2gqbpg/pbDfjPc8W4bvxtGuf535ha/1sf4TtvdWlMVfP+1dP5j9PUukHrOGqicWn0ioH1h3FYx2o8/jYu+QOCKGIYXImIElhDQ2+grsq88zaFR3O4M0Kmy+1S4zbtLVW9PpjDYPX9qkOosbyJylif+o8DXPcNuOa/1Y8JczDG7QjHpvswGFMyYnCliMDR2z/vdMnuMk2aZTqkR3Onz1HcRJRcXZWZe3Uor9LEmRLeaYmTYn26ohPPfVt/8WNDfzzYXlgmqan66Z19wjJCr9GC7BeM9TBt/GiJyiIT1RmDKzXY3I2V8s+lZbLrQPWHcUGWQ24+KlNOap9m6bIRESVbMBbV0K35/HgornCJs8od4WDsVxLh/bu6lMIIxWK63RyKWYdNdcXgGkH4gFi4fo/8/Ee5NEl3J0WrI0LrI98dqDEeIRbjR/UThlciogQNxpFiDsWqTMJcEpFiut17PXhLMUsoEhuDa4RM/3mrjPl8pWwtLEuaVkcEdbS01mbisjLp2zY14QM8ERFFJhTXtX/lcA9Q9LYEe0KuPs639jhQ+URdvr30quRqVS69lftAhUsqneE/r4h9Yzrqf9cUp0syJP4wuEYotN70r6U13mqxaHXEgRDY+6MGTT8IoxJvfLdIhcstm0tEHHtcatkqjelUlzz635Wmv2te1/+uDHAfvBd3HXD7lAcEsrNUky9+r1ThtXGGfnAHERFRzA9QtODQP6NUY4vf2fvsILNRhrRvKXGHwTUCL0q0tNb2dvi/hQdkzd4qcWuOGoHQFSAsBguY+rSm8SqohrOJS8VKLywukxcWY/ePSNNMhzTLTlGt0QVZKdIs2+H9G5e4LSvVXm9uIiIiig0G1wZCTau5PCCQ0kqR91bqZ1OPhTT0x+gZHJomGWkpnnE4HaVxm8M0nT7eO41DJNVZPQ0Cp3FbmtO47pCtxS75YHXo55WXLlJcid1AIjsPaLLzgN5FTjA5aSizSJFmCLQ1Qq5+vXGmviuHiIiIkkdcB9exY8fKxx9/LL/++qtkZWVJ3759Zdy4cdK1a1eJFzv21x5aDUe1ckrnfGd1cMQ5zZ1+YVGFR328cd0bHo3bPAHTGSR4GkdyGq3Bqzfula7t86KyiwLzn/1nVa3lAs2zHfLOWY3U33vLNDWtKjEo1WS3p9RgV6lbdnvGH6gSKanE4JYNRepRAs5Xtd76tdT6ttzqf2fGoPWWXYERERHFRlwH1zlz5sgtt9wixxxzjFRVVckDDzwggwYNkpUrV0pOTo7Egxa5mWFNd1n3DOnZMq5Xd50hnOHgs0C9Chhu6p3pDXEFCJPZ6p5Bpy+p9A2yCLi4NF/fV67prbelmuwsrb31tlGaSLMott7W1hUY6noTGQM7ERHFWlx/s06fPt3n+ltvvSUtWrSQJUuWyMknnyzx4NjOTaV1fqZsKywLWueKVkd8qSciHHSGg8/8wxueM0JrXQ9Ky0lzSE6+Uzrm1x6Y9pSZW259W3HNrbcoUSiOUuttqK7AHuybKc0lMbHvXiIiskJcB1d/hYWF6rJp06ZBpykvL1eDoahIJRaprKxUQzT844yuctuUFarXiUDh9e+9MnyOLowV4/Gi/bhoWTyudY78vMulAiUOwOpRoLe+Reux8RhNM51yaBOptfXWKEdAmN1dWv230YqL8oX6tN42yxSZt7n26ScuK5fhPUQqVXcoidOZ4LxNlfLY/LJaArsmJ7aLj/AarfeAam0O8HpPdHV93rH6DKLguA2sZef173K5o5abAgn3sRwa+lOyAbfbLeecc47s27dP5s2bF3S60aNHy5gxY2qMf/fddyU7W+2njooVux3y8R8psq+i+kO8cbomF3RyS89mtljFSQmhdX+FyL4KkcIKhxSqv/VLfXCo2yrcDQslKaKpOmS07povU83XVb+Cmu84Yxp1m/lSn5/P+EDz9MyvtmkwhFspgc/eMUudap0E7iBQk8bpIg8f5dLPlpOAkvW9nqzPm4hio7S0VC677DLVSJmXl2f/4HrTTTfJl19+qUJru3bt6tTi2r59e9m1a1etKyIS8Itq8Ya98suqVdI4w215KwyWZ+3mfXJI28ZJ0RoULXiLoGeI6lpbTRZtrZS5m2pvcbUTHNhn7jnCe6CgZ7wanA45UKnJb3tDnzby7IPTpFN+irpPuucgxHTv3zXHpXse1zjwMF7fA8Famw0oD4mX1uZIqu/zTqTPILu2sifSNrAjO6//zJx8aXvQYTF7POS1goKCkMHVFqUCt956q0ybNk2+/fbbWkMrZGRkqMFfWlqaGqIJcz/x0JbSuny9uF2xa14PBW8Wu71h4otD8pwieZkpcpCnNKFNoxSZuyl0/7g3dHPJwK75qrq2ylXdZ2+F0WevGlc9Xr9u9OXrmdbl279vhcv3xBA156FPG/R2t95yamZM71vsUv/ftJ+vq//rHy3AKswi1HoCc42g67kNl3qo9g3BanpP7xy7Cx2yXaokI9XhDcjm6QIFaqNnDv8voJeXVf8oDuSV5eUqwMX7+w0/xlR37Jr+WvD+7blUg6eFvUpzy8SlDXvedv8MSoSa7vpuAx6EGRl2fA84nSlRz01m4T5Warx/uN52220ydepU+eabb6Rz585WLxKRgg9vfHHV2hVYlkO6N9YkLw7PGOYyBeFKU7g1ArIKuabAa0y7bm+VTFkVOpT2apEiOekpar4I2hWeQF3huY75qb/d+jhzkEb5Bg6sO4AzbCgN3SnkFFkbXrd1Bj0kV4dZhFuss3DOFHfH/0qkUbrDGwrBCIfq0hsUNW9QNIdH73Tm28xB0zS+5rxrTucfUKNRaofnfcHH+9XBlfihkWH8OHA6pLIiRZpsOCAZqfr6xKX5R4M+rXG9elyoaYP9wIi0UAdhRvPMiFZLhMBOiSeugyu6wkJt6qeffiq5ubmybds2NT4/P1/160oUz12B/b13hqRo8dPyXvPXv3Et/C//k9qlyv/+CN1375P9c+oU1l2eAIuQrIfb6lCrgq/ptkq/2/QQ7LluCsiYvrxKZG9JhaSlp3mnq/QL0bgP/q69BTr8tLd6T+hSikRUWoUh0Gk1U0QKq6LymAiw3pDrE4Crr6OFPcNooVet7vp9fFrvPdP4T4ta8heX1P6jZ+KyMnWAarz9OG2oZA7sBrY2x6e4Dq4TJ05Ul/379/cZP2nSJLn66qstWiqi8LoCw5fZ6o2S1H331mW+WSkiWXUI0eHQT8JRJl3bZ9e6TGipNMKqCrd+oRZBd9Vul7y2ovZd5nBJ9zTpmOdUfQOjQRCPqp8YRP8blynGpXecI+R0YMzTGB/8PuibuHpcwOnU337TBZj3jzurZPjs4NvbcO+xmdK5sdP7I6LcpUlZlSYbdpZIs/xsfd16ymFwm/lHif918w+Tclf1NsDf5lhs/Mipzw+MSLY2n//xftVdnnEgJF5r3oMi1UGQer249+BIzw/H6oM1q89qqH5Umg6iVPPyzqfmvNW8TAdgGvM25usQTbaUimQXuVW5jHdaz4lwqqf1PXkNPteSMbAbkr212eXWZNmWMllavFn1V4+uP+NlW8d1cLXJcWOUxPABhg/vQL/K7dj9iRV998YDfGEbu6qxuzuQwwqcMvW3ipCtzdccUffgHs+ObJ4auiwm2yEDO9WscVU/HESTru3TI7JO8J1g1H1XeFrU/Vvmq0NwdfA1WusDBeZKn3n53lZSoamW5FB8S1vUkkp8SRVZURJyKmwi/VThIuXu0IH9tpkl0iTTOKW46VTiplOG+x/oWWN8gINB/U9DXmP+xinNo1QqkuytzXO9oX0/zg+qxqG/+ofPPkyG9Ght9eLFd3AlsgN8ISfaWdEaEtgTVbRam+NdPD1vBBXV+wR+YES4dT6QFdur5N7ZpWG1Nndp6lSh2qVpgq6b8bdxXb/0XPccJImc6zLd7r3NO776AE01P891jK+ev+m65j9vz3IghKK52+HwmXeg39UY5182U5s1qocR60pjjADrG3DNwdgUgp3hBWK8jD9arfr7C+r5xQckC/XXTk9Luum07cZjGC3geJUaB9iaT8ker+YGCe04ydJN/1oqE684yvLwmlzftkQUMcka2BOttTkcyfq8wzoIM0hrc7zQy2X2Stf2TXyW0a0FD8U/7ayScd+HPqDxsu7p0iY3xdtbidFziXHQp7n3E+NgTyOMmw8Grarlb3OPK/5bwXgsfUljVy5SWC4yck7o8plqqSILi9Vf1eUi1WUe5uvVJSXVJSI1ykkcetD2LxcxuhM0l5agJdu/NKT6vuayE5QNBa/pxlrFq2fM5yvl9MNaWfp6T65vHSKiBkrG1uZkfd7x1NocaaiXRmkMOt3QVT+Hgqw0eWNFecjAftURGTF97v69oYQTksMNxkZXg38WuWXFjtDNzvhBk5Xq0B/PE/grTa3mmF+gVm3140CVouCa/wTxVmJSc+m2FpbJwvV75ISDm4lVGFyJiOooGVubk/V5J2Nrc7wG9vr2hlLX8pAVO0KXh9x/fFbI9wJatcurNPl14z7p3KaxCn7mMg8j4Oqt3nrg9rZ+Gy3hapy5dVzzBm1zeYl3nn6t6P4lK9WD3sJuvo7TpJeE0RHOjv11614w0pLrE4iIiKiOkrG1ORkDe13KQzBduK3a6H4tNz3++vOub003ehmwEoMrERFRCMna2pxsgT1eW5vjIbQ7RKRVvt41lpW8je5EREREgQL7aR3T1GUiBrbArc1ZKsT5t7RifKK2Njs9oT0QY02gSyyrXwPJ9fORiIiIKIRkbG2urUQELa3sx5WIiIgoTiVjeYg5tP9WnCVaXhueOYuIiIiI4pczxSG922RK+0PaSrxhjSsRERER2QKDKxERERHZAksFiIgoIJxa0unEqSBxGklcYhdiimiCDtL1TtLdbrfqFN3lwlgiouhicCUiSgI4rAIhFOcrT0lJUWcgUsFUBVL9UoVTdXYinPvcIY46Hovh9p6hx60uKyrcslpEGmelqQVQt6sz9iD0ugOeEpOIqDYMrkRECdQaaoTOFKc+TSrGqbAa/WXCY6SIQ9Kc+lmFKp1uddmsUbqkVZ+r00vDKSc9rbUuza2CLU49iVNlVrn0YGtc16dh0iVKdgyuRETx0BrqbfEM3BpqDGgRTa1Ha2g8wnNIczgkTXWzEzpZI7fqrbW+gwq6fteNvxl1iRILgysRUYR5Wz/R4ukM3hqqX9eDKoUXdBHaEfDDhTpclC643Zpq3dXLGdzeFlzW6RLZC4MrEUVMoDhhtAw6PLd6Wwr9xvtO6z8/vxvM03juVH29ehoXUomINMpwSqopHQa7b6Dl8l8m88TYLZ6oraGJQrVe16FOQpUrINh6anHdruq6XYRfBNtgm9d4PfmMC/ZAAW4wvxd85xvW3QPegJILaJaT7vMeqMNi1aquQT/oTwMt/HkHqxgJOO/wH84z75q3YAxGo3QFrfm4grWKvzW35zLI/CjyGFyJEpTxBYRdzQ7UHqKlT92g735GoxW+ENVOWhW2jHH6PY1pMAEuEdJwG27WpzfGBf5ijQeVni/tlnmZAWssifwh46bjnaKX6dqe8R5onJ3G90CU4UePG/HZE3BxWVnlktUb8RmUoerNjfCr+U1nXPr8LSh70cN09ThicCWKMZUFHX5hUXwDoU84NMZ52mO81z0HwvjeD+G0OpQSEVFsGAcn6vRL47dCo4zUiPxw0IyWXxV8/cIvxrlN4dfTguwTfhOg9ZjBlRoMb8/01BQ1IEAZ44JOLA3bRRbJ+ddxNgF3BRrTO/xaJ7GrcfXGvdKhaba+bhAo2eBBRET15EC9PAb9WyeqrcdaRpbEIwZXqhOEsoxUp2SkpqhLBDL8zda94Lvo0px1O5iEiIjISiloZInT7y0GVwoKuzX0gJoi6WkpkuF0qhBGREREZAUGV1I7GzLS9FbUdIRVT0jlbm0iIiKKJwyuSQZ9SmZ6dvEbIRWXRERERPGOwTWBW1EzUx3e/vtwRGN6qpMdnRMREZFtMbhGgZaSip7PcVxeTB4PB/4glHrrUT2tqegwe8X6Pey/j4iIiBICg2sUVDQ5VHWFpPcWXCUOnHpFc4tD/Y0+JqrEoXnGuX3/9l4GCL3mbqf0I/od6pJHrBMREVEyYHCNJtUjfJpoKWnqal069cXB+5mpmmQ6RbKcmmSmimQ6NXGoHoJdIirk4tIzqL+rqv8mIiIiSjAMrnEAB0jhgKnM9BTJTHN6D55qEATY8jIR2STSpLOehNX56MyBV2/hrRGE4/68GURERJSMGFxj3ACble5U4TQLAdUTWFOisas/xSmSmq7/nZErkqa3+obFaMUN2KoborWXoZeIiIiihME1StJSHZ5wagx6XaotIPRiEE/wrQvPuY9VgFV/o1bXPM5dy+0S4vZg95cw52+anoiIiGyHwTUKurXKjU4rqm3qeuP8ufuE67oGZdPftd7fLVKJg+w2iaTn6qUa/rcHejwiIiIKisE1CpI2tNoyXEexFbyyUkR+EWnaObxSDSMYBw235pBs3OZ/PdR9JbxpLWFsF9OlGu03LuBlkOmq8Fw2iWQ3x26QeswnyHKZl8/nh0xtl+Y9A1od7xtqXnW4jGtBtmWdx5kuA23j+owzz9O8/Wq9Hs40Db0utd/uqPJ81GXiDDQRWMZ4fw1RomNwJYq7QB0Hfe6GG6KN6yEDYYjLaLXSqx8PK0TyWtetzjuRBdxzUJ/wG0b4M/Y6FHTT139tgTDe99TYlXoPrBFpfmhk3wM+P6DUH7UH3YhNJ9Y8bth72xjuo43BlYjiO0RTZMU0JFZ6vmnSRVL5wyGh1GjZJh9GmK2oMP14Sw2jPC2Gx4nYFIMrERERUcR/IDpFnKnx++NNCxFuHfHZcMHgSkRERJRsHPbcs2avpSUiIiKipMXgSkRERES2wOBKRERERLbA4EpEREREtsDgSkRERES2YIvgOmHCBOnUqZNkZmbKcccdJwsXLrR6kYiIiIgoxuI+uP7nP/+Ru+++Wx5++GFZunSp9OzZUwYPHiw7duywetGIiIiIKIbiPrg+++yzcv3118s111wjhx12mLz88suSnZ0tb775ptWLRkREREQxFNcnIKioqJAlS5bIyJEjveNSUlJk4MCBsmDBgoD3KS8vV4OhqKhIXVZWVqohmRjPN9med7zg+rcet4G1uP6tx21gLa7/8IW7juI6uO7atUtcLpe0bNnSZzyu//rrrwHvM3bsWBkzZkyN8TNmzFAttclo5syZVi9CUuP6tx63gbW4/q3HbWAtrv/QSktL7R9c6wOts6iJNbe4tm/fXgYNGiR5eXmSbL9e8GY5/fTTJS0tzs6RnAS4/q3HbWAtrn/rcRtYi+s/fMYeclsH14KCAnE6nbJ9+3af8bjeqlWrgPfJyMhQgz+8YJL1RZPMzz0ecP1bj9vAWlz/1uM2sBbXf2jhrp+4PjgrPT1djj76aJk1a5Z3nNvtVtdPOOEES5eNiIiIiGIrrltcAbv9hw0bJn369JFjjz1Wxo8fLyUlJaqXASIiIiJKHnEfXC+55BLZuXOnjBo1SrZt2ya9evWS6dOn1zhgi4iIiIgSW9wHV7j11lvVUB+aptWp6DfRisJxlB6eO2trYo/r33rcBtbi+rcet4G1uP7DZ+Q0I7fZOrg2xP79+9UlehYgIiIiovjObfn5+UFvd2ihoq3N4WCuLVu2SG5urjgcDkkmRldgGzduTLquwOIB17/1uA2sxfVvPW4Da3H9hw9xFKG1TZs26mRTSdviiiffrl07SWZ4s/ANYx2uf+txG1iL69963AbW4voPT20trbboDouIiIiIyMDgSkRERES2wOCawHAGsYcffjjgmcQo+rj+rcdtYC2uf+txG1iL6z/yEv7gLCIiIiJKDGxxJSIiIiJbYHAlIiIiIltgcCUiIiIiW2BwJSIiIiJbYHC1mW+//VbOPvtsdWYJnAnsk08+8bkdx9qNGjVKWrduLVlZWTJw4EBZs2aNzzR79uyRyy+/XHWG3LhxY7nuuuukuLg4xs/EnsaOHSvHHHOMOhNbixYt5LzzzpPVq1f7TFNWVia33HKLNGvWTBo1aiQXXnihbN++3WeaP//8U4YOHSrZ2dlqPvfdd59UVVXF+NnY08SJE+XII4/0duh9wgknyJdffum9nes/tp588kn1WXTnnXd6x3EbRNfo0aPVOjcP3bp1897O9R99mzdvliuuuEKtY3zXHnHEEbJ48WLv7fwujh4GV5spKSmRnj17yoQJEwLe/tRTT8kLL7wgL7/8svzwww+Sk5MjgwcPVh9kBrxRfvnlF5k5c6ZMmzZNheEbbrghhs/CvubMmaO+EL7//nu1/iorK2XQoEFquxjuuusu+fzzz+WDDz5Q0+OUwxdccIH3dpfLpb4wKioqZP78+fL222/LW2+9pT7kKDScCQ9hacmSJeqL4rTTTpNzzz1XvaaB6z92Fi1aJK+88or6IWHGbRB9hx9+uGzdutU7zJs3z3sb13907d27V/r16ydpaWnqR/PKlSvlmWeekSZNmnin4XdxFKE7LLInbL6pU6d6r7vdbq1Vq1ba008/7R23b98+LSMjQ3vvvffU9ZUrV6r7LVq0yDvNl19+qTkcDm3z5s0xfgb2t2PHDrU+58yZ413faWlp2gcffOCdZtWqVWqaBQsWqOtffPGFlpKSom3bts07zcSJE7W8vDytvLzcgmdhf02aNNFef/11rv8Y2r9/v9alSxdt5syZ2imnnKLdcccdajy3QfQ9/PDDWs+ePQPexvUffSNGjNBOPPHEoLfzuzi62OKaQNavXy/btm1TuyTM5/097rjjZMGCBeo6LrFLok+fPt5pMH1KSor6VUh1U1hYqC6bNm2qLtEKiFZY8zbALrwOHTr4bAPsVmrZsqV3GvwSLyoq8rYaUnjQcjRlyhTV4o2SAa7/2MGeB7Tamdc1cBvEBnY7o2TsoIMOUi132PUPXP/R99lnn6nv0IsuukiVWfTu3Vtee+017+38Lo4uBtcEgjcKmD+MjOvGbbjEG80sNTVVBS9jGgqP2+1WdX3YZdSjRw81DuswPT1dfSDVtg0CbSPjNgrtp59+UrV7OBvNjTfeKFOnTpXDDjuM6z9G8GNh6dKlqubbH7dB9CEAYdf+9OnTVc03gtJJJ50k+/fv5/qPgd9//12t9y5dushXX30lN910k9x+++2q5AL4XRxdqVGeP1FCtzj9/PPPPrVlFBtdu3aV5cuXqxbvDz/8UIYNG6Zq+Sj6Nm7cKHfccYeqy8vMzLR6cZLSGWec4f0b9cUIsh07dpT3339fHQhE0W+0QEvpE088oa6jxRXfBahnxWcRRRdbXBNIq1at1KX/0aO4btyGyx07dvjcjiNJcXSjMQ2Fduutt6pi+tmzZ6uDhQxYhzjgYd++fbVug0DbyLiNQkOL0iGHHCJHH320avXDAYvPP/88138MYFc0PkOOOuoo1UKEAT8acCAK/karErdBbKF19dBDD5W1a9fyPRAD6CkAe3jMunfv7i3X4HdxdDG4JpDOnTurF/ysWbO841CzhHoZ1P8BLvGBhi8fw9dff61+QeJXO9UOx8QhtGLXNNYb1rkZghSONDVvA3SXhQ808zbArm7zhxZar9Aliv+HIYUHr9/y8nKu/xgYMGCAWn9o8TYGtD6hztL4m9sgttCF0rp161Sg4nsg+lAe5t8N4m+//aZavYHfxVEW5YO/KApH8i5btkwN2HzPPvus+nvDhg3q9ieffFJr3Lix9umnn2o//vijdu6552qdO3fWDhw44J3HkCFDtN69e2s//PCDNm/ePHVk8KWXXmrhs7KPm266ScvPz9e++eYbbevWrd6htLTUO82NN96odejQQfv666+1xYsXayeccIIaDFVVVVqPHj20QYMGacuXL9emT5+uNW/eXBs5cqRFz8pe7r//ftWLw/r169VrHNdxJO6MGTPU7Vz/sWfuVQC4DaLrnnvuUZ9BeA9899132sCBA7WCggLVywlw/UfXwoULtdTUVO3xxx/X1qxZo/373//WsrOztX/961/eafhdHD0MrjYze/ZsFVj9h2HDhnm74XjooYe0li1bqq43BgwYoK1evdpnHrt371ZvjkaNGqnuT6655hoViCm0QOsew6RJk7zT4IPp5ptvVl004cPs/PPPV+HW7I8//tDOOOMMLSsrS33h4IuosrLSgmdkP9dee63WsWNHLT09XX3Z4jVuhFbg+rc+uHIbRNcll1yitW7dWr0H2rZtq66vXbvWezvXf/R9/vnnKvzje7Zbt27aq6++6nM7v4ujx4F/ot2qS0RERETUUKxxJSIiIiJbYHAlIiIiIltgcCUiIiIiW2BwJSIiIiJbYHAlIiIiIltgcCUiIiIiW2BwJSIiIiJbYHAlooSE87U/8cQTsmrVKqsXhWKsrKxMHnvsMXVaUyJKLAyuREmoU6dOMn78eO91h8Mhn3zyifr7jz/+UNdx3vlIeuutt6Rx48YNmsfVV18t5513XljT3nPPPSq4dOvWLez59+/fX+68806xgv96/+abb9R1nM88UusvUq+XeDdq1CiZP3++XHnlleoHDBElDgZXIhtCoKltGD16dK33X7Rokdxwww1idwiygZ7r+++/L7/88ou8/fbban3Em0ABvH379rJ161bp0aOHxDsEWQTreAzHCxculB9++EE+++wzufjii0O+F+wgWj8miewo1eoFIKK6Q8Ax/Oc//1EtTKtXr/aOa9SoUa33b968uSQyBBYMduJ0OqVVq1ZWL0Zcc7lcKsClpARvczn22GNlzpw56u8HHngghktHRLHAFlciG0LAMYb8/Hz1ZW5cLykpkcsvv1xatmypAuwxxxwj//vf/xrUuvXzzz/LGWecoeaH+WIX7K5du2q9D3Ztd+jQQbKzs+X888+X3bt315jm008/laOOOkoyMzPloIMOkjFjxkhVVZXUV3l5udx7773Stm1bycnJkeOOO65Gy+B3332nSgKwXE2aNJHBgwfL3r17vbe73W4ZPny4NG3aVK1P/xa7Z599Vo444gg1f7SS3nzzzVJcXOzzvLFL/6uvvpLu3burdTZkyBDvjw3MDy3BeO5GCzmWsT6tahMnTpSDDz5Y0tPTpWvXrvLOO+/43I75vf7662r94/l26dJFtUTWZseOHXL22WdLVlaWdO7cWf7973+HXI6NGzeqHwp43lhv5557rno+/i3M//d//yetW7eWZs2ayS233CKVlZXqdmyPDRs2yF133eVdJ+Z1iWU+7LDDJCMjQ/7880+1x+D000+XgoIC9fo/5ZRTZOnSpTWeu3/5y8cffyynnnqqWhc9e/aUBQsW+Nxn3rx5ctJJJ6nnjm17++23q/eT+X2D2tmrrrpKbdeOHTuqZdu5c6d6zhh35JFHyuLFi+s8X9RjX3vttZKbm6veN6+++qr3dmwH6N27t3oeWF9EyYrBlSjBIESdeeaZMmvWLFm2bJkKTQgi+MKvD9RYnnbaaepLE1/I06dPl+3bt9faooldtdddd53ceuutKoghLOAL32zu3LkqANxxxx2ycuVKeeWVV1RQefzxx6W+8HgII1OmTJEff/xRLrroIvX816xZo27HsgwYMECFIEyHQIF1g5Y8A0IlQimew1NPPSWPPPKIzJw503s7WvteeOEFbynC119/rYKuWWlpqQppCJLffvutWvcI1IBLrDsjzGLo27dvnZ/r1KlT1bpDLS9+WPz973+Xa665RmbPnu0zHX4M4PGwPvC6wI+aPXv2BJ0vQiaCKObz4Ycfyj//+U8VZoNB+ET4R+DCNsUPAyOsm+tLMb9169apS6w3bGsMgEDZrl07ta6NdWJel+PGjVMBHOu8RYsWsn//fhk2bJjaft9//70K5HhuGF+bf/zjH2r943Vw6KGHyqWXXur9oYRlwzJfeOGFal1hTwbmj9eU2XPPPSf9+vVT762hQ4eqH3F4HV9xxRUqPOOHBK5rmlan+T7zzDPSp08fNV/8GLrpppu8e1FQ/gD4AYp1g/VFlLQ0IrK1SZMmafn5+bVOc/jhh2svvvii93rHjh215557znsdHwVTp05Vf69fv15dX7Zsmbr+6KOPaoMGDfKZ38aNG9U0q1evDvh4l156qXbmmWf6jLvkkkt8lnPAgAHaE0884TPNO++8o7Vu3Tro8xg2bJh27rnnBrxtw4YNmtPp1DZv3uwzHo8zcuRI73L169cv6PxPOeUU7cQTT/QZd8wxx2gjRowIep8PPvhAa9asmc/2wLpZu3atd9yECRO0li1b1vo8/Nf77Nmz1fW9e/d652tef3379tWuv/56n3lcdNFFPusd93/wwQe914uLi9W4L7/8MuBzwfbE7QsXLvSOW7VqlRpnfr34b7OuXbtqbrfbO668vFzLysrSvvrqK+/zxWuuqqrKZ1nxmgj2mjSvy+XLl2u1cblcWm5urvb555/X+pp+/fXXvbf/8ssvahyeH1x33XXaDTfc4DPfuXPnaikpKdqBAwe8y3jFFVd4b9+6dauax0MPPeQdt2DBAjUOt9V3vliXLVq00CZOnBjwtUGUzNjiSpSALa5oVcJuauxmResXuoSqb4vrihUrVCsZ5mMMxpH6aE0KBI+H3fRmJ5xwQo35ooXNPN/rr79etSihla2u0IMAWk7RkmaeJ+odjeU0Wlxrg129Zti1bW5xRKsX5oFyBLQyosUNZRDmZcauaLS8BZtHJGAdo+XPDNf9u/8yPx+0JOfl5QVdFtw3NTVVjj76aO84bOvaejPAdly7dq1aF8Y6R7kAuqQyvz4OP/xwVcdb13WCMgj/bYIWf7xW0NKKUgE8J7zuQ73GzfPB44OxDHgeaAE2v3bQkozSkfXr1wecB8pmAKUj/uMaMl+j9CfSrxmiRMCDs4gSDEIrdm1jV/Uhhxyi6ur+8pe/1LtbIAQC7E7H7lp/xpd/feeL3dgXXHBBjdtQ81qf+SEYLVmyxCcgmQ9Ww7oIJS0tzec6QgRChlEredZZZ6nduChpQEDDbl+URWD9IrAGm4ex6zjWans+kYD1jqAbqBbWfBBgfZcD28y/ZwiUCeDHwvPPP6/qTFH7ih9GoV7j5mUw5mksA54Hyi1Qf+oPNae1zSPS843GdiJKFAyuRAkGNYaoU8QBOcYXp/lAmbrCwVMfffSROoAErXHhQGsvakTNUIvoP1/U8CFcRwJqcNHiilYqHAgTCFq1UPuLwFwfCMUIE6hHNI5sR9dbdYVWRHNdbX1gHWNbI8QZcB31u/WF1lXUfOJ54qA+wDYy+pINBNsRdZuoPUXLZ33VZZ3geaL2FnWtgJrcUAcLhoLngVrrSL0eIzlfrBto6GuGKBGwVIAowWD3KQ7ewG5x7Ka87LLLGtRyg6O/cTAPDmTB0dzY/Ysj5nEgULAvUrQu4SAutPriwKiXXnpJXTdDF16TJ09WIRIH3WA3NQ6qevDBB+u1nCgRwIFHODAGzx+7YXFQy9ixY+W///2vmmbkyJHqOeDgFxwo8+uvv6oj88MNPQgfOBjpxRdflN9//10dfPXyyy/XeVnxIwCPj1CIxzaOrq+L++67T+2CxvJjHaO3Azxv4yCw+kDPBDiQCC2E+OGBAPu3v/2t1pZqrHMc3Y+j6nFwFtY7eknAa2DTpk11Wic4kG3z5s0htwde41j3eM1gObEM4bSm12bEiBHqpAXGAYVYp+j5wf8gKivmix8FeH7GgZGFhYUNWiYiO2NwJUowCDDo5glHqmMXP+rp0OpTX23atFEtXAipgwYNUvV8OLsU6h6D9ad5/PHHy2uvvaZ25aLboRkzZtQIpFiuadOmqdvQuof74Iht7Pqtr0mTJqngiiPtEcLQBROCqrFLFuEWj4dAj/4+sXsZISLclmQ8F6xflE3gRAHYPY5gXFeoz8Ty4Shy7E7H+q0rPDesX/w4QP0oemXA829oV0mYB7Y5uphCGQdOVIHgFAzKIxA4sY4xPVqCUTqBGte6tMCi3hl7BlAbHKqf4TfeeEN1YYbXNWqMEZJrW8ZwoDUe9dC//fabarFHCz5+XGFdWD1fvD7RkwW2Me6HHwlEycqBI7SsXggiIiIiolDY4kpEREREtsDgSkRERES2wOBKRERERLbA4EpEREREtsDgSkRERES2wOBKRERERLbA4EpEREREtsDgSkRERES2wOBKRERERLbA4EpEREREtsDgSkRERERiB/8PsMtnalJYVV4AAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -4145,21 +4672,32 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[np.float64(4.586051136945035),\n",
+       " np.float64(4.299691619702497),\n",
+       " np.float64(4.232697661885681)]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "\n",
-    "from scripts.rf import plot_learning_curve, plot_mse\n",
     "\n",
     "plot_learning_curve_rmse(best_rf, X, y)\n",
-    "plot_mse(best_rf, X_train, X_test, y_train, y_test)\n"
+    "plot_rmse(best_rf, X_train, X_test, y_train, y_test)\n",
+    "\n"
    ]
   },
   {
diff --git a/scripts/Data_filter_Jess.py b/scripts/Data_filter_Jess.py
index 0e6170d..a9a0c48 100644
--- a/scripts/Data_filter_Jess.py
+++ b/scripts/Data_filter_Jess.py
@@ -3,7 +3,7 @@
 import numpy as np
 import seaborn as sns
 
-def filter_nutriscore_data(df):
+def filter_nutriscore_data(df, df_test):
     """
     Filters the DataFrame to include only rows with a valid nutriscore_score.
     
@@ -19,11 +19,12 @@ def filter_nutriscore_data(df):
     
     # Filter out rows where 'nutriscore_score' is NaN
     filtered_df = df[df['nutriscore_score'].notna()]
+    filtered_df_test = df_test[df_test['nutriscore_score'].notna()]
     
-    return filtered_df
+    return filtered_df, filtered_df_test
 
 
-def categorical_filter(df, cat_keep):
+def categorical_filter(df, df_test, cat_keep):
     """"
     Filters the DataFrame to include only rows with specified categories.
     
@@ -37,9 +38,13 @@ def categorical_filter(df, cat_keep):
     categorical = df.select_dtypes(exclude=['float64','int64']) 
     cat_keep = ['categories','pnns_groups_1', 'pnns_groups_2', 'brands_tags', 'ingredients_analysis_tags']
     df_cat = categorical[cat_keep]
-    return df_cat
 
-def numerical_filter(df, num_drop):
+    categorical = df_test.select_dtypes(exclude=['float64','int64']) 
+    df_cat_test = categorical[cat_keep]
+    
+    return df_cat, df_cat_test
+
+def numerical_filter(df, df_test, num_drop):
     """"
     Filters the DataFrame to include only relevant numerical values.
     
@@ -52,14 +57,17 @@ def numerical_filter(df, num_drop):
     """
     num_drop = ['created_t', 'last_updated_t', 'last_modified_t', 'last_image_t','serving_quantity', 'nova_group', 'product_quantity', 'unique_scans_n', 'completeness', 'energy-kj_100g', 'energy-kcal_100g']
     numerical = df.select_dtypes(include=['float64','int64'])
+    numerical_test = df_test.select_dtypes(include=['float64','int64'])
     percent_to_keep = numerical.isnull().sum()*100 /len(df)
     percent_to_keep = percent_to_keep[percent_to_keep.values < 80]  
     num_keep = numerical[percent_to_keep.index]
+    num_keep_test = numerical_test[percent_to_keep.index]
     num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')
-
-    return num_keep
+    num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')
+    
+    return num_keep, num_keep_test
     
-def final_df(df_cat, df_num):
+def final_df(df_cat, df_num, df_cat_test, df_num_test):
     """
     Combines categorical and numerical filtered DataFrames.
     
@@ -71,7 +79,8 @@ def final_df(df_cat, df_num):
     DataFrame: A combined DataFrame with both categorical and numerical data.
     """
     filtered_df = pd.concat([df_cat, df_num], axis=1)
+    filtered_df_test = pd.concat([df_cat_test, df_num_test], axis=1)
 
-    return filtered_df
+    return filtered_df, filtered_df_test
 
 

From c673df0f639906a7ba300cf5e48867d550b24a38 Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Sat, 30 Aug 2025 16:22:22 +0200
Subject: [PATCH 06/12] updates

---
 notebooks/project_starter.ipynb | 2035 ++-----------------------------
 scripts/Data_filter_Jess.py     |    5 +-
 scripts/rf.py                   |   69 +-
 3 files changed, 148 insertions(+), 1961 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index 111b3bb..bf7ac3f 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -53,9 +53,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_23304\\962525088.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\962525088.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_23304\\962525088.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\962525088.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n"
      ]
     }
@@ -65,13 +65,12 @@
     "df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
     "\n",
     "df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n",
-    "               # read 5000 rows (5001 → 10000)\n",
-    "\n"
+    "               # read 5000 rows (5001 → 10000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -1154,7 +1153,7 @@
          "type": "float"
         }
        ],
-       "ref": "48d506fa-f248-4661-9ba0-e8f156efa833",
+       "ref": "376a82dd-1d4a-44f1-bf65-bedfdd08961f",
        "rows": [
         [
          "0",
@@ -2461,7 +2460,7 @@
        "[5 rows x 214 columns]"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2486,30 +2485,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "filter_nutriscore_data() takes 1 positional argument but 2 were given",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[33], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m filtered_df, filtered_df_test \u001b[38;5;241m=\u001b[39m \u001b[43mdfj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfilter_nutriscore_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      2\u001b[0m cat_df, cat_df_test \u001b[38;5;241m=\u001b[39m dfj\u001b[38;5;241m.\u001b[39mcategorical_filter(filtered_df, filtered_df_test, cat_keep\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[0;32m      3\u001b[0m num_df, num_df_test \u001b[38;5;241m=\u001b[39m dfj\u001b[38;5;241m.\u001b[39mnumerical_filter(filtered_df,filtered_df_test, num_drop\u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "\u001b[1;31mTypeError\u001b[0m: filter_nutriscore_data() takes 1 positional argument but 2 were given"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:65: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n",
+      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:66: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
+    "### taking out lines without nutriscore because it's non informative (both fot the train and the test df)\n",
     "filtered_df, filtered_df_test = dfj.filter_nutriscore_data(df_train, df_test)\n",
+    "\n",
+    "### Cleaning the variables \n",
     "cat_df, cat_df_test = dfj.categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n",
     "num_df, num_df_test = dfj.numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n",
     "final_df, final_df_test = dfj.final_df(cat_df, num_df, cat_df_test, num_df_test)\n",
+    "\n",
+    "### Droping some additionnal columns \n",
     "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
     "final_df_test = final_df_test.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
-    "final_df.head()\n",
-    "final_df_test.head()"
+    "\n",
+    "final_df.columns.equals(final_df_test.columns) #checking if we have the same variables in each df "
    ]
   },
   {
@@ -2523,28 +2542,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              pnns_groups_1      PNNS_pro\n",
-      "6                   unknown            NA\n",
-      "9                   unknown            NA\n",
-      "11    Fruits and vegetables   Plant_based\n",
-      "12                  unknown            NA\n",
-      "14          Composite foods     Processed\n",
-      "...                     ...           ...\n",
-      "4926   Cereals and potatoes   Plant_based\n",
-      "4935         Fish Meat Eggs  Animal_based\n",
-      "4936                unknown            NA\n",
-      "4943        Composite foods     Processed\n",
-      "4987          Sugary snacks        Snacks\n",
-      "\n",
-      "[438 rows x 2 columns]\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pnns_mapping = {\"unknown\" : 'NA', \n",
     "                'Beverages' : 'Drinks',\n",
@@ -2567,259 +2565,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "PNNS_pro",
-         "rawType": "object",
-         "type": "string"
-        }
-       ],
-       "ref": "f5cf055f-f21b-4126-a537-93bd9532e793",
-       "rows": [
-        [
-         "0",
-         "Plant_based"
-        ],
-        [
-         "7",
-         "Animal_based"
-        ],
-        [
-         "42",
-         "Plant_based"
-        ],
-        [
-         "45",
-         "NA"
-        ],
-        [
-         "47",
-         "NA"
-        ],
-        [
-         "51",
-         "Animal_based"
-        ],
-        [
-         "63",
-         "Snacks"
-        ],
-        [
-         "70",
-         "Plant_based"
-        ],
-        [
-         "73",
-         "Snacks"
-        ],
-        [
-         "118",
-         "NA"
-        ],
-        [
-         "119",
-         "Snacks"
-        ],
-        [
-         "125",
-         "NA"
-        ],
-        [
-         "126",
-         "Snacks"
-        ],
-        [
-         "128",
-         "NA"
-        ],
-        [
-         "129",
-         "NA"
-        ],
-        [
-         "131",
-         "Snacks"
-        ],
-        [
-         "132",
-         "Snacks"
-        ],
-        [
-         "134",
-         "Snacks"
-        ],
-        [
-         "137",
-         "Snacks"
-        ],
-        [
-         "138",
-         "Snacks"
-        ],
-        [
-         "139",
-         "NA"
-        ],
-        [
-         "140",
-         "Snacks"
-        ],
-        [
-         "141",
-         "Snacks"
-        ],
-        [
-         "142",
-         "Snacks"
-        ],
-        [
-         "143",
-         "NA"
-        ],
-        [
-         "144",
-         "NA"
-        ],
-        [
-         "145",
-         "Snacks"
-        ],
-        [
-         "146",
-         "Snacks"
-        ],
-        [
-         "147",
-         "Snacks"
-        ],
-        [
-         "148",
-         "Snacks"
-        ],
-        [
-         "149",
-         "Snacks"
-        ],
-        [
-         "150",
-         "Snacks"
-        ],
-        [
-         "218",
-         "NA"
-        ],
-        [
-         "223",
-         "Plant_based"
-        ],
-        [
-         "237",
-         "Snacks"
-        ],
-        [
-         "238",
-         "NA"
-        ],
-        [
-         "267",
-         "NA"
-        ],
-        [
-         "316",
-         "NA"
-        ],
-        [
-         "317",
-         "NA"
-        ],
-        [
-         "318",
-         "NA"
-        ],
-        [
-         "319",
-         "Plant_based"
-        ],
-        [
-         "322",
-         "NA"
-        ],
-        [
-         "323",
-         "NA"
-        ],
-        [
-         "324",
-         "NA"
-        ],
-        [
-         "325",
-         "NA"
-        ],
-        [
-         "326",
-         "NA"
-        ],
-        [
-         "329",
-         "NA"
-        ],
-        [
-         "330",
-         "NA"
-        ],
-        [
-         "335",
-         "NA"
-        ],
-        [
-         "336",
-         "NA"
-        ]
-       ],
-       "shape": {
-        "columns": 1,
-        "rows": 769
-       }
-      },
-      "text/plain": [
-       "0        Plant_based\n",
-       "7       Animal_based\n",
-       "42       Plant_based\n",
-       "45                NA\n",
-       "47                NA\n",
-       "            ...     \n",
-       "4949          Snacks\n",
-       "4963       Processed\n",
-       "4972    Animal_based\n",
-       "4978          Snacks\n",
-       "4987          Snacks\n",
-       "Name: PNNS_pro, Length: 769, dtype: object"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "final_df_test['PNNS_pro']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2834,1353 +2589,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "code",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "additives_n",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "nutriscore_score",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "energy_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "saturated-fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "carbohydrates_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "sugars_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fiber_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "proteins_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "salt_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "sodium_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Animal_based",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Drinks",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_NA",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Plant_based",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Processed",
-         "rawType": "float64",
-         "type": "float"
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-       "shape": {
-        "columns": 19,
-        "rows": 5
-       }
-      },
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>additives_n</th>\n",
-       "      <th>nutriscore_score</th>\n",
-       "      <th>energy_100g</th>\n",
-       "      <th>fat_100g</th>\n",
-       "      <th>saturated-fat_100g</th>\n",
-       "      <th>carbohydrates_100g</th>\n",
-       "      <th>sugars_100g</th>\n",
-       "      <th>fiber_100g</th>\n",
-       "      <th>proteins_100g</th>\n",
-       "      <th>salt_100g</th>\n",
-       "      <th>sodium_100g</th>\n",
-       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
-       "      <th>PNNS_pro_Animal_based</th>\n",
-       "      <th>PNNS_pro_Drinks</th>\n",
-       "      <th>PNNS_pro_NA</th>\n",
-       "      <th>PNNS_pro_Plant_based</th>\n",
-       "      <th>PNNS_pro_Processed</th>\n",
-       "      <th>PNNS_pro_Snacks</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>10.50</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>9.00</td>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1520.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.950</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1510.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>10.714286</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>1.500</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>9.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-11.0</td>\n",
-       "      <td>293.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>88.000000</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.275</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6    4.0          0.0              15.0       2401.0      12.0   \n",
-       "9    6.0          NaN               4.0       1520.0      11.0   \n",
-       "11   7.0          0.0               4.0          4.0       1.0   \n",
-       "12   8.0          1.0               6.0       1510.0       2.0   \n",
-       "14   9.0          NaN             -11.0        293.0       0.5   \n",
-       "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6                10.50                13.0         9.00   36.000000   \n",
-       "9                 2.00                25.0         0.98    9.000000   \n",
-       "11                1.00                 1.0         1.00    1.000000   \n",
-       "12                0.50                 6.7         1.70   10.714286   \n",
-       "14                0.06                 2.0         0.24   88.000000   \n",
-       "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6            23.0      0.300         0.12   \n",
-       "9            22.0      0.950         0.38   \n",
-       "11            1.0      1.000         0.40   \n",
-       "12           76.0      1.500         0.60   \n",
-       "14           18.0      0.275         0.11   \n",
-       "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                                 0.0       \n",
-       "9                                                 NaN       \n",
-       "11                                                0.0       \n",
-       "12                                                0.0       \n",
-       "14                                                NaN       \n",
-       "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0          0.0                   0.0   \n",
-       "9                     0.0              0.0          0.0                   0.0   \n",
-       "11                    0.0              0.0          0.0                   1.0   \n",
-       "12                    0.0              0.0          1.0                   0.0   \n",
-       "14                    0.0              0.0          1.0                   0.0   \n",
-       "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              1.0  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "filtered_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.3 Imputing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ": shape of df with only numeric features=(809, 19)\n",
-      ": shape of df with only numeric features=(769, 142)\n"
-     ]
-    },
-    {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>additives_n</th>\n",
-       "      <th>nutriscore_score</th>\n",
-       "      <th>energy_100g</th>\n",
-       "      <th>fat_100g</th>\n",
-       "      <th>saturated-fat_100g</th>\n",
-       "      <th>carbohydrates_100g</th>\n",
-       "      <th>sugars_100g</th>\n",
-       "      <th>fiber_100g</th>\n",
-       "      <th>proteins_100g</th>\n",
-       "      <th>salt_100g</th>\n",
-       "      <th>sodium_100g</th>\n",
-       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
-       "      <th>PNNS_pro_Animal_based</th>\n",
-       "      <th>PNNS_pro_Drinks</th>\n",
-       "      <th>PNNS_pro_NA</th>\n",
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-       "      <th>PNNS_pro_Snacks</th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <th>6</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>10.50</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>9.00</td>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <th>9</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1520.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.950</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>20.400223</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <th>11</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000</td>\n",
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-       "      <th>12</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1510.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>1.70</td>\n",
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-       "      <td>76.0</td>\n",
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-       "      <th>14</th>\n",
-       "      <td>9.0</td>\n",
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-       "      <td>293.0</td>\n",
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-       "      <td>0.24</td>\n",
-       "      <td>88.000000</td>\n",
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-       "      <td>0.275</td>\n",
-       "      <td>0.11</td>\n",
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-      "text/plain": [
-       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6    4.0          0.0              15.0       2401.0      12.0   \n",
-       "9    6.0          1.8               4.0       1520.0      11.0   \n",
-       "11   7.0          0.0               4.0          4.0       1.0   \n",
-       "12   8.0          1.0               6.0       1510.0       2.0   \n",
-       "14   9.0          0.6             -11.0        293.0       0.5   \n",
-       "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6                10.50                13.0         9.00   36.000000   \n",
-       "9                 2.00                25.0         0.98    9.000000   \n",
-       "11                1.00                 1.0         1.00    1.000000   \n",
-       "12                0.50                 6.7         1.70   10.714286   \n",
-       "14                0.06                 2.0         0.24   88.000000   \n",
-       "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6            23.0      0.300         0.12   \n",
-       "9            22.0      0.950         0.38   \n",
-       "11            1.0      1.000         0.40   \n",
-       "12           76.0      1.500         0.60   \n",
-       "14           18.0      0.275         0.11   \n",
-       "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                            0.000000       \n",
-       "9                                           20.400223       \n",
-       "11                                           0.000000       \n",
-       "12                                           0.000000       \n",
-       "14                                          20.000291       \n",
-       "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0          0.0                   0.0   \n",
-       "9                     0.0              0.0          0.0                   0.0   \n",
-       "11                    0.0              0.0          0.0                   1.0   \n",
-       "12                    0.0              0.0          1.0                   0.0   \n",
-       "14                    0.0              0.0          1.0                   0.0   \n",
-       "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              1.0  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from scripts.Imputing import knn_impute_numeric\n",
-    "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
-    "imputed_df_test = knn_impute_numeric(filtered_df_test, n_neighbors = 5)\n",
-    "imputed_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.4 Scaling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ": shape of df with only numeric features=(809, 18)\n",
-      ": shape of df with only numeric features=(769, 141)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scripts.Scaling import scaler_numeric\n",
-    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
-    "work_df_test = scaler_numeric(imputed_df_test, 'nutriscore_score')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "code",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "additives_n",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "nutriscore_score",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "energy_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "saturated-fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "carbohydrates_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "sugars_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fiber_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "proteins_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "salt_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "sodium_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Animal_based",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Drinks",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_NA",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Plant_based",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Processed",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Snacks",
-         "rawType": "float64",
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-       "shape": {
-        "columns": 19,
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-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>additives_n</th>\n",
-       "      <th>nutriscore_score</th>\n",
-       "      <th>energy_100g</th>\n",
-       "      <th>fat_100g</th>\n",
-       "      <th>saturated-fat_100g</th>\n",
-       "      <th>carbohydrates_100g</th>\n",
-       "      <th>sugars_100g</th>\n",
-       "      <th>fiber_100g</th>\n",
-       "      <th>proteins_100g</th>\n",
-       "      <th>salt_100g</th>\n",
-       "      <th>sodium_100g</th>\n",
-       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
-       "      <th>PNNS_pro_Animal_based</th>\n",
-       "      <th>PNNS_pro_Drinks</th>\n",
-       "      <th>PNNS_pro_NA</th>\n",
-       "      <th>PNNS_pro_Plant_based</th>\n",
-       "      <th>PNNS_pro_Processed</th>\n",
-       "      <th>PNNS_pro_Snacks</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>-0.000001</td>\n",
-       "      <td>-0.375</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>1.454104</td>\n",
-       "      <td>0.021973</td>\n",
-       "      <td>1.157949</td>\n",
-       "      <td>-0.332297</td>\n",
-       "      <td>-0.096843</td>\n",
-       "      <td>2.084031</td>\n",
-       "      <td>0.094930</td>\n",
-       "      <td>-0.242461</td>\n",
-       "      <td>-0.242461</td>\n",
-       "      <td>-0.152158</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>-0.333333</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>-0.000001</td>\n",
-       "      <td>0.750</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.196431</td>\n",
-       "      <td>-0.039062</td>\n",
-       "      <td>-0.295043</td>\n",
-       "      <td>-0.021255</td>\n",
-       "      <td>-0.454432</td>\n",
-       "      <td>0.012059</td>\n",
-       "      <td>0.065283</td>\n",
-       "      <td>-0.045461</td>\n",
-       "      <td>-0.045461</td>\n",
-       "      <td>0.296067</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>-0.333333</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>-0.000001</td>\n",
-       "      <td>-0.375</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>-1.967737</td>\n",
-       "      <td>-0.649414</td>\n",
-       "      <td>-0.465983</td>\n",
-       "      <td>-0.643339</td>\n",
-       "      <td>-0.453540</td>\n",
-       "      <td>-0.601859</td>\n",
-       "      <td>-0.557308</td>\n",
-       "      <td>-0.030308</td>\n",
-       "      <td>-0.030308</td>\n",
-       "      <td>-0.152158</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>1.333333</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>-0.000001</td>\n",
-       "      <td>0.250</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.182156</td>\n",
-       "      <td>-0.588379</td>\n",
-       "      <td>-0.551453</td>\n",
-       "      <td>-0.495594</td>\n",
-       "      <td>-0.422329</td>\n",
-       "      <td>0.143613</td>\n",
-       "      <td>1.666232</td>\n",
-       "      <td>0.121230</td>\n",
-       "      <td>0.121230</td>\n",
-       "      <td>-0.152158</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>-0.333333</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>-0.000001</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-11.0</td>\n",
-       "      <td>-1.555175</td>\n",
-       "      <td>-0.679932</td>\n",
-       "      <td>-0.626667</td>\n",
-       "      <td>-0.617418</td>\n",
-       "      <td>-0.487426</td>\n",
-       "      <td>6.074496</td>\n",
-       "      <td>-0.053306</td>\n",
-       "      <td>-0.250038</td>\n",
-       "      <td>-0.250038</td>\n",
-       "      <td>0.287280</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>-0.333333</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6  -0.000001       -0.375              15.0     1.454104  0.021973   \n",
-       "9  -0.000001        0.750               4.0     0.196431 -0.039062   \n",
-       "11 -0.000001       -0.375               4.0    -1.967737 -0.649414   \n",
-       "12 -0.000001        0.250               6.0     0.182156 -0.588379   \n",
-       "14 -0.000001        0.000             -11.0    -1.555175 -0.679932   \n",
-       "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6             1.157949           -0.332297    -0.096843    2.084031   \n",
-       "9            -0.295043           -0.021255    -0.454432    0.012059   \n",
-       "11           -0.465983           -0.643339    -0.453540   -0.601859   \n",
-       "12           -0.551453           -0.495594    -0.422329    0.143613   \n",
-       "14           -0.626667           -0.617418    -0.487426    6.074496   \n",
-       "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6        0.094930  -0.242461    -0.242461   \n",
-       "9        0.065283  -0.045461    -0.045461   \n",
-       "11      -0.557308  -0.030308    -0.030308   \n",
-       "12       1.666232   0.121230     0.121230   \n",
-       "14      -0.053306  -0.250038    -0.250038   \n",
-       "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                           -0.152158       \n",
-       "9                                            0.296067       \n",
-       "11                                          -0.152158       \n",
-       "12                                          -0.152158       \n",
-       "14                                           0.287280       \n",
-       "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0         -0.5             -0.333333   \n",
-       "9                     0.0              0.0         -0.5             -0.333333   \n",
-       "11                    0.0              0.0         -0.5              1.333333   \n",
-       "12                    0.0              0.0          2.0             -0.333333   \n",
-       "14                    0.0              0.0          2.0             -0.333333   \n",
-       "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              5.0  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
+   "source": [
+    "filtered_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.3 Imputing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scripts.Imputing import knn_impute_numeric\n",
+    "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
+    "imputed_df_test = knn_impute_numeric(filtered_df_test, n_neighbors = 5)\n",
+    "imputed_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.4 Scaling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scripts.Scaling import scaler_numeric\n",
+    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
+    "work_df_test = scaler_numeric(imputed_df_test, 'nutriscore_score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "work_df.head()"
    ]
@@ -4215,7 +2672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4269,7 +2726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4281,127 +2738,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n",
-      "540 fits failed out of a total of 1620.\n",
-      "The score on these train-test partitions for these parameters will be set to nan.\n",
-      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
-      "\n",
-      "Below are more details about the failures:\n",
-      "--------------------------------------------------------------------------------\n",
-      "342 fits failed with the following error:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
-      "    estimator._validate_params()\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
-      "    validate_parameter_constraints(\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
-      "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "198 fits failed with the following error:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
-      "    estimator._validate_params()\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
-      "    validate_parameter_constraints(\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
-      "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
-      "\n",
-      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
-      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [        nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan -4.17441534 -3.9850685  -3.869491\n",
-      " -4.43024577 -4.14775395 -3.76619056 -4.46170689 -4.46606186 -3.9911324\n",
-      " -4.36181718 -4.13390446 -3.94874007 -4.59103923 -4.30176094 -3.96973599\n",
-      " -4.30587871 -4.21863185 -3.99385929 -4.30387585 -4.14318382 -4.06357957\n",
-      " -4.30387585 -4.14318382 -4.06357957 -4.3324884  -4.26769304 -4.15057326\n",
-      " -4.17441534 -3.9850685  -3.869491   -4.43024577 -4.14775395 -3.76619056\n",
-      " -4.46170689 -4.46606186 -3.9911324  -4.36181718 -4.13390446 -3.94874007\n",
-      " -4.59103923 -4.30176094 -3.96973599 -4.30587871 -4.21863185 -3.99385929\n",
-      " -4.30387585 -4.14318382 -4.06357957 -4.30387585 -4.14318382 -4.06357957\n",
-      " -4.3324884  -4.26769304 -4.15057326         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      " -4.21952674 -4.18778716 -3.86417073 -4.34736341 -4.17625035 -3.91895388\n",
-      " -4.70056037 -4.46621254 -4.00376246 -4.23921756 -4.20411889 -3.90976299\n",
-      " -4.489981   -4.11515935 -3.96809022 -4.33314957 -4.31300635 -3.99178119\n",
-      " -4.48692777 -4.26130711 -4.09123967 -4.48692777 -4.26130711 -4.09123967\n",
-      " -4.45914192 -4.34581783 -4.16687368 -4.21952674 -4.18778716 -3.86417073\n",
-      " -4.34736341 -4.17625035 -3.91895388 -4.70056037 -4.46621254 -4.00376246\n",
-      " -4.23921756 -4.20411889 -3.90976299 -4.489981   -4.11515935 -3.96809022\n",
-      " -4.33314957 -4.31300635 -3.99178119 -4.48692777 -4.26130711 -4.09123967\n",
-      " -4.48692777 -4.26130711 -4.09123967 -4.45914192 -4.34581783 -4.16687368\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan -4.16498725 -3.98663985 -3.87277587\n",
-      " -4.43024577 -4.14775395 -3.76619075 -4.46187139 -4.46607952 -3.99388922\n",
-      " -4.36181718 -4.13390446 -3.94882138 -4.59103923 -4.30176094 -3.96973599\n",
-      " -4.30587871 -4.21863185 -3.99385929 -4.30387585 -4.14318382 -4.06357957\n",
-      " -4.30387585 -4.14318382 -4.06357957 -4.3324884  -4.26769304 -4.15057326\n",
-      " -4.16498725 -3.98663985 -3.87277587 -4.43024577 -4.14775395 -3.76619075\n",
-      " -4.46187139 -4.46607952 -3.99388922 -4.36181718 -4.13390446 -3.94882138\n",
-      " -4.59103923 -4.30176094 -3.96973599 -4.30587871 -4.21863185 -3.99385929\n",
-      " -4.30387585 -4.14318382 -4.06357957 -4.30387585 -4.14318382 -4.06357957\n",
-      " -4.3324884  -4.26769304 -4.15057326         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      "         nan         nan         nan         nan         nan         nan\n",
-      " -4.17441534 -3.9850685  -3.869491   -4.43024577 -4.14775395 -3.76619056\n",
-      " -4.46170689 -4.46606186 -3.9911324  -4.36181718 -4.13390446 -3.94874007\n",
-      " -4.59103923 -4.30176094 -3.96973599 -4.30587871 -4.21863185 -3.99385929\n",
-      " -4.30387585 -4.14318382 -4.06357957 -4.30387585 -4.14318382 -4.06357957\n",
-      " -4.3324884  -4.26769304 -4.15057326 -4.17441534 -3.9850685  -3.869491\n",
-      " -4.43024577 -4.14775395 -3.76619056 -4.46170689 -4.46606186 -3.9911324\n",
-      " -4.36181718 -4.13390446 -3.94874007 -4.59103923 -4.30176094 -3.96973599\n",
-      " -4.30587871 -4.21863185 -3.99385929 -4.30387585 -4.14318382 -4.06357957\n",
-      " -4.30387585 -4.14318382 -4.06357957 -4.3324884  -4.26769304 -4.15057326]\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "ename": "ValueError",
-     "evalue": "The feature names should match those that were passed during fit.\nFeature names unseen at fit time:\n- abbreviated_product_name\n- acidity_100g\n- added-salt_100g\n- added-sugars_100g\n- additives\n- ...\nFeature names seen at fit time, yet now missing:\n- PNNS_pro_Animal_based\n- PNNS_pro_Drinks\n- PNNS_pro_NA\n- PNNS_pro_Plant_based\n- PNNS_pro_Processed\n- ...\n",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[32], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m best_rf, X_train, X_test, y_train, y_test, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mrandom_forest_GS\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\n",
-      "Cell \u001b[1;32mIn[30], line 37\u001b[0m, in \u001b[0;36mrandom_forest_GS\u001b[1;34m(X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[0;32m     34\u001b[0m grid_search\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[0;32m     36\u001b[0m \u001b[38;5;66;03m# Prédictions\u001b[39;00m\n\u001b[1;32m---> 37\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbest_estimator_\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     39\u001b[0m \u001b[38;5;66;03m# Meilleurs paramètres\u001b[39;00m\n\u001b[0;32m     40\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMeilleurs paramètres trouvés : \u001b[39m\u001b[38;5;124m\"\u001b[39m, grid_search\u001b[38;5;241m.\u001b[39mbest_params_)\n",
-      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:1066\u001b[0m, in \u001b[0;36mForestRegressor.predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m   1064\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m   1065\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[1;32m-> 1066\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1068\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[0;32m   1069\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n",
-      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\ensemble\\_forest.py:638\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    635\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    636\u001b[0m     ensure_all_finite \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 638\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mvalidate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    639\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    640\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    641\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    642\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    643\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    644\u001b[0m \u001b[43m    \u001b[49m\u001b[43mensure_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    645\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    646\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[0;32m    647\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\validation.py:2919\u001b[0m, in \u001b[0;36mvalidate_data\u001b[1;34m(_estimator, X, y, reset, validate_separately, skip_check_array, **check_params)\u001b[0m\n\u001b[0;32m   2835\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mvalidate_data\u001b[39m(\n\u001b[0;32m   2836\u001b[0m     _estimator,\n\u001b[0;32m   2837\u001b[0m     \u001b[38;5;241m/\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2843\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params,\n\u001b[0;32m   2844\u001b[0m ):\n\u001b[0;32m   2845\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate input data and set or check feature names and counts of the input.\u001b[39;00m\n\u001b[0;32m   2846\u001b[0m \n\u001b[0;32m   2847\u001b[0m \u001b[38;5;124;03m    This helper function should be used in an estimator that requires input\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   2917\u001b[0m \u001b[38;5;124;03m        validated.\u001b[39;00m\n\u001b[0;32m   2918\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 2919\u001b[0m     \u001b[43m_check_feature_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_estimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2920\u001b[0m     tags \u001b[38;5;241m=\u001b[39m get_tags(_estimator)\n\u001b[0;32m   2921\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m tags\u001b[38;5;241m.\u001b[39mtarget_tags\u001b[38;5;241m.\u001b[39mrequired:\n",
-      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\validation.py:2777\u001b[0m, in \u001b[0;36m_check_feature_names\u001b[1;34m(estimator, X, reset)\u001b[0m\n\u001b[0;32m   2774\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m missing_names \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m unexpected_names:\n\u001b[0;32m   2775\u001b[0m     message \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFeature names must be in the same order as they were in fit.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m-> 2777\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(message)\n",
-      "\u001b[1;31mValueError\u001b[0m: The feature names should match those that were passed during fit.\nFeature names unseen at fit time:\n- abbreviated_product_name\n- acidity_100g\n- added-salt_100g\n- added-sugars_100g\n- additives\n- ...\nFeature names seen at fit time, yet now missing:\n- PNNS_pro_Animal_based\n- PNNS_pro_Drinks\n- PNNS_pro_NA\n- PNNS_pro_Plant_based\n- PNNS_pro_Processed\n- ...\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y, X_test, y_test)"
    ]
@@ -4410,129 +2749,7 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n",
-      "540 fits failed out of a total of 1620.\n",
-      "The score on these train-test partitions for these parameters will be set to nan.\n",
-      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
-      "\n",
-      "Below are more details about the failures:\n",
-      "--------------------------------------------------------------------------------\n",
-      "275 fits failed with the following error:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
-      "    estimator._validate_params()\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
-      "    validate_parameter_constraints(\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
-      "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "265 fits failed with the following error:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
-      "    estimator._validate_params()\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
-      "    validate_parameter_constraints(\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
-      "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
-      "\n",
-      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
-      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [         nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan -24.47451567 -17.51118198 -17.58537547\n",
-      " -28.06035404 -18.22279658 -17.89023802 -27.04168073 -19.63694956\n",
-      " -19.58299176 -25.40079344 -19.1005516  -18.73631141 -25.74814605\n",
-      " -19.73786442 -19.128602   -29.23809438 -20.54209621 -20.13463464\n",
-      " -26.0040655  -20.90029875 -20.57899564 -26.0040655  -20.90029875\n",
-      " -20.57899564 -29.50884242 -22.06421854 -21.63159919 -24.47451567\n",
-      " -17.51118198 -17.58537547 -28.06035404 -18.22279658 -17.89023802\n",
-      " -27.04168073 -19.63694956 -19.58299176 -25.40079344 -19.1005516\n",
-      " -18.73631141 -25.74814605 -19.73786442 -19.128602   -29.23809438\n",
-      " -20.54209621 -20.13463464 -26.0040655  -20.90029875 -20.57899564\n",
-      " -26.0040655  -20.90029875 -20.57899564 -29.50884242 -22.06421854\n",
-      " -21.63159919          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan -26.18493585 -18.49932398\n",
-      " -18.03499343 -27.47932759 -19.17392547 -18.56475395 -29.03894481\n",
-      " -20.93822886 -20.42710061 -25.2441473  -19.71794472 -18.88071978\n",
-      " -25.11245421 -19.17820697 -19.14722588 -28.50097379 -20.36063732\n",
-      " -20.47120177 -28.83965768 -21.28475519 -21.07701    -28.83965768\n",
-      " -21.28475519 -21.07701    -28.6957801  -22.88387847 -22.20248943\n",
-      " -26.18493585 -18.49932398 -18.03499343 -27.47932759 -19.17392547\n",
-      " -18.56475395 -29.03894481 -20.93822886 -20.42710061 -25.2441473\n",
-      " -19.71794472 -18.88071978 -25.11245421 -19.17820697 -19.14722588\n",
-      " -28.50097379 -20.36063732 -20.47120177 -28.83965768 -21.28475519\n",
-      " -21.07701    -28.83965768 -21.28475519 -21.07701    -28.6957801\n",
-      " -22.88387847 -22.20248943          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan -24.17555381\n",
-      " -17.53245455 -17.56714795 -28.24716711 -18.22477793 -17.91956499\n",
-      " -27.04168073 -19.6452736  -19.59832376 -25.40079344 -19.1005516\n",
-      " -18.73631141 -25.74814605 -19.73786442 -19.128602   -29.23809438\n",
-      " -20.54209621 -20.13463464 -26.0040655  -20.90029875 -20.57899564\n",
-      " -26.0040655  -20.90029875 -20.57899564 -29.50884242 -22.06421854\n",
-      " -21.63159919 -24.17555381 -17.53245455 -17.56714795 -28.24716711\n",
-      " -18.22477793 -17.91956499 -27.04168073 -19.6452736  -19.59832376\n",
-      " -25.40079344 -19.1005516  -18.73631141 -25.74814605 -19.73786442\n",
-      " -19.128602   -29.23809438 -20.54209621 -20.13463464 -26.0040655\n",
-      " -20.90029875 -20.57899564 -26.0040655  -20.90029875 -20.57899564\n",
-      " -29.50884242 -22.06421854 -21.63159919          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      " -24.47451567 -17.51118198 -17.58537547 -28.06035404 -18.22279658\n",
-      " -17.89023802 -27.04168073 -19.63694956 -19.58299176 -25.40079344\n",
-      " -19.1005516  -18.73631141 -25.74814605 -19.73786442 -19.128602\n",
-      " -29.23809438 -20.54209621 -20.13463464 -26.0040655  -20.90029875\n",
-      " -20.57899564 -26.0040655  -20.90029875 -20.57899564 -29.50884242\n",
-      " -22.06421854 -21.63159919 -24.47451567 -17.51118198 -17.58537547\n",
-      " -28.06035404 -18.22279658 -17.89023802 -27.04168073 -19.63694956\n",
-      " -19.58299176 -25.40079344 -19.1005516  -18.73631141 -25.74814605\n",
-      " -19.73786442 -19.128602   -29.23809438 -20.54209621 -20.13463464\n",
-      " -26.0040655  -20.90029875 -20.57899564 -26.0040655  -20.90029875\n",
-      " -20.57899564 -29.50884242 -22.06421854 -21.63159919]\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Meilleurs paramètres trouvés :  {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 50}\n",
-      "MSE : 18.487348024539877\n",
-      "R² : 0.7469523387400461\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "### Random forest \n",
     "\n",
@@ -4546,7 +2763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4657,42 +2874,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": [
-       "[np.float64(4.586051136945035),\n",
-       " np.float64(4.299691619702497),\n",
-       " np.float64(4.232697661885681)]"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "\n",
     "plot_learning_curve_rmse(best_rf, X, y)\n",
@@ -4702,30 +2886,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x950 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
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",
-      "text/plain": [
-       "<Figure size 800x950 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "import shap\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/scripts/Data_filter_Jess.py b/scripts/Data_filter_Jess.py
index a9a0c48..b6f9605 100644
--- a/scripts/Data_filter_Jess.py
+++ b/scripts/Data_filter_Jess.py
@@ -8,10 +8,11 @@ def filter_nutriscore_data(df, df_test):
     Filters the DataFrame to include only rows with a valid nutriscore_score.
     
     Parameters:
-    df (DataFrame): The input DataFrame containing food data.
+    df (DataFrame): The input train DataFrame containing food data.
+    df_test (DataFrame): The input test DataFrame containing food data.
     
     Returns:
-    DataFrame: A filtered DataFrame with only valid nutriscore_score entries.
+    DataFrame: 2 filtered DataFrame with only valid nutriscore_score entries.
     """
     # Check if 'nutriscore_score' column exists
     if 'nutriscore_score' not in df.columns:
diff --git a/scripts/rf.py b/scripts/rf.py
index f8f2651..ce52c93 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -4,9 +4,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-def random_forest_GS(X, y): 
+def random_forest_GS(X_train, y_train, X_test, y_test): 
     # Séparer en train/test
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
     
     # Définir le modèle
     rf = RandomForestRegressor(random_state=42)
@@ -74,43 +74,46 @@ def plot_learning_curve_rmse(model, X, y, cv=5):
     test_scores_std   = np.std(test_scores, axis=1)
 
     # Couleurs personnalisées
-    color_train = "#2ca02c"  # vert
-    color_test  = "#9467bd"  # violet
+    #color_train = "#2ca02c"  # vert
+    #color_test  = "#9467bd"  # violet
 
     plt.figure(figsize=(8, 5), facecolor="white")
     ax = plt.gca()
-    ax.set_facecolor("white")  # fond blanc
+    #ax.set_facecolor("white")  # fond blanc
 
     # Tracer les courbes
-    plt.plot(train_sizes, train_scores_mean, "o-", color=color_train, label="RMSE entraînement", linewidth=2)
-    plt.plot(train_sizes, test_scores_mean, "o-", color=color_test, label="RMSE validation", linewidth=2)
+    plt.plot(train_sizes, train_scores_mean, "o-", label="RMSE entraînement", linewidth=2)
+    plt.plot(train_sizes, test_scores_mean, "o-", label="RMSE validation", linewidth=2)
 
     # Zones ombrées
     plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
-                                  train_scores_mean + train_scores_std,
-                                  color=color_train, alpha=0.2)
+                                  train_scores_mean + train_scores_std, alpha=0.2)
     plt.fill_between(train_sizes,test_scores_mean - test_scores_std,
-                                test_scores_mean + test_scores_std,
-                                color=color_test,alpha=0.2)
+                                test_scores_mean + test_scores_std,alpha=0.2)
 
     # Style épuré : enlever spines gauche et bas
-    ax.spines['top'].set_visible(False)
-    ax.spines['right'].set_visible(False)
-    ax.spines['bottom'].set_visible(False)
-    ax.spines['left'].set_visible(False)
+    #ax.spines['top'].set_visible(False)
+    #ax.spines['right'].set_visible(False)
+    #ax.spines['bottom'].set_visible(False)
+    #ax.spines['left'].set_visible(False)
 
     # Axes et grille
     plt.xlabel("Taille de l'échantillon d'entraînement")
     plt.ylabel("RMSE")
     plt.title(f"Courbe d'apprentissage - {model.__class__.__name__}")
-    plt.legend()
+    plt.legend(frameon=False)
     plt.grid(True, linestyle='--', alpha=0.5)
     plt.tight_layout()
     plt.show()
 
 
 # Loss en fonction du nombre d'arbres / Loss function // number of threes
-def plot_mse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]):
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+
+def plot_rmse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]):
     errors = []
 
     for n in n_estimators_range:
@@ -125,14 +128,34 @@ def plot_mse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,5
         )
         rf_tmp.fit(X_train, y_train)
         y_pred_tmp = rf_tmp.predict(X_test)
+        
+        # Compute RMSE explicitly
         mse = mean_squared_error(y_test, y_pred_tmp)
-        errors.append(mse)
+        rmse = np.sqrt(mse)
+        errors.append(rmse)
+
+    # Couleurs personnalisées
+    #color = "#9467bd"  # violet
+
+    plt.figure(figsize=(8, 5), facecolor="white")
+    ax = plt.gca()
+    ax.set_facecolor("white")  # fond blanc
+
+    # Style épuré : enlever spines
+    #ax.spines['top'].set_visible(False)
+    #ax.spines['right'].set_visible(False)
+    #ax.spines['bottom'].set_visible(False)
+    #ax.spines['left'].set_visible(False)
 
-    plt.figure(figsize=(8,5))
-    plt.plot(n_estimators_range, errors, marker="o")
+    # Courbe RMSE
+    plt.plot(n_estimators_range, errors, marker="o", label="RMSE")
     plt.xlabel("Nombre d'arbres (n_estimators)")
-    plt.ylabel("MSE sur test")
-    plt.title("Évolution de la loss (MSE) vs nombre d'arbres")
-    plt.grid(True)
+    plt.ylabel("RMSE sur test data")
+    plt.legend(frameon=False)
+    plt.grid(True, linestyle='--', alpha=0.5)
+    plt.tight_layout()
     plt.show()
+    
+    return errors  # also return RMSE values for later use
+
 

From 5b346641fe07fe980c74ab89e29969353a304f41 Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Sat, 30 Aug 2025 16:47:41 +0200
Subject: [PATCH 07/12] update n 544324

---
 scripts/rf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/scripts/rf.py b/scripts/rf.py
index ce52c93..58acd6d 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -26,7 +26,7 @@ def random_forest_GS(X_train, y_train, X_test, y_test):
         param_grid=param_grid,
         cv=5,                 
         n_jobs=-1,            
-        scoring="root_mean_squared_error",  
+        scoring="neg_mean_squared_error",  
         verbose=2
     )
 

From f21e91c5de8e78031fad6e8d1693697631049f4d Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Sat, 30 Aug 2025 16:48:50 +0200
Subject: [PATCH 08/12] h

---
 notebooks/project_starter.ipynb | 1439 +++++++++++++++++++++++++++++--
 1 file changed, 1365 insertions(+), 74 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index bf7ac3f..4d1abf1 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,16 +46,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\962525088.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\962525088.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n"
      ]
     }
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1153,7 @@
          "type": "float"
         }
        ],
-       "ref": "376a82dd-1d4a-44f1-bf65-bedfdd08961f",
+       "ref": "30902a37-e4da-4941-81d0-dd6221fe140d",
        "rows": [
         [
          "0",
@@ -2460,7 +2460,7 @@
        "[5 rows x 214 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2485,7 +2485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -2510,7 +2510,7 @@
        "True"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2540,9 +2540,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              pnns_groups_1      PNNS_pro\n",
+      "6                   unknown            NA\n",
+      "9                   unknown            NA\n",
+      "11    Fruits and vegetables   Plant_based\n",
+      "12                  unknown            NA\n",
+      "14          Composite foods     Processed\n",
+      "...                     ...           ...\n",
+      "4926   Cereals and potatoes   Plant_based\n",
+      "4935         Fish Meat Eggs  Animal_based\n",
+      "4936                unknown            NA\n",
+      "4943        Composite foods     Processed\n",
+      "4987          Sugary snacks        Snacks\n",
+      "\n",
+      "[437 rows x 2 columns]\n"
+     ]
+    }
+   ],
    "source": [
     "pnns_mapping = {\"unknown\" : 'NA', \n",
     "                'Beverages' : 'Drinks',\n",
@@ -2574,7 +2595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2589,9 +2610,436 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
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+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "additives_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutriscore_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "saturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbohydrates_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sugars_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sodium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Animal_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Drinks",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_NA",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Plant_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Processed",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Snacks",
+         "rawType": "float64",
+         "type": "float"
+        }
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+       ],
+       "shape": {
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+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>code</th>\n",
+       "      <th>additives_n</th>\n",
+       "      <th>nutriscore_score</th>\n",
+       "      <th>energy_100g</th>\n",
+       "      <th>fat_100g</th>\n",
+       "      <th>saturated-fat_100g</th>\n",
+       "      <th>carbohydrates_100g</th>\n",
+       "      <th>sugars_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
+       "      <th>PNNS_pro_Animal_based</th>\n",
+       "      <th>PNNS_pro_Drinks</th>\n",
+       "      <th>PNNS_pro_NA</th>\n",
+       "      <th>PNNS_pro_Plant_based</th>\n",
+       "      <th>PNNS_pro_Processed</th>\n",
+       "      <th>PNNS_pro_Snacks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1520.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1510.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>10.714286</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-11.0</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.275</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6    4.0          0.0              15.0       2401.0      12.0   \n",
+       "9    6.0          NaN               4.0       1520.0      11.0   \n",
+       "11   7.0          0.0               4.0          4.0       1.0   \n",
+       "12   8.0          1.0               6.0       1510.0       2.0   \n",
+       "14   9.0          NaN             -11.0        293.0       0.5   \n",
+       "\n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6                10.50                13.0         9.00   36.000000   \n",
+       "9                 2.00                25.0         0.98    9.000000   \n",
+       "11                1.00                 1.0         1.00    1.000000   \n",
+       "12                0.50                 6.7         1.70   10.714286   \n",
+       "14                0.06                 2.0         0.24   88.000000   \n",
+       "\n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6            23.0      0.300         0.12   \n",
+       "9            22.0      0.950         0.38   \n",
+       "11            1.0      1.000         0.40   \n",
+       "12           76.0      1.500         0.60   \n",
+       "14           18.0      0.275         0.11   \n",
+       "\n",
+       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "6                                                 0.0       \n",
+       "9                                                 NaN       \n",
+       "11                                                0.0       \n",
+       "12                                                0.0       \n",
+       "14                                                NaN       \n",
+       "\n",
+       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
+       "6                     0.0              0.0          0.0                   0.0   \n",
+       "9                     0.0              0.0          0.0                   0.0   \n",
+       "11                    0.0              0.0          0.0                   1.0   \n",
+       "12                    0.0              0.0          1.0                   0.0   \n",
+       "14                    0.0              0.0          1.0                   0.0   \n",
+       "\n",
+       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "6                  0.0              1.0  \n",
+       "9                  1.0              0.0  \n",
+       "11                 0.0              0.0  \n",
+       "12                 0.0              0.0  \n",
+       "14                 0.0              0.0  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "filtered_df.head()"
    ]
@@ -2605,9 +3053,444 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ": shape of df with only numeric features=(807, 19)\n",
+      ": shape of df with only numeric features=(772, 142)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
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+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "additives_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutriscore_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "saturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbohydrates_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sugars_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sodium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Animal_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Drinks",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_NA",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Plant_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Processed",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Snacks",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "eb45bc85-0ff4-41e1-b908-d48e54ab910d",
+       "rows": [
+        [
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>code</th>\n",
+       "      <th>additives_n</th>\n",
+       "      <th>nutriscore_score</th>\n",
+       "      <th>energy_100g</th>\n",
+       "      <th>fat_100g</th>\n",
+       "      <th>saturated-fat_100g</th>\n",
+       "      <th>carbohydrates_100g</th>\n",
+       "      <th>sugars_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
+       "      <th>PNNS_pro_Animal_based</th>\n",
+       "      <th>PNNS_pro_Drinks</th>\n",
+       "      <th>PNNS_pro_NA</th>\n",
+       "      <th>PNNS_pro_Plant_based</th>\n",
+       "      <th>PNNS_pro_Processed</th>\n",
+       "      <th>PNNS_pro_Snacks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.12</td>\n",
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+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1520.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>20.400223</td>\n",
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+       "      <td>1.0</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1510.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>10.714286</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>-11.0</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.275</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>20.000291</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6    4.0          0.0              15.0       2401.0      12.0   \n",
+       "9    6.0          1.8               4.0       1520.0      11.0   \n",
+       "11   7.0          0.0               4.0          4.0       1.0   \n",
+       "12   8.0          1.0               6.0       1510.0       2.0   \n",
+       "14   9.0          0.6             -11.0        293.0       0.5   \n",
+       "\n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6                10.50                13.0         9.00   36.000000   \n",
+       "9                 2.00                25.0         0.98    9.000000   \n",
+       "11                1.00                 1.0         1.00    1.000000   \n",
+       "12                0.50                 6.7         1.70   10.714286   \n",
+       "14                0.06                 2.0         0.24   88.000000   \n",
+       "\n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6            23.0      0.300         0.12   \n",
+       "9            22.0      0.950         0.38   \n",
+       "11            1.0      1.000         0.40   \n",
+       "12           76.0      1.500         0.60   \n",
+       "14           18.0      0.275         0.11   \n",
+       "\n",
+       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "6                                            0.000000       \n",
+       "9                                           20.400223       \n",
+       "11                                           0.000000       \n",
+       "12                                           0.000000       \n",
+       "14                                          20.000291       \n",
+       "\n",
+       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
+       "6                     0.0              0.0          0.0                   0.0   \n",
+       "9                     0.0              0.0          0.0                   0.0   \n",
+       "11                    0.0              0.0          0.0                   1.0   \n",
+       "12                    0.0              0.0          1.0                   0.0   \n",
+       "14                    0.0              0.0          1.0                   0.0   \n",
+       "\n",
+       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "6                  0.0              1.0  \n",
+       "9                  1.0              0.0  \n",
+       "11                 0.0              0.0  \n",
+       "12                 0.0              0.0  \n",
+       "14                 0.0              0.0  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "from scripts.Imputing import knn_impute_numeric\n",
     "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
@@ -2624,9 +3507,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ": shape of df with only numeric features=(807, 18)\n",
+      ": shape of df with only numeric features=(772, 141)\n"
+     ]
+    }
+   ],
    "source": [
     "from scripts.Scaling import scaler_numeric\n",
     "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
@@ -2635,9 +3527,436 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
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+         "name": "index",
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+         "type": "float"
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+        {
+         "name": "additives_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutriscore_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "saturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbohydrates_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sugars_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sodium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
+         "rawType": "float64",
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+        {
+         "name": "PNNS_pro_Animal_based",
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+        {
+         "name": "PNNS_pro_Drinks",
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+        {
+         "name": "PNNS_pro_NA",
+         "rawType": "float64",
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+        {
+         "name": "PNNS_pro_Plant_based",
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+        {
+         "name": "PNNS_pro_Processed",
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+        {
+         "name": "PNNS_pro_Snacks",
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+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "    .dataframe thead th {\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>code</th>\n",
+       "      <th>additives_n</th>\n",
+       "      <th>nutriscore_score</th>\n",
+       "      <th>energy_100g</th>\n",
+       "      <th>fat_100g</th>\n",
+       "      <th>saturated-fat_100g</th>\n",
+       "      <th>carbohydrates_100g</th>\n",
+       "      <th>sugars_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
+       "      <th>PNNS_pro_Animal_based</th>\n",
+       "      <th>PNNS_pro_Drinks</th>\n",
+       "      <th>PNNS_pro_NA</th>\n",
+       "      <th>PNNS_pro_Plant_based</th>\n",
+       "      <th>PNNS_pro_Processed</th>\n",
+       "      <th>PNNS_pro_Snacks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
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+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>0.375</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.180364</td>\n",
+       "      <td>-0.039062</td>\n",
+       "      <td>-0.280365</td>\n",
+       "      <td>-0.022608</td>\n",
+       "      <td>-0.600802</td>\n",
+       "      <td>0.003842</td>\n",
+       "      <td>0.065283</td>\n",
+       "      <td>-0.054149</td>\n",
+       "      <td>-0.054149</td>\n",
+       "      <td>0.384014</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.750</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-2.024727</td>\n",
+       "      <td>-0.649414</td>\n",
+       "      <td>-0.442802</td>\n",
+       "      <td>-0.684312</td>\n",
+       "      <td>-0.599623</td>\n",
+       "      <td>-0.805038</td>\n",
+       "      <td>-0.557308</td>\n",
+       "      <td>-0.038223</td>\n",
+       "      <td>-0.038223</td>\n",
+       "      <td>-0.197357</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.125</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>0.165818</td>\n",
+       "      <td>-0.588379</td>\n",
+       "      <td>-0.524020</td>\n",
+       "      <td>-0.527157</td>\n",
+       "      <td>-0.558359</td>\n",
+       "      <td>0.177174</td>\n",
+       "      <td>1.666232</td>\n",
+       "      <td>0.121038</td>\n",
+       "      <td>0.121038</td>\n",
+       "      <td>-0.197357</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.375</td>\n",
+       "      <td>-11.0</td>\n",
+       "      <td>-1.604364</td>\n",
+       "      <td>-0.679932</td>\n",
+       "      <td>-0.595492</td>\n",
+       "      <td>-0.656741</td>\n",
+       "      <td>-0.644423</td>\n",
+       "      <td>7.991539</td>\n",
+       "      <td>-0.053306</td>\n",
+       "      <td>-0.269151</td>\n",
+       "      <td>-0.269151</td>\n",
+       "      <td>0.372616</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6  -0.000002       -0.750              15.0     1.461818  0.021973   \n",
+       "9  -0.000002        0.375               4.0     0.180364 -0.039062   \n",
+       "11 -0.000002       -0.750               4.0    -2.024727 -0.649414   \n",
+       "12 -0.000002       -0.125               6.0     0.165818 -0.588379   \n",
+       "14 -0.000002       -0.375             -11.0    -1.604364 -0.679932   \n",
+       "\n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6             1.100345           -0.353460    -0.128036    2.733814   \n",
+       "9            -0.280365           -0.022608    -0.600802    0.003842   \n",
+       "11           -0.442802           -0.684312    -0.599623   -0.805038   \n",
+       "12           -0.524020           -0.527157    -0.558359    0.177174   \n",
+       "14           -0.595492           -0.656741    -0.644423    7.991539   \n",
+       "\n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6        0.094930  -0.261188    -0.261188   \n",
+       "9        0.065283  -0.054149    -0.054149   \n",
+       "11      -0.557308  -0.038223    -0.038223   \n",
+       "12       1.666232   0.121038     0.121038   \n",
+       "14      -0.053306  -0.269151    -0.269151   \n",
+       "\n",
+       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "6                                           -0.197357       \n",
+       "9                                            0.384014       \n",
+       "11                                          -0.197357       \n",
+       "12                                          -0.197357       \n",
+       "14                                           0.372616       \n",
+       "\n",
+       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
+       "6                     0.0              0.0         -0.5                   0.0   \n",
+       "9                     0.0              0.0         -0.5                   0.0   \n",
+       "11                    0.0              0.0         -0.5                   5.0   \n",
+       "12                    0.0              0.0          2.0                   0.0   \n",
+       "14                    0.0              0.0          2.0                   0.0   \n",
+       "\n",
+       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "6                  0.0              2.5  \n",
+       "9                  1.0              0.0  \n",
+       "11                 0.0              0.0  \n",
+       "12                 0.0              0.0  \n",
+       "14                 0.0              0.0  "
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "work_df.head()"
    ]
@@ -2672,76 +3991,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from sklearn.metrics import mean_squared_error, r2_score\n",
-    "\n",
-    "def random_forest_GS(X_train, y_train, X_test, y_test): \n",
-    "    # Séparer en train/test\n",
-    "    #X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
-    "    \n",
-    "    # Définir le modèle\n",
-    "    rf = RandomForestRegressor(random_state=42)\n",
-    "\n",
-    "    # Grille des hyperparamètres à tester\n",
-    "    param_grid = {\n",
-    "        \"n_estimators\": [5, 10, 60],              \n",
-    "        \"max_depth\": [None, 10, 20, 50],         \n",
-    "        \"min_samples_split\": [2, 5, 10],         \n",
-    "        \"min_samples_leaf\": [1, 2, 4],           \n",
-    "        \"max_features\": [\"auto\", \"sqrt\", \"log2\"] \n",
-    "    }\n",
-    "\n",
-    "    # Grid Search avec validation croisée\n",
-    "    grid_search = GridSearchCV(\n",
-    "        estimator=rf,\n",
-    "        param_grid=param_grid,\n",
-    "        cv=5,                 \n",
-    "        n_jobs=-1,            \n",
-    "        scoring=\"neg_root_mean_squared_error\",  \n",
-    "        verbose=2\n",
-    "    )\n",
-    "\n",
-    "    # Entraînement\n",
-    "    grid_search.fit(X_train, y_train)\n",
-    "    \n",
-    "    # Prédictions\n",
-    "    y_pred = grid_search.best_estimator_.predict(X_test)\n",
-    "\n",
-    "    # Meilleurs paramètres\n",
-    "    print(\"Meilleurs paramètres trouvés : \", grid_search.best_params_)\n",
-    "    \n",
-    "    # Évaluation\n",
-    "    print(\"MSE :\", mean_squared_error(y_test, y_pred))\n",
-    "    print(\"R² :\", r2_score(y_test, y_pred))\n",
-    "\n",
-    "    return grid_search.best_estimator_, X_train, X_test, y_train, y_test, y_pred"
+    "X = work_df.drop(\"nutriscore_score\", axis=1)  \n",
+    "y = work_df[\"nutriscore_score\"]\n",
+    "X_test = work_df_test.drop(\"nutriscore_score\", axis=1) \n",
+    "y_test = work_df_test[\"nutriscore_score\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
-    "X = work_df.drop(\"nutriscore_score\", axis=1)  \n",
-    "y = work_df[\"nutriscore_score\"]\n",
-    "X_test = work_df_test.drop(\"nutriscore_score\", axis=1) \n",
-    "y_test = work_df_test[\"nutriscore_score\"]"
+    "from scripts.rf import random_forest_GS"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "InvalidParameterError",
+     "evalue": "The 'scoring' parameter of GridSearchCV must be a str among {'precision_samples', 'neg_mean_squared_log_error', 'neg_root_mean_squared_log_error', 'neg_max_error', 'f1_weighted', 'top_k_accuracy', 'f1_micro', 'precision_weighted', 'neg_mean_poisson_deviance', 'matthews_corrcoef', 'neg_negative_likelihood_ratio', 'f1', 'jaccard', 'fowlkes_mallows_score', 'precision_macro', 'precision_micro', 'average_precision', 'neg_mean_gamma_deviance', 'precision', 'recall_weighted', 'neg_mean_absolute_percentage_error', 'v_measure_score', 'f1_macro', 'balanced_accuracy', 'explained_variance', 'neg_mean_absolute_error', 'neg_brier_score', 'recall_macro', 'neg_mean_squared_error', 'mutual_info_score', 'roc_auc_ovr_weighted', 'recall_samples', 'accuracy', 'recall_micro', 'adjusted_mutual_info_score', 'homogeneity_score', 'jaccard_macro', 'jaccard_micro', 'r2', 'positive_likelihood_ratio', 'normalized_mutual_info_score', 'neg_log_loss', 'd2_absolute_error_score', 'roc_auc_ovo_weighted', 'roc_auc', 'rand_score', 'neg_root_mean_squared_error', 'roc_auc_ovr', 'jaccard_weighted', 'completeness_score', 'roc_auc_ovo', 'neg_median_absolute_error', 'recall', 'f1_samples', 'jaccard_samples', 'adjusted_rand_score'}, a callable, an instance of 'list', an instance of 'tuple', an instance of 'dict' or None. Got 'root_mean_squared_error' instead.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m best_rf, X_train, X_test, y_train, y_test, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mrandom_forest_GS\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\Documents\\OFFProject\\scripts\\rf.py:34\u001b[0m, in \u001b[0;36mrandom_forest_GS\u001b[1;34m(X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[0;32m     24\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(\n\u001b[0;32m     25\u001b[0m     estimator\u001b[38;5;241m=\u001b[39mrf,\n\u001b[0;32m     26\u001b[0m     param_grid\u001b[38;5;241m=\u001b[39mparam_grid,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     30\u001b[0m     verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m\n\u001b[0;32m     31\u001b[0m )\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Entraînement\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     36\u001b[0m \u001b[38;5;66;03m# Prédictions\u001b[39;00m\n\u001b[0;32m     37\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m grid_search\u001b[38;5;241m.\u001b[39mbest_estimator_\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
+      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py:1382\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1377\u001b[0m partial_fit_and_fitted \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1378\u001b[0m     fit_method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpartial_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[0;32m   1379\u001b[0m )\n\u001b[0;32m   1381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[1;32m-> 1382\u001b[0m     \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1384\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1385\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1386\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1387\u001b[0m     )\n\u001b[0;32m   1388\u001b[0m ):\n\u001b[0;32m   1389\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py:436\u001b[0m, in \u001b[0;36mBaseEstimator._validate_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    429\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[0;32m    430\u001b[0m \n\u001b[0;32m    431\u001b[0m \u001b[38;5;124;03m    The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[38;5;124;03m    accepted constraints.\u001b[39;00m\n\u001b[0;32m    435\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 436\u001b[0m     \u001b[43mvalidate_parameter_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    437\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parameter_constraints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    438\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    439\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    440\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py:98\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     94\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     95\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     96\u001b[0m     )\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m     99\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    101\u001b[0m )\n",
+      "\u001b[1;31mInvalidParameterError\u001b[0m: The 'scoring' parameter of GridSearchCV must be a str among {'precision_samples', 'neg_mean_squared_log_error', 'neg_root_mean_squared_log_error', 'neg_max_error', 'f1_weighted', 'top_k_accuracy', 'f1_micro', 'precision_weighted', 'neg_mean_poisson_deviance', 'matthews_corrcoef', 'neg_negative_likelihood_ratio', 'f1', 'jaccard', 'fowlkes_mallows_score', 'precision_macro', 'precision_micro', 'average_precision', 'neg_mean_gamma_deviance', 'precision', 'recall_weighted', 'neg_mean_absolute_percentage_error', 'v_measure_score', 'f1_macro', 'balanced_accuracy', 'explained_variance', 'neg_mean_absolute_error', 'neg_brier_score', 'recall_macro', 'neg_mean_squared_error', 'mutual_info_score', 'roc_auc_ovr_weighted', 'recall_samples', 'accuracy', 'recall_micro', 'adjusted_mutual_info_score', 'homogeneity_score', 'jaccard_macro', 'jaccard_micro', 'r2', 'positive_likelihood_ratio', 'normalized_mutual_info_score', 'neg_log_loss', 'd2_absolute_error_score', 'roc_auc_ovo_weighted', 'roc_auc', 'rand_score', 'neg_root_mean_squared_error', 'roc_auc_ovr', 'jaccard_weighted', 'completeness_score', 'roc_auc_ovo', 'neg_median_absolute_error', 'recall', 'f1_samples', 'jaccard_samples', 'adjusted_rand_score'}, a callable, an instance of 'list', an instance of 'tuple', an instance of 'dict' or None. Got 'root_mean_squared_error' instead."
+     ]
+    }
+   ],
    "source": [
+    "\n",
     "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y, X_test, y_test)"
    ]
   },

From 36754207e865fc27ccc606992d1a14432e308ce7 Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Sat, 30 Aug 2025 17:16:45 +0200
Subject: [PATCH 09/12] updating the update

---
 notebooks/project_starter.ipynb | 4278 ++++++-------------------------
 scripts/rf.py                   |    2 +-
 2 files changed, 784 insertions(+), 3496 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index 4d1abf1..28f8f99 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,16 +46,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_32200\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12196\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_32200\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n"
      ]
     }
@@ -70,3000 +70,155 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "code",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "url",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "creator",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "created_t",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "created_datetime",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "last_modified_t",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "last_modified_datetime",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "last_modified_by",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "last_updated_t",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "last_updated_datetime",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "product_name",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "abbreviated_product_name",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "generic_name",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "quantity",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "packaging",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "packaging_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "packaging_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "packaging_text",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "brands",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "brands_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "brands_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "categories",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "categories_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "categories_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "origins",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "origins_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "origins_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "manufacturing_places",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "manufacturing_places_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "labels",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "labels_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "labels_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "emb_codes",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "emb_codes_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "first_packaging_code_geo",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "cities",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "cities_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "purchase_places",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "stores",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "countries",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "countries_tags",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "countries_en",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "ingredients_text",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "ingredients_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "ingredients_analysis_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "allergens",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "allergens_en",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "traces",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "traces_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "traces_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "serving_size",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "serving_quantity",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "no_nutrition_data",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "additives_n",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "additives",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "additives_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "additives_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "nutriscore_score",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "nutriscore_grade",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "nova_group",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "pnns_groups_1",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "pnns_groups_2",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "food_groups",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "food_groups_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "food_groups_en",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "states",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "states_tags",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "states_en",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "brand_owner",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "environmental_score_score",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "environmental_score_grade",
-         "rawType": "object",
-         "type": "string"
-        },
-        {
-         "name": "nutrient_levels_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "product_quantity",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "owner",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "data_quality_errors_tags",
-         "rawType": "object",
-         "type": "unknown"
-        },
-        {
-         "name": "unique_scans_n",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "popularity_tags",
-         "rawType": "object",
-         "type": "unknown"
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-         null
-        ],
-        [
-         "1",
-         "63",
-         "http://world-en.openfoodfacts.org/product/000000000063/m-amp-m-white-fitpiggy",
-         "kiliweb",
-         "1673620307",
-         "2023-01-13T14:31:47Z",
-         "1750061386",
-         "2025-06-16T08:09:46Z",
-         "bodysupport",
-         "1750061386",
-         "2025-06-16T08:09:46Z",
-         "M&amp;M white",
-         null,
-         null,
-         "80 gram",
-         null,
-         null,
-         null,
-         null,
-         "Fitpiggy",
-         "xx:fitpiggy",
-         "fitpiggy",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
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-         null,
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-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "en:fr",
-         "en:france",
-         "France",
-         "Weizenmehl, Rapsöl, Speisesalz, 1,7% Meersalz, Fefe, Gerstenmaizextrakt, Säureregulator: Natrium - hydroxid; Backtriebnittel: Ammoniumcarbonate. Das Produkt kann Spuren von Sesam enthalten. DURCHSCHNITTLICHE NÄHRWERTE - pro %RM* 100g pro 100g Brennwert kJ/kcal 1630/385 19% Fett 3,7g 5% davon: - gesättigte Fettsäuren 0,6g 3% Kohlenhydrate 74,0g 28% davon: - Zucker 2,4g 3% Ballaststoffe 3,9g 12,0g 24% Eiweiß 3,9g 65% Salz *Referenzmenge für einen durchschnittlichen Erwachsenen (8400 kJ/2000 kcal) AP EDEKA KUNDEN UND ERNÄHRUNGSSERVICE 2509 (",
-         "en:weizenmehl,en:rapsol,en:speisesalz,en:meersalz,en:fefe,en:gerstenmaizextrakt,en:saureregulator,en:hydroxid,en:backtriebnittel,en:pro-rm-100g-pro-100g-brennwert-kj,en:kcal-1630,en:385-19-fett-3-7-5-davon,en:gesattigte-fettsauren-0-6-3-kohlenhydrate-74-28-davon,en:zucker-2-4-3-ballaststoffe-3-9-12-24-eiweiss-3-9-65-salz-referenzmenge-fur-einen-durchschnittlichen-erwachsenen,en:ap-edeka-kunden-und-ernahrungsservice-2509,en:sodium,en:minerals,en:ammoniumcarbonate,en:das-produkt-kann-spuren-von-sesam-enthalten,en:durchschnittliche-nahrwerte,en:8400-kj,en:2000-kcal",
-         "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown",
-         null,
-         null,
-         null,
-         null,
-         null,
-         "80 gram",
-         "80.0",
-         null,
-         "0.0",
-         null,
-         null,
-         null,
-         null,
-         "unknown",
-         null,
-         "unknown",
-         "unknown",
-         null,
-         null,
-         null,
-         "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded",
-         "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded",
-         "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded",
-         null,
-         null,
-         "unknown",
-         null,
-         "80.0",
-         null,
-         null,
-         "1.0",
-         "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-us-scans-2024,top-100000-us-scans-2024,top-country-us-scans-2024",
-         "0.6625",
-         "1746257766.0",
-         "2025-05-03T07:36:06Z",
-         null,
-         null,
-         "https://images.openfoodfacts.org/images/products/invalid/front_en.12.400.jpg",
-         "https://images.openfoodfacts.org/images/products/invalid/front_en.12.200.jpg",
-         "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.400.jpg",
-         "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.200.jpg",
-         null,
-         null,
-         null,
-         "359.0",
-         "1502.0",
-         null,
-         "19.0",
-         "17.3",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "26.0",
-         "1.0",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "17.0",
-         null,
-         null,
-         null,
-         "1.2",
-         null,
-         "0.48",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
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-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "0.0",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null
-        ],
-        [
-         "2",
-         "114",
-         "http://world-en.openfoodfacts.org/product/000000000114/chocolate-n3-jeff-de-bruges",
-         "kiliweb",
-         "1580066482",
-         "2020-01-26T19:21:22Z",
-         "1751035658",
-         "2025-06-27T14:47:38Z",
-         "teolemon",
-         "1751035658",
-         "2025-06-27T14:47:38Z",
-         "Chocolate n3",
-         null,
-         null,
-         "80 g",
-         null,
-         null,
-         null,
-         null,
-         "Jeff de Bruges",
-         "xx:jeff-de-bruges",
-         "jeff-de-bruges",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "Green Dot,Made in France",
-         "en:green-dot,en:made-in-france",
-         "Green Dot,Made in France",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "France",
-         "en:france",
-         "France",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "unknown",
-         null,
-         "unknown",
-         "unknown",
-         null,
-         null,
-         null,
-         "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded",
-         "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded",
-         "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded",
-         null,
-         null,
-         "unknown",
-         null,
-         "80.0",
-         null,
-         null,
-         "1.0",
-         "bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024",
-         "0.475",
-         "1737247860.0",
-         "2025-01-19T00:51:00Z",
-         null,
-         null,
-         "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.400.jpg",
-         "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.200.jpg",
-         null,
-         null,
-         "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.400.jpg",
-         "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.200.jpg",
-         "2415.0",
-         null,
-         "2415.0",
-         null,
-         "44.0",
-         "28.0",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "30.0",
-         "27.0",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "7.1",
-         null,
-         null,
-         null,
-         "0.025",
-         null,
-         "0.01",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null
-        ],
-        [
-         "3",
-         "105",
-         "http://world-en.openfoodfacts.org/product/0000000105/paleta-gran-reserva-sierra-nevada-advocare",
-         "kiliweb",
-         "1572117743",
-         "2019-10-26T19:22:23Z",
-         "1738073570",
-         "2025-01-28T14:12:50Z",
-         null,
-         "1743653496",
-         "2025-04-03T04:11:36Z",
-         "Paleta gran reserva - Sierra nevada-",
-         null,
-         null,
-         "750ml",
-         null,
-         null,
-         null,
-         null,
-         "AdvoCare",
-         "xx:advocare",
-         "advocare",
-         "Bebidas y preparaciones de bebidas, Bebidas",
-         "en:beverages-and-beverages-preparations,en:beverages",
-         "Beverages and beverages preparations,Beverages",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "Spanien, Germany",
-         "en:germany,en:spain",
-         "Germany,Spain",
-         "Thiamin, Biotin, Chromium, Garcinia cambogia fruit extract, Taurine, Green coffee fruit extract, Caffeine, Inositol, Citric Acid, Natural ,  Artificial Flavors, Sucralose, Spirulina Extract, Beta Carotene",
-         "en:thiamin,en:biotin,en:vitamins,en:chromium,en:minerals,en:garcinia-cambogia-fruit-extract,en:taurine,en:green-coffee-fruit-extract,en:caffeine,en:inositol,en:e330,en:natural,en:artificial-flavouring,en:flavouring,en:e955,en:spirulina-concentrate,en:algae,en:spirulina,en:e160ai,en:e160a",
-         "en:may-contain-palm-oil,en:vegan-status-unknown,en:vegetarian-status-unknown",
-         null,
-         null,
-         null,
-         null,
-         null,
-         "5g",
-         "5.0",
-         null,
-         "2.0",
-         null,
-         "en:e330,en:e955",
-         "E330 - Citric acid,E955 - Sucralose",
-         null,
-         "unknown",
-         "4.0",
-         "Beverages",
-         "Artificially sweetened beverages",
-         "en:artificially-sweetened-beverages",
-         "en:beverages,en:artificially-sweetened-beverages",
-         "Beverages,Artificially sweetened beverages",
-         "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded",
-         "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded",
-         "To be completed,Nutrition facts to be completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded",
-         null,
-         null,
-         "unknown",
-         null,
-         "750.0",
-         null,
-         null,
-         "1.0",
-         "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-500-az-scans-2024,top-1000-az-scans-2024,top-5000-az-scans-2024,top-10000-az-scans-2024,top-50000-az-scans-2024,top-100000-az-scans-2024,top-country-az-scans-2024",
-         "0.675",
-         "1738073557.0",
-         "2025-01-28T14:12:37Z",
-         "en:beverages",
-         "Beverages",
-         "https://images.openfoodfacts.org/images/products/invalid/front_es.21.400.jpg",
-         "https://images.openfoodfacts.org/images/products/invalid/front_es.21.200.jpg",
-         null,
-         null,
-         "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.400.jpg",
-         "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.200.jpg",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "0.0113351004464306",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null
-        ],
-        [
-         "4",
-         "2",
-         "http://world-en.openfoodfacts.org/product/00000002/filets-de-poulet-blanc-x2-solo",
-         "kiliweb",
-         "1722606455",
-         "2024-08-02T13:47:35Z",
-         "1749171851",
-         "2025-06-06T01:04:11Z",
-         "altroconsumo",
-         "1749171851",
-         "2025-06-06T01:04:11Z",
-         "Filets de poulet blanc x2",
-         null,
-         null,
-         "240-400 g",
-         null,
-         null,
-         null,
-         null,
-         "SoLo, selbstgemacht 2 Liter",
-         "xx:solo,xx:selbstgemacht-2-liter",
-         "solo,selbstgemacht-2-liter",
-         "Protein powders",
-         "en:dietary-supplements,en:bodybuilding-supplements,en:protein-powders",
-         "Dietary supplements,Bodybuilding supplements,Protein powders",
-         "île d’Orléans,Québec,Canada",
-         "en:canada,en:quebec,fr:ile-d-orleans",
-         "Canada,Québec,fr:ile-d-orleans",
-         "Ancenis",
-         "ancenis",
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-       "      <td>2020-01-26T19:21:22Z</td>\n",
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-       "      <td>2025-06-27T14:47:38Z</td>\n",
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-       "   code                                                url  creator  \\\n",
-       "0    54  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
-       "1    63  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
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-       "4     2  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
-       "\n",
-       "    created_t      created_datetime  last_modified_t last_modified_datetime  \\\n",
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-       "4  1722606455  2024-08-02T13:47:35Z       1749171851   2025-06-06T01:04:11Z   \n",
-       "\n",
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-       "\n",
-       "  water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g  \\\n",
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-       "\n",
-       "  inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g  \\\n",
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-       "4           NaN            NaN           NaN          NaN          NaN   \n",
-       "\n",
-       "  carbohydrates-total_100g  \n",
-       "0                      NaN  \n",
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-       "\n",
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-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2. Pre-processing "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.1 Preliminary data curation (of the train and test df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:65: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n",
-      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:66: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "### taking out lines without nutriscore because it's non informative (both fot the train and the test df)\n",
-    "filtered_df, filtered_df_test = dfj.filter_nutriscore_data(df_train, df_test)\n",
-    "\n",
-    "### Cleaning the variables \n",
-    "cat_df, cat_df_test = dfj.categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n",
-    "num_df, num_df_test = dfj.numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n",
-    "final_df, final_df_test = dfj.final_df(cat_df, num_df, cat_df_test, num_df_test)\n",
-    "\n",
-    "### Droping some additionnal columns \n",
-    "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
-    "final_df_test = final_df_test.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
-    "\n",
-    "final_df.columns.equals(final_df_test.columns) #checking if we have the same variables in each df "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.2 Enconding "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              pnns_groups_1      PNNS_pro\n",
-      "6                   unknown            NA\n",
-      "9                   unknown            NA\n",
-      "11    Fruits and vegetables   Plant_based\n",
-      "12                  unknown            NA\n",
-      "14          Composite foods     Processed\n",
-      "...                     ...           ...\n",
-      "4926   Cereals and potatoes   Plant_based\n",
-      "4935         Fish Meat Eggs  Animal_based\n",
-      "4936                unknown            NA\n",
-      "4943        Composite foods     Processed\n",
-      "4987          Sugary snacks        Snacks\n",
-      "\n",
-      "[437 rows x 2 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "pnns_mapping = {\"unknown\" : 'NA', \n",
-    "                'Beverages' : 'Drinks',\n",
-    "                'nan' : 'NA',\n",
-    "                'Fruits and vegetables' : 'Plant_based',\n",
-    "                'Composite foods' : 'Processed',\n",
-    "                'Sugary snacks' : 'Snacks',\n",
-    "                'Salty snacks' : 'Snacks',\n",
-    "                'Cereals and potatoes' : 'Plant_based',\n",
-    "                'Fat and sauces' : 'Processed', \n",
-    "                'Milk and dairy products' : 'Animal_based',\n",
-    "                'Fish Meat Eggs' : 'Animal_based',\n",
-    "                'Alcoholic beverages' : 'Drinks'\n",
-    "}\n",
-    "\n",
-    "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
-    "final_df_test['PNNS_pro'] = final_df_test['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
-    "print(final_df[['pnns_groups_1', 'PNNS_pro']])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "final_df_test['PNNS_pro']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scripts.encoding_func import one_hot_encode_column\n",
-    "\n",
-    "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n",
-    "filterd_df_test = one_hot_encode_column(final_df_test, 'PNNS_pro')\n",
-    "filtered_df.head()\n",
-    "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n",
-    "filtered_df_test = filtered_df_test.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
-       "columns": [
-        {
-         "name": "index",
-         "rawType": "int64",
-         "type": "integer"
-        },
-        {
-         "name": "code",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "additives_n",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "nutriscore_score",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "energy_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "saturated-fat_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "carbohydrates_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "sugars_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fiber_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "proteins_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "salt_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "sodium_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Animal_based",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Drinks",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_NA",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Plant_based",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Processed",
-         "rawType": "float64",
-         "type": "float"
-        },
-        {
-         "name": "PNNS_pro_Snacks",
-         "rawType": "float64",
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-       "shape": {
-        "columns": 19,
-        "rows": 5
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-      },
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>additives_n</th>\n",
-       "      <th>nutriscore_score</th>\n",
-       "      <th>energy_100g</th>\n",
-       "      <th>fat_100g</th>\n",
-       "      <th>saturated-fat_100g</th>\n",
-       "      <th>carbohydrates_100g</th>\n",
-       "      <th>sugars_100g</th>\n",
-       "      <th>fiber_100g</th>\n",
-       "      <th>proteins_100g</th>\n",
-       "      <th>salt_100g</th>\n",
-       "      <th>sodium_100g</th>\n",
-       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
-       "      <th>PNNS_pro_Animal_based</th>\n",
-       "      <th>PNNS_pro_Drinks</th>\n",
-       "      <th>PNNS_pro_NA</th>\n",
-       "      <th>PNNS_pro_Plant_based</th>\n",
-       "      <th>PNNS_pro_Processed</th>\n",
-       "      <th>PNNS_pro_Snacks</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>10.50</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>9.00</td>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1520.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.950</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1510.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>10.714286</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>1.500</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>9.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>-11.0</td>\n",
-       "      <td>293.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>88.000000</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.275</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Pre-processing "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.1 Preliminary data curation (of the train and test df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:66: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n",
+      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:67: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n"
+     ]
+    },
+    {
+     "data": {
       "text/plain": [
-       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6    4.0          0.0              15.0       2401.0      12.0   \n",
-       "9    6.0          NaN               4.0       1520.0      11.0   \n",
-       "11   7.0          0.0               4.0          4.0       1.0   \n",
-       "12   8.0          1.0               6.0       1510.0       2.0   \n",
-       "14   9.0          NaN             -11.0        293.0       0.5   \n",
-       "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6                10.50                13.0         9.00   36.000000   \n",
-       "9                 2.00                25.0         0.98    9.000000   \n",
-       "11                1.00                 1.0         1.00    1.000000   \n",
-       "12                0.50                 6.7         1.70   10.714286   \n",
-       "14                0.06                 2.0         0.24   88.000000   \n",
-       "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6            23.0      0.300         0.12   \n",
-       "9            22.0      0.950         0.38   \n",
-       "11            1.0      1.000         0.40   \n",
-       "12           76.0      1.500         0.60   \n",
-       "14           18.0      0.275         0.11   \n",
-       "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                                 0.0       \n",
-       "9                                                 NaN       \n",
-       "11                                                0.0       \n",
-       "12                                                0.0       \n",
-       "14                                                NaN       \n",
-       "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0          0.0                   0.0   \n",
-       "9                     0.0              0.0          0.0                   0.0   \n",
-       "11                    0.0              0.0          0.0                   1.0   \n",
-       "12                    0.0              0.0          1.0                   0.0   \n",
-       "14                    0.0              0.0          1.0                   0.0   \n",
-       "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              1.0  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
+       "True"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "filtered_df.head()"
+    "### taking out lines without nutriscore because it's non informative (both fot the train and the test df)\n",
+    "filtered_df, filtered_df_test = dfj.filter_nutriscore_data(df_train, df_test)\n",
+    "\n",
+    "### Cleaning the variables \n",
+    "cat_df, cat_df_test = dfj.categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n",
+    "num_df, num_df_test = dfj.numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n",
+    "final_df, final_df_test = dfj.final_df(cat_df, num_df, cat_df_test, num_df_test)\n",
+    "\n",
+    "### Droping some additionnal columns \n",
+    "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
+    "final_df_test = final_df_test.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
+    "\n",
+    "final_df.columns.equals(final_df_test.columns) #checking if we have the same variables in each df "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### 2.3 Imputing"
+    "##### 2.2 Enconding "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(807, 19)\n",
-      ": shape of df with only numeric features=(772, 142)\n"
+      "              pnns_groups_1      PNNS_pro\n",
+      "6                   unknown            NA\n",
+      "9                   unknown            NA\n",
+      "11    Fruits and vegetables   Plant_based\n",
+      "12                  unknown            NA\n",
+      "14          Composite foods     Processed\n",
+      "...                     ...           ...\n",
+      "4926   Cereals and potatoes   Plant_based\n",
+      "4935         Fish Meat Eggs  Animal_based\n",
+      "4936                unknown            NA\n",
+      "4943        Composite foods     Processed\n",
+      "4987          Sugary snacks        Snacks\n",
+      "\n",
+      "[437 rows x 2 columns]\n",
+      "                pnns_groups_1      PNNS_pro\n",
+      "0        Cereals and potatoes   Plant_based\n",
+      "7              Fish Meat Eggs  Animal_based\n",
+      "42      Fruits and vegetables   Plant_based\n",
+      "45                    unknown            NA\n",
+      "47                    unknown            NA\n",
+      "...                       ...           ...\n",
+      "4952            Sugary snacks        Snacks\n",
+      "4966          Composite foods     Processed\n",
+      "4975  Milk and dairy products  Animal_based\n",
+      "4981            Sugary snacks        Snacks\n",
+      "4990            Sugary snacks        Snacks\n",
+      "\n",
+      "[772 rows x 2 columns]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "pnns_mapping = {\"unknown\" : 'NA', \n",
+    "                'Beverages' : 'Drinks',\n",
+    "                'nan' : 'NA',\n",
+    "                'Fruits and vegetables' : 'Plant_based',\n",
+    "                'Composite foods' : 'Processed',\n",
+    "                'Sugary snacks' : 'Snacks',\n",
+    "                'Salty snacks' : 'Snacks',\n",
+    "                'Cereals and potatoes' : 'Plant_based',\n",
+    "                'Fat and sauces' : 'Processed', \n",
+    "                'Milk and dairy products' : 'Animal_based',\n",
+    "                'Fish Meat Eggs' : 'Animal_based',\n",
+    "                'Alcoholic beverages' : 'Drinks'\n",
+    "}\n",
+    "\n",
+    "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
+    "final_df_test['PNNS_pro'] = final_df_test['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
+    "print(final_df[['pnns_groups_1', 'PNNS_pro']])\n",
+    "print(final_df_test[['pnns_groups_1', 'PNNS_pro']])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "application/vnd.microsoft.datawrangler.viewer.v0+json": {
@@ -3073,6 +228,31 @@
          "rawType": "int64",
          "type": "integer"
         },
+        {
+         "name": "categories",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "pnns_groups_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "pnns_groups_2",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "brands_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "ingredients_analysis_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
         {
          "name": "code",
          "rawType": "float64",
@@ -3138,6 +318,11 @@
          "rawType": "float64",
          "type": "float"
         },
+        {
+         "name": "PNNS_pro",
+         "rawType": "object",
+         "type": "string"
+        },
         {
          "name": "PNNS_pro_Animal_based",
          "rawType": "float64",
@@ -3169,89 +354,113 @@
          "type": "float"
         }
        ],
-       "ref": "eb45bc85-0ff4-41e1-b908-d48e54ab910d",
+       "ref": "558f6e86-a31f-4bd8-afcd-23b65f850648",
        "rows": [
         [
-         "6",
-         "4.0",
+         "0",
+         "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Céréales et pommes de terre, Pains, Baguettes",
+         "Cereals and potatoes",
+         "Bread",
+         "xx:la-campaniere",
+         "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown",
+         "584019351.0",
          "0.0",
-         "15.0",
-         "2401.0",
-         "12.0",
-         "10.5",
-         "13.0",
-         "9.0",
-         "36.0",
-         "23.0",
+         "4.0",
+         "1125.0",
+         "3.0",
          "0.3",
-         "0.12",
-         "0.0",
+         "47.9",
+         "3.8",
+         "5.5",
+         "9.4",
+         "1.3",
+         "0.52",
          "0.0",
+         "Plant_based",
          "0.0",
          "0.0",
          "0.0",
+         "1.0",
          "0.0",
-         "1.0"
+         "0.0"
         ],
         [
-         "9",
-         "6.0",
-         "1.8",
-         "4.0",
-         "1520.0",
-         "11.0",
-         "2.0",
-         "25.0",
-         "0.98",
-         "9.0",
-         "22.0",
-         "0.95",
-         "0.38",
-         "20.400223270165018",
+         "7",
+         "Produits de la mer, Poissons et dérivés, Poissons, Poissons gras, Saumons, Poissons fumés, Saumons fumés, Saumons fumés à la ficelle",
+         "Fish Meat Eggs",
+         "Fish and seafood",
+         null,
+         null,
+         "5869.0",
+         null,
+         "17.0",
+         "1059.0",
+         "17.0",
+         "2.6",
+         "0.5",
          "0.0",
+         null,
+         "23.0",
+         "2.5",
+         "1.0",
+         null,
+         "Animal_based",
          "0.0",
          "0.0",
          "0.0",
          "1.0",
+         "0.0",
          "0.0"
         ],
         [
-         "11",
-         "7.0",
-         "0.0",
-         "4.0",
-         "4.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "0.4",
-         "0.0",
+         "42",
+         "Fresh papayas",
+         "Fruits and vegetables",
+         "Fruits",
+         "xx:curate",
+         "en:palm-oil-free,en:vegan,en:vegetarian",
+         "599990534.0",
          "0.0",
+         "-3.0",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "100.0",
+         "Plant_based",
          "0.0",
          "0.0",
          "1.0",
          "0.0",
+         "0.0",
          "0.0"
-        ],
-        [
-         "12",
-         "8.0",
-         "1.0",
-         "6.0",
-         "1510.0",
-         "2.0",
-         "0.5",
-         "6.7",
-         "1.7",
-         "10.714286",
-         "76.0",
-         "1.5",
-         "0.6",
+        ],
+        [
+         "45",
+         "Snacks, Snacks sucrés",
+         "unknown",
+         "unknown",
+         null,
+         null,
+         "600002458.0",
+         null,
+         "25.0",
+         "2464.0",
+         "47.0",
+         "29.0",
+         "34.0",
+         "31.0",
+         null,
+         "5.9",
+         "0.0",
          "0.0",
+         null,
+         "NA",
          "0.0",
          "0.0",
          "1.0",
@@ -3260,20 +469,26 @@
          "0.0"
         ],
         [
-         "14",
-         "9.0",
-         "0.6",
-         "-11.0",
-         "293.0",
-         "0.5",
-         "0.06",
-         "2.0",
-         "0.24",
-         "88.0",
-         "18.0",
-         "0.275",
-         "0.11",
-         "20.00029130415483",
+         "47",
+         "Drink mix",
+         "unknown",
+         "unknown",
+         "tclinics-usa",
+         "en:may-contain-palm-oil,en:non-vegan,en:vegetarian-status-unknown",
+         "600020002.0",
+         "6.0",
+         "25.0",
+         "33472.0",
+         "0.0",
+         "0.0",
+         "300.0",
+         "0.0",
+         "200.0",
+         "1500.0",
+         "18.75000125",
+         "7.5000005",
+         "0.429687499999986",
+         "NA",
          "0.0",
          "0.0",
          "1.0",
@@ -3283,7 +498,7 @@
         ]
        ],
        "shape": {
-        "columns": 19,
+        "columns": 25,
         "rows": 5
        }
       },
@@ -3306,19 +521,21 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
+       "      <th>categories</th>\n",
+       "      <th>pnns_groups_1</th>\n",
+       "      <th>pnns_groups_2</th>\n",
+       "      <th>brands_tags</th>\n",
+       "      <th>ingredients_analysis_tags</th>\n",
        "      <th>code</th>\n",
        "      <th>additives_n</th>\n",
        "      <th>nutriscore_score</th>\n",
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
-       "      <th>saturated-fat_100g</th>\n",
-       "      <th>carbohydrates_100g</th>\n",
-       "      <th>sugars_100g</th>\n",
-       "      <th>fiber_100g</th>\n",
-       "      <th>proteins_100g</th>\n",
+       "      <th>...</th>\n",
        "      <th>salt_100g</th>\n",
        "      <th>sodium_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
+       "      <th>PNNS_pro</th>\n",
        "      <th>PNNS_pro_Animal_based</th>\n",
        "      <th>PNNS_pro_Drinks</th>\n",
        "      <th>PNNS_pro_NA</th>\n",
@@ -3329,86 +546,94 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>4.0</td>\n",
+       "      <th>0</th>\n",
+       "      <td>Aliments et boissons à base de végétaux, Alime...</td>\n",
+       "      <td>Cereals and potatoes</td>\n",
+       "      <td>Bread</td>\n",
+       "      <td>xx:la-campaniere</td>\n",
+       "      <td>en:palm-oil-free,en:vegan-status-unknown,en:ve...</td>\n",
+       "      <td>584019351.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>10.50</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>9.00</td>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>0.12</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1125.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>0.52</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>Plant_based</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1520.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.950</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>20.400223</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>7</th>\n",
+       "      <td>Produits de la mer, Poissons et dérivés, Poiss...</td>\n",
+       "      <td>Fish Meat Eggs</td>\n",
+       "      <td>Fish and seafood</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5869.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1059.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animal_based</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>42</th>\n",
+       "      <td>Fresh papayas</td>\n",
+       "      <td>Fruits and vegetables</td>\n",
+       "      <td>Fruits</td>\n",
+       "      <td>xx:curate</td>\n",
+       "      <td>en:palm-oil-free,en:vegan,en:vegetarian</td>\n",
+       "      <td>599990534.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>Plant_based</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1510.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>10.714286</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>1.500</td>\n",
-       "      <td>0.60</td>\n",
+       "      <th>45</th>\n",
+       "      <td>Snacks, Snacks sucrés</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600002458.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2464.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>0.000000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NA</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3417,20 +642,22 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.6</td>\n",
-       "      <td>-11.0</td>\n",
-       "      <td>293.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>88.000000</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.275</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>20.000291</td>\n",
+       "      <th>47</th>\n",
+       "      <td>Drink mix</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>tclinics-usa</td>\n",
+       "      <td>en:may-contain-palm-oil,en:non-vegan,en:vegeta...</td>\n",
+       "      <td>600020002.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>33472.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.750001</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>0.429687</td>\n",
+       "      <td>NA</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -3440,96 +667,115 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6    4.0          0.0              15.0       2401.0      12.0   \n",
-       "9    6.0          1.8               4.0       1520.0      11.0   \n",
-       "11   7.0          0.0               4.0          4.0       1.0   \n",
-       "12   8.0          1.0               6.0       1510.0       2.0   \n",
-       "14   9.0          0.6             -11.0        293.0       0.5   \n",
+       "                                           categories          pnns_groups_1  \\\n",
+       "0   Aliments et boissons à base de végétaux, Alime...   Cereals and potatoes   \n",
+       "7   Produits de la mer, Poissons et dérivés, Poiss...         Fish Meat Eggs   \n",
+       "42                                      Fresh papayas  Fruits and vegetables   \n",
+       "45                              Snacks, Snacks sucrés                unknown   \n",
+       "47                                          Drink mix                unknown   \n",
        "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6                10.50                13.0         9.00   36.000000   \n",
-       "9                 2.00                25.0         0.98    9.000000   \n",
-       "11                1.00                 1.0         1.00    1.000000   \n",
-       "12                0.50                 6.7         1.70   10.714286   \n",
-       "14                0.06                 2.0         0.24   88.000000   \n",
+       "       pnns_groups_2       brands_tags  \\\n",
+       "0              Bread  xx:la-campaniere   \n",
+       "7   Fish and seafood               NaN   \n",
+       "42            Fruits         xx:curate   \n",
+       "45           unknown               NaN   \n",
+       "47           unknown      tclinics-usa   \n",
        "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6            23.0      0.300         0.12   \n",
-       "9            22.0      0.950         0.38   \n",
-       "11            1.0      1.000         0.40   \n",
-       "12           76.0      1.500         0.60   \n",
-       "14           18.0      0.275         0.11   \n",
+       "                            ingredients_analysis_tags         code  \\\n",
+       "0   en:palm-oil-free,en:vegan-status-unknown,en:ve...  584019351.0   \n",
+       "7                                                 NaN       5869.0   \n",
+       "42            en:palm-oil-free,en:vegan,en:vegetarian  599990534.0   \n",
+       "45                                                NaN  600002458.0   \n",
+       "47  en:may-contain-palm-oil,en:non-vegan,en:vegeta...  600020002.0   \n",
        "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                            0.000000       \n",
-       "9                                           20.400223       \n",
-       "11                                           0.000000       \n",
-       "12                                           0.000000       \n",
-       "14                                          20.000291       \n",
+       "    additives_n  nutriscore_score  energy_100g  fat_100g  ...  salt_100g  \\\n",
+       "0           0.0               4.0       1125.0       3.0  ...   1.300000   \n",
+       "7           NaN              17.0       1059.0      17.0  ...   2.500000   \n",
+       "42          0.0              -3.0          NaN       NaN  ...        NaN   \n",
+       "45          NaN              25.0       2464.0      47.0  ...   0.000000   \n",
+       "47          6.0              25.0      33472.0       0.0  ...  18.750001   \n",
        "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0          0.0                   0.0   \n",
-       "9                     0.0              0.0          0.0                   0.0   \n",
-       "11                    0.0              0.0          0.0                   1.0   \n",
-       "12                    0.0              0.0          1.0                   0.0   \n",
-       "14                    0.0              0.0          1.0                   0.0   \n",
+       "    sodium_100g  fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "0          0.52                                           0.000000       \n",
+       "7          1.00                                                NaN       \n",
+       "42          NaN                                         100.000000       \n",
+       "45         0.00                                                NaN       \n",
+       "47         7.50                                           0.429687       \n",
        "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              1.0  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
+       "        PNNS_pro  PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  \\\n",
+       "0    Plant_based                    0.0              0.0          0.0   \n",
+       "7   Animal_based                    0.0              0.0          0.0   \n",
+       "42   Plant_based                    0.0              0.0          1.0   \n",
+       "45            NA                    0.0              0.0          1.0   \n",
+       "47            NA                    0.0              0.0          1.0   \n",
+       "\n",
+       "    PNNS_pro_Plant_based PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "0                    1.0                0.0              0.0  \n",
+       "7                    1.0                0.0              0.0  \n",
+       "42                   0.0                0.0              0.0  \n",
+       "45                   0.0                0.0              0.0  \n",
+       "47                   0.0                0.0              0.0  \n",
+       "\n",
+       "[5 rows x 25 columns]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from scripts.Imputing import knn_impute_numeric\n",
-    "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
-    "imputed_df_test = knn_impute_numeric(filtered_df_test, n_neighbors = 5)\n",
-    "imputed_df.head()"
+    "from scripts.encoding_func import one_hot_encode_column\n",
+    "\n",
+    "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n",
+    "filtered_df_test = one_hot_encode_column(final_df_test, 'PNNS_pro')\n",
+    "filtered_df.head()\n",
+    "filtered_df_test.head()\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 29,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "##### 2.4 Scaling"
+    "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n",
+    "filtered_df_test = filtered_df_test.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ": shape of df with only numeric features=(807, 18)\n",
-      ": shape of df with only numeric features=(772, 141)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "from scripts.Scaling import scaler_numeric\n",
-    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
-    "work_df_test = scaler_numeric(imputed_df_test, 'nutriscore_score')"
+    "filtered_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.3 Imputing"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ": shape of df with only numeric features=(807, 19)\n",
+      ": shape of df with only numeric features=(1450, 19)\n"
+     ]
+    },
     {
      "data": {
       "application/vnd.microsoft.datawrangler.viewer.v0+json": {
@@ -3635,114 +881,114 @@
          "type": "float"
         }
        ],
-       "ref": "cd29a021-657d-43dc-8880-d22ab74e3406",
+       "ref": "a145b9fe-a807-455c-b868-d4d83b78a957",
        "rows": [
         [
          "6",
-         "-2.445750371869255e-06",
-         "-0.7499999999999999",
+         "4.0",
+         "0.0",
          "15.0",
-         "1.461818181818182",
-         "0.021972656250000073",
-         "1.1003451776649746",
-         "-0.3534601599117728",
-         "-0.12803584060363116",
-         "2.7338144044616133",
-         "0.09493032908390153",
-         "-0.26118808727504383",
-         "-0.2611880872750438",
-         "-0.19735657983213245",
+         "2401.0",
+         "12.0",
+         "10.5",
+         "13.0",
+         "9.0",
+         "36.0",
+         "23.0",
+         "0.3",
+         "0.12",
+         "0.0",
          "0.0",
          "0.0",
-         "-0.5",
          "0.0",
          "0.0",
-         "2.5"
+         "0.0",
+         "1.0"
         ],
         [
          "9",
-         "-2.426058339889631e-06",
-         "0.37500000000000006",
+         "6.0",
+         "1.8",
          "4.0",
-         "0.18036363636363636",
-         "-0.039062499999999924",
-         "-0.28036548223350255",
-         "-0.02260821615660326",
-         "-0.6008016977128036",
-         "0.0038421831263237664",
-         "0.06528313074414463",
-         "-0.05414874980092376",
-         "-0.05414874980092372",
-         "0.3840135991997579",
+         "1520.0",
+         "11.0",
+         "2.0",
+         "25.0",
+         "0.98",
+         "9.0",
+         "22.0",
+         "0.95",
+         "0.38",
+         "20.400223270165018",
+         "0.0",
          "0.0",
          "0.0",
-         "-0.5",
          "0.0",
          "1.0",
          "0.0"
         ],
         [
          "11",
-         "-2.4162123238998194e-06",
-         "-0.7499999999999999",
+         "7.0",
+         "0.0",
+         "4.0",
          "4.0",
-         "-2.0247272727272727",
-         "-0.6494140624999999",
-         "-0.4428020304568528",
-         "-0.6843121036669423",
-         "-0.5996227304880924",
-         "-0.8050384750470954",
-         "-0.5573080343907502",
-         "-0.03822264691829912",
-         "-0.03822264691829908",
-         "-0.19735657983213245",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "0.4",
+         "0.0",
+         "0.0",
          "0.0",
          "0.0",
-         "-0.5",
-         "5.0",
+         "1.0",
          "0.0",
          "0.0"
         ],
         [
          "12",
-         "-2.4063663079100075e-06",
-         "-0.12499999999999997",
+         "8.0",
+         "1.0",
          "6.0",
-         "0.1658181818181818",
-         "-0.5883789062499999",
-         "-0.5240203045685279",
-         "-0.5271574303832368",
-         "-0.5583588776232021",
-         "0.17717378162350847",
-         "1.666231841091017",
-         "0.12103838190794709",
-         "0.12103838190794709",
-         "-0.19735657983213245",
+         "1510.0",
+         "2.0",
+         "0.5",
+         "6.7",
+         "1.7",
+         "10.714286",
+         "76.0",
+         "1.5",
+         "0.6",
          "0.0",
          "0.0",
-         "2.0",
+         "0.0",
+         "1.0",
          "0.0",
          "0.0",
          "0.0"
         ],
         [
          "14",
-         "-2.3965202919201957e-06",
-         "-0.37499999999999994",
+         "9.0",
+         "0.6",
          "-11.0",
-         "-1.6043636363636364",
-         "-0.6799316406249999",
-         "-0.595492385786802",
-         "-0.6567411083540116",
-         "-0.6444234850271162",
-         "7.991538682588836",
-         "-0.053305662614882954",
-         "-0.26915113871635615",
-         "-0.2691511387163561",
-         "0.37261624753351613",
+         "293.0",
+         "0.5",
+         "0.06",
+         "2.0",
+         "0.24",
+         "88.0",
+         "18.0",
+         "0.275",
+         "0.11",
+         "20.00029130415483",
          "0.0",
          "0.0",
-         "2.0",
+         "1.0",
          "0.0",
          "0.0",
          "0.0"
@@ -3796,110 +1042,110 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>-0.000002</td>\n",
-       "      <td>-0.750</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>15.0</td>\n",
-       "      <td>1.461818</td>\n",
-       "      <td>0.021973</td>\n",
-       "      <td>1.100345</td>\n",
-       "      <td>-0.353460</td>\n",
-       "      <td>-0.128036</td>\n",
-       "      <td>2.733814</td>\n",
-       "      <td>0.094930</td>\n",
-       "      <td>-0.261188</td>\n",
-       "      <td>-0.261188</td>\n",
-       "      <td>-0.197357</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2.5</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>-0.000002</td>\n",
-       "      <td>0.375</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.8</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>0.180364</td>\n",
-       "      <td>-0.039062</td>\n",
-       "      <td>-0.280365</td>\n",
-       "      <td>-0.022608</td>\n",
-       "      <td>-0.600802</td>\n",
-       "      <td>0.003842</td>\n",
-       "      <td>0.065283</td>\n",
-       "      <td>-0.054149</td>\n",
-       "      <td>-0.054149</td>\n",
-       "      <td>0.384014</td>\n",
+       "      <td>1520.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>20.400223</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>-0.000002</td>\n",
-       "      <td>-0.750</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>-2.024727</td>\n",
-       "      <td>-0.649414</td>\n",
-       "      <td>-0.442802</td>\n",
-       "      <td>-0.684312</td>\n",
-       "      <td>-0.599623</td>\n",
-       "      <td>-0.805038</td>\n",
-       "      <td>-0.557308</td>\n",
-       "      <td>-0.038223</td>\n",
-       "      <td>-0.038223</td>\n",
-       "      <td>-0.197357</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>5.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>-0.000002</td>\n",
-       "      <td>-0.125</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>6.0</td>\n",
-       "      <td>0.165818</td>\n",
-       "      <td>-0.588379</td>\n",
-       "      <td>-0.524020</td>\n",
-       "      <td>-0.527157</td>\n",
-       "      <td>-0.558359</td>\n",
-       "      <td>0.177174</td>\n",
-       "      <td>1.666232</td>\n",
-       "      <td>0.121038</td>\n",
-       "      <td>0.121038</td>\n",
-       "      <td>-0.197357</td>\n",
+       "      <td>1510.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>10.714286</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>-0.000002</td>\n",
-       "      <td>-0.375</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.6</td>\n",
        "      <td>-11.0</td>\n",
-       "      <td>-1.604364</td>\n",
-       "      <td>-0.679932</td>\n",
-       "      <td>-0.595492</td>\n",
-       "      <td>-0.656741</td>\n",
-       "      <td>-0.644423</td>\n",
-       "      <td>7.991539</td>\n",
-       "      <td>-0.053306</td>\n",
-       "      <td>-0.269151</td>\n",
-       "      <td>-0.269151</td>\n",
-       "      <td>0.372616</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.275</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>20.000291</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3909,267 +1155,309 @@
        "</div>"
       ],
       "text/plain": [
-       "        code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6  -0.000002       -0.750              15.0     1.461818  0.021973   \n",
-       "9  -0.000002        0.375               4.0     0.180364 -0.039062   \n",
-       "11 -0.000002       -0.750               4.0    -2.024727 -0.649414   \n",
-       "12 -0.000002       -0.125               6.0     0.165818 -0.588379   \n",
-       "14 -0.000002       -0.375             -11.0    -1.604364 -0.679932   \n",
+       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6    4.0          0.0              15.0       2401.0      12.0   \n",
+       "9    6.0          1.8               4.0       1520.0      11.0   \n",
+       "11   7.0          0.0               4.0          4.0       1.0   \n",
+       "12   8.0          1.0               6.0       1510.0       2.0   \n",
+       "14   9.0          0.6             -11.0        293.0       0.5   \n",
        "\n",
        "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6             1.100345           -0.353460    -0.128036    2.733814   \n",
-       "9            -0.280365           -0.022608    -0.600802    0.003842   \n",
-       "11           -0.442802           -0.684312    -0.599623   -0.805038   \n",
-       "12           -0.524020           -0.527157    -0.558359    0.177174   \n",
-       "14           -0.595492           -0.656741    -0.644423    7.991539   \n",
+       "6                10.50                13.0         9.00   36.000000   \n",
+       "9                 2.00                25.0         0.98    9.000000   \n",
+       "11                1.00                 1.0         1.00    1.000000   \n",
+       "12                0.50                 6.7         1.70   10.714286   \n",
+       "14                0.06                 2.0         0.24   88.000000   \n",
        "\n",
        "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6        0.094930  -0.261188    -0.261188   \n",
-       "9        0.065283  -0.054149    -0.054149   \n",
-       "11      -0.557308  -0.038223    -0.038223   \n",
-       "12       1.666232   0.121038     0.121038   \n",
-       "14      -0.053306  -0.269151    -0.269151   \n",
+       "6            23.0      0.300         0.12   \n",
+       "9            22.0      0.950         0.38   \n",
+       "11            1.0      1.000         0.40   \n",
+       "12           76.0      1.500         0.60   \n",
+       "14           18.0      0.275         0.11   \n",
        "\n",
        "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                           -0.197357       \n",
-       "9                                            0.384014       \n",
-       "11                                          -0.197357       \n",
-       "12                                          -0.197357       \n",
-       "14                                           0.372616       \n",
+       "6                                            0.000000       \n",
+       "9                                           20.400223       \n",
+       "11                                           0.000000       \n",
+       "12                                           0.000000       \n",
+       "14                                          20.000291       \n",
        "\n",
        "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0         -0.5                   0.0   \n",
-       "9                     0.0              0.0         -0.5                   0.0   \n",
-       "11                    0.0              0.0         -0.5                   5.0   \n",
-       "12                    0.0              0.0          2.0                   0.0   \n",
-       "14                    0.0              0.0          2.0                   0.0   \n",
+       "6                     0.0              0.0          0.0                   0.0   \n",
+       "9                     0.0              0.0          0.0                   0.0   \n",
+       "11                    0.0              0.0          0.0                   1.0   \n",
+       "12                    0.0              0.0          1.0                   0.0   \n",
+       "14                    0.0              0.0          1.0                   0.0   \n",
        "\n",
        "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              2.5  \n",
+       "6                  0.0              1.0  \n",
        "9                  1.0              0.0  \n",
        "11                 0.0              0.0  \n",
        "12                 0.0              0.0  \n",
        "14                 0.0              0.0  "
       ]
      },
-     "execution_count": 19,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "work_df.head()"
+    "from scripts.Imputing import knn_impute_numeric\n",
+    "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
+    "imputed_df_test = knn_impute_numeric(filtered_df_test, n_neighbors = 5)\n",
+    "imputed_df.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Outliers?"
+    "##### 2.4 Scaling"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 31,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ": shape of df with only numeric features=(807, 18)\n",
+      ": shape of df with only numeric features=(1450, 18)\n"
+     ]
+    }
+   ],
    "source": [
-    "### 3. Predicting the nutriscore"
+    "from scripts.Scaling import scaler_numeric\n",
+    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
+    "work_df_test = scaler_numeric(imputed_df_test, 'nutriscore_score')"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "##### 3.1 : Decision tree"
+    "work_df.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### 3.2 Random forest"
+    "#### Outliers?"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 20,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "X = work_df.drop(\"nutriscore_score\", axis=1)  \n",
-    "y = work_df[\"nutriscore_score\"]\n",
-    "X_test = work_df_test.drop(\"nutriscore_score\", axis=1) \n",
-    "y_test = work_df_test[\"nutriscore_score\"]"
+    "### 3. Predicting the nutriscore"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 24,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "from scripts.rf import random_forest_GS"
+    "##### 3.1 : Decision tree"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 25,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "InvalidParameterError",
-     "evalue": "The 'scoring' parameter of GridSearchCV must be a str among {'precision_samples', 'neg_mean_squared_log_error', 'neg_root_mean_squared_log_error', 'neg_max_error', 'f1_weighted', 'top_k_accuracy', 'f1_micro', 'precision_weighted', 'neg_mean_poisson_deviance', 'matthews_corrcoef', 'neg_negative_likelihood_ratio', 'f1', 'jaccard', 'fowlkes_mallows_score', 'precision_macro', 'precision_micro', 'average_precision', 'neg_mean_gamma_deviance', 'precision', 'recall_weighted', 'neg_mean_absolute_percentage_error', 'v_measure_score', 'f1_macro', 'balanced_accuracy', 'explained_variance', 'neg_mean_absolute_error', 'neg_brier_score', 'recall_macro', 'neg_mean_squared_error', 'mutual_info_score', 'roc_auc_ovr_weighted', 'recall_samples', 'accuracy', 'recall_micro', 'adjusted_mutual_info_score', 'homogeneity_score', 'jaccard_macro', 'jaccard_micro', 'r2', 'positive_likelihood_ratio', 'normalized_mutual_info_score', 'neg_log_loss', 'd2_absolute_error_score', 'roc_auc_ovo_weighted', 'roc_auc', 'rand_score', 'neg_root_mean_squared_error', 'roc_auc_ovr', 'jaccard_weighted', 'completeness_score', 'roc_auc_ovo', 'neg_median_absolute_error', 'recall', 'f1_samples', 'jaccard_samples', 'adjusted_rand_score'}, a callable, an instance of 'list', an instance of 'tuple', an instance of 'dict' or None. Got 'root_mean_squared_error' instead.",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mInvalidParameterError\u001b[0m                     Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[25], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m best_rf, X_train, X_test, y_train, y_test, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mrandom_forest_GS\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mX_test\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_test\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32m~\\Documents\\OFFProject\\scripts\\rf.py:34\u001b[0m, in \u001b[0;36mrandom_forest_GS\u001b[1;34m(X_train, y_train, X_test, y_test)\u001b[0m\n\u001b[0;32m     24\u001b[0m grid_search \u001b[38;5;241m=\u001b[39m GridSearchCV(\n\u001b[0;32m     25\u001b[0m     estimator\u001b[38;5;241m=\u001b[39mrf,\n\u001b[0;32m     26\u001b[0m     param_grid\u001b[38;5;241m=\u001b[39mparam_grid,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     30\u001b[0m     verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m\n\u001b[0;32m     31\u001b[0m )\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Entraînement\u001b[39;00m\n\u001b[1;32m---> 34\u001b[0m \u001b[43mgrid_search\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     36\u001b[0m \u001b[38;5;66;03m# Prédictions\u001b[39;00m\n\u001b[0;32m     37\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m grid_search\u001b[38;5;241m.\u001b[39mbest_estimator_\u001b[38;5;241m.\u001b[39mpredict(X_test)\n",
-      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py:1382\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1377\u001b[0m partial_fit_and_fitted \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1378\u001b[0m     fit_method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpartial_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[0;32m   1379\u001b[0m )\n\u001b[0;32m   1381\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[1;32m-> 1382\u001b[0m     \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1384\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1385\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1386\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1387\u001b[0m     )\n\u001b[0;32m   1388\u001b[0m ):\n\u001b[0;32m   1389\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
-      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py:436\u001b[0m, in \u001b[0;36mBaseEstimator._validate_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    428\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    429\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[0;32m    430\u001b[0m \n\u001b[0;32m    431\u001b[0m \u001b[38;5;124;03m    The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[38;5;124;03m    accepted constraints.\u001b[39;00m\n\u001b[0;32m    435\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 436\u001b[0m     \u001b[43mvalidate_parameter_constraints\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    437\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parameter_constraints\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    438\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    439\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcaller_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    440\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py:98\u001b[0m, in \u001b[0;36mvalidate_parameter_constraints\u001b[1;34m(parameter_constraints, params, caller_name)\u001b[0m\n\u001b[0;32m     92\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m     93\u001b[0m     constraints_str \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m     94\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\u001b[38;5;28mstr\u001b[39m(c)\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39mconstraints[:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]])\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m or\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     95\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     96\u001b[0m     )\n\u001b[1;32m---> 98\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidParameterError(\n\u001b[0;32m     99\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m parameter of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcaller_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m must be\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    100\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconstraints_str\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m. Got \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mparam_val\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    101\u001b[0m )\n",
-      "\u001b[1;31mInvalidParameterError\u001b[0m: The 'scoring' parameter of GridSearchCV must be a str among {'precision_samples', 'neg_mean_squared_log_error', 'neg_root_mean_squared_log_error', 'neg_max_error', 'f1_weighted', 'top_k_accuracy', 'f1_micro', 'precision_weighted', 'neg_mean_poisson_deviance', 'matthews_corrcoef', 'neg_negative_likelihood_ratio', 'f1', 'jaccard', 'fowlkes_mallows_score', 'precision_macro', 'precision_micro', 'average_precision', 'neg_mean_gamma_deviance', 'precision', 'recall_weighted', 'neg_mean_absolute_percentage_error', 'v_measure_score', 'f1_macro', 'balanced_accuracy', 'explained_variance', 'neg_mean_absolute_error', 'neg_brier_score', 'recall_macro', 'neg_mean_squared_error', 'mutual_info_score', 'roc_auc_ovr_weighted', 'recall_samples', 'accuracy', 'recall_micro', 'adjusted_mutual_info_score', 'homogeneity_score', 'jaccard_macro', 'jaccard_micro', 'r2', 'positive_likelihood_ratio', 'normalized_mutual_info_score', 'neg_log_loss', 'd2_absolute_error_score', 'roc_auc_ovo_weighted', 'roc_auc', 'rand_score', 'neg_root_mean_squared_error', 'roc_auc_ovr', 'jaccard_weighted', 'completeness_score', 'roc_auc_ovo', 'neg_median_absolute_error', 'recall', 'f1_samples', 'jaccard_samples', 'adjusted_rand_score'}, a callable, an instance of 'list', an instance of 'tuple', an instance of 'dict' or None. Got 'root_mean_squared_error' instead."
-     ]
-    }
-   ],
    "source": [
-    "\n",
-    "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y, X_test, y_test)"
+    "##### 3.2 Random forest"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "### Random forest \n",
-    "\n",
     "X = work_df.drop(\"nutriscore_score\", axis=1)  \n",
     "y = work_df[\"nutriscore_score\"]\n",
-    "\n",
-    "from scripts.rf import random_forest_GS\n",
-    "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y)\n",
-    "\n"
+    "X_test = work_df_test.drop(\"nutriscore_score\", axis=1) \n",
+    "y_test = work_df_test[\"nutriscore_score\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n",
+      "540 fits failed out of a total of 1620.\n",
+      "The score on these train-test partitions for these parameters will be set to nan.\n",
+      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
+      "\n",
+      "Below are more details about the failures:\n",
+      "--------------------------------------------------------------------------------\n",
+      "206 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
+      "    estimator._validate_params()\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
+      "    validate_parameter_constraints(\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
+      "    raise InvalidParameterError(\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "334 fits failed with the following error:\n",
+      "Traceback (most recent call last):\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
+      "    estimator.fit(X_train, y_train, **fit_params)\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
+      "    estimator._validate_params()\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
+      "    validate_parameter_constraints(\n",
+      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
+      "    raise InvalidParameterError(\n",
+      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
+      "\n",
+      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
+      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [         nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan -28.40078216 -27.41119183 -25.43973985\n",
+      " -30.92752695 -28.69548775 -24.98124643 -34.10000593 -28.11079336\n",
+      " -25.7229842  -32.69205128 -25.4949763  -25.28107748 -35.82649297\n",
+      " -29.12476986 -25.78841879 -42.7811845  -31.4564862  -27.04992778\n",
+      " -34.56585429 -29.66038082 -27.98264181 -34.56585429 -29.66038082\n",
+      " -27.98264181 -32.81587154 -29.45784358 -28.0036475  -28.40078216\n",
+      " -27.41119183 -25.43973985 -30.92752695 -28.69548775 -24.98124643\n",
+      " -34.10000593 -28.11079336 -25.7229842  -32.69205128 -25.4949763\n",
+      " -25.28107748 -35.82649297 -29.12476986 -25.78841879 -42.7811845\n",
+      " -31.4564862  -27.04992778 -34.56585429 -29.66038082 -27.98264181\n",
+      " -34.56585429 -29.66038082 -27.98264181 -32.81587154 -29.45784358\n",
+      " -28.0036475           nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan -32.6001312  -28.26068731\n",
+      " -25.19694896 -31.56943702 -27.8065814  -25.4765719  -35.33856308\n",
+      " -29.8607632  -27.39657916 -31.78543528 -27.9463427  -24.56787522\n",
+      " -31.10488548 -27.41086252 -25.98672186 -36.72497321 -30.40072803\n",
+      " -26.59289105 -38.18719755 -30.36983308 -28.15365161 -38.18719755\n",
+      " -30.36983308 -28.15365161 -36.31265589 -31.65254105 -27.91489056\n",
+      " -32.6001312  -28.26068731 -25.19694896 -31.56943702 -27.8065814\n",
+      " -25.4765719  -35.33856308 -29.8607632  -27.39657916 -31.78543528\n",
+      " -27.9463427  -24.56787522 -31.10488548 -27.41086252 -25.98672186\n",
+      " -36.72497321 -30.40072803 -26.59289105 -38.18719755 -30.36983308\n",
+      " -28.15365161 -38.18719755 -30.36983308 -28.15365161 -36.31265589\n",
+      " -31.65254105 -27.91489056          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan -28.40078216\n",
+      " -27.41119183 -25.34621528 -30.92752695 -28.75560499 -25.00224768\n",
+      " -34.75251947 -28.358578   -25.76613456 -33.00587297 -25.64588925\n",
+      " -25.31263651 -35.82649297 -29.12476986 -25.78841879 -42.7811845\n",
+      " -31.4564862  -27.04992778 -34.56585429 -29.66038082 -27.98264181\n",
+      " -34.56585429 -29.66038082 -27.98264181 -32.81587154 -29.45784358\n",
+      " -28.0036475  -28.40078216 -27.41119183 -25.34621528 -30.92752695\n",
+      " -28.75560499 -25.00224768 -34.75251947 -28.358578   -25.76613456\n",
+      " -33.00587297 -25.64588925 -25.31263651 -35.82649297 -29.12476986\n",
+      " -25.78841879 -42.7811845  -31.4564862  -27.04992778 -34.56585429\n",
+      " -29.66038082 -27.98264181 -34.56585429 -29.66038082 -27.98264181\n",
+      " -32.81587154 -29.45784358 -28.0036475           nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      "          nan          nan          nan          nan          nan\n",
+      " -28.40078216 -27.41119183 -25.43973985 -30.92752695 -28.69548775\n",
+      " -24.98124643 -34.10000593 -28.11079336 -25.7229842  -32.69205128\n",
+      " -25.4949763  -25.28107748 -35.82649297 -29.12476986 -25.78841879\n",
+      " -42.7811845  -31.4564862  -27.04992778 -34.56585429 -29.66038082\n",
+      " -27.98264181 -34.56585429 -29.66038082 -27.98264181 -32.81587154\n",
+      " -29.45784358 -28.0036475  -28.40078216 -27.41119183 -25.43973985\n",
+      " -30.92752695 -28.69548775 -24.98124643 -34.10000593 -28.11079336\n",
+      " -25.7229842  -32.69205128 -25.4949763  -25.28107748 -35.82649297\n",
+      " -29.12476986 -25.78841879 -42.7811845  -31.4564862  -27.04992778\n",
+      " -34.56585429 -29.66038082 -27.98264181 -34.56585429 -29.66038082\n",
+      " -27.98264181 -32.81587154 -29.45784358 -28.0036475 ]\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Meilleurs paramètres trouvés :  {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 60}\n",
+      "MSE : 7.595541984511499\n",
+      "R² : 0.10416152266041212\n"
+     ]
+    }
+   ],
    "source": [
-    "def plot_learning_curve_rmse(model, X, y, cv=5):\n",
-    "    train_sizes, train_scores, test_scores = learning_curve(\n",
-    "        model,\n",
-    "        X, y,\n",
-    "        cv=cv,\n",
-    "        scoring=\"neg_root_mean_squared_error\",\n",
-    "        n_jobs=-1,\n",
-    "        train_sizes=np.linspace(0.1, 1.0, 10),\n",
-    "        shuffle=True,\n",
-    "        random_state=42\n",
-    "    )\n",
-    "\n",
-    "    # Convertir scores négatifs en positifs\n",
-    "    train_scores = -train_scores\n",
-    "    test_scores  = -test_scores\n",
-    "\n",
-    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
-    "    test_scores_mean  = np.mean(test_scores, axis=1)\n",
-    "    train_scores_std  = np.std(train_scores, axis=1)\n",
-    "    test_scores_std   = np.std(test_scores, axis=1)\n",
-    "\n",
-    "    # Couleurs personnalisées\n",
-    "    color_train = \"#2ca02c\"  # vert\n",
-    "    color_test  = \"#9467bd\"  # violet\n",
-    "\n",
-    "    plt.figure(figsize=(8, 5), facecolor=\"white\")\n",
-    "    ax = plt.gca()\n",
-    "    ax.set_facecolor(\"white\")  # fond blanc\n",
-    "\n",
-    "    # Tracer les courbes\n",
-    "    plt.plot(train_sizes, train_scores_mean, \"o-\", color=color_train, label=\"RMSE entraînement\", linewidth=2)\n",
-    "    plt.plot(train_sizes, test_scores_mean, \"o-\", color=color_test, label=\"RMSE validation\", linewidth=2)\n",
-    "\n",
-    "    # Zones ombrées\n",
-    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
-    "                                  train_scores_mean + train_scores_std,\n",
-    "                                  color=color_train, alpha=0.2)\n",
-    "    plt.fill_between(train_sizes,test_scores_mean - test_scores_std,\n",
-    "                                test_scores_mean + test_scores_std,\n",
-    "                                color=color_test,alpha=0.2)\n",
-    "\n",
-    "    # Style épuré : enlever spines gauche et bas\n",
-    "    ax.spines['top'].set_visible(False)\n",
-    "    ax.spines['right'].set_visible(False)\n",
-    "    ax.spines['bottom'].set_visible(False)\n",
-    "    ax.spines['left'].set_visible(False)\n",
-    "\n",
-    "    # Axes et grille\n",
-    "    plt.xlabel(\"Taille de l'échantillon d'entraînement\")\n",
-    "    plt.ylabel(\"RMSE\")\n",
-    "    plt.title(f\"Courbe d'apprentissage - {model.__class__.__name__}\")\n",
-    "    plt.legend(frameon=False)\n",
-    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "\n",
-    "def plot_rmse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]):\n",
-    "    errors = []\n",
-    "\n",
-    "    for n in n_estimators_range:\n",
-    "        rf_tmp = RandomForestRegressor(\n",
-    "            n_estimators=n,\n",
-    "            max_depth=best_rf.max_depth,\n",
-    "            min_samples_split=best_rf.min_samples_split,\n",
-    "            min_samples_leaf=best_rf.min_samples_leaf,\n",
-    "            max_features=best_rf.max_features,\n",
-    "            random_state=42,\n",
-    "            n_jobs=-1\n",
-    "        )\n",
-    "        rf_tmp.fit(X_train, y_train)\n",
-    "        y_pred_tmp = rf_tmp.predict(X_test)\n",
-    "        \n",
-    "        # Compute RMSE explicitly\n",
-    "        mse = mean_squared_error(y_test, y_pred_tmp)\n",
-    "        rmse = np.sqrt(mse)\n",
-    "        errors.append(rmse)\n",
-    "\n",
-    "    # Couleurs personnalisées\n",
-    "    color = \"#9467bd\"  # violet\n",
-    "\n",
-    "    plt.figure(figsize=(8, 5), facecolor=\"white\")\n",
-    "    ax = plt.gca()\n",
-    "    ax.set_facecolor(\"white\")  # fond blanc\n",
-    "\n",
-    "    # Style épuré : enlever spines\n",
-    "    ax.spines['top'].set_visible(False)\n",
-    "    ax.spines['right'].set_visible(False)\n",
-    "    ax.spines['bottom'].set_visible(False)\n",
-    "    ax.spines['left'].set_visible(False)\n",
-    "\n",
-    "    # Courbe RMSE\n",
-    "    plt.plot(n_estimators_range, errors, marker=\"o\", color=color, label=\"RMSE\")\n",
-    "    plt.xlabel(\"Nombre d'arbres (n_estimators)\")\n",
-    "    plt.ylabel(\"RMSE sur test\")\n",
-    "    plt.legend(frameon=False)\n",
-    "    plt.grid(True, linestyle='--', alpha=0.5)\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "    \n",
-    "    return errors  # also return RMSE values for later use\n",
-    "\n"
+    "from scripts.rf import random_forest_GS\n",
+    "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X, y, X_test, y_test)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SCPAEWuB7BE8C8g7wPcR3DTkmEHdbb711Sp9vF0kQzxho46488jmQNG21Bx6mZIIWeQ4jIdFvJj4PbfrjH/+Y8D1ok/VeeOjgwYBXCt4SFJaAIIMXAvaEOPjiiy+U5xHb4XmByIeYwk2DbLWZkHyEwoKQEYK7dLiT9sYbb6hEy6FApRf84cJdMfzRsScnYnCE7fY7qFhnB3dtkZiYLvg8+50uJBmiHVYSJgZiGMBgH+uufqrgzh7+2Fl3Ou3gj+pwYACNQSJCXuyg7xhEJuqLHbQb/YkfXHz99deDyjEi+TKVmv9WYjAGqonuDI+UdAUl7gJjQcI6klCR2Is7ulYoDe62YmCHBfaEFwOJnxjg4Q5pqucW3zskc+Nc2tuI85rN8xIftmW/E5sqCGeDFwfeiW9/+9vRaj8IzUJIH5KiLeClGSk4J7hhEH9O4r/T1jWL9fF3+bHOfk2PNrgWEa6V6LpDOBhEpTUwToZlYwjXVGyM/eG1wIJrE4ITAhgV5kbynb/qqquU5wLfeSSwo08IfcQNleHak+r3eLj+oGoaPBDDtR3nE/thgRCBkEeSOsSG1Vb8/sC7gQW/3xDD6NsFF1yQFXsRkq8wx4KQEYJ4b/zxwGAPAiEelE20SoDijitAtRQ71t0xxGXb/8DhjpgdCJhkHouhsMpZWqDaE7Aq4OCPHe6w4S5a/B1SvMagLRl4H2KdcdcS1WAsEE6Au5jDgffHfybinONLdVqgkpU99AyDZ4gtqy8WqExlL3mLP+p4jT/mqGg0FNiO8/9///d/KkQjHoQwjAR8T+LFYiIQ/hJ/TiwPgVXyMt4mGIRY4sraJ9VzC/th3WOPPRZdhwo2qGqTzfOCwZZ9SVSeeThwxxyDWHy/EN9u9RPY+wp74+7wSMG1CnGK75cF7qbHh+Mg7wODcAyC7eVI4UFCG+3X9GiD84BwG5wXe2ggfpcgTOEFGC4sD98F7INBcqL8HsvGOBfxJavx3YAIsJ+HVL/z9mMgDBCVthDahT7hNe72J/IM279zqX6PhwLhhDhGovcg5NOqxIRSzvEMd43iRgDmYMH3FOc2G/YiJF+hx4KQEYI/hPgjgDtS8ELYZ95+/fXX1UDOCvfYcsstlTsfgxMrfOOtt95SoSsIA8DdZQsIFSQE448qZh/GXTQM1BPdxR8O3LlFQiPi7+FZwd1ExPqiPVYfLrvsMnUXDX/g0BYMEPA+3D1EwipKsSYDggSufsRK4645BvVW/XZ73Hoyjw/CV5BEiZK9KO2IEpz2ZEY7COfBH1zsjz/AEGm4Ox+foIkQNcSAoz/wwiBMAaFDOPdD5YxYg3TET0OsoA/4LMRjY8CBu5H4Y48QpXTBwBwx1xCSaB88RPHlJAG+DxgU4448bAMhhYEOPtcSp/h+YHCDu+QIF0J8Oc45BjeWNyzVcwsvwE033aQSdlEWEwN+7GclI1t3bkfrvKQLrieEk8C++K6ib/Dw4dpCeU+0F6E8mYQR4fuEc4LrGcn+OCc4ZvwEavguoR04F7iecQ6tcrPwjGEehVyC69iaXwHXIoQYBDUGu4nm6ogHNkRpWcwGv80226hSrBDjuGmAkB/kB+C8wPuHO/UYiGOwjM/BbwX6jvfYv/M4HtqF6xQiLFn+hsW5556r8odwbcODgQXfL1wrsAs+D999hLrherIG+al+j4cC/cZn47cXn4n+4mYOPAhYj99giElcV7jxA+EITwlyL3DN4lrEuQcQDQgnwzGQhwKhifbhPVYBikztRUje4nRZKkLczoIFC1QpxmnTpqnygSgXuMsuuxg33nhjTLnL/v5+45JLLlHlOYuKiowpU6YYF1xwQcw+AGVCzz//fKOurk6VzESJz0WLFiUtN5uoNKlVChWlX7/zne+oNtXW1hqnn356TGlMi4ceekiVM0RZVCwo03jaaacZX3zxxbD9f+mll4xtt91W9R3lW1Ge0fr8oUC/zznnHFVOEmU3cc7eeOMNVQ7SXqbSKk953333qfOFEp/Y/+CDD44pcwvwPpR5RHnSnXbaSZXCxXm76aabYvazjvnggw8mbNv7779vHHHEEapsLEq04hjf/e53jeeee27QOY4vOxlfQtYqIYoSkmg3tll2jN/3vffeM44++mhjgw02UJ+Lvh5yyCGqPxb/+te/jP32209twznHviiv2tjYmPa5BV9++aU6l9hv3Lhx6n34PqBd8+bNS/u8ZAN8Nr5/ibj44otjSoiiDOg3vvEN1f6JEyca5513nvH0008nLDOaqAQobIF+2MH3CqWacf3hOkSJX5REjT8m+Oc//2lsvfXW6nyMGTPGOOaYY4wVK1YM+gxcV/EkaxPaA5uk+n21wPcHvxcVFRWq7Xvuuafx+uuvx+wz1O+G9Vk4Bsq54vqZMWOGcfzxx0e/gyj9CtvgNwJ9wn477rhjTHleq0wt+oDfHnye9b0bri977LGHKqfc0tKiXq9evVp9Hn4v8buJ8sB77723cdttt43oe5zsnFslhK+++mq1HfbEbyZ+2/C7jRLjAN91lJPFdw3XHx5xzeLvgMWtt96qrnfrOsE5PPfcc6PHyKa9CMk3PPjPaXFDCCHJQBw9PDrwANlLgCYCVZRQIjLdpHoSC+4Y4447SmbCM0GIG+H3mJDcwxwLQggpYBA/bgex6QjJQHlfDsaIW+D3mJD8gDkWhBBSwCCBH3NxIEcD8z4gDwdx5YhRJ8Qt8HtMSH5AYUEIIQUMKuogMRsDMCSrIkEWpW1RlIAQt8DvMSH5AXMsCCGEEEIIIRnDHAtCCCGEEEJIxlBYEEIIIYQQQgo7xyIcDqsZUjHhTCoT4BBCCCGEEEJSB1kTmLAVE7xiwlRthQVExZQpU5xuBiGEEEIIIVqzfPlyNcu8tsICngqro1VVVU43J69BlYzFixfLjBkzxOfzOd0cMkJoR32gLfWAdtQH2lIPaMfs09bWpm7kW+NubYWFFf4EUUFhMfyFVlFRoc4TLzT3QjvqA22pB7SjPtCWekA7jh6ppB0weZsQQgghhBCSMRQWhBBCCCGEkMKeIA8xX9XV1dLa2spQqGGAmVFFC9n8rKDlXmhHfaAt9YB21AfaUg9oR2fH2/RYFBDBYNDpJpAsQDvqA22pB7SjPtCWekA7OgeFRYEA9b5kyRL1SNwL7agPtKUe0I76QFvqAe3oLBQWhBBCCCGEkIyhsCCEEEIIIYRkDIVFATHcNOzEHdCO+kBb6gHtqA+0pR7Qjs7BqlCEEEIIIYSQhLAqFBkE9GNHR4d6JO6FdtQH2lIPaEd9oC31IFM7rlmzRi6++GJZvnx51ttWCFBYFAiojrBixQpWSXA5tKM+0JZ6QDvqA22pB5na8bTTTpN3331XfvzjH2e9bYUAhQUhhBBCyCgRChvyxuJ18ugHK9UjXo8mxx9/vJoYDktRUZFMnz5dzjvvPOnp6YnZz9pn3rx5Met7e3tl7NixatuLL74YXf/SSy/JXnvtJWPGjJGysjKZNWuWHHfccdLX16e2Y1/rmPHLqlWrRrXP+IxHHnkk4+M89NBD6liPP/64TJ06VW677TZxOy9G7NLS0pKTz/Pn5FMIIYQQQgqMpz5plEseny+NrQOD+obqErno0E3lgDkNo/a5BxxwgNx1113S39+v7r5DAGBwefXVV8fsN2XKFLXfN77xjei6hx9+WCoqKqS5uTm6bv78+eqYP//5z+VPf/qTlJaWysKFC9VAPBQKxRzziy++GBSHX19fL04DARQIBIbc58gjj1QL0EFUOAE9FgUCflBwQXF6e3dDO+oDbakHtKM+ZNuWEBWn/O29GFEBVrX2qPXYPloUFxfLhAkTlHA4/PDDZZ999pFnn3120H4QHPfff790d3dH1/3lL39R6+0888wz6nh/+MMfZM6cOTJjxgwlNG6//XYlMuJFBPa1L0NVafrkk0/kwAMPVGJm/Pjx8sMf/lDWrl0b3b7HHnvIGWecobwu8JbgeMiBsJg2bZp6/Pa3v61sh7bBjpdeeqlstdVWcscddyivTUlJidrvqaeekl133VVqamqUZ+aQQw6RxYsXR4+3dOlSdZwPPvgg5o7/c889J9ttt53y1uy8885KQNl59NFHZZtttlGfs+GGG8oll1wSMwM4jnHrrbeqz8MxNtlkE3njjTdk0aJFqo/l5eXquPa2pHpc9BH9tzxJjz32WLQve+65p3peW1ur9oVHazShsCgQcFHjC8kSbO6GdtQH2lIPaEd9yKYtEe4ET0WioCdrHbaPdliUNXB//fXXE96t33bbbdXAHJ4HsGzZMnn55ZfV4N4OBvONjY1qWzZBeA7Cq7beemt555131KB/9erV8t3vfjdmv3vuuUcNvN98800lbiAaLKH09ttvq0d4XtBGvIYdMYjGoB19+/e//x0VCp2dnfKLX/xCfR7EAuyNQflwORm/+c1v5Nprr1Xv8/v9cuKJJ0a3vfLKK/KjH/1IzjzzTOXdgYC4++675fLLL485xu9//3u1H9qy8cYbyw9+8AP52c9+JhdccIE6LhLOTz/99LSPC7GBc/bRRx/JQQcdJMccc4zyOEFYWraFEML5ueGGG2Q0YShUNgj2ifiKIBslX8GXFWXCUC6Md9bcC+2oD7SlHtCOhWfLQ298Vda09w55rN5gSNZ39Sf/LBHlydjusmel2O8btm3jKovl8Z/vKqnyxBNPKA8A7mwjZwKD55tuuinhvhggw0tx7LHHqkErBqbjxo2L2eeoo46Sp59+WnbffXclMhA6tffee6tBb3zY0+TJk2NeI1fh008/TfjZaBNExRVXXBFdh7ZgQLxgwQKZPXu2WrfFFlvIRRddpJ7jjjzeB1Gw7777RtsKDwTaBjtCsOAR4U/33ntvTH+sUCf752E7Bu7wxiQDg3n0H/zqV7+Sgw8+WOWtwJOAgT3WHRfx9EDYQETAy2K1G5xwwglR0XT++efLTjvtJL/73e9k//33V+sgILCPRarHhRfi6KOPVs9xLhGu9tZbbymvErw8licJ52i0obDIBh2rRbqbRYorRYqrzMU/dBxfroESR/JUZWWl+HzD/4iR/IR21AfaUg9ox8KzJUTFqrbY8KaRYoqP5AJkpCD8Ze7cueru/HXXXafusMcPqC0gKDB4/fLLL5WwwKA0HpwPeAQuu+wyef7555XnAANY5GxgANvQ0BBzlx3n0AIJ5Mn48MMP5YUXXlAiKB6EBNmFhR18XlNT05B2hLCAqIkXScgNufDCC1UfEHJleSrgrRlKWNjbYPUXbdhggw1UP1577bUYTwJyTyA8urq6VIhS/DEQ9gU233zzmHV4D+aNgGAbyXHh2cF7k52f0YbCIlsYYZGeVnMB/pKIyIDYqMxrbwYhhBBCUgPeg+EYzmNhUVtWlLLHIh0wuJw5c2b0jvyWW24pd955p5x00kmD9rXyDLANA1bkO7S3tyc87qRJk1SYFBbcOcfA/5ZbblF31i2Qz5DqnXHMN3HooYcOSioHdrESL07gUUqlnCzOQzz4PAgO5IdMnDhRHQeCwqpulQx7GyyPltUG9APn4Igjjhj0Piu3I9kxsn1c6zhOlU2msBgtgj3m0tkk4vGKBCpESqpNkeFP7weCEEIIIflBKiFJyJ3Y9ernVaJ2oiwKDB8nVJfIq+fvJT7v6N54RBjUr3/9a5VXgJj++GRrKxwKIVAIz0nV84ZkYAz+4RUZKUhKRg4A8jzgVRkpGFjHV6dKxLp161SuAUTFbrvtpta9+uqrkinoB447MyLmskU2jmvl1qRyfrIBs81y5c3obRNpXS7SNF+k6TOR1hUiPW0I6sxJE6BeodwZA+xuaEd9oC31gHbUh2zaEmIBJWXVceM/J/KI7aMtKuw5EhAMN998c8LtiMXHjNNIik4EkoZPOeUUVR0KIUrImYAIwSM8AHYQgoNQJPuCsrfJJqNDkjHyA5B0jWMjlwN5BukMhCFMkHOBz0J+RTI7QgzBQ4NSskjsRlgXBFemILQKuRyXXHKJOiefffaZqrb129/+1vHjwjuDc4G8G9gYXpDRhMLCCZQnY41I82KRVR+JrFss0rlWJDh0MlimdyyQDMXKJe6GdtQH2lIPaEd9yLYtMU/F3GO3UZ4JO3iN9aM5j0U88Aag2hAqKiXyMGDgWVdXl3Sehx122EENSE8++WTZbLPNVBIzJtbDpHRWQrPFRhttpDwZ9gVzaSQCoUjIIYCI2G+//VS+wVlnnaVCqdKxA6o1oUoU7IdKV3hMJCxwTAzM0R6EP5199tlyzTXXSKYg+RoD92eeeUa23357ldyO3BYM6p0+LsLXrCRw5HDYq06NBh4D2S0uBcktqN6AKg7xVQlySstyka6BmssZoXIzIkngCJ/K0g8cYu1wVwDVAfgH0L3QjvpAW+oB7agPo2VLhEW9taRZmtp7pL6yRHaYPiZnnopChNeks+Nt5ljkbW7GmoHcDIiMkqqMcjOgH1H9AG5A4l5oR32gLfWAdtSH0bIlRMROM8Zm9ZgkObwmnYXCwg25GVja8OtUbAoMeDQClVnzZhBCCCGEEJIpFBZuItRrejKy7M0ghBBCCCEkUygsdPJmwJMBkZHAm4EkJs4M635oR32gLfWAdtQH2lIPaEdnYfJ2viVvZwO7NwNioyi2IgUhhBBCCCHZHm8zSF9HLG9G2wqRNZ+JrJ4v4eZl0rhssYSDQadbRzKsdtHY2OjYjJoke9CWekA76gNtqQe0o7NQWBQCoV4xutZK6+plYqz62Jw3o6NJpL/H6ZaRNIGDEXcMXOxoJBFoSz2gHfWBttQD2tFZmGNRcBgDuRmyUsQXGAiZwiMrTRFCCCGEkBFAYVHohPrM/BCVI+IxczNUSdsq5mYQQgghhJCU4e3pAgHFEeoqS9RjcgyRvnaRtpWR3IxPzcT07hYELeausSQpqHJRV1fHahcaQFvqAe2oD7SlO7n77rulpqYm+vrSSy+V73znO0Pa8fjjj5fDDz8848/O1nF0gsKiQPDiB7OqRD2m7c1Yv0Rk1UciaxdFcjO6R7OpZAi8Xq/6w4dH4m5oSz2gHfVh1GwZDokseUXk43+Zj3g9imCwi0E1lqKiIpk+fbqcd9550tMTm1dp7TNv3ryY9b29vTJ27Fi17cUXX4yuf+mll2SvvfaSMWPGSFlZmcyaNUuOO+446evrU9uxr3XM+GXVqlWSK84991zV1mzacenSpaofH3zwQcz6G264QQkbMgBDoQqEcNiQlc1dMmlMmXi9I7kbE/FmYAEqNyOSl4FHry/bTSYJQJWLlStXyqRJkziQcTm0pR7QjvowKrac/5jIU+eLtH09sK5qosgBV4ts+i0ZLQ444AC56667pL+/X959910lADAwvvrqq2P2mzJlitrvG9/4RnTdww8/LBUVFdLc3DzQjfnz1TF//vOfy5/+9CcpLS2VhQsXykMPPSShUKxQ+uKLLwaVJK2vr5dcAdGzfv16qa2tHfVrEiVYSSz8FSwQUBuhs7dfPWYF5c1YF/FmfGx6M9pX05sxyqDKRWdnJ6tdaABtqQe0oz5k3ZYQFQ/8KFZUgLZGcz22jxLFxcUyYcIEJRwQqrPPPvvIs88+O2g/CI77779fursH/nb/5S9/UevtPPPMM+p4f/jDH2TOnDkyY8YMJTRuv/12JTLiRQT2tS+JBvgQcpMnT5a5c+fGrH///ffV/l999ZV6/cc//lE233xzKS8vV/059dRTpaOjI2nfL774Ytlvv/2idoTw+cUvfqHCpeCJgfcm3sZPPfWU7LrrrtF9DjnkEFm8eHF0O7w+YOutt1YCbY899kgYCtXb2ytnnHGGOgclJSXqmG+//XZ0u+XVee6552S77bZTImjnnXdWYkwXKCxIFoh4M9q/FlnzeSQ3Y1kkN2N0Xb6EEEJI3oG/ffBUJLydF1n31K9y8jfyk08+kddff10CgcCgbdtuu61MmzZNeR7AsmXL5OWXX5Yf/vCHMftBHGBuCGzLFhAPRx99tPzjH/+IWf/3v/9ddtllF5k6dWp0P3hJPv30U7nnnnvk+eefV+IgVa699loVrgTB9OqrrypPDLwydiAoIT7eeecdNejHZ37729+OzoXx1ltvqcf//e9/6jz8+9//TvhZ5513njqXaOd7770nM2fOlP333z/G+wN+85vfqHbh8/x+v5x44omiCwyFItnH8mZgUZWmys2QKVSbKoq9s0EIIYS4ilt3N/MNhyLYK9KNv4HJMMxCKdfMEvEXD/+ZFfUiP3sp5SY+8cQTKpwpGAyqu+gYKN90000J98WgFoPuY489Vg3ADzroIBk3blzMPkcddZQ8/fTTsvvuuyuRgdCpvffeW370ox8NCnuCF8IOBAJEQSKOOeYYNcCGoNlggw3UQB4elN/+9rfRfc4666zoc4igyy67TE4++WT585//nNK5uP766+WCCy6QI444Qr2+5ZZbVF/sHHnkkTGvcT5wDhACBg+NdT7gzUD/EwFxMnfuXHUODzzwQLUOHh14iu68806V+2Fx+eWXq3MJfvWrX8nBBx+scmDg5XA7FBYFAtIqJtSUqcfcezM6zAUeDa/fLGmLpbiCQiNN8MchmVuZuAvaUg9oxwK0JUQF/p5lgyHFx8jZc8891SAXg93rrrtO3RWPHzxbQFBgcPvll1+qQTG8A/H4fD6Vi4FBPTwGb775plxxxRUqZwN38xsaGqL7vvLKK1JZWRl9jQTyZGy11VayySabKK8F2oCk66amJiVkLOAluPLKK+Xzzz+XtrY2JZYwCO/q6lKhRPEg1Aj9hR0xUR48DDvuuGN0O7YhDMkeDoV8kQsvvFD1a+3atVFPBQQPhEUqIHSqv79feVvsfd9hhx3ks88+i9l3iy22iD63zh36DXHldvhLWCDgQqspDzhfRi8cFOlpEWlbYYZNIT+j+UuRjjUifV3Ots0tdqypcd6OJGNoSz2gHQvQlvAeVE4ceikdm9qHYr/hjoUFn5kGyEdAGM6WW26p7r5jwIy75omwcgpOOukkNWC37rYnAontCJOC9wNeCOwPD4Ad5CPgs63FCmlKBrwWVjgUHpG7gTZZ1ZjQNgzEEWKERPSbb75ZbbOqUcUD+0EIpXNNHnrooSpcCR4GnCssQ31GphTZxJbVTkvMuB16LAqoKtTSNR0ybVzFCKtCjabQaDUX4PGZngzLqxEYfDeikMEPD35o4Q7mHVJ3Q1vqAe1YgLZMJSQJuRPXzzETtRPmWXjM6lBnfTzqVRXRl1//+tcqh+AHP/jBoGRrKxwKIVDnn3++GpSnAqou4W47vCKZgDYh9Ami4V//+leMUME62AXhUpZNHnjggSGPB08EBAHeh6pNaCOEwje/+U21HR4PHHebbbZRr9etW6eSpyEqdtttN7UOuRh2rPyU+ApYdpDQHggE5LXXXouKKXgwkLxtD+fSHQqLAgE/a33BUPaqQo0WRmiw0FA5GpWm0EDoVAHfGbR+MFmBxv3QlnpAO+pDVm0JsYCSsqj+BBER89c38jfsgKtyVqodoUWI8cfd/l/+8peDtsNLsGbNmkH5Eha33nqrmsMBCc0YQMNTce+99yqvxY033hizL0J64ufMgAciWUgUhBwqI8FjgoH7t741UIYXHg8MzvEZ8Cpg0B7vIYkH9rMWcOaZZ8pVV12l5t3YeOONVZWplpaWGIGE9t12221KhCD8CWFZdlDlCYIM1aOQQ4JciPhSs/ASnXLKKeo8Y64PhDWhihZCttC3QoG3V0j+C43eNjPJbe0XZujUusVmjGtfJ35BnG4hIYQQMhjMU/Hde0WqBvIPFPBUYP0ozmMRD/IKTj/9dDXQTeRhsGYdT1Q5CiBPACVekTS92WabqcRjTKz3yCOPRJOQLTbaaCM1QLcv8BAMFw714YcfKuFi96gglAtCALkcyHVAxSjkW6TDOeeco8K3UEJ3p512Uvkf+BwLeEKQMI424jPOPvtsueaaawadP+SeQGBNnDhRDjvssISfddVVV6lcFnwePCKLFi1SieIQL4WCx3DxbRYk8UAxIjknmcrOCS3LzRmq85hQ2JCFja0yq6FafPkUCpUplkcjmgxeprVHA3dzkGSGOy+puqtJfkJb6gHtqA+jZkuERX31ukjHapGK8SJTd+aksqMIr0lnx9sMhSoQoCUmjy13oCpUjjwaWDApuMdry8+A4CjXSmjgzgrcsIzldj+0pR7QjvowaraEiJhuxu6T0YfXpLNQWBQIcHNWlCQv+aYNRnhAaIhdaES8Gi4XGsqOFRVON4NkAdpSD2hHfaAt9YB2dBbKuQIBoVALvm5VjwWFJTTaG0XWLRRZ9ZHI2kUi7atEejtcl6MBF++CBQuGrExB3AFtqQe0oz7QlnpAOzoLPRYFRNhlg+hRExp97eZieTSKIiFTKkejHH5UyWd0qXVNaEtdoB31gbbUA9rROSgsSGFjFxodWOGJSwbPf6FBCCGEEJIPUFgQEoMh0tdhLvFCw3qk0CCEEEIIGQSFRYGAalDT6yv1qwqVS6Gh8Jglbe2zg+dQaKDKxfTp01ntQgNoSz2gHfWBttQD2tFZKCwKCL+PF1nmGCL9neYiq+OEhuXRGN262Zioh+gBbakHtKM+0JZ6QDs6B0eaBQKKQWGCvEIrCpUzoYGJj5q/NGcGX7NApHWlSE+rOTFSlhPSMPEPE9PcD22pB7SjPtCWekA7OgslHSGj5dHobIp4NEoHksFz4NEghBBCCHECCgtCRl1odJmLEhpihk5FK09VUmgQQgghRAsoLAjJNVGhscZ87S+NTQb38bIkhBBCiPvwGIZ7Z01ra2uT6upqaW1tlaqqKuca0rJcpGut5DMwM/IrUBUK092TPMZXLOKPLOp5QMRfIuILwP+h4kZR7YJ2dDfqmqQtXQ/tqA+0pR7Qjs6Ot3lrtIAIhsIS8DNfP+8J9ZpLb/wGj4i3SIKGXwKlZabYiAqQABSjM+0lIyYYDEogEHC6GSRDaEd9oC31gHZ0Do4yCwR4K5Y0tbMqlKsxJBzslSUrvpZwxxqRthUizYtFmuaLNH4osnq+yLrFIq0rRLC9p00k2IvbN043nCQAd9SWLFnCyiUuh3bUB9pSD2hHZ6HHghAtMIb2dFheDeXhMMOqot4OQgghhJAsQGFBiPYYIsEecxlSdJTEPacbmRBCCCGpQ2FRQHgZg68F2bWjXXS0xW2j6BhtkFxI3A/tqA+0pR7Qjs7BqlAFUhWKkOziiatcFVfFihBCCCFawKpQZBDQj529QSkv9rP8movJHzvaPB3xeLwDORxKaEQ8HBQdg23Z2Snl5eW8Jl0M7agPtKUe0I7OQl9RgYBqUCvWdbIqlMtxhR2NsCk4elrN2cZbl4msWyTS9KlZvarpc5HmL0VaV4p0rhPpbRcJ9kmhgYolK1asYOUSl0M76gNtqQe0o7PQY0EIybHo6DaXhJ4O+4SA9udFTrSWEEIIIWlAYUEIyUPR0ZqC6CgWKSoV8fqcajEhhBBCbFBYFAiIMgz4feqRuJeCteNQogP5HBAY/lLzsajMFbkciP3FzLCMAXY3tKM+0JZ6QDs6C6tCZQNWhSIkv/D4IiLDEhol5nP+oSGEEELSglWhyCCgH1u7+qW6rIgq3sXQjilihET6OswlpkRuRGBYC7wcPr9ztmxtVT/WtKV7oR31gbbUA9qxgKtChUIh+d3vfifTp0+X0tJSmTFjhvz+979XXwqSXVBFaFVLV35XEyLDQjtmWiK3W6S7WaRtpVmpavXHIqs/FVm3WKStUaS7RSQ4aHryUQEVS1atWsXKJS6HdtQH2lIPaEdncdRjcfXVV8vcuXPlnnvukc0220zeeecdOeGEE5TKPOOMM5xsGiGkUAj1mYt95nF7KFU0d4OhVIQQQkjeCovXX39dDjvsMDn44IPV62nTpsl9990nb731lpPNIoQUOkOGUpXY8jbKHAulIoQQQvINR/8i7rzzznLbbbfJggULZPbs2fLhhx/Kq6++Kn/84x8T7t/b26sWezKJFVKFBSCezuv1KheYPaQq2Xqsw7Zk663j2teDGBdbOCzeyHvjQ1R8Xo86rn097nl6h1gfNgwxEq0PGwjmsPVJxOsZvN7rMftrX4/nZQG/OlYorpHYP1Hbk63Plz7Z1xdMnwxDSgN+9R5t+uQaO4XF6OsSwSLNA33Cz6glNopKxBsoV6Vw493w8b8deEQIKI6h2m7b3/q9SrY+1d+3Uf3dG2K9z+crmD5hKSsr06pPOtoplT5Z16TVLh36NNL1bu4TnmPWbTza2+nmPnkctlP8cfJWWPzqV79S4mDjjTdWjUfDL7/8cjnmmGMS7n/llVfKJZdcMmj94sWLpaKiQj1HGFVDQ4OsXr1aJe9Y1NXVqWXlypVqqneLCRMmSE1NjSxdulT6+gZm/508ebI6Jo5tP9HIB/H7/bJw4cKBBvS0yqy6IgmGwrKkqT26GoOU2ROrpbM3qGZLtkC50A3HV6okXMTLW5QXF8mUunJpbu+Vte090fXVZQFpqC2T1a3d0to10Ma6yhKpqyqRlc1d0tnbP9CnmjKpKQ/I0jUd0hcc+DJMHluuBlQLvm5Vg61on+orxe/zysLG2DKesxqqXdGnipIiWbyqrWD61N0XlMWr27Tqk6vt9PW6wX3yF8nC1R0iXr+It0h5NWbN3kSC4bAsWbJkoE9er1o6OjrUTLHRPgUCsuGGG6rfMMQKR/tUXi5TpkyR5uZmWbt2oBKdI797sNOsWRIMBgf1CTeK8HmF0idUScHxdeqTjnZKtU9r1qzRrk862mmoPmH/5cuXa9WnOgftZP99y+tys/fff7+ce+65cs0116gciw8++EDOOuss5bE47rjjUvJYWIaxyl85ovZaV4i3e11+eywMQ1o6emVsVUnMsa393XPXWMc74an3CZ+5rq1XaiuL1TF16FOituvZJ4RSFUs4EkIV9gZkfXuv1I0fH/0N0u0uVyH0Caxbt079cbfe6/Y+FbLHYv369TJmzBg1+NKhTyNd7+Y+4XlLS4u6Ju1VodzcJ4/DdsL5xHWRSrlZR4UFRAG8Fqeddlp03WWXXSZ/+9vf5PPPPx/2/ZzHInUw+MFdYdwJxuCMuBPaUUNbTqoTX3FZXLJ4idPNIymCP9y4+4c7fvgjTNwLbakHtGMBz2PR1dUVVUkW+BLEKyZCCNGWcL9ZkSqmKpU3thqVJTjifi8JIYSQfMJRYXHooYeqnIoNNthAhUK9//77KgzqxBNPdLJZhBDiLEZYpL/TXKKYoVSDSuD6ihxsKCGEEJInwuLGG29UE+Sdeuqp0tTUJBMnTpSf/exncuGFFzrZLC3xRJJWGTzjbmjHQrYlJvjrMRdZP7AayeFRr8ZAdSqSGxDHzBl+9YC21APa0VkczbHIFOZYEEJIAuyhVPBywKvhC0QWejgIIYRomGNBcgcq2qC85vjqUlURh7gT2lEfRtWWCUOpLDyxIgOP8eKDd/pSBjmBKA05fvz4QTmDxF3QlnpAOzoLhUWBALcUavbXV5c63RSSAbSjPjhnS0Mk1GsuyVBzb9iEx6Dn/NNhAac/7uLV19c73RSSIbSlHtCOzsK/DoQQQgZXqsIyME/g4FCrqMhIJDyK6PUghJAChMKCEEJI+qFW0URySRJulczrgbCrAEvnEkKIhlBYFAi4eVhXWcKbiC6HdtQHvW2JcKs+c0mG1z9MuJU7ksxReaauro4VaDSAttQD2tFZKCwKBC8utCqWoHQ7tKM+FLwtw0FzSRZu5ZIkcySHYhBD3A9tqQe0o7NQWBRQBZqVzV0yaUwZqwm5GNpRH2hLPZLMUYFm5cqVMmnSJFagcTm0pR7Qjs5CYVFAFWg6e/vVI3EvtKM+0Ja5SDL3xQmN7CeZowJNZ2eneiTuhrbUA9rRWSgsCCGE6IkREgmGUksyR84Hql0lXTyJ14cN3CI1F58vxx0khJD8gsKCEEJIgZJCkvlwQFh0toqs7kHizMjEyUj2IYSQPITCokDA37sJNWXqkbgX2lEfaEtN7YhSvFhGFc/oi5cCjE1HPP6ECRMYl+9yaEdnobAoEFB2raY84HQzSIbQjvpAW+qBM3Y0zDAvLKPJsALFEiG+NLb58la0KFvW1DjdDJIhtKOzUFgUUAWapWs6ZNq4ClagcTG0oz7QlnqgtR1H0/tiFxrxnpKk23w2b0uibZmFiaGa0NKlS2XatGm82+1iaEdnobAoEFAboS8YYgUal0M76gNtqQe0Y6aiJZjlA3tG5kXxeARFhPq62sXo7RTxW8n8kX05QHUNqAbV19fHqlAOQWFBCCGEEE2IhImFQiNLxO9uFVkXMpNn4knkSYnxotiFiC8S9mUt/tjXhGgKhQUhhBBCSM68LB6b8PAPiA1PEhFCUUJcBIVFgYCbL5PHlrMCjcuhHfWBttQD2lEfcmdLzH0CcRIcemb5hFCUDAfyKiZPnsz8CoegsCigKgkVJUVON4NkCO2oD7SlHtCO+uAOW1KUpGTHigqnm1GwUFgUCKGwIYtXtcmMCVXi460110I76gNtqQe0oz7ob8vCECWhUEgWL14sM2bMEJ/PPYJIFygsCogwKyRoAe2oD7SlHtCO+kBb5lCU4LkvIOIrEvEVR577s1ZyljgDhQUhhBBCCHFelKCilhIZEBsBEb/tuVrPYWu+QwsRQgghhBDnQdWtYLe5JBUegdjFb3+d7zky+kNhUSAgXHR6fSUrl7gc2lEfaEs9oB31gbZ0i/DoMZdEeLzi9RbJ9GqfeNtWiPhLTLGhPB8UHrmAwqKA8PtYek0HaEd9oC31gHbUB9pSD+HhR6pMV6c5eWEMngQhVnHeD5IRFBYFAiYUXdjYKrMaqsXHuzGuhXbUB9pSD2hHfaAtC8GOhpnnkTTXw5MkxIrCI1UoLAghhBBCCLELj75hhEd8iJWVdO4pbFVKYUEIIYQQQsiwpOLxKBqislVAe+FBYUEIIYQQQkhWhEefuQwpPIaobOVy4eExDPfOBtPW1ibV1dXS2toqVVVVzjWkZblI11rJZ2BmxB2i2gWmuyfuhHbUB9pSD2hHfaAt9cD1dvRaIVYRARIoFympds14m+UPCohgiDNR6gDtqA+0pR7QjvpAW+qBq+0Y7hfp6xDpXi/SsVqkY424CQqLAgHqfUlTu3ok7oV21AfaUg9oR32gLfWAdnQWCgtCCCGEEEJIxlBYEEIIIYQQQjKGwqKA8LoxiYkMgnbUB9pSD2hHfaAt9YB2dA5WhSqQqlCEEEIIIcRlBCpF6mY62gRWhSKDgH7s6OlXj8S90I76QFvqAe2oD7SlHtCOzkJhUSCgOsKKdZ2skuByaEd9oC31gHbUB9pSD2hHZ6GwIIQQQgghhGQMhQUhhBBCCCEkYygsCgTURwj4feqRuBfaUR9oSz2gHfWBttQD2tFZ/A5/PskRXq9HNhxf6XQzSIbQjvpAW+oB7agPtKUe0I7OQo9FgYDqCC2dfayS4HJoR32gLfWAdtQH2lIPaEdnobAoEFAdYVVLF6skuBzaUR9oSz2gHfWBttQD2tFZKCwIIYQQQgghGUNhQQghhBBCCMkYCosCAdURyouLWCXB5dCO+kBb6gHtqA+0pR7Qjs7CqlAFVCVhSl25080gGUI76gNtqQe0oz7QlnpAOzoLPRYFQtgwZG1bj3ok7oV21AfaUg9oR32gLfWAdnQWCosCAdfX2vYe9UjcC+2oD7SlHtCO+kBb6gHt6CwUFoQQQgghhJCMobAghBBCCCGEZAyFRYGA6gjVZQFWSXA5tKM+0JZ6QDvqA22pB7Sjs7AqVAFVSWioLXO6GSRDaEd9oC31gHbUB9pSD2hHZ6HHokAIhw1pXN+lHol7oR31gbbUA9pRH2hLPaAdnYXCokDA5dXa1aceiXuhHfWBttQD2lEfaEs9oB2dhcKCEEIIIYQQkjEUFoQQQgghhJCMobAoEDwekbrKEvVI3AvtqA+0pR7QjvpAW+oB7egsrApVIHg9HqmrKnG6GSRDaEd9oC31gHbUB9pSD2hHZ6HHokBAdYTlaztZJcHl0I76QFvqAe2oD7SlHtCOzkJhUSDg8urs7WeVBJdDO+oDbakHtKM+0JZ6QDs6C4UFIYQQQgghJGMoLAghhBBCCCEZQ2FRIHg9IhNqytQjcS+0oz7QlnpAO+oDbakHtKOzsCpUgeDxeKSmPOB0M0iG0I76QFvqAe2oD7SlHtCOzkKPRYGA6ghfrm5nlQSXQzvqA22pB7SjPtCWekA7OguFRYGAy6svGGKVBJdDO+oDbakHtKM+0JZ6QDs6C4UFIYQQQgghJGMoLAghhBBCCCEZQ2FRIKA6wuSx5ayS4HJoR32gLfWAdtQH2lIPaEdnYVWoAqqSUFFS5HQzSIbQjvpAW+oB7agPtKUe0I7OQo9FgRAKG7Lg61b1SNwL7agPtKUe0I76QFvqAe3oLBQWBUTY4EWmA7SjPtCWekA76gNtqQe0o3NQWBBCCCGEEELcLyxWrlwpxx57rIwdO1ZKS0tl8803l3feecfpZhFCCCGEEELckry9fv162WWXXWTPPfeUJ598UsaNGycLFy6U2tpaJ5ulJaiOML2+klUSXA7tqA+0pR7QjvpAW+oB7VjAwuLqq6+WKVOmyF133RVdN336dCebpDV+n+MOKpIFaEd9oC31gHbUB9pSD2hH53D0zD/22GOy3XbbyVFHHSX19fWy9dZby+233+5kk7QFxREWNraqR+JeaEd9oC31gHbUB9pSD2jHAvZYfPnllzJ37lz5xS9+Ib/+9a/l7bffljPOOEMCgYAcd9xxg/bv7e1Vi0VbW5t6DIVCarHqF3u9XgmHw2LYqgIkW4912JZsvXVc+3qA/aOEw+KNvDf+i+zzetRx7evhnfMOsR7VDIxE68OG2A/v8cDlN3g93H+euPUou2b1L74Em+UujG97svX50if7+kLpk9kWI2ab2/uUqO2F0Ce8z6pcokufdLTTcH0CWG8/vtv7pKOdUumTdU1i8YkefRq0vgD6ZP2uYr3o0KdwWDwYZ47WGHaI9T6fz/x9iztO3goLdAAeiyuuuEK9hsfik08+kVtuuSWhsLjyyivlkksuGbR+8eLFUlFRoZ5XV1dLQ0ODrF69WlpbW6P71NXVqQXJ4p2dndH1EyZMkJqaGlm6dKn09fVF10+ePFkdE8e2n2iEavn9fpULEqWnVWbVFUkwFJYlTe3R1bhYZ0+sls7eoKxYN/CZAb9PNhxfKa1d/bKqpSu6vry4SKbUlUtze6+sbe+Jrq8uC0hDbZmsbu2W1q6BNtZVlkhdVYmsbO6Szt7+gT7VlElNeUCWrumQvqD5ZcAXOhgyv3SLV7XFlGJDLCLchlD4dmY1VOd1n5SdxpariXAKpU/4ocDnLpK26I+b2/uko51S6ROuyfUd5ufr0icd7TRcn6aOq5De/rAsWjVwTbq9TzraKZU+4ZrE86ayHpk0Ro8+6Win4fpUW1GsHr9e3yXdfUH398nXI3XeMaM3hkWfZs2SYDAoS5YsGeiT1yuzZ89Wn4fjpIrHsEucHDN16lTZd9995Y477oiugwfjsssuUycvFY8FcjSam5ulqqrKOY9F6wrxdq/L6zsNUOKLV7XK7Ik1g85r3qhyTe+eZLNPauKfxlaZOaFatVmHPiVqeyH0Ce9btKpVNppYo7bp0Ccd7TRcn/BywdctMsN2Tbq9T4XsscA1iYFjkc+rRZ8GrS+APmE/iIQZ46vUdtf3KVAhnrqZjnosWlpaZMyYMeqGvTXezkuPBSpCffHFFzHrFixYoARHIoqLi9USDzqOJdFJiifd9fHHTbge78XVifUD3+EoMG4663GRR33sMW1MsDLF9XgKUWFdQIlI1JZ87lNMG72F0Sfko22UxI5u7ZOOdkqlT3ib3ZY69MlOofQJf3ST/ba6tU9Drde5T/ZrUpc+pdpGnfrkNUyvQrLxjuv65PWay2iNYYdZr9qeZP+EbREHOfvss2XevHkqFGrRokXyj3/8Q2677TY57bTTnGyWtsB1R9wP7agPtKUe0I76QFvqAe3oHI4Ki+23314efvhhue+++2TOnDny+9//Xq6//no55phjnGyWlsAFh3jAeNcdcRe0oz7QlnpAO+oDbakHtKOzOBoKBQ455BC1EEIIIYQQQtwLZxAhhBBCCCGEZAyFRQGhkoSI66Ed9YG21APaUR9oSz2gHZ3D0XKzmYJys5i3IpXyV6NKy3KRrrXOfT4hhBBCCNGPQKVI3UzXjLfpsSgQoB87evpj6hwT90E76gNtqQe0oz7QlnpAOzoLhUWBgOoImA2SVRLcDe2oD7SlHtCO+kBb6gHtWOBVoVxNOCTy1esiq+eL+ItEJmwh4k19EhFCCCGEEEJ0gcJipMx/TOSp80Xavh5YVz5OZOefi0z/ppMtI4QQQgghJOcwFGqkouKBH8WKCtC5RuTZC0WWvCz5BuojBPy+RDPHExdBO+oDbakHtKM+0JZ6QDs6C6tCjST86fo5g0WFnfJ6kaPvY1gUIYQQQggZOawKpTnIqRhKVIDOJpFVH0k+Af3Y0tnHKgkuh3bUB9pSD2hHfaAt9YB2dBYKi3TpWJ3afusWSz6B6girWrpYJcHl0I76QFvqAe2oD7SlHtCOzsLk7XSpGJ/afm/cJLL0VZGNDjCTuYvKRrtlhBBCCCGEOAaFRbpM3VmkaqJIWyMcbkPv2/iBubx6vcj03U2R0bCliIeOIkIIIYQQohcc4aYLErIPuDryIknNgRl7i1RPGXgd7BFZ+LTIE2eL3He0yNt3irSukFyClpYXF7FKgsuhHfWBttQD2lEfaEs9oB2dhVWhsjqPRb3IzqeboU84rU3zRRY8JbL4eZG+zsHHGD9HZPYBIjP2EAlU5LT5hBBCCCEkzwm4qyoUhUUuZt4O9op89ZrIgqdFVrwtYoRjt/sCItN2M0OlJm4zKmVqw4Yhze29MqayWLwe6ni3QjvqA22pB7SjPtCWeqCdHQPuEhbMscgECIDpu4nUThPpWpt8P3+xyIy9zKVrncjCZ01Pxvql5vZQn8ji58ylvE5k5r6mJ6N2ataaCvm4tr1HaiuKk0ZwkfyHdtQH2lIPaEd9oC31gHZ0FgqLXFM2VmTL74ts8T2RtQtMgbHoOZHeNnN751qRD+8zl3GbiMze3xQkJQ54ZAghhBBCCBkNYdHU1CT19fVJtweDQXnvvfdkhx12SOewhQncc+M2MpdvnCqybJ4pMvBohMx91nxmLm/cbFajgsiYsoOIl3qQEEIIIYTkF2mNUBsaGqSxsTEqLjbffHP573//K1OmmBWQ1q1bJzvttJOEQpGBMUkNX5EZUoWle73Iov+Z+RjrFpnbw/0iS14yl9LaSKjU/iJjZ6T8EfAGVpcF6BV0ObSjPtCWekA76gNtqQe0o4uERXye99KlS6W/v3/IfUiaQDhsfpS5QFhAYEBoQHAAPH78gLmMnWUKjJn7iJTWDHlYr9cjDbWcpM/t0I76QFvqAe2oD7SlHtCOms1j4dEhAz9fGDtTZKfTRI55UGT/K8wytt6ige3rFpozfP/tSJGnfyOy5BWRUKzQswiHDWlc36UeiXuhHfWBttQD2lEfaEs9oB2dhcH6bgA5FcixwNLTZs6LgXyMNZ+b25GTgXK2WIqrTA8GPBl1s81cjsgc4a1dfVJfXepsX0hG0I76QFvqAe2oD7SlHtCOLhIW8Ea0t7dLSUmJCnnC646ODlXfFliPZBRBdajNDjcXlKtFqBTK11rlblFd6tN/m0vtdFNgzNpXpGSM0y0nhBBCCCEak3aOxezZs2Neb7311jGvGQqVQzB/xo4/E9n+xyIr3zW9GEtfNefFAOuXiLx5i8hbt4l38vZSWbebyLh9RQLFTrecEEIIIYQUsrB44YUXRq8lJLOJ+lCGFktvu8iXL5qejNWfmNuNsHiWvymTlr8pxqe3mPNiwJNRv2k0VIq4A5irrrKEZtMA2lIPaEd9oC31gHZ0Fo/h4jJO6UwxPqq0LB965m0n27XwaZEFz4h0Ng3eXj3FnOEboVIVyecnIYQQQgghDhCoFKmb6ZrxdlrCAhPgYY6K4uKBUJrVq1fLLbfcIp2dnfKtb31Ldt11V8kVFBYpYoQlvPJ96f7kP1L29eviCfbE7eARmbSt6cXAXBr+EocaSoYDVS5WNnfJpDFlqqQecS+0pR7QjvpAW+qBdnYMuEtYpBUK9ZOf/EQCgYDceuut6jUSubfffnvp6elRk+ddd9118uijj8pBBx2UWQ9IdvF4xZi4jSz3zJBZe5wjvq9eNvMxGj+M7GCIrHzHXF4tE9lwD9OTMWFzhkrlGbgL0Nnbrx6Ju6Et9YB21AfaUg9oR2dJS1i89tprctNNN0Vf33vvvcqDsXDhQqVkzj//fLnmmmsoLPKZQJnIRgeaS1ujyMJnzHyM9q/N7f1dIl/811wqJ5pejNn7iVQ2ON1yQgghhBCiywR5K1eulFmzZkVfP/fcc3LkkUcqUQGOO+44+fTTT7PfSjI6VDWIbHucyPf/LnLon0Q2OkikyDZbJcTGu3eJ3He0yONninzxpCk8CCGEEEIIycRjgfkruru7o6/nzZunPBT27ZjXguQfCDOcUFOmHgeBcKeGLcxllzPMGbwRKrXyvYhTUcywKSyv3SAyfXfTkzFxKxVmRfLEjsRV0JZ6QDvqA22pB7Sjs6Q1Ktxqq63kr3/9q3r+yiuvqMTtvfbaK7p98eLFMnHixOy3kmQM5hepKQ8MP88IErdRJerga0V+8E9zjgxUj7JA4jcqTf3nF6Yn4+07RVpXjHr7SZp2JHkPbakHtKM+0JZ6QDs6S1pVoV566SU58MADVaJ2Y2OjHH300XLnnXdGt5966qmqOtQ999wjuYBVodKrkrB0TYdMG1eRfpUEfEWa5ptejMXPi/R1Dt5n/Bwz4XvGHiKBigQNCIms+kikq1mkbIzIhC3M+TdI7uxI8graUg9oR32gLfVAOzsGNK4Ktfvuu8u7774rzzzzjEyYMEGOOuqoQR6NHXbYYWStJqMK1GNfMDSyKglQ/eM3M5edThf56jUz4XvF26qUrQKT8WF5/U8i03Y1RQZK2EI8LHlZ5PUbRTrXDByzfJzIzj8Xmf7NbHWxIMjIjiSvoC31gHbUB9pSD2hHZ0lLWIBNNtlELYn46U9/mo02kXzGX2zO3I2la53IwmdNT8b6peb2UJ/p1cBSVicybmORr14dfByIjGcvFNn3UooLQgghhJBCExYvv/xySvt985scKBYEZWNFtvy+yBbfE1m7wBQYi54T6W0ztyM8LJGosPP6TSJTd2FYFCGEEEJIIeVYeL3eaDJMsrdhO+a2yAXMsUgd2KuzNyjlxf7RTWgK9Yssm2eKjK/eQLTj8O/Z8WSRmfuYQoXJVvlhRzLq0JZ6QDvqA22pB9rZMeCuHIu0hMXYsWOlsrJSjj/+ePnhD38odXV1Cfez5rUYbSgs8pz5j4m8+sfU9y+uFKmZKlI7XWTMNJHayFI6hoKDEEIIIYVHwF3CIq1QKFSCevjhh+Uvf/mL/OEPf1AzbJ900klywAEH6KEKNSYUNmTxqjaZMaFKfLmqklBjK1ObCr3tA0ng8YLDEhkQHepxakEKDkfsSEYF2lIPaEd9oC31gHZ0lrSERSAQkO9973tqWbZsmdx9991y+umnS29vr5p1+5JLLhG/P+18cJIjwqk7p7IDSsqi+pO9GlQ8EA2z9hdp+cpMAE+0LwTHqo/NJea9VTbBMU1kzHTT41Faq7XgyLkdyahBW+oB7agPtKUe0I7OMWIVsMEGG8iFF16oQqLgtbjqqqvknHPOkTFjxmS3hcS9ICEbJWVR/SkZ3zw3tipUX4fIeoiMJSLNS02x0QLBkSDUDEnimBsDS7zggMiwiw4VUlWbxc4RQgghhJCMhQU8FA899JAKiXrjjTfk4IMPlv/85z8UFWQwEA0oKTtoHot6kZ1PH1xqFpPrWXNmxHstlOCA2Fgy8LwrieBo/NBc7JRUx4VTWYKjJps9JoQQQggpSNJK3n7rrbfkrrvukvvvv1+mTZsmJ5xwghx77LGOCQomb6cOzNwXDEvAP1DZK6eM1szbSnAsHbxgjo1UKamJDaeycjiwPs9w3I4ka9CWekA76gNtqQfa2THgruTttMvNIgQK+RTbbrtt0v2+9a1vSS6gsEgdmDlsiCCPSYsLbTh62swQKns4FZ53N6d+DIROxYRTTY8IjtxUPUtEwdlRY2hLPaAd9YG21APt7BjQXFgMB+exyN8qCQsbW2VWQ3VhV0mA4IgPp8Lz7vVpCg5bOBVK49ZMEympGnWvDu2oD7SlHtCO+kBb6oF2dgy4S1iklWMRDg8/2VlXV1c6hyQkt2Dw37CFudjpaU0cUpVIcGAdlq/fi12P8rdq/g1bOBWeo/LVcCx5OUEeyjgz+T0+D4UQQgghJA/JWm1YJHTffPPNan6LVatWZeuwhOQGhDc1bGkudnpaBsKp7AvWx4Mwq5VY4gQHZhSPD6fCc0twQFQkqpwFkYH1SH6nuCCEEEKITsIC4uHiiy+WZ599Vs1pcd5558nhhx+uqkP99re/FZ/PJ2efffbotZaQXIME7olbmYud7pZIONXSgfK4SnC0Dj4GEsmxrHw3dn1ZnTnvRtP8odvw+k0iU3fJTrI7IYQQQsgokVaOxfnnny+33nqr7LPPPvL666/LmjVrVGWoefPmya9//Ws56qijlLjIFcyxKOBkpnwFIVIQGM1LBib9w3OUwM2EjQ9RJXiNkhoJl9SKt6xWPGW1Ir5AtlpOcgyvST2gHfWBttQD7ewY0DjH4sEHH5R7771XVX365JNPZIsttpBgMCgffvihHsbTnGDILL9GRhEkdmOZuPXAOmh3S3DEL6kKjs+fUAuushjpHig3Pw+eFeR4YE4Oqw0xS405Rwiv07yC16Qe0I76QFvqAe3oHGkJixUrVkTLzM6ZM0eKi4tV6BNFRf4D9b6kqd2skkBz5RZcH6jyhGXSNrGCA/kV/7to5Mfu6zSX1hXD7+stihMeQ4gR5Jx4s5aCRRLAa1IPaEd9oC31gHZ0lrRGDigji9yK6Jv9fqmoqBiNdhFSGIJj2q5m9Sd7Nah44I3Y5Uzl3Qh3NkvrutVS4+0UDxLIrQpVEBfDEe43P2eoz7JTXGWKoZIUxEhRqWgx4SIhhBBCciMsELd2/PHHK08F6OnpkZNPPlnKy8tj9vv3v/898hYRUkhgMIySsomqQlns9otoVSgjbMjqxlapiq/PHew1E8dRmarbJjjU87h1ECTG8KWjVZhWqqFa/pLBwqOkVgR5IBAmlkDBawgWTwYuapbmJYQQQtwvLDDjtp1jjz022+0ho4iXIWv5CQbDKCk7aLBcL7Lz6YMGywnt6C8Wqag3l+GAqIBg6IqIjKgISSJGgj3DHxP7tDeay3BAVAzrBbGtsyeoa1aal9ekHtCO+kBb6gHt6JKqUPkGq0IRrcjX8J7+7sSeD/sCgYJ2Z1r9KhH2BPW1C0RCfcn3hRg7+r78OG+EEEJIpuhcFYq4F+jHzt6glBf7mWyfr2AwbK8mlS92RP4ElqqG4fcNByMhWUnCsOI9I8j7yGaCemeTyL9/KjJmuum9QYgUxEZF5BFJ6Xny/ec1qQe0oz7QlnpAOzoLhUUBVUlYsa6TVRJcTt7bEZWkMNM4luGAs7SvwxQYPesHQrPg+VCPeL0+vQR10LzYXBLhKxIpGxcRGuMSiI9xpmckB3+M8t6WJCVoR32gLfWAdnQWCgtCiDNg8F5caS4yZfj9V7wt8t9zM/vMUL9I+9fmkoyk4sP2PEfigxBCCHETFBaEEHcwcZvhS/PC8/CdO80QrI5IaV0sHU2xz+EpyUR8YE4QJTTGDRYdFB+EEEIKFAqLAgHDm4Dfpx6JeyloO6ZSmhdVtCwvSM3U5Pv1d0VExgjFRzhz8eEprZOAzz96tszXYgCaUdDXpGbQlnpAOzoLq0JlA1aFIiR3JJzHInFp3ozIVHykwlDiw3quEs7TnPeDc30QQogeBNxVFYrCokCEBczc2tUv1WVFrJLgYmjHPLsbn1B84LFpdMVHfOiVXXwkm+vDwmVzfeQ7vCb1gbbUA+3sGHCXsGAoVAFVSVjV0iWVpayS4GZox9RL8+aEojIz5CrtsKs1YnQ0SW/rKinuXSeebIVdldeJrPli6DbDkzF1F4ZFZQlek/pAW+oB7egsFBaEEOKA+AiHDVna2GqWRAx1Z+b5SEV8WOB49xxiejkCFeYEhIMebc+L49dVmLOh63AnkBBCSFahsCCEEBd7PkYUdoXZ1LFkMl9JvNiIvk6yrjhuHcRJPpAvYXWEEKIBFBYFAu4tlhcXsUqCy6EdC9iWqYiPZfNEnvrV8MfCBIaYJR2TDuIxXawZ1rGMFAiLZF4S+7qoxyTBNgicTMhCknvBXJMFIMAKxpaaQzs6C5O3CyR5mxBSAGDwd9/3h5/r4+j7zEEhfv5DfaanQy2dA0tvgnWD9os8N8LiCP6SJOIkgQclJqSrXKTxI5EXr0x+bCa5D8AqY4Q4R4DJ2yQPCRuGNLf3ypjKYvEyNtq10I76MCq2THWuD+tOMz7XX2wu8GKMBIiTYHecILGJjkRCZNC6Lhwo/c8O9phL1zrJOs/9XqRhS3MmdiTH45yp535ziTw3vD7p7vdISWmJeK3t6tEXeZ9/8DpfZF3Mdtvr6HOfiAeLg9d6sipjEBlYr5EAy8o1WQCenXyHfyedhcKiQMDf/rXtPVJbUWz6CYkroR31YdRsiUEeBnu5mOsD4A83wrSw4C72SIDHAzkkdrHRO5y3JG5dJjkjyRLiV74z7G4wXVl2P3nwJyQVIpF10edJxEm8mEm6f6xoUiWMX7526Oa99iezQltRqfMiyOlrkp6dvIB/J52FwoIQQnQDgxiUlHXLnVMMYK0QppGicka6UvCSdIg0fynS9Jm4A4Sr9ZuLZFk8ZQOEAd9z6IAdlXDBErA92p8Psw3v9wdEvIHI4wiPl+6kkplSQJ4dQoaCwoIQQnQkX+b6yBW4w15SZS7D8fX7Ik+cPfx++18hUr+p6b2AcAkFI89D5mMoKKFQv3y9plUm1gTEZ2A99onsp/aPLBAG9m04hn1dzPYk6wbtH2kHXjuV52IHbQj1movTRD0vqQsVj7dIxvcY4llWKeJPQ/jgWnv1uqHb8/pNnD/GguFiWkNhUSDAG1hdFqBX0OXQjvpAWzoIBjIIURkuyX3KjsMOeDxhQ/zl3eKpLhXxOmhNJTLihMkgMWKJmgTiJCqCbNvXLxP54j/Df3bdLDORHoUAlHfF/mh7nkusPsDLk6LOgY+jdrTag7LQ9x4uUhwptTxI2MQtlmjBY8L9bc/tQifRdhXWlie/NDkIF+Nvq7NQWBQIXq9HGmpHNxKYjD60oz7Qlg6SbpK7G+yocinQ3uLsHROiY8Vbwwuww28Z/lwh8N0SM1HBEREdEDPBPttjIoGSQKhYIWLpHm8kJZazQV+7ueQcT2JREiNEkgmYuP0HiZ1Aavvi+dJXchIuljfXZIFCYVEgYJbf1a3dMr66VF10xJ3QjvpAWzpMlpLctbZjFgWYumOuBp1Fo57unlrI1mChEu7vk+a2DhlT6hGv8uKkKFTavhb56rXhP7ek1rydjmOEIsfLCZGy0rn2GqULyj+vXypSUmOGNBZX2R6rzep1hX5NuoC8ERZXXXWVXHDBBXLmmWfK9ddf73RztAOFHFu7+qQe7nriWmhHfaAt9Uhy196Oua4ylguQ2G2VWbZhhA1Z29cqtROq0wtrS3f+mIEPTOKRiXttFzUx+6SwbyrvyxdQ2e2dvyTfDs+HEhrVkcfKgeeWACmuEiNQKd1tHjECk0RKqzKfSJOkRV6c7bfffltuvfVW2WKLLZxuCiGEkEKi0JLcC6HKmFs8O0kETk5RIWpx4iYqROLFTgJRknDfBO+D6IJnJxPUcdaayxDgLG9oX4EJMeO9H0qURJ7HixQ8R4W6fMhLCSMc8R2Rxg9EKsaLTN057687x4VFR0eHHHPMMXL77bfLZZdd5nRzCCGEEBIPBZienh0VohbJgxhNUq3Ett2JImV1Ir1tIj2tkcd2kd5WkZ62yOu29MLIrNLT7Y2pvweizy5GrOeDXseJFH/x6Ca6V00UOeBqkU2/JfmK48LitNNOk4MPPlj22WefYYVFb2+vWuxTjINQKKQW4PF4xOv1SjgcFgNKPEKy9ViHbcnWW8e1rwfYP0o4LN7Ie8Nxk8f6vB51XPt6aGDvEOsxa6SRaH3YiJmbFr8HmFUyfj08uJ649Tjm2Ipi9Z5QXCMtj29825Otz5c+2dcXTp8MGVNerD5bwrr0SUc7Dd8ntAu2xOfq0icd7TRcn9Ae/Lbar0m390lHO6XSJ+uaNMcCI+jT1N3EmLKzyKqPxdO9TjxlY8XTsIWEUG/K9sEFaaf6zcUbqcSWyA+g3lY+TjxbHSOGxzt0n/BfsEc8vW3i7WuXcLclQCLCo7dN+jpaJBDqUPtIb7spUvo6xJNqWWbs19NiLmlgQKBFBYkpOjwl1WKUVIoRgEekUoziKvEUV4m3rFrCAayvinohon1d/JJ4nrsoui56/LZGkQd+JOHv3C2yyaHZGcMOsd7n86njxh8nb4XF/fffL++9954KhUqFK6+8Ui655JJB6xcvXiwVFebEStXV1dLQ0CCrV6+W1tbW6D51dXVqWblypXR2dkbXT5gwQWpqamTp0qXS1zcQazh58mR1TBzbfqKnT58ufr9fFi5cONCAnlaZVVckwVBYljQNVHzAxTp7YrV09gZlxbqBzwz4fbLh+Epp7eqXVS1d0fXlxUUypa5cTUWPWSMtUDYNFQ6QjIRY3mifKkukrqpEVjZ3SWfvgHqfUFMmNeUBWbqmQ/qCA1+GyWPLVZsWNLaafwStPtVXit/nlYWNA+cLzGqodkWfKkqKZPGqtoLpU3Nnr1p06pOOdkq1T/U1pdLR069Vn3S001B9KvL71PF16pOOdkq1TyHDyKxPnukiZdPNPnl9svhr/s1d0tQhFRudKJPeu1qJiJjBcuRx9aY/lglen7R29qXYpyppqJsgq9d3Sau3T6Q0tk/L13bG9qm6RGr8fbJsZaMSI77+NvH1tcvYQJ8UhzqlZd0aJVR8/e1qfXG4U4kUD3I/UsSDcC1MGonFvj6uzxbWNI4hf5mEiiqVIAmUVStxar0v9jim0Av/9zxZ7J0lEyZOynwMCzvNmiXBYFCWLFky0DavV2bPnq3GzDhOyufAsEucHLJ8+XLZbrvt5Nlnn43mVuyxxx6y1VZbJU3eTuSxmDJlijQ3N0tVVZVzHovWFeLtXpfXdxrwvHF9l/pRiDd4Qd49cWmfsO+K5i6ZWFsWrXbh9j4lansh9AnH+3p9l0wZW64+W4c+6Win4foEMOBrsF2Tbu9TwXosItfkpDFlapCsQ58GrXe6T0teFu+8m8RjC+8xysdJ+Buni2f6N7PSJ7y/cX23NNSUiseWhD/iPgX7Ta9HJBwL4gM3lI2oh6RdPD1t4ulrE0OFb2F7m3hGuepX6IePiXfDb+bEY9HS0iJjxoxRN+yt8XbeeSzeffddaWpqkm222Sa6Difg5ZdflptuukkJCHTITnFxsVriwX7x+1onKZ5018cfN+F6vDeS5ONLIElh3HTW4yJPJG2TlU1LdX1XX1BdTPiRSkSituR7n6JtLJQ+eUS6+4LqPfHbXdsnHe2UYp+6I9ekV6M+6WinodqOwUhXkmvSrX0aar3ufcI1if106lMqbcxZn2bsLjJ915hCAJ4JW4jPlpCcaZ9CYVGeCo+3LGE70+5TUUCkaKxIxdiY9Yn29lhPVLhWdyQ8KxKKZQ/Xin+MPoe3KrX7/b6uNdGxZ0Zj2GHWK3sk2T+vhMXee+8tH39sunosTjjhBNl4443l/PPPT6sThBBCCCHEBRRCIQCPR6SozFwqJ6RXBWrZPJFnfjP8vqgSlYc4JiwqKytlzpw5MevKy8tl7Nixg9YTQgghhBCiveja4BsqkT35vCgeszoUSs/mIYl9J0Q74OFDMlYSTx9xCbSjPtCWekA76gNtqQeut6M3Mi9KQiKdOuCqvJ3PwrHk7WyA5G1UgUolmWRUaVk+qAIAIYQQQggh2ZvHYpIpKnI8j0U6423H57EguQFVEFA2btq4iqSJSyT/oR31gbbUA9pRH2hLPdDGjtMjM96vXSTi83PmbZJfwC2FWtSudU8RBe2oD7SlHtCO+kBb6oFWdvT6RCZvJ1I3U9wCcywIIYQQQgghGUNhQQghhBBCCMkYCosCAWGGmHXbzeGGhHbUCdpSD2hHfaAt9YB2dBbmWBQImDmxoqTI6WaQDKEd9YG21APaUR9oSz2gHZ2FHosCIRQ2ZMHXreqRuBfaUR9oSz2gHfWBttQD2tFZKCyyQHtvUC3BPP8Sh907ZQmxQTvqA22pB7SjPtCWekA7OgdDobJAJ4RFa496HvB7pbTIp5aSgE+KGORHCCGEEEIKAAqLLNMXDKultbtfvS7yeaWkCGLDL6UBrwR8dBIRQgghhBD9oLAYZfpDYbW09wTVa7/XIyURj0ZpwCfF/twIDThOptdXskqCy6Ed9YG21APaUR9oSz2gHZ2FwiLHIA+jozeoFuDzeJTAsMQGhIZnlC4GP70lWkA76gNtqQe0oz7QlnpAOzoHz7zDhAxTaKzt6JXl67vky7UdsrKlR5o7+6S7LyTZyj9CXvnCxlb1SNwL7agPtKUe0I76QFvqAe3oLPRY5Bm4ELr6gtLVZ76G86K4yCdlyqvhlVK/X7yUg4QQQgghJM+gsMhzILh7+kNqiQoNPypOeVUIFYQGPX6EEEIIIcRpKCwyAJOvvLWkWRYubZOyYFDmjPOJb5SzhZTQCIbU0tJlVp5CXoYqb8sSt4QQQgghxCE8huHeWUTa2tqkurpaWltbpaqqKqef/dQnjXLJ4/OlMTJ/Bagr9cip25TIblOcnUoeJW5LoyVufVLk8wjMjDAraA5Md0/cCe2oD7SlHtCO+kBb6oF2dgxUitTNdM14m0E0IxQVp/ztvRhRAdZ2G3Lpa93yynLTk+AUKG/b1hOU1e09snRdpyxZ1ymr2syE8N5g2NG2kcwJhmhDXaAt9YB21AfaUg9oR+egsBhB+BM8FUO5eea+36P2yxeCIUMJjQWNrfJVc5d8uaZTiaL1Xf3S08+Lz03ga7WkqZ3VLjSAttQD2lEfaEs9oB2dhTkWaYKcinhPRTxrugy5+b0eOWhGQDas8Yo3z1xxVolbay4NuAsRNqUSwpGn4feN2lwahBBCCCFETygs0qSpfWhRYfH4on61VAY8smW9T7as98tW430ytQoT4OXXqB2qvrMvKJ22Ercl0RK3ptBgiVtCCCGEEDIUFBZpUl9Zktb+7X2GvLoiqBZQU2wKja3G+2Wrep9Mqsyd0EjVcwLvYXd/SC32uTSUNwNeDZa4dZR884CRkUNb6gHtqA+0pR7Qjs7BqlBpgtyJXa9+Xla19iTNs6gt8cj3NwnIR00h+WhNUNojnoBEjC31yFb1/qjYaKjI/xG7OZeGN1reFoLDzxK3hBBCCCEFXRWKwiKDqlAg0cm7cJfSaMlZCJElrWH5YHVQPoTQaApKl+m8SMj4Mo9sGfFmIHyqvjw7QgNm7g+Gpcg/Oh6SQGQuDdOrwbk0RgvYsbM3KOXF/rwLqSPpQVvqAe2oD7SlHmhnxwCFRcHOYzGuzCOnbD30PBYQGgvXh+WDpqB8uDoon6wJSY8ZcZSQiRVeU2RExMaY0pEJjbBhyNrWbqmrLs2JixBzaZRE59LwSoCxU1lBfX8aW2VWQ/WoT8ZIRhfaUg9oR32gLfVAOzu6TFgwx2KEHDCnQfbddEJk5u2lUhZsS2nmbWzfeKxPLd/fpFj6Q4Z80RxS3gx4NeavC0mfTWh83RFWy3+/NOfGmFJlCg2ETSF8qro4PwfsmEsDS3uP6Z5BqBQm6yv2m14NhFLpcCOBEEIIIYSYUFhkAETCTjPGyvSiZmlv7hrRMTAr9pxxfrUcs1mx9IUM+WxtyPRoNIXks3Uhsc9pt7wtrBZUnALTq73Km7F1vU82r/erKlT5SDBsKJHRLsGYylNKbMCzwYRwQgghhBBXQ2GRZwR8Zo4FFtATNGT+2pC8H8nRgHfDPukL8jeWtPbJIwvMwfrMWm+0tC3ESnmRKTTwv9/nVY/5QHzlKStPo0TlaviV2IBXg8QC+wUwz4jTDSEZQ1vqAe2oD7SlHtCOzsIciyywatlCaW9elZPP6uo35JM1psiAV2PR+nDS2SURlTV7jC+aCL7ZOJ+U+t1zqfk8nkjlKXg0zDAqzqdBCCGEkIIhwBwLMoqUFXlkh4lFagEdfYYqafvhalNofNkyEDcFwfH5upBa7v+sT6ApNhqL/AyIDb9sWudTHpJ8BTOEx0/cB3GBpPCSAq0+hfsArV39Ul1WpEe1iwKGttQD2lEfaEs9oB2dhcLC5VQEPLLzpCK1gNbesJo/w6w6FZKv2gaERtAQ+XRtSC1//7RPiryixAVEBsTGRmN8KucjX4FjpicYUot0mzkmfp8nkgxeGEnhEIurWrqksrRa8thUJAVoSz2gHfWBttQD2tFZKCw0A1WidpuCxRQazd3haMWp91b1yypbjnl/WNQ2LPd8IlLiiwgNlLYd75PZtcNXuXKaYMiQ9hCTwgkhhBBCnIbCIgvUVxZLZbhEuvvC0t0XlB57GSeHwbwXe071yu4b+GVtqyFGUbF8vAZCA4IiKKs6BxI0MJ/Ge6tDagFlflEJ4BAZyNGYUePNe6HBpHBCCCGEEGegsMgCmHCuPOCX8gBeBVRp1e6+kHT1BaWrP6TuqudLlYTqMq/sM80n+0wz16/qiEzWF/FqrO0eaCtmCH+rMagWkV6pKBLZot6cPwNejWnV3pxMtpcpfcGwWtoic2q4OSkcZ7u8uIjVLjSAttQD2lEfaEs9oB2dhVWhskHLcpGutUk394XC0tVr3kWH2EhWxclp8FVY2RGOJoJDbKzvSd7Y6mKPbAGREREbG1Qhv8GTdCZMzDK+rseQsSWYuyN/wqyiSeEBeDYKMymcEEIIIXlIwF1VoSgsciAs4rFCdSA2evpDKnxntIGZu3qDUlbsT7lKAt6zrA0ejZB8GJlHo60veWvHlAwIDXg0JlZ41Ge9srxf/vxeT4w3pK7UI6duUxLNBck3rKRwJTTyKCk8bBjS3N4rYyqLXeEtIsmhLfWAdtQH2lIPtLNjwF3CgqFQDoABK5YxZSLhsEh3MChdfSEVPtU7SvkZGNJ39vRLKYRFiu+BKJha7VPLYbMC6mJd0oJk8KASGx81BaXTLM6kaO4x5MVlQbWAcaUeaajwykdrBvIdLCAyLn2tWy7cRfJSXAyVFK7K3TqUFI7bAGvbe6S2othsFHEttKUe0I76QFvqAe3oLBQWDoPY/nKVn2GawsrPwPwN3XmSn2EB5T+j1qeWIzYyw5sWt4RVbgaEBibu6zbH4Yo13Yas6R4sKuzc/F6P7DTRp2YFd1tSOLwYxZGkcIRRBfK8D4QQQgghowmFRZ7h93qkssSvFgAPBrwZPXmYn4EcCczsjeW7m5iiaEGzWb4WYgPVp1DSdijWdRvyrYc6ZEqVVyZVeGVipVcmVgw8H1vqyVtXJmzTG5cUPlDm1l1J4YQQQgghmUJhkedYd8VFipR7D5PDmR6NkPSmkZ+BoXlpIPUwqJGKok3rMKO3X47etFieWdIn17zZM+z7ID4wY7h91nCLgE+koXyw4MDz+jJP3iSAWzOFd/QGpaN39JLC8e7qsgC9uxpAW+oB7agPtKUe0I7OQmHhInDjPpqfUW7mZ3QFg9IdqTg1VH4G8iUqy1Q93Jwxviy12/VI5Eb1qURRX30hUbOH22cQt4DemgDREREbSnRUeNRzrIfQyZuZwqU/K0nhXq9HGmrLRq/RJGfQlnpAO+oDbakHtKOzUFi4GITZVAT8agH9yM/oDUlXf1B5NRCaZK/w1NHdLxWlRSlXhcoUlJSFaLBXg4pnXJlH/npIhXre1GXIyvawfN1hLtbzxo5wwpAq6KgV7WG1SGPsNmiK8WWeAU9H5BELEsoDPk9eJIWjnfBqpJoUHg4bsrq1W8ZXl6ofT+JeaEs9oB31gbbUA9rRWSgsNAJhNkWlfqkqjc3PgMjo6O1Xs4KXQ1jkqD0IU0JJWVR/SsYpW5dEw5kaKswqUvEgSRy5GJhj4+uI2LA/702QHw5N1dhpSGNnSN6V2B3waXVlnoiHwxZmVWmKjlJ/7n6I0M74pHB4NZCvAQGIRwhI89Gj3CCrW3ukrLhI/H5s96rtyEMx35OzppMMgdxu7eqT+upSp5tCMoB21AfaUg9oR2ehsCiA/IzasiIJhoqltzcoY8oC0oOk4xzNn4FSsigpGz+PBTwVEBWplJqF8Kgvx+KVrcfHboMnBpPuNbZHxIZdfLSH1ezh8aAVa7oMWdOFiQBDCefjsHs47M/LA56ceDWCSayDkr+oGLaqvSdhUrsncr6w2IUJ9lXiw2s+QqPEr6cwIYQQQkgmUFgUCBgwFvk8MqY8oAaRIeRn9Aelpw+hUyHpG6X5MwDEw86T/KMy8zYGzwi3qiv1yub1g0VHa68RE1ZlCY+VHYa0J5nsD/NxNPeEVFWreGqKPTFeDvO56f2oDJiD+ZGSjdnJ0SOEwNnD4NLBEibwjnglIkRsAkQ9T7jeI34KE0IIIaSgobAoEDDgq6ssiQ78EMdfWexXC+gPmfNnJMrPyAYYiG45PrdfNwzya0qweGXTusHb23oNlb9hD6uyFiSTJ6Kl15CW3pDMXzdYdFQUScLqVfB4QJAMJTpSnZ0cRygvGb1wNkuYSJaEiXr0RNbFCROPFeIVESY+tb8U7DVJ3AntqA+0pR7Qjs7iMXBb16WkM8X4qNKyXKRrreiEPT8D82e49ksyQrr6jTgPx4D4GCoZPRlIe4mtXjXwfP66oPz+teRleS/cpTQvZycfDfB3ICo0Io8qbEuFbkU8JTZhguf444H34BHiTQka9dzp3hBCCCEZEqgUqZspbhlv02NRQFUSVjZ3yaQxZSlVSbDnZxi2BOOuXpS1zU1+hpOUFXlkZq1PLfH0BE1PR3z1KggPVLZKdG4wIzlmKceSLje+2yOzx3ilqtgrxV5D2rr6pbo8kLPqXrnEiMwHEgoZkQK9IwdnZ0B0RMTGICFiC+eKCBNL3Kjtoyhe0r0mSX5CO+oDbakHtKOzUFgUCBiwdfb2j0gQYPBUFvCpZWy5RPMzTG9GSPqxooAo8Xtkeo1PLfH0hQxZ1WmWyI0tnWuuH0mEEcKyjn28Uz1HldwyP8LY+qQi4JGKIo9KKMcjXpcXeVSuh7leBu1T7DMHxoUATjVEMRLezVfZJx3xErMO7zREmjt7pao8oCqBmQImdj8znGxUmk7y4LeV5Be0pR7Qjs5CYUHSJlF+RlckZApiA3ecCxXMj7FBlU8t8SB3oakzUjY34uH4qCkoi9LwYmASwfZ+LCMbLGNSwQGhMSA8LFESK0QkVqwEPI7N/zEaie5Oi5dwZKb2VW2JK3zZiXpRrIR5tdLuaYkNI7PEi3pPRPzgSVT4RHKQLOGj1kSEjDoOxQwhhJARQGFBMgbVpqpL/WoBPf1hM2xK5WgUXn5GMjATOCpIIbfC4sPVQfnlC13DvndOnTmTeEe/IW09YekKwWuUfo41in+ZCegjs0qRmpQxsfiwi5SBdaaQsV5nQ5ikmuiuY4iYOSVLbq4oy1KW98QUJhFREhEiMWFjkTdY+8TsH9lo31+93zqGJWys/exhaREPUIE42gghxNVQWBQIuFM5oaZMPY42agbpotj8DEtkwLuBO7UUG+nNTv5/e5WrO/KotYASwSUBnzqHyN3o7DOU4OiIPA68FunsN8vq2vfpjD6mP0TFDOgIzUpWNWs4Aj67x8QSI4nFSXmC7fO+DiaccBHnD+sxZ4pbxAUuxcrSQM4mrEwXy8JK0ETN7eyVaw89M7WIFX5mbrBEjPU7ZwmXqHcmsr/13pj9Ub3MmusljSplufxtJaMLbakHtKOzUFgUCKr0KoLuc/65A/kZIgOfHw6LhMUUGXiOATNSwo2wGeaC4Yu5zVADGzPcxEzKGngfHsXVpDs7OexYGglBU6Vni7B4JG4Kj5TAOezulxhRoh5t4qMD22NemyIFoVjwmKRLX0ikOYR5QkbHcP/3ZresaA9JRZFXSovMJPwyv0dK1aOox3K/R0r8zuea2G2pc7jY6IWeWWtGD/uEkwMTTGKdOeO9NfN9oMin8qvwnPO5uBen/k6S7EI7OguFRYGAQfjSNR0ybVxFXlRJsOYzMG8TZnYsCBOIEvTRGnQoMQLxAgGitkWeR0SKEi9qm3luLDHjBOnMTo42r+/oldqK4owHxhgo4bcX3oPx5em/H+cMM5vHC4+Y130DwiVenCSaFT1TcMy/fNQ37H44c4jcK40KDyTFe5QQKVWP2G6+tj+3tsXvizC1dMmmLeMpxHCx0SCVCScT2VEJEl+kZHKcILHP6eK3Sipzgsm8IN/+TpKRQTs6C4VFgYA/i32alomNipQs/ICEbAIkuiiPihkSojwrUQ+Kuc0MFcH6iEdlBKFeqc5OrgY6IUilgRh4p0DbKgOiErtHAoQJwrEswaFCtoYI6YInYkV7dr7BRkSEdAUNWZeFqwK5J6Z3xC5WbF4SJUJkQIxEvCb9fSGZ0BdS4s56T0mGlbsgKnQJF8sFmXp2El2T5jpD0s0woyBxFp3/ThYStKOzUFgQElfxCrHVmQ7bLSFieUiUVyQS6mVObG0XLmZIF5YdJ/uj4gVzOej8w4iBUlWxSFVxauc61UT3H80JSH2Z18w/6TekO4iwLTwi3weeEvN1l+01QsIyOdfIPWntNaS1F6/SPVJsnzCQVCIkTpwMhHVFtiXwqBT7DDXvyVDMfb9HiVi3h0Xp6NnJVJCYIsMzKHzLF6koRkFSmFiRg9FHtZgv1P/R9ebfq/j3Rv8S2dKt1L5xx7d/be2pWfibFv+NVmvs+ydpc8z+sSuSvjcUCquKe02ouIfcxLjtQ33O0G2I3z6wZtBhjOQrBp+L5J+LfpeU+aWhTlwDhQUhowD+YOMPdzYFCu6s4s5oT0+/1FeUiOGJDfFS3hYrB8Xh8C4nE91/sGlx2oNmlRQf8WCYYsMuQuzrTRFiiRNTtCCJPiJWIs8hNEYKBKblyZERzPI+HGu6DPnOw+1qPhZ1N1zdEcfgMyKsI84/M5HZ9traR82CHnlfZJ+B40TWRZ5H19uONbA+9jPNmdat59Z+A58ZfZ+9HUk/0/Z5tmPp6tnJpSAZ9MHRp7aBU5JmxA+YEh8nyTGTHEdS+iz7+uQDPPxudveHZX1X30Bfkx4ntb5Yn5f0vXED8Oi+toIJ9nH7gDiwDfaTCAOdfv/TAX8Pe4NhaesNDlvK2w0E8YfeRVBYFAj4jZw8tpxVEtwsUHwiht8rM8ZXSnmxP6VwGXv+iT33JGR7bXlMrLyTTEK68inRPe1Eatz5L4K7IMOGRuZ2sYsO03OCEsEDHpSuSNhXb8is7mUXL0rMRPbFtmyjkvLVXCgW+WTp0SMqMkTUeR+Kq+d1y+sr+lWYGjxClleoxBbSpvJ0/PhDWiStPWEpD3hVWJzThQFGQ5DoWAwgHvz+IRG/ubNvxDYshPOUKaN9jnCkmvJix8OFCxUKiwIBP5IVJe64+0ayZ8dM809ick7gMbGEiZVvYqvalUisOJno7vTcLlgQ6pUpYcubEucxSeRR+aotLK+tGF6J1JaYghUiUi3Kjqa3BI+Y70RHVF+HERQWEB7/+yp9VYdLTYWv2ULZ4B2KipFIKJu5LrJf5LlaF9nHysVB4TCn77rmW8jYaP6+QliMlEI5T5mQi3OUqR1JZngMu1/QZbS1tUl1dbW0trZKVVWVcw1pWS7StVbyGQwcFq9qkxkTqnj3xMW4yY72MC4rZMsUH/bwrYHtA/sOH8alw11B9LW5rUfGVJVkbeCI83Ls4x3Dhov99ZCKYc9XNLzOEh82ARK73tzP2sd6n7VfdJ39fRF7R8VM9P0D77W8aAOfbX3mwLEG1sfuZ7XV3nazdLX5vK3XGPIc5ROwUklSoWKvWDawjz1HZ8CzMrBfOtdKspAxiwt3KdVm0JzJNVlI52mk5OocjcZvq1OEwoYs6CgWo2qy1FeWyA7Txzjyty6d8TY9FgXEQO134mbcYsf4MK6RCpOo+LAESCSPZI+KOLESWY+k93wL48qVLbMZLqYqEA2ym7v/SKdbCOCXO5TI1GqfGZJmhbNFnw+Et7V0BSXk8SrPkn19NkLZrIkwcSzJ0vwvmFbIqkwWE9Zlq1oGr0mxT+TBz4cu33zDOz3K2xKdcd3+LbEmKoxfH/l9kASvE33DBrZ5hjx+zGfFHz+Fz8GvRkt7WNqNUMyA1H6sRH3EdTxc0YSb3u2RGTVmda/4z45pU5JzlrANMfsOzFo/6P3J7OAZyeckXjdc6Bh+p+GpyFVhCbf8nUzNu9OO7C+1rqG6RC46dFM5YE6D5Cv0WBSQx2JhY6vMaqh23Z1dMgDtmDox+SW2qlz5kl+Cz1jb2i111aVZv6uWKNwg38LFnCTbnp2h7GgmkkYESSSUzRQcAzk49vVWuNvA+kgxAdtzt08MSvQmkUixJ6kPRVXA9K5FizLEFIeILeaQeB2KDxjSHwxJWbFf/HHFHXxJC1YMLvgwuAjE0MUk4gtJ+JKsw/uGE2LJvDvWu+Yeu01OxQU9FoSQgke3/JLRmBelUBnNQgDxeO2FAbIAvnuoOhYtoRwvUuIKBVhek2hVs8hzu2clkypmhMQzqAxtGrT1YUlQv3ZE9Eu+4o0XSPYqfGLI2iTOHWu+nEseny/7bjohL3/TKSwKBHz3ptdXsiqUy6Ed3TOniRnrHx7IL7HlmqjXobCUFfnUwBPjuqhYyVKZYPzB2XI8f+JHuxAAvh1jKktyFiSmElN9CGfySE2WjomZxe2Cw/KazF8blHs+GX4m+72m+tXcMakOLuO3DV3HP26fuHKr6XzOsMeP5APZ7yYnLXNr29bcHZb3Vg9fFWDLeq/UFHuT9it5X4foS8LzaCQoZZv8ffH9SeRdiJ0DY3D7oo9x5XWtR0x4urJj+F+26oD52xWT02XLndKFcCSPbEDUp9457NnY2iNvLWmWnWaMlXyDf3UKCD9GSsT10I5uEibqv4RASOAPSyK3ePz8JQMJ8LFipRDmL3GDZ2fQ/A4uw+/1SGVApDIQ248t633yn8X9w4aMnbdjaV7eOU0XayI3dTshjfDEVEPrrt6jXIvz5FT4ofWbOVCQIXExh2DEw6x+JwUiJUlBCVvRiWhBiZh1CYpHDBI7gz8/FA2rTVDgItE6exEKeCNVifLhz2lT+9A5K05BYVEg4IKIxuYX5u+aFtCOhWHLTBPf7fklkmJYQkpyZNhjZPYZqQiiYcMrRqCqJtUM3KW1BpfWzMA4hdbEY9Ft1p1aVZo3LI3NXTKhtlQNRg0rcTQycZmVROpGsZfLkLF8ADay8mXS6VGhnaeRkI1zpCZxHPSTmDivSdmxNvv5a/lUXKK+skTyEQoLQgjRjEzzS0jq4A5mV3efTK4tGz7RO2yFkkSESlSwxK3DPpEQCSVs4sSOXbzYCw0MHMsmbqzjWceJ+Sx95o5xGp6n4eE5Sg14TTG3RzLvDn5lJlSbpWfzEQoLQgghJEeCL3kBVmcwvSp2ARIrdvDvezUlcuRmFfJuY7+s6QzLuDKvbDOhKHo3eEDYJPbORKWPPU4/LldiULx+smMlyZdI+L64ZID4uP/4NkD4qepAqrIQvE8DuQqx/UkMiyYMD89RZt4d6yyh5Gy+njMKC0IIIaRAgTYwQ/GGFzt7TS/S3vvU3xeU6XWp5ULYQ+LUY2T9zPqIJLEJF/u+lmCzdrF7leyqJT48zzqW9caoJ8rmyTLfF+vFim9jdHvkv/jk8OjxbN6taJskc1hYYuTeHXgqOI/FKMJ5LFJnqERR4h5oR32gLfWAdtQH2jI1LI9WvDCKht4NEkcDwie6r01ImRWSzC1WXlJ8TpO1D16E40MB48L6RpqEn4+EOPM2yWeCobAEMFsMcTW0oz7QlnpAO+oDbTk8GKvHjtcTzX2eW6LiQ02EakhPf0iKIhUUVcU8m4gZECzmevPRnotkFyxx+6SYm5QtICK2biiRKbMmiVugsCgQcGEsaWpnNSGXQzvqA22pB7SjPtCW7g/pw9xDmFHk6+ZO046jdHffLGUb52GxXos5mWp0m82LYu074JWxFWCweWPwfnthBjdBYUEIIYQQQki6lfcUo6tCjUCZuAn6+wghhBBCCMlDPHlQPS4dKCwKCDdOFEMGQzvqA22pB7SjPtCWekA7FqiwuPLKK2X77beXyspKqa+vl8MPP1y++OILJ5ukLYgznD1x9OINSW6gHfWBttQD2lEfaEs9oB0LWFi89NJLctppp8m8efPk2Weflf7+ftlvv/2ks7PTyWZpCRKHOnr6o5MHEXdCO+oDbakHtKM+0JZ6QDsWsLB46qmn5Pjjj5fNNttMttxyS7n77rtl2bJl8u677zrZLG2rXaxY12mWXCOuhXbUB9pSD2hHfaAt9YB2dJa8qgqFiTfAmDFjEm7v7e1Vi33CDhAKhdQikclQvF6vhMPhGLWabD3WYVuy9dZx7esB9o8SDos38t74LzJccVat5Ghb1HGSr7fKkA1aH44tPYYQQsQRxq+3Jvexr8ckK1b/8DymTxFvYXzbk63Plz7Z1xdKn6xa2vZtbu9TorYXQp/wPjXJk62eutv7pKOdhusTwHr78d3eJx3tlEqfrGsSC8qW6tCnQesLoE/W7yrWiw59CofFg3HmaI1hh1jv8/nM37e447hCWKAzZ511luyyyy4yZ86cpDkZl1xyyaD1ixcvloqKCvUcMwM2NDTI6tWro0IF1NXVqWXlypUxoVYTJkyQmpoaWbp0qfT19UXXT548WR0Tx7af6OnTp4vf75eFCxcONKCnVWbVFamJdVAD2wIXK+L8OnuDSj1bBPw+2XB8pbR29cuqlq7o+vLiIplSVy7N7b2ytr0nur66LCANtWWyurVbWrsG2lhXWSJ1VSWysrlLOnv7B/pUUyY15QFZuqZD+oLmlwFf6GDI/NItXtUWvfBUn+orxe/zysLGgfMFUAM6n/uk7DS2XCpKigqmT/ihwOcukrboj5vb+6SjnVLpE67J9R3m5+vSJx3tNFyfpo6rkN7+sCxaNXBNur1POtoplT7hmsTzprIemTRGjz7paKfh+lRbUawev17fJd19Qff3ydcjdd4xozeGRZ9mzZJgMChLliwZ6JPXK7Nnz1afh+OkisfIkyC0U045RZ588kl59dVX1QlJ1WMxZcoUaW5ujk4x7ojHonWFeLvX5fWdBjxftrZDXSjxBs8bVa7p3ZNs9gn7LlnTIVPrKtRn6dCnRG0vhD7heF+t7ZANlWAULfqko52G6xPAAGMD2zXp9j7paKdU+mRdk9PGVagBpQ59GrS+APqE9y9b2ykbjC0Xjy2B27V9ClSIp26mox6LlpYWFU2EG/bWeDuvhcXpp58ujz76qLz88stKTaUKhAU8FKl0dFRpWS7Stda5zyeEEEIIIfoRqBSpm+loE9IZbzuavA1NA1Hx8MMPy/PPP5+WqCDpoRRnZx+rJLgc2lEfaEs9oB31gbbUA9rRWRwVFig1+7e//U3+8Y9/qLksVq1apZbu7m4nm6UlcMEhvi/edUfcBe2oD7SlHtCO+kBb6gHtWMDCYu7cucqtsscee6iEa2v55z//Ka6ibIxIsYOhWIQQQgghhDiMo1WhtHFTBcpFxs4Q6esS6WwS6W5B75xuFSGEEEIIITkjb8rNakGgTCQwTaSyzxQYXetEjNgMe6fwRMqbDdRHIG6EdtQH2lIPaEd9oC31gHZ0lryoCjVS8qYqVDJCQbNaVOcakfBALWVCCCGEEEKGhVWhSBSfX6Rygkj9ZiLVU0R85qQtToBayWvbMAGQa3UkoR21grbUA9pRH2hLPaAdnYXCIhdg4pHyOpHxm4rUThMpKs95E3B9YXZHXmfuhnbUB9pSD2hHfaAt9YB2dBbmWOSa0lpz6W0X6WgS6W1zukWEEEIIIYRkDIWFUxRXmkt/tykwutezkhQhhBBCCHEtDIVymqJSkdqpIvWbipTXi3h8o/IxqI5QXRZglQSXQzvqA22pB7SjPtCWekA7Ogs9FvmCPyBSPclM9u60Kkn1Z+3wXq9HGmrLsnY84gy0oz7QlnpAO+oDbakHtKOz0GORb3h9IpXjRcajktQGIv6SrBw2HDakcX2XeiTuhXbUB9pSD2hHfaAt9YB2dBYKi3zF4xEpHytSv4lI7XSRQEVGh8Pl1drVxywOl0M76gNtqQe0oz7QlnpAOzoLQ6HcQGmNufR2mDN697Q63SJCCCGEEEJioLBwE8UV5tLfYwqMrmZWkiKEEEIIIXkBQ6HcSFGJSM0GZh5GxfiUKkkhsqquskQ9EvdCO+oDbakHtKM+0JZ6QDs6Cz0WbsZXJFI10RQXXevM+TCSVJLyejxSV5WdRHDiHLSjPtCWekA76gNtqQe0o7PQY6FLJamKetODUTNVxF86aBdUR1i+tpNVElwO7agPtKUe0I76QFvqAe3oLPRY6AT8fmVjzAUJ3vBg9HWoTbi8Onv7mZHhcmhHfaAt9YB21AfaUg9oR2ehsNCVkmpz6esU6Vgt0tXidIsIIYQQQojGMBRKdwLlImM2FBm3iUhRmYiHJieEEEIIIdmHHosCwRsokQnTNhJvRblI11qRzjUiRsjpZpE08XpEJtSUqUfibmhLPaAd9YG21APa0VkoLAoEj8cjNTU15ouqhoFKUpgPI9TndPNIOnYsDzjdDJIFaEs9oB31gbbUA9rRWRgXUyCEw2H58ssv1aPC6xWpGCdSv2nSSlIk/0CViy9Xt7PahQbQlnpAO+oDbakHtKOz0GNRIBiGIX19feoxeSWptkglqXanmkmGAdbrC4ZY7UIDaEs9oB31gbbUA9rRWSgsyAAlVebS12VWkkLJWl6ahBBCCCEkBSgsyGACZSJjposEe00PRneziBEJoSKEEEIIISQBFBYFgtfrlcmTJ6vHlPEXi9RMEalsGKgkFQ6OZjPJMKDKxeSx5ax2oQG0pR7QjvpAW+oB7egsFBYFVCWhoqJiZG/2+UUqJ4iU15veC3gxQr3ZbiJJ1Y4lRU43g2QB2lIPaEd9oC31gHZ0FlaFKhBCoZAsWLBAPY4YeDvK60TqNxGpnWZOuEdySihsyIKvW9UjcTe0pR7QjvpAW+oB7egs9FgUENFSs5mCSlKltebS2256MHrbsnNsMizh+MpexLXQlnpAO+oDbakHtKNzUFiQzCiuNJf+brOSVHcLK0kRQgghhBQgFBYkOxSVmuFRlX3mbN6Y1ZuVpAghhBBCCgYKiwIB1aCmT5+eXlWokeAPiFRPNitJoYpU51qRcP/ofmYBgSoX0+srWe1CA2hLPaAd9YG21APa0VkoLAoIvz+H5vb6WElqlPD7WHNBF2hLPaAd9YG21APa0Tl45gsocXvhwoXZS+BOt5LU+E1FaqebQqN0jEhxlVlVyhdANnhu2+RiUORiYWOreiTuhrbUA9pRH2hLPaAdnYUeC5I7SmvMJRHhkDn5XqjffLSW6Gtst20jhBBCCCF5BYUFyQ8QOoUFs30PB8rIDRIeSV5jYRI5IYQQQsioQ2FB3Afm0fAVmQuqUQ3HIG+Izfuh1tEbQgghhBCSKR7DcO8sIm1tbVJdXS2tra1SVVXldHPyGpgZ+RWoCoXp7kk63pAhhEiOvSHKjoZZ9YJ2dDe0pR7QjvpAW+qBdnYMVIrUzXTNeJseiwIiGAxKIIBkaZI9b0g4gegI2tYFY19ngWAoLAE/6y7oAG2pB7SjPtCWekA7OgeFRYEAb8WSJUtk1qxZ4vP5nG6OPqDqlRd5ISnkhgAlNIYIw8J2I2R6QtRiRLwipmMRd2GWNLXLrIZq8WlwI6aQoS31gHbUB9pSD2hHZ6GwICSX+PzmMhLgHQn2i7QtEhm3oenntQQIhEeMGDESrMtgX0IIIYSQYaCwIMRN3hGIElU9KyCSS8+TXWzYvSgJxYhkd9+It4YQQggh+Q2FRQGBxG3ifhyxI3JPPBAyDoTRWV6VpB6XcBKhkmSfVPbLkZjx6pBYSGhHjaAt9YB2dA5WhSKEkOHETEJRE87ufoQQQkg8rApF8hHox87OTikvL9ej/FqBQjvm0kOD8+vNnS3DiQRJIo+LkeZ+rr135DBx5y3mPBqxduzpk/LiIts1OdR7R2E7yQrKlr1BKS/28/fVxdCOzkJhUUBVoVasWMGqUC6HdtTYltEQN9rVTYRDIVmxcKHMmuzgNWnkUMjY855G9DzuONF1o/E8vTaGw4asaO6QWRNrxKeKYyRoY14QN1iOGTx7Rnn9SN+TZL11bu3nOurNtZ//uG3DVIVasa6TVaEcgsKCEEIIcTPxgzXepR0ZoZBI60KR8bOGLo4RraanXiR/ns1BO20aizGE8FB2/FJk7DSzemJCwTICMRMvNNN9X4FAYUEIIYQQknaoJMlLG3hD5iS3gbLcVk9MVwwZKYoSr7uG6u5qLRkxiDPErNuMN3Q3tKM+0JZ6QDvqA22pB3lrR09hCFJWhSKEEEIIIYRkPN7mxAYFAvRjS0uLeiTuhXbUB9pSD2hHfaAt9YB2dBYKiwKqQLNq1Sr1SNwL7agPtKUe0I76QFvqAe3oLBQWhBBCCCGEkIyhsCCEEEIIIYRkDIVFgYDqCJyt2f3QjvpAW+oB7agPtKUe0I7OwqpQhBBCCCGEkISwKhQZBJKY1q5dy2Qml0M76gNtqQe0oz7QlnpAOzoLhUWBAMcULjQXO6gI7agVtKUe0I76QFvqAe3oLBQWhBBCCCGEkIyhsCCEEEIIIYRkDIVFgYDqCEi8YZUEd0M76gNtqQe0oz7QlnpAOzoLq0IRQgghhBBCEsKqUGQQqI7Q2NjIKgkuh3bUB9pSD2hHfaAt9YB2dBYKiwIBjikoTRc7qAjtqBW0pR7QjvpAW+oB7egsFBaEEEIIIYSQjPGLi7HUKGK/yNCEQiHp6OhQ58rn8zndHDJCaEd9oC31gHbUB9pSD2jH7GONs1PxArlaWLS3t6vHKVOmON0UQgghhBBCtAXjbiRxa1sVCok5X3/9tVRWVrKsWApqEwJs+fLlrKDlYmhHfaAt9YB21AfaUg9ox+wDqQBRMXHiRPF6vfp6LNC5yZMnO90MV4GLjBea+6Ed9YG21APaUR9oSz2gHbPLcJ4KCyZvE0IIIYQQQjKGwoIQQgghhBCSMRQWBUJxcbFcdNFF6pG4F9pRH2hLPaAd9YG21APa0VlcnbxNCCGEEEIIyQ/osSCEEEIIIYRkDIUFIYQQQgghJGMoLAghhBBCCCEZQ2HhYl5++WU59NBD1YQlmCDwkUceidmO9JkLL7xQGhoapLS0VPbZZx9ZuHBhzD7Nzc1yzDHHqFrPNTU1ctJJJ0lHR0eOe1LYXHnllbL99turiR7r6+vl8MMPly+++CJmn56eHjnttNNk7NixUlFRIUceeaSsXr06Zp9ly5bJwQcfLGVlZeo45557rgSDwRz3prCZO3eubLHFFtH66TvttJM8+eST0e20ozu56qqr1G/sWWedFV1HW7qDiy++WNnOvmy88cbR7bSje1i5cqUce+yxylYY02y++ebyzjvvRLdzzJMfUFi4mM7OTtlyyy3l5ptvTrj9D3/4g/zpT3+SW265Rd58800pLy+X/fffX/2QWuAC+/TTT+XZZ5+VJ554QomVn/70pznsBXnppZfUH7Z58+YpO/T398t+++2n7Gtx9tlny+OPPy4PPvig2h8zzh9xxBHR7aFQSP3h6+vrk9dff13uueceufvuu9WPLMkdmLATg9B3331X/cHba6+95LDDDlPXGKAd3cfbb78tt956qxKMdmhL97DZZptJY2NjdHn11Vej22hHd7B+/XrZZZddpKioSN2smT9/vlx77bVSW1sb3YdjnjwBVaGI+4EpH3744ejrcDhsTJgwwbjmmmui61paWozi4mLjvvvuU6/nz5+v3vf2229H93nyyScNj8djrFy5Msc9IBZNTU3KLi+99FLUbkVFRcaDDz4Y3eezzz5T+7zxxhvq9X//+1/D6/Uaq1atiu4zd+5co6qqyujt7XWgF8SitrbWuOOOO2hHF9Le3m7MmjXLePbZZ43dd9/dOPPMM9V62tI9XHTRRcaWW26ZcBvt6B7OP/98Y9ddd026nWOe/IEeC01ZsmSJrFq1SrkC7dOx77jjjvLGG2+o13iEK3C77baL7oP9vV6vUvvEGVpbW9XjmDFj1CPufsOLYbclXPkbbLBBjC3hFh4/fnx0H9ypaWtri94tJ7kFdzrvv/9+5XlCSBTt6D7gScTdarvNAG3pLhAOg5DhDTfcUN2xRmgToB3dw2OPPabGKkcddZQKR9t6663l9ttvj27nmCd/oLDQFFxgwP5jaL22tuERF6gdv9+vBrTWPiS3hMNhFccNl++cOXPUOtgiEAioH8ShbJnI1tY2kjs+/vhjFauNyZlOPvlkefjhh2XTTTelHV0GROF7772ncqDioS3dAwaWCF166qmnVA4UBqC77babtLe3044u4ssvv1T2mzVrljz99NNyyimnyBlnnKFC0wDHPPmD3+kGEEJi75B+8sknMTHAxF1stNFG8sEHHyjP07/+9S857rjjVOw2cQ/Lly+XM888U8Vhl5SUON0ckgEHHnhg9DnyZCA0pk6dKg888IBK8CXuuekGT8MVV1yhXsNjgb+VyKfAbyzJH+ix0JQJEyaox/jqFnhtbcNjU1NTzHZUukDVBGsfkjtOP/10lUz2wgsvqCRgC9gCiYMtLS1D2jKRra1tJHfgDujMmTNl2223VXe7UWDhhhtuoB1dBEJk8Nu4zTbbqDuaWCAOkRiK57gLSlu6E3gnZs+eLYsWLeI16SJQ6QmeXzubbLJJNKyNY578gcJCU6ZPn64ulOeeey66DjGhiCNEvDfAI35Q8UfU4vnnn1d3BnBXh+QG5N5DVCBkBucftrODASoqYdhtiXK0+EG12xIhOPYfTdxtRUm9+B9jkltwPfX29tKOLmLvvfdWdoDnyVpwtxTx+dZz2tKdoLTo4sWL1UCV16R7QHhwfBn2BQsWKO8T4Jgnj3A6e5xkVrHk/fffVwtM+cc//lE9/+qrr9T2q666yqipqTEeffRR46OPPjIOO+wwY/r06UZ3d3f0GAcccICx9dZbG2+++abx6quvqgooRx99tIO9KjxOOeUUo7q62njxxReNxsbG6NLV1RXd5+STTzY22GAD4/nnnzfeeecdY6eddlKLRTAYNObMmWPst99+xgcffGA89dRTxrhx44wLLrjAoV4VJr/61a9UNa8lS5aoaw6vUXHkmWeeUdtpR/dirwoFaEt3cM4556jfVlyTr732mrHPPvsYdXV1qvoeoB3dwVtvvWX4/X7j8ssvNxYuXGj8/e9/N8rKyoy//e1v0X045skPKCxczAsvvKAERfxy3HHHRcuv/e53vzPGjx+vSq7tvffexhdffBFzjHXr1qmLqqKiQpXPO+GEE5RgIbkjkQ2x3HXXXdF98MN46qmnqtKl+DH99re/rcSHnaVLlxoHHnigUVpaqv5w4g9qf3+/Az0qXE488URj6tSpRiAQUIMPXHOWqAC0oz7CgrZ0B9/73veMhoYGdU1OmjRJvV60aFF0O+3oHh5//HEl8jCe2XjjjY3bbrstZjvHPPmBB/857TUhhBBCCCGEuBvmWBBCCCGEEEIyhsKCEEIIIYQQkjEUFoQQQgghhJCMobAghBBCCCGEZAyFBSGEEEIIISRjKCwIIYQQQgghGUNhQQghhBBCCMkYCgtCCMlD+vr65IorrpDPPvvM6aaQHNPT0yOXXXaZfPzxx043hRBC0oLCghDiOqZNmybXX3999LXH45FHHnlEPV+6dKl6/cEHH2T1M++++26pqanJ6BjHH3+8HH744Snte84556iB5cYbb5zy8ffYYw8566yzxAniz/uLL76oXre0tGTt/GXr+5LvXHjhhfL666/LD3/4QyUwCSHELVBYEEJyDgacQy0XX3zxkO9/++235ac//am4HQiNRH194IEH5NNPP5V77rlHnY98I5FAmjJlijQ2NsqcOXMk34HQgPDJR/Hy1ltvyZtvvimPPfaYfPe73x32WnADoyX2CSH5h9/pBhBCCg8MQC3++c9/qju0X3zxRXRdRUXFkO8fN26c6AwGlFjchM/nkwkTJjjdjLwmFAqpAbbXm/ye3g477CAvvfSSev7rX/86h60jhJDMoceCEJJzMAC1lurqajXYsl53dnbKMcccI+PHj1cCY/vtt5f//e9/Gd0d/uSTT+TAAw9Ux8NxEWKydu3aId+D0J0NNthAysrK5Nvf/rasW7du0D6PPvqobLPNNlJSUiIbbrihXHLJJRIMBmWk9Pb2yi9/+UuZNGmSlJeXy4477jjozvprr72mQp7QrtraWtl///1l/fr10e3hcFjOO+88GTNmjDqf8Xe8//jHP8rmm2+ujg8vw6mnniodHR0x/UbI0tNPPy2bbLKJOmcHHHBAVAziePCkoO+WhwltHMld6blz58qMGTMkEAjIRhttJH/9619jtuN4d9xxhzr/6O+sWbPUnfyhaGpqkkMPPVRKS0tl+vTp8ve//33YdixfvlwJOfQb5+2www5T/Yn30Pzf//2fNDQ0yNixY+W0006T/v5+tR32+Oqrr+Tss8+OnhP7uUSbN910UykuLpZly5Ypj9u+++4rdXV16vu/++67y3vvvTeo7/Hhff/+979lzz33VOdiyy23lDfeeCPmPa+++qrstttuqu+w7RlnnKGuJ/t1g9yNH/3oR8quU6dOVW1bs2aN6jPWbbHFFvLOO++kfVzkA5144olSWVmprpvbbrstuh12AFtvvbXqB84XIURPKCwIIXkFBrkHHXSQPPfcc/L++++rQS0GihiQjQTE+O+1115qUIMB01NPPSWrV68e0iOAUJSTTjpJTj/9dDVQxmAOAzI7r7zyihqgnXnmmTJ//ny59dZb1UDy8ssvl5GCz8Ng8f7775ePPvpIjjrqKNX/hQsXqu1oy957760GqdgPAz6cG9wJt8CgH6IBffjDH/4gl156qTz77LPR7bhb/qc//SkaavX8888rIWKnq6tLDaIx0H/55ZfVuYfgAXjEubPEBpadd9457b4+/PDD6twhlwTC72c/+5mccMIJ8sILL8TsB7GGz8P5wPcCorO5uTnpcSECIBRwnH/961/y5z//WYmNZEAcQJxhQAybQrhZYsqe34DjLV68WD3ivMHWWAAG/JMnT1bn2jon9nN59dVXK4GEc15fXy/t7e1y3HHHKfvNmzdPCSb0DeuH4je/+Y06//gezJ49W44++uiokEXb0OYjjzxSnSt4AnF8fKfsXHfddbLLLruoa+vggw9WIhvf42OPPVaJGwg9vDYMI63jXnvttbLddtup40KsnnLKKVEvJMK7AG4Q4NzgfBFCNMUghBAHueuuu4zq6uoh99lss82MG2+8Mfp66tSpxnXXXRd9jZ+yhx9+WD1fsmSJev3++++r17///e+N/fbbL+Z4y5cvV/t88cUXCT/v6KOPNg466KCYdd/73vdi2rn33nsbV1xxRcw+f/3rX42Ghoak/TjuuOOMww47LOG2r776yvD5fMbKlStj1uNzLrjggmi7dtlll6TH33333Y1dd901Zt32229vnH/++Unf8+CDDxpjx46NsQfOzaJFi6Lrbr75ZmP8+PFD9iP+vL/wwgvq9fr166PHtZ+/nXfe2fjJT34Sc4yjjjoq5rzj/b/97W+jrzs6OtS6J598MmFfYE9sf+utt6LrPvvsM7XO/n2Jt9lGG21khMPh6Lre3l6jtLTUePrpp6P9xXcuGAzGtBXfiWTfSfu5/OCDD4yhCIVCRmVlpfH4448P+Z2+4447ots//fRTtQ79AyeddJLx05/+NOa4r7zyiuH1eo3u7u5oG4899tjo9sbGRnWM3/3ud9F1b7zxhlqHbSM9Ls5lfX29MXfu3ITfDUKIvtBjQQjJO48F7soiDAdhJLh7jJKrI/VYfPjhh+ouM45jLValJdyNTQQ+D2FIdnbaaadBx8Udavtxf/KTn6g7srhLnS6oAAXPA+5E24+JeHurnZbHYigQymIHoTv2O/a4a4xjINwKd+lxxxphXvY2I9QGd66THSMb4BzjzrkdvI4vr2vvDzwxVVVVSduC9/r9ftl2222j62DroapRwY6LFi1S58I65wiHQslX+/djs802U3kk6Z4ThHnF2wQeM3xX4KlAKBT6hO/9cN9x+3Hw+cBqA/oBD4r9uwNPDELjlixZkvAYCAsECI2LX5fJca3Qxmx/Zwgh+Q+TtwkheQVEBUJ3EIozc+ZMFdf9ne98Z8RlNzFgQ7gQwlHisQZnIz0uwnSOOOKIQduQczGS42Hg+u6778YMYO3J7DgXw1FUVBTzGoM8DAKtWP1DDjlEhakgZAsDaIS1IOwL5xeCItkxrNCYXDNUf7IBzjuESKJcDHuRgJG2AzaLr+yFMCiIuRtuuEHlOSD3AsJ1uO+4vQ3WMa02oB8IJ0P+QzzIeRjqGNk+7mjYiRDiDigsCCF5BWLcESePhF1rYGNPpE0XJFc/9NBDKsEUd7NTAd4S5CjYQSx8/HERQw7xkw2QAwKPBe7yIlE2EbgrjNwTCJqRANGCwR7i4a3KRChtmy64C2/P6xgJOMewNQbZFniN/JGRAu8Ecg7QTyT9A9jImksjEbAj8gaQ+wDPwUhJ55ygn8j9QF4FQE7IcMUEhgP9QK5Ptr6P2Twuzg3I9DtDCMl/GApFCMkrEB6C5E6E/SAM4wc/+EFGdz5RvQfJvkh0RTUehLeg4hEShZMNdHB3Fkne8Jogcfqmm25Sr+2gRO69996rBvlIykUYDpKuf/vb346onQiBQmIyEmfRf4SZIOn1yiuvlP/85z9qnwsuuED1AcmxSKT9/PPPVWWlVAelGBwiWfnGG2+UL7/8UiVn33LLLWm3FSINn49BOz7bqo6UDueee64KsUH7cY5RrQr9tpLERwIqSyHRGHfYIQwhMH784x8P6enBOUd1JlRFQvI2zjuqXOE7sGLFirTOCRLdV65cOaw98B3Hucd3Bu1EG1LxRg3F+eefrybVswoO4Jyicld8krUTx4VoQ/+swgmtra0ZtYkQkr9QWBBC8goMMFFGFZWGEMKEeG7cNR0pEydOVHeIISL2228/FU+O2akRd59sPoFvfOMbcvvtt6tQFZT1fOaZZwYJBrTriSeeUNtwdxzvQcUdhLaMlLvuuksJC1RKwiAZJU4hJKyQE4gPfB4EF+Y7QPgMBnmpemLQF5xfhIVhIjuE/0C4pAvyA9A+VAFCuBDOb7qgbzi/EG/IX0BVLfQ/01KkOAZsjhKuCFPDRIoY2CYD4V8QBDjH2B+eFISGIcciHQ8G8m3gWUNuynDzrNx5552qRDC+18hxgYgZqo2pAG8W8nEWLFigPF7wgEH84lw4fVx8P1GJDDbG+yDiCCF64kEGt9ONIIQQQgghhLgbeiwIIYQQQgghGUNhQQghhBBCCMkYCgtCCCGEEEJIxlBYEEIIIYQQQjKGwoIQQgghhBCSMRQWhBBCCCGEkIyhsCCEEEIIIYRkDIUFIYQQQgghJGMoLAghhBBCCCEZQ2FBCCGEEEIIyRgKC0IIIYQQQkjGUFgQQgghhBBCJFP+H99r2tzcAHbsAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[np.float64(8.142660153833225),\n",
+       " np.float64(7.637385552703454),\n",
+       " np.float64(7.494284341019172)]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "\n",
+    "from scripts.rf import plot_learning_curve_rmse, plot_rmse\n",
     "plot_learning_curve_rmse(best_rf, X, y)\n",
     "plot_rmse(best_rf, X_train, X_test, y_train, y_test)\n",
     "\n"
diff --git a/scripts/rf.py b/scripts/rf.py
index 58acd6d..ea07ffa 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -13,7 +13,7 @@ def random_forest_GS(X_train, y_train, X_test, y_test):
 
     # Grille des hyperparamètres à tester
     param_grid = {
-        "n_estimators": [5, 10, 60],              
+        "n_estimators": [5, 50, 100],              
         "max_depth": [None, 10, 20, 50],         
         "min_samples_split": [2, 5, 10],         
         "min_samples_leaf": [1, 2, 4],           

From 60532445238a748dd6016f4ae92339f308fb2eed Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Sat, 30 Aug 2025 19:05:20 +0200
Subject: [PATCH 10/12] update date date date date

---
 notebooks/project_starter.ipynb | 4781 +++++++++++++++++++++++++------
 scripts/rf.py                   |   54 +-
 2 files changed, 4003 insertions(+), 832 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index 28f8f99..0fe66a9 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -53,9 +53,9 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_32200\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12412\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_32200\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12412\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n"
      ]
     }
@@ -70,153 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_train.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 2. Pre-processing "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.1 Preliminary data curation (of the train and test df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:66: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n",
-      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:67: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "### taking out lines without nutriscore because it's non informative (both fot the train and the test df)\n",
-    "filtered_df, filtered_df_test = dfj.filter_nutriscore_data(df_train, df_test)\n",
-    "\n",
-    "### Cleaning the variables \n",
-    "cat_df, cat_df_test = dfj.categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n",
-    "num_df, num_df_test = dfj.numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n",
-    "final_df, final_df_test = dfj.final_df(cat_df, num_df, cat_df_test, num_df_test)\n",
-    "\n",
-    "### Droping some additionnal columns \n",
-    "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
-    "final_df_test = final_df_test.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
-    "\n",
-    "final_df.columns.equals(final_df_test.columns) #checking if we have the same variables in each df "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.2 Enconding "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "              pnns_groups_1      PNNS_pro\n",
-      "6                   unknown            NA\n",
-      "9                   unknown            NA\n",
-      "11    Fruits and vegetables   Plant_based\n",
-      "12                  unknown            NA\n",
-      "14          Composite foods     Processed\n",
-      "...                     ...           ...\n",
-      "4926   Cereals and potatoes   Plant_based\n",
-      "4935         Fish Meat Eggs  Animal_based\n",
-      "4936                unknown            NA\n",
-      "4943        Composite foods     Processed\n",
-      "4987          Sugary snacks        Snacks\n",
-      "\n",
-      "[437 rows x 2 columns]\n",
-      "                pnns_groups_1      PNNS_pro\n",
-      "0        Cereals and potatoes   Plant_based\n",
-      "7              Fish Meat Eggs  Animal_based\n",
-      "42      Fruits and vegetables   Plant_based\n",
-      "45                    unknown            NA\n",
-      "47                    unknown            NA\n",
-      "...                       ...           ...\n",
-      "4952            Sugary snacks        Snacks\n",
-      "4966          Composite foods     Processed\n",
-      "4975  Milk and dairy products  Animal_based\n",
-      "4981            Sugary snacks        Snacks\n",
-      "4990            Sugary snacks        Snacks\n",
-      "\n",
-      "[772 rows x 2 columns]\n"
-     ]
-    }
-   ],
-   "source": [
-    "pnns_mapping = {\"unknown\" : 'NA', \n",
-    "                'Beverages' : 'Drinks',\n",
-    "                'nan' : 'NA',\n",
-    "                'Fruits and vegetables' : 'Plant_based',\n",
-    "                'Composite foods' : 'Processed',\n",
-    "                'Sugary snacks' : 'Snacks',\n",
-    "                'Salty snacks' : 'Snacks',\n",
-    "                'Cereals and potatoes' : 'Plant_based',\n",
-    "                'Fat and sauces' : 'Processed', \n",
-    "                'Milk and dairy products' : 'Animal_based',\n",
-    "                'Fish Meat Eggs' : 'Animal_based',\n",
-    "                'Alcoholic beverages' : 'Drinks'\n",
-    "}\n",
-    "\n",
-    "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
-    "final_df_test['PNNS_pro'] = final_df_test['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
-    "print(final_df[['pnns_groups_1', 'PNNS_pro']])\n",
-    "print(final_df_test[['pnns_groups_1', 'PNNS_pro']])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -229,124 +83,3616 @@
          "type": "integer"
         },
         {
-         "name": "categories",
+         "name": "code",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "url",
          "rawType": "object",
          "type": "string"
         },
         {
-         "name": "pnns_groups_1",
+         "name": "creator",
          "rawType": "object",
          "type": "string"
         },
         {
-         "name": "pnns_groups_2",
+         "name": "created_t",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "created_datetime",
          "rawType": "object",
          "type": "string"
         },
         {
-         "name": "brands_tags",
+         "name": "last_modified_t",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "last_modified_datetime",
          "rawType": "object",
-         "type": "unknown"
+         "type": "string"
         },
         {
-         "name": "ingredients_analysis_tags",
+         "name": "last_modified_by",
          "rawType": "object",
          "type": "unknown"
         },
         {
-         "name": "code",
-         "rawType": "float64",
-         "type": "float"
+         "name": "last_updated_t",
+         "rawType": "int64",
+         "type": "integer"
         },
         {
-         "name": "additives_n",
-         "rawType": "float64",
-         "type": "float"
+         "name": "last_updated_datetime",
+         "rawType": "object",
+         "type": "string"
         },
         {
-         "name": "nutriscore_score",
-         "rawType": "float64",
-         "type": "float"
+         "name": "product_name",
+         "rawType": "object",
+         "type": "string"
         },
         {
-         "name": "energy_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "abbreviated_product_name",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "fat_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "generic_name",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "saturated-fat_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "quantity",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "carbohydrates_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "packaging",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "sugars_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "packaging_tags",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "fiber_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "packaging_en",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "proteins_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "packaging_text",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "salt_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "brands",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "sodium_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "brands_tags",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
-         "rawType": "float64",
-         "type": "float"
+         "name": "brands_en",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "PNNS_pro",
+         "name": "categories",
          "rawType": "object",
-         "type": "string"
+         "type": "unknown"
         },
         {
-         "name": "PNNS_pro_Animal_based",
-         "rawType": "float64",
-         "type": "float"
+         "name": "categories_tags",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "PNNS_pro_Drinks",
-         "rawType": "float64",
-         "type": "float"
+         "name": "categories_en",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "PNNS_pro_NA",
-         "rawType": "float64",
-         "type": "float"
+         "name": "origins",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "PNNS_pro_Plant_based",
-         "rawType": "float64",
-         "type": "float"
+         "name": "origins_tags",
+         "rawType": "object",
+         "type": "unknown"
         },
         {
-         "name": "PNNS_pro_Processed",
-         "rawType": "float64",
-         "type": "float"
+         "name": "origins_en",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "manufacturing_places",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "manufacturing_places_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "labels",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "labels_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "labels_en",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "emb_codes",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "emb_codes_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "first_packaging_code_geo",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "cities",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cities_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "purchase_places",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "stores",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "countries",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "countries_tags",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "countries_en",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "ingredients_text",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "ingredients_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "ingredients_analysis_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "allergens",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "allergens_en",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "traces",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "traces_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "traces_en",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "serving_size",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "serving_quantity",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "no_nutrition_data",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "additives_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "additives",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "additives_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "additives_en",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "nutriscore_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutriscore_grade",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "nova_group",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "pnns_groups_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "pnns_groups_2",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "food_groups",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "food_groups_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "food_groups_en",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "states",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "states_tags",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "states_en",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "brand_owner",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "environmental_score_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "environmental_score_grade",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "nutrient_levels_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "product_quantity",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "owner",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "data_quality_errors_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "unique_scans_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "popularity_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "completeness",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "last_image_t",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "last_image_datetime",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "main_category",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "main_category_en",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "image_url",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "image_small_url",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "image_ingredients_url",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "image_ingredients_small_url",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "image_nutrition_url",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "image_nutrition_small_url",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "energy-kj_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy-kcal_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy-from-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "saturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "butyric-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "caproic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "caprylic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "capric-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "lauric-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "myristic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "palmitic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "stearic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "arachidic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "behenic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "lignoceric-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cerotic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "montanic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "melissic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "unsaturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "monounsaturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "omega-9-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "polyunsaturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "omega-3-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "omega-6-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "alpha-linolenic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "eicosapentaenoic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "docosahexaenoic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "linoleic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "arachidonic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "gamma-linolenic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "dihomo-gamma-linolenic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "oleic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "elaidic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "gondoic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "mead-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "erucic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nervonic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
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+         "rawType": "float64",
+         "type": "float"
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+        {
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+         "rawType": "float64",
+         "type": "float"
+        },
+        {
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+         "rawType": "float64",
+         "type": "float"
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+        {
+         "name": "sugars_100g",
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+        },
+        {
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+         "type": "float"
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+        {
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+        },
+        {
+         "name": "glucose_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
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+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "galactose_100g",
+         "rawType": "float64",
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+        {
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+        },
+        {
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+         "rawType": "float64",
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+        {
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+         "type": "float"
+        },
+        {
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+         "type": "float"
+        },
+        {
+         "name": "starch_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "polyols_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "erythritol_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "isomalt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "maltitol_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sorbitol_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "soluble-fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "insoluble-fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "casein_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "serum-proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nucleotides_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "added-salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sodium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "alcohol_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-a_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "beta-carotene_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-d_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-e_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-k_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-c_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-b1_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-b2_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-pp_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-b6_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-b9_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "folates_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-b12_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "biotin_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "pantothenic-acid_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "silica_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "bicarbonate_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "potassium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "chloride_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "calcium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "phosphorus_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "iron_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "magnesium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "zinc_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "copper_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "manganese_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fluoride_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "selenium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "chromium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "molybdenum_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "iodine_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "caffeine_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "taurine_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "methylsulfonylmethane_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "ph_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-dried_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "collagen-meat-protein-ratio_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cocoa_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "chlorophyl_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbon-footprint_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbon-footprint-from-meat-or-fish_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutrition-score-fr_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutrition-score-uk_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "glycemic-index_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "water-hardness_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "choline_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "phylloquinone_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "beta-glucan_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "inositol_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carnitine_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sulphate_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nitrate_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "acidity_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbohydrates-total_100g",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "af76bee0-c025-406d-81f4-4a1e1bd0b78c",
+       "rows": [
+        [
+         "0",
+         "54",
+         "http://world-en.openfoodfacts.org/product/000000000054/limonade-artisanale-a-la-rose",
+         "kiliweb",
+         "1582569031",
+         "2020-02-24T18:30:31Z",
+         "1733085204",
+         "2024-12-01T20:33:24Z",
+         null,
+         "1740205422",
+         "2025-02-22T06:23:42Z",
+         "Limonade artisanale a la rose",
+         null,
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+         "en:fr",
+         "en:france",
+         "France",
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+         "unknown",
+         "unknown",
+         null,
+         null,
+         null,
+         "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-to-be-completed, en:packaging-to-be-completed, en:quantity-to-be-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded",
+         "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-to-be-completed,en:packaging-to-be-completed,en:quantity-to-be-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded",
+         "To be completed,Nutrition facts to be completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands to be completed,Packaging to be completed,Quantity to be completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded",
+         null,
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+         "0.1625",
+         "1733085204.0",
+         "2024-12-01T20:33:24Z",
+         null,
+         null,
+         "https://images.openfoodfacts.org/images/products/invalid/front_en.6.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/front_en.6.200.jpg",
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+        [
+         "1",
+         "63",
+         "http://world-en.openfoodfacts.org/product/000000000063/m-amp-m-white-fitpiggy",
+         "kiliweb",
+         "1673620307",
+         "2023-01-13T14:31:47Z",
+         "1750061386",
+         "2025-06-16T08:09:46Z",
+         "bodysupport",
+         "1750061386",
+         "2025-06-16T08:09:46Z",
+         "M&amp;M white",
+         null,
+         null,
+         "80 gram",
+         null,
+         null,
+         null,
+         null,
+         "Fitpiggy",
+         "xx:fitpiggy",
+         "fitpiggy",
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+         "en:fr",
+         "en:france",
+         "France",
+         "Weizenmehl, Rapsöl, Speisesalz, 1,7% Meersalz, Fefe, Gerstenmaizextrakt, Säureregulator: Natrium - hydroxid; Backtriebnittel: Ammoniumcarbonate. Das Produkt kann Spuren von Sesam enthalten. DURCHSCHNITTLICHE NÄHRWERTE - pro %RM* 100g pro 100g Brennwert kJ/kcal 1630/385 19% Fett 3,7g 5% davon: - gesättigte Fettsäuren 0,6g 3% Kohlenhydrate 74,0g 28% davon: - Zucker 2,4g 3% Ballaststoffe 3,9g 12,0g 24% Eiweiß 3,9g 65% Salz *Referenzmenge für einen durchschnittlichen Erwachsenen (8400 kJ/2000 kcal) AP EDEKA KUNDEN UND ERNÄHRUNGSSERVICE 2509 (",
+         "en:weizenmehl,en:rapsol,en:speisesalz,en:meersalz,en:fefe,en:gerstenmaizextrakt,en:saureregulator,en:hydroxid,en:backtriebnittel,en:pro-rm-100g-pro-100g-brennwert-kj,en:kcal-1630,en:385-19-fett-3-7-5-davon,en:gesattigte-fettsauren-0-6-3-kohlenhydrate-74-28-davon,en:zucker-2-4-3-ballaststoffe-3-9-12-24-eiweiss-3-9-65-salz-referenzmenge-fur-einen-durchschnittlichen-erwachsenen,en:ap-edeka-kunden-und-ernahrungsservice-2509,en:sodium,en:minerals,en:ammoniumcarbonate,en:das-produkt-kann-spuren-von-sesam-enthalten,en:durchschnittliche-nahrwerte,en:8400-kj,en:2000-kcal",
+         "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown",
+         null,
+         null,
+         null,
+         null,
+         null,
+         "80 gram",
+         "80.0",
+         null,
+         "0.0",
+         null,
+         null,
+         null,
+         null,
+         "unknown",
+         null,
+         "unknown",
+         "unknown",
+         null,
+         null,
+         null,
+         "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-to-be-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded",
+         "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-to-be-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded",
+         "To be completed,Nutrition facts completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo to be selected,Ingredients photo to be selected,Front photo selected,Photos uploaded",
+         null,
+         null,
+         "unknown",
+         null,
+         "80.0",
+         null,
+         null,
+         "1.0",
+         "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-50000-us-scans-2024,top-100000-us-scans-2024,top-country-us-scans-2024",
+         "0.6625",
+         "1746257766.0",
+         "2025-05-03T07:36:06Z",
+         null,
+         null,
+         "https://images.openfoodfacts.org/images/products/invalid/front_en.12.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/front_en.12.200.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/ingredients_de.8.200.jpg",
+         null,
+         null,
+         null,
+         "359.0",
+         "1502.0",
+         null,
+         "19.0",
+         "17.3",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "26.0",
+         "1.0",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "17.0",
+         null,
+         null,
+         null,
+         "1.2",
+         null,
+         "0.48",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "0.0",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null
+        ],
+        [
+         "2",
+         "114",
+         "http://world-en.openfoodfacts.org/product/000000000114/chocolate-n3-jeff-de-bruges",
+         "kiliweb",
+         "1580066482",
+         "2020-01-26T19:21:22Z",
+         "1751035658",
+         "2025-06-27T14:47:38Z",
+         "teolemon",
+         "1751035658",
+         "2025-06-27T14:47:38Z",
+         "Chocolate n3",
+         null,
+         null,
+         "80 g",
+         null,
+         null,
+         null,
+         null,
+         "Jeff de Bruges",
+         "xx:jeff-de-bruges",
+         "jeff-de-bruges",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "Green Dot,Made in France",
+         "en:green-dot,en:made-in-france",
+         "Green Dot,Made in France",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "France",
+         "en:france",
+         "France",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "unknown",
+         null,
+         "unknown",
+         "unknown",
+         null,
+         null,
+         null,
+         "en:to-be-completed, en:nutrition-facts-completed, en:ingredients-to-be-completed, en:expiration-date-to-be-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-to-be-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded",
+         "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-to-be-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-to-be-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded",
+         "To be completed,Nutrition facts completed,Ingredients to be completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories to be completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded",
+         null,
+         null,
+         "unknown",
+         null,
+         "80.0",
+         null,
+         null,
+         "1.0",
+         "bottom-25-percent-scans-2022,bottom-20-percent-scans-2022,top-85-percent-scans-2022,top-90-percent-scans-2022,top-country-fr-scans-2022,top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-country-fr-scans-2024",
+         "0.475",
+         "1737247860.0",
+         "2025-01-19T00:51:00Z",
+         null,
+         null,
+         "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/front_fr.21.200.jpg",
+         null,
+         null,
+         "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/nutrition_fr.5.200.jpg",
+         "2415.0",
+         null,
+         "2415.0",
+         null,
+         "44.0",
+         "28.0",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "30.0",
+         "27.0",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "7.1",
+         null,
+         null,
+         null,
+         "0.025",
+         null,
+         "0.01",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null
+        ],
+        [
+         "3",
+         "105",
+         "http://world-en.openfoodfacts.org/product/0000000105/paleta-gran-reserva-sierra-nevada-advocare",
+         "kiliweb",
+         "1572117743",
+         "2019-10-26T19:22:23Z",
+         "1738073570",
+         "2025-01-28T14:12:50Z",
+         null,
+         "1743653496",
+         "2025-04-03T04:11:36Z",
+         "Paleta gran reserva - Sierra nevada-",
+         null,
+         null,
+         "750ml",
+         null,
+         null,
+         null,
+         null,
+         "AdvoCare",
+         "xx:advocare",
+         "advocare",
+         "Bebidas y preparaciones de bebidas, Bebidas",
+         "en:beverages-and-beverages-preparations,en:beverages",
+         "Beverages and beverages preparations,Beverages",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "Spanien, Germany",
+         "en:germany,en:spain",
+         "Germany,Spain",
+         "Thiamin, Biotin, Chromium, Garcinia cambogia fruit extract, Taurine, Green coffee fruit extract, Caffeine, Inositol, Citric Acid, Natural ,  Artificial Flavors, Sucralose, Spirulina Extract, Beta Carotene",
+         "en:thiamin,en:biotin,en:vitamins,en:chromium,en:minerals,en:garcinia-cambogia-fruit-extract,en:taurine,en:green-coffee-fruit-extract,en:caffeine,en:inositol,en:e330,en:natural,en:artificial-flavouring,en:flavouring,en:e955,en:spirulina-concentrate,en:algae,en:spirulina,en:e160ai,en:e160a",
+         "en:may-contain-palm-oil,en:vegan-status-unknown,en:vegetarian-status-unknown",
+         null,
+         null,
+         null,
+         null,
+         null,
+         "5g",
+         "5.0",
+         null,
+         "2.0",
+         null,
+         "en:e330,en:e955",
+         "E330 - Citric acid,E955 - Sucralose",
+         null,
+         "unknown",
+         "4.0",
+         "Beverages",
+         "Artificially sweetened beverages",
+         "en:artificially-sweetened-beverages",
+         "en:beverages,en:artificially-sweetened-beverages",
+         "Beverages,Artificially sweetened beverages",
+         "en:to-be-completed, en:nutrition-facts-to-be-completed, en:ingredients-completed, en:expiration-date-completed, en:packaging-code-to-be-completed, en:characteristics-to-be-completed, en:origins-to-be-completed, en:categories-completed, en:brands-completed, en:packaging-to-be-completed, en:quantity-completed, en:product-name-completed, en:photos-to-be-validated, en:packaging-photo-to-be-selected, en:nutrition-photo-selected, en:ingredients-photo-to-be-selected, en:front-photo-selected, en:photos-uploaded",
+         "en:to-be-completed,en:nutrition-facts-to-be-completed,en:ingredients-completed,en:expiration-date-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-to-be-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-to-be-validated,en:packaging-photo-to-be-selected,en:nutrition-photo-selected,en:ingredients-photo-to-be-selected,en:front-photo-selected,en:photos-uploaded",
+         "To be completed,Nutrition facts to be completed,Ingredients completed,Expiration date completed,Packaging code to be completed,Characteristics to be completed,Origins to be completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos to be validated,Packaging photo to be selected,Nutrition photo selected,Ingredients photo to be selected,Front photo selected,Photos uploaded",
+         null,
+         null,
+         "unknown",
+         null,
+         "750.0",
+         null,
+         null,
+         "1.0",
+         "top-75-percent-scans-2024,top-80-percent-scans-2024,top-85-percent-scans-2024,top-90-percent-scans-2024,top-500-az-scans-2024,top-1000-az-scans-2024,top-5000-az-scans-2024,top-10000-az-scans-2024,top-50000-az-scans-2024,top-100000-az-scans-2024,top-country-az-scans-2024",
+         "0.675",
+         "1738073557.0",
+         "2025-01-28T14:12:37Z",
+         "en:beverages",
+         "Beverages",
+         "https://images.openfoodfacts.org/images/products/invalid/front_es.21.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/front_es.21.200.jpg",
+         null,
+         null,
+         "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/nutrition_es.5.200.jpg",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
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+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "0.0113351004464306",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null
+        ],
+        [
+         "4",
+         "2",
+         "http://world-en.openfoodfacts.org/product/00000002/filets-de-poulet-blanc-x2-solo",
+         "kiliweb",
+         "1722606455",
+         "2024-08-02T13:47:35Z",
+         "1749171851",
+         "2025-06-06T01:04:11Z",
+         "altroconsumo",
+         "1749171851",
+         "2025-06-06T01:04:11Z",
+         "Filets de poulet blanc x2",
+         null,
+         null,
+         "240-400 g",
+         null,
+         null,
+         null,
+         null,
+         "SoLo, selbstgemacht 2 Liter",
+         "xx:solo,xx:selbstgemacht-2-liter",
+         "solo,selbstgemacht-2-liter",
+         "Protein powders",
+         "en:dietary-supplements,en:bodybuilding-supplements,en:protein-powders",
+         "Dietary supplements,Bodybuilding supplements,Protein powders",
+         "île d’Orléans,Québec,Canada",
+         "en:canada,en:quebec,fr:ile-d-orleans",
+         "Canada,Québec,fr:ile-d-orleans",
+         "Ancenis",
+         "ancenis",
+         "Organic, EU Organic, French meat, Bee Friendly, French poultry, AB Agriculture Biologique, en:no-additives",
+         "en:organic,en:eu-organic,en:french-meat,en:bee-friendly,en:french-poultry,en:no-additives,fr:ab-agriculture-biologique",
+         "Organic,EU Organic,French meat,Bee Friendly,French poultry,No additives,AB Agriculture Biologique",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "Brasilien, Germany",
+         "en:brazil,en:germany",
+         "Brazil,Germany",
+         "48% Tomatenpulver, Stärke, Zucker, jodiertes Speisesalz, WEIZENMEHL, Würze, Maiskeimöl, Kaliumchlorid, Kräuter (Basilikum, Thymian, Oregano), Hefeextrakt, Zwiebeln, Gewürze (Knoblauch, Pfeffer), Rote-Bete-Pulver, Aromen, Speisesalz. Kann ROGGEN, GERSTE, HAFER, EI, SOJA, MILCH, SELLERIE, SENF enthalten. Kochsalzersatz, gewonnen aus natürlichen Kaliummineralien. NUTRI-SCORE berechnet pro 100 g zubereitetes Gericht. Unilever Deutschland GmbH Konsumenten - Postfach 57 05 50 22774 Hamburg Ene Fett da Fe Kol da Ball Elw Salz 196 Erw service Tel.: 0800 5858 555 E (gebührenfrei) ww Unilever www.knorr.de",
+         "en:tomatenpulver,en:starke,en:zucker,en:jodiertes-speisesalz,en:weizenmehl,en:wurze,en:maiskeimol,en:kaliumchlorid,en:krauter,en:hefeextrakt,en:zwiebeln,en:gewurze,en:rote-bete-pulver,en:aromen,en:speisesalz,en:kann-roggen,en:gerste,en:hafer,en:ei,en:soya,en:milch,en:sellerie,en:senf-enthalten,en:kochsalzersatz,en:gewonnen-aus-naturlichen-kaliummineralien,en:nutri-score-berechnet-pro-100-g-zubereitetes-gericht,en:unilever-deutschland-gmbh-konsumenten,en:postfach-57-05-50-22774-hamburg-ene-fett-da-fe-kol-da-ball-elw-salz-196-erw-service-tel,en:basilikum,en:thymian,en:oregano,en:herb,en:knoblauch,en:pfeffer,en:0800-5858-555-e,en:ww-unilever-www-knorr-de,en:gebuhrenfrei",
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+         null,
+         null,
+         null,
+         null,
+         null,
+         "35gm",
+         "35.0",
+         null,
+         "0.0",
+         null,
+         null,
+         null,
+         null,
+         "not-applicable",
+         null,
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+         null,
+         null,
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+         "en:to-be-completed,en:nutrition-facts-completed,en:ingredients-completed,en:expiration-date-to-be-completed,en:packaging-code-to-be-completed,en:characteristics-to-be-completed,en:origins-completed,en:categories-completed,en:brands-completed,en:packaging-to-be-completed,en:quantity-completed,en:product-name-completed,en:photos-validated,en:packaging-photo-selected,en:nutrition-photo-selected,en:ingredients-photo-selected,en:front-photo-selected,en:photos-uploaded",
+         "To be completed,Nutrition facts completed,Ingredients completed,Expiration date to be completed,Packaging code to be completed,Characteristics to be completed,Origins completed,Categories completed,Brands completed,Packaging to be completed,Quantity completed,Product name completed,Photos validated,Packaging photo selected,Nutrition photo selected,Ingredients photo selected,Front photo selected,Photos uploaded",
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+         "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.75.400.jpg",
+         "https://images.openfoodfacts.org/images/products/invalid/nutrition_en.75.200.jpg",
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+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>code</th>\n",
+       "      <th>url</th>\n",
+       "      <th>creator</th>\n",
+       "      <th>created_t</th>\n",
+       "      <th>created_datetime</th>\n",
+       "      <th>last_modified_t</th>\n",
+       "      <th>last_modified_datetime</th>\n",
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+       "      <th>last_updated_t</th>\n",
+       "      <th>last_updated_datetime</th>\n",
+       "      <th>...</th>\n",
+       "      <th>water-hardness_100g</th>\n",
+       "      <th>choline_100g</th>\n",
+       "      <th>phylloquinone_100g</th>\n",
+       "      <th>beta-glucan_100g</th>\n",
+       "      <th>inositol_100g</th>\n",
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+       "      <th>nitrate_100g</th>\n",
+       "      <th>acidity_100g</th>\n",
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+       "    </tr>\n",
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+       "      <th>0</th>\n",
+       "      <td>54</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>kiliweb</td>\n",
+       "      <td>1582569031</td>\n",
+       "      <td>2020-02-24T18:30:31Z</td>\n",
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+       "      <td>2024-12-01T20:33:24Z</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>63</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>kiliweb</td>\n",
+       "      <td>1673620307</td>\n",
+       "      <td>2023-01-13T14:31:47Z</td>\n",
+       "      <td>1750061386</td>\n",
+       "      <td>2025-06-16T08:09:46Z</td>\n",
+       "      <td>bodysupport</td>\n",
+       "      <td>1750061386</td>\n",
+       "      <td>2025-06-16T08:09:46Z</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>114</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>kiliweb</td>\n",
+       "      <td>1580066482</td>\n",
+       "      <td>2020-01-26T19:21:22Z</td>\n",
+       "      <td>1751035658</td>\n",
+       "      <td>2025-06-27T14:47:38Z</td>\n",
+       "      <td>teolemon</td>\n",
+       "      <td>1751035658</td>\n",
+       "      <td>2025-06-27T14:47:38Z</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>105</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>kiliweb</td>\n",
+       "      <td>1572117743</td>\n",
+       "      <td>2019-10-26T19:22:23Z</td>\n",
+       "      <td>1738073570</td>\n",
+       "      <td>2025-01-28T14:12:50Z</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1743653496</td>\n",
+       "      <td>2025-04-03T04:11:36Z</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2</td>\n",
+       "      <td>http://world-en.openfoodfacts.org/product/0000...</td>\n",
+       "      <td>kiliweb</td>\n",
+       "      <td>1722606455</td>\n",
+       "      <td>2024-08-02T13:47:35Z</td>\n",
+       "      <td>1749171851</td>\n",
+       "      <td>2025-06-06T01:04:11Z</td>\n",
+       "      <td>altroconsumo</td>\n",
+       "      <td>1749171851</td>\n",
+       "      <td>2025-06-06T01:04:11Z</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 214 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   code                                                url  creator  \\\n",
+       "0    54  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "1    63  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "2   114  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "3   105  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "4     2  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "\n",
+       "    created_t      created_datetime  last_modified_t last_modified_datetime  \\\n",
+       "0  1582569031  2020-02-24T18:30:31Z       1733085204   2024-12-01T20:33:24Z   \n",
+       "1  1673620307  2023-01-13T14:31:47Z       1750061386   2025-06-16T08:09:46Z   \n",
+       "2  1580066482  2020-01-26T19:21:22Z       1751035658   2025-06-27T14:47:38Z   \n",
+       "3  1572117743  2019-10-26T19:22:23Z       1738073570   2025-01-28T14:12:50Z   \n",
+       "4  1722606455  2024-08-02T13:47:35Z       1749171851   2025-06-06T01:04:11Z   \n",
+       "\n",
+       "  last_modified_by  last_updated_t last_updated_datetime  ...  \\\n",
+       "0              NaN      1740205422  2025-02-22T06:23:42Z  ...   \n",
+       "1      bodysupport      1750061386  2025-06-16T08:09:46Z  ...   \n",
+       "2         teolemon      1751035658  2025-06-27T14:47:38Z  ...   \n",
+       "3              NaN      1743653496  2025-04-03T04:11:36Z  ...   \n",
+       "4     altroconsumo      1749171851  2025-06-06T01:04:11Z  ...   \n",
+       "\n",
+       "  water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g  \\\n",
+       "0                 NaN          NaN                NaN              NaN   \n",
+       "1                 NaN          NaN                NaN              NaN   \n",
+       "2                 NaN          NaN                NaN              NaN   \n",
+       "3                 NaN          NaN                NaN              NaN   \n",
+       "4                 NaN          NaN                NaN              NaN   \n",
+       "\n",
+       "  inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g  \\\n",
+       "0           NaN            NaN           NaN          NaN          NaN   \n",
+       "1           NaN            NaN           NaN          NaN          NaN   \n",
+       "2           NaN            NaN           NaN          NaN          NaN   \n",
+       "3           NaN            NaN           NaN          NaN          NaN   \n",
+       "4           NaN            NaN           NaN          NaN          NaN   \n",
+       "\n",
+       "  carbohydrates-total_100g  \n",
+       "0                      NaN  \n",
+       "1                      NaN  \n",
+       "2                      NaN  \n",
+       "3                      NaN  \n",
+       "4                      NaN  \n",
+       "\n",
+       "[5 rows x 214 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2. Pre-processing "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.1 Preliminary data curation (of the train and test df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:66: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  num_keep.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n",
+      "C:\\Users\\jessi\\Documents\\OFFProject\\scripts\\Data_filter_Jess.py:67: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  num_keep_test.drop(num_drop, axis = 'columns', inplace=True, errors='ignore')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### taking out lines without nutriscore because it's non informative (both fot the train and the test df)\n",
+    "filtered_df, filtered_df_test = dfj.filter_nutriscore_data(df_train, df_test)\n",
+    "\n",
+    "### Cleaning the variables \n",
+    "cat_df, cat_df_test = dfj.categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n",
+    "num_df, num_df_test = dfj.numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n",
+    "final_df, final_df_test = dfj.final_df(cat_df, num_df, cat_df_test, num_df_test)\n",
+    "\n",
+    "### Droping some additionnal columns \n",
+    "final_df = final_df.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
+    "final_df_test = final_df_test.drop(columns=['environmental_score_score','nutrition-score-fr_100g'])\n",
+    "\n",
+    "final_df.columns.equals(final_df_test.columns) #checking if we have the same variables in each df "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.2 Enconding "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              pnns_groups_1      PNNS_pro\n",
+      "6                   unknown            NA\n",
+      "9                   unknown            NA\n",
+      "11    Fruits and vegetables   Plant_based\n",
+      "12                  unknown            NA\n",
+      "14          Composite foods     Processed\n",
+      "...                     ...           ...\n",
+      "4926   Cereals and potatoes   Plant_based\n",
+      "4935         Fish Meat Eggs  Animal_based\n",
+      "4936                unknown            NA\n",
+      "4943        Composite foods     Processed\n",
+      "4987          Sugary snacks        Snacks\n",
+      "\n",
+      "[437 rows x 2 columns]\n",
+      "                pnns_groups_1      PNNS_pro\n",
+      "0        Cereals and potatoes   Plant_based\n",
+      "7              Fish Meat Eggs  Animal_based\n",
+      "42      Fruits and vegetables   Plant_based\n",
+      "45                    unknown            NA\n",
+      "47                    unknown            NA\n",
+      "...                       ...           ...\n",
+      "4952            Sugary snacks        Snacks\n",
+      "4966          Composite foods     Processed\n",
+      "4975  Milk and dairy products  Animal_based\n",
+      "4981            Sugary snacks        Snacks\n",
+      "4990            Sugary snacks        Snacks\n",
+      "\n",
+      "[772 rows x 2 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pnns_mapping = {\"unknown\" : 'NA', \n",
+    "                'Beverages' : 'Drinks',\n",
+    "                'nan' : 'NA',\n",
+    "                'Fruits and vegetables' : 'Plant_based',\n",
+    "                'Composite foods' : 'Processed',\n",
+    "                'Sugary snacks' : 'Snacks',\n",
+    "                'Salty snacks' : 'Snacks',\n",
+    "                'Cereals and potatoes' : 'Plant_based',\n",
+    "                'Fat and sauces' : 'Processed', \n",
+    "                'Milk and dairy products' : 'Animal_based',\n",
+    "                'Fish Meat Eggs' : 'Animal_based',\n",
+    "                'Alcoholic beverages' : 'Drinks'\n",
+    "}\n",
+    "\n",
+    "final_df['PNNS_pro'] = final_df['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
+    "final_df_test['PNNS_pro'] = final_df_test['pnns_groups_1'].map(pnns_mapping).fillna('Other')\n",
+    "print(final_df[['pnns_groups_1', 'PNNS_pro']])\n",
+    "print(final_df_test[['pnns_groups_1', 'PNNS_pro']])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "categories",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "pnns_groups_1",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "pnns_groups_2",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "brands_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "ingredients_analysis_tags",
+         "rawType": "object",
+         "type": "unknown"
+        },
+        {
+         "name": "code",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "additives_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutriscore_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "saturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbohydrates_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sugars_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sodium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "PNNS_pro_Animal_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Drinks",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_NA",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Plant_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Processed",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Snacks",
+         "rawType": "float64",
+         "type": "float"
+        }
+       ],
+       "ref": "de68c5e6-3592-4420-ae4d-0c858fd51822",
+       "rows": [
+        [
+         "0",
+         "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Céréales et pommes de terre, Pains, Baguettes",
+         "Cereals and potatoes",
+         "Bread",
+         "xx:la-campaniere",
+         "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown",
+         "584019351.0",
+         "0.0",
+         "4.0",
+         "1125.0",
+         "3.0",
+         "0.3",
+         "47.9",
+         "3.8",
+         "5.5",
+         "9.4",
+         "1.3",
+         "0.52",
+         "0.0",
+         "Plant_based",
+         "0.0",
+         "0.0",
+         "0.0",
+         "1.0",
+         "0.0",
+         "0.0"
+        ],
+        [
+         "7",
+         "Produits de la mer, Poissons et dérivés, Poissons, Poissons gras, Saumons, Poissons fumés, Saumons fumés, Saumons fumés à la ficelle",
+         "Fish Meat Eggs",
+         "Fish and seafood",
+         null,
+         null,
+         "5869.0",
+         null,
+         "17.0",
+         "1059.0",
+         "17.0",
+         "2.6",
+         "0.5",
+         "0.0",
+         null,
+         "23.0",
+         "2.5",
+         "1.0",
+         null,
+         "Animal_based",
+         "0.0",
+         "0.0",
+         "0.0",
+         "1.0",
+         "0.0",
+         "0.0"
+        ],
+        [
+         "42",
+         "Fresh papayas",
+         "Fruits and vegetables",
+         "Fruits",
+         "xx:curate",
+         "en:palm-oil-free,en:vegan,en:vegetarian",
+         "599990534.0",
+         "0.0",
+         "-3.0",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         "100.0",
+         "Plant_based",
+         "0.0",
+         "0.0",
+         "1.0",
+         "0.0",
+         "0.0",
+         "0.0"
+        ],
+        [
+         "45",
+         "Snacks, Snacks sucrés",
+         "unknown",
+         "unknown",
+         null,
+         null,
+         "600002458.0",
+         null,
+         "25.0",
+         "2464.0",
+         "47.0",
+         "29.0",
+         "34.0",
+         "31.0",
+         null,
+         "5.9",
+         "0.0",
+         "0.0",
+         null,
+         "NA",
+         "0.0",
+         "0.0",
+         "1.0",
+         "0.0",
+         "0.0",
+         "0.0"
+        ],
+        [
+         "47",
+         "Drink mix",
+         "unknown",
+         "unknown",
+         "tclinics-usa",
+         "en:may-contain-palm-oil,en:non-vegan,en:vegetarian-status-unknown",
+         "600020002.0",
+         "6.0",
+         "25.0",
+         "33472.0",
+         "0.0",
+         "0.0",
+         "300.0",
+         "0.0",
+         "200.0",
+         "1500.0",
+         "18.75000125",
+         "7.5000005",
+         "0.429687499999986",
+         "NA",
+         "0.0",
+         "0.0",
+         "1.0",
+         "0.0",
+         "0.0",
+         "0.0"
+        ]
+       ],
+       "shape": {
+        "columns": 25,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>categories</th>\n",
+       "      <th>pnns_groups_1</th>\n",
+       "      <th>pnns_groups_2</th>\n",
+       "      <th>brands_tags</th>\n",
+       "      <th>ingredients_analysis_tags</th>\n",
+       "      <th>code</th>\n",
+       "      <th>additives_n</th>\n",
+       "      <th>nutriscore_score</th>\n",
+       "      <th>energy_100g</th>\n",
+       "      <th>fat_100g</th>\n",
+       "      <th>...</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
+       "      <th>PNNS_pro</th>\n",
+       "      <th>PNNS_pro_Animal_based</th>\n",
+       "      <th>PNNS_pro_Drinks</th>\n",
+       "      <th>PNNS_pro_NA</th>\n",
+       "      <th>PNNS_pro_Plant_based</th>\n",
+       "      <th>PNNS_pro_Processed</th>\n",
+       "      <th>PNNS_pro_Snacks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Aliments et boissons à base de végétaux, Alime...</td>\n",
+       "      <td>Cereals and potatoes</td>\n",
+       "      <td>Bread</td>\n",
+       "      <td>xx:la-campaniere</td>\n",
+       "      <td>en:palm-oil-free,en:vegan-status-unknown,en:ve...</td>\n",
+       "      <td>584019351.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1125.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>0.52</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Plant_based</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Produits de la mer, Poissons et dérivés, Poiss...</td>\n",
+       "      <td>Fish Meat Eggs</td>\n",
+       "      <td>Fish and seafood</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5869.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>1059.0</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animal_based</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>42</th>\n",
+       "      <td>Fresh papayas</td>\n",
+       "      <td>Fruits and vegetables</td>\n",
+       "      <td>Fruits</td>\n",
+       "      <td>xx:curate</td>\n",
+       "      <td>en:palm-oil-free,en:vegan,en:vegetarian</td>\n",
+       "      <td>599990534.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>100.000000</td>\n",
+       "      <td>Plant_based</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>Snacks, Snacks sucrés</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>600002458.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>2464.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>47</th>\n",
+       "      <td>Drink mix</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>unknown</td>\n",
+       "      <td>tclinics-usa</td>\n",
+       "      <td>en:may-contain-palm-oil,en:non-vegan,en:vegeta...</td>\n",
+       "      <td>600020002.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>33472.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>18.750001</td>\n",
+       "      <td>7.50</td>\n",
+       "      <td>0.429687</td>\n",
+       "      <td>NA</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           categories          pnns_groups_1  \\\n",
+       "0   Aliments et boissons à base de végétaux, Alime...   Cereals and potatoes   \n",
+       "7   Produits de la mer, Poissons et dérivés, Poiss...         Fish Meat Eggs   \n",
+       "42                                      Fresh papayas  Fruits and vegetables   \n",
+       "45                              Snacks, Snacks sucrés                unknown   \n",
+       "47                                          Drink mix                unknown   \n",
+       "\n",
+       "       pnns_groups_2       brands_tags  \\\n",
+       "0              Bread  xx:la-campaniere   \n",
+       "7   Fish and seafood               NaN   \n",
+       "42            Fruits         xx:curate   \n",
+       "45           unknown               NaN   \n",
+       "47           unknown      tclinics-usa   \n",
+       "\n",
+       "                            ingredients_analysis_tags         code  \\\n",
+       "0   en:palm-oil-free,en:vegan-status-unknown,en:ve...  584019351.0   \n",
+       "7                                                 NaN       5869.0   \n",
+       "42            en:palm-oil-free,en:vegan,en:vegetarian  599990534.0   \n",
+       "45                                                NaN  600002458.0   \n",
+       "47  en:may-contain-palm-oil,en:non-vegan,en:vegeta...  600020002.0   \n",
+       "\n",
+       "    additives_n  nutriscore_score  energy_100g  fat_100g  ...  salt_100g  \\\n",
+       "0           0.0               4.0       1125.0       3.0  ...   1.300000   \n",
+       "7           NaN              17.0       1059.0      17.0  ...   2.500000   \n",
+       "42          0.0              -3.0          NaN       NaN  ...        NaN   \n",
+       "45          NaN              25.0       2464.0      47.0  ...   0.000000   \n",
+       "47          6.0              25.0      33472.0       0.0  ...  18.750001   \n",
+       "\n",
+       "    sodium_100g  fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "0          0.52                                           0.000000       \n",
+       "7          1.00                                                NaN       \n",
+       "42          NaN                                         100.000000       \n",
+       "45         0.00                                                NaN       \n",
+       "47         7.50                                           0.429687       \n",
+       "\n",
+       "        PNNS_pro  PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  \\\n",
+       "0    Plant_based                    0.0              0.0          0.0   \n",
+       "7   Animal_based                    0.0              0.0          0.0   \n",
+       "42   Plant_based                    0.0              0.0          1.0   \n",
+       "45            NA                    0.0              0.0          1.0   \n",
+       "47            NA                    0.0              0.0          1.0   \n",
+       "\n",
+       "    PNNS_pro_Plant_based PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "0                    1.0                0.0              0.0  \n",
+       "7                    1.0                0.0              0.0  \n",
+       "42                   0.0                0.0              0.0  \n",
+       "45                   0.0                0.0              0.0  \n",
+       "47                   0.0                0.0              0.0  \n",
+       "\n",
+       "[5 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scripts.encoding_func import one_hot_encode_column\n",
+    "\n",
+    "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n",
+    "filtered_df_test = one_hot_encode_column(final_df_test, 'PNNS_pro')\n",
+    "filtered_df.head()\n",
+    "filtered_df_test.head()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n",
+    "filtered_df_test = filtered_df_test.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "code",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "additives_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutriscore_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "saturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbohydrates_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sugars_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sodium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Animal_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Drinks",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_NA",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Plant_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Processed",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Snacks",
+         "rawType": "float64",
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+        ]
+       ],
+       "shape": {
+        "columns": 19,
+        "rows": 5
+       }
+      },
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>code</th>\n",
+       "      <th>additives_n</th>\n",
+       "      <th>nutriscore_score</th>\n",
+       "      <th>energy_100g</th>\n",
+       "      <th>fat_100g</th>\n",
+       "      <th>saturated-fat_100g</th>\n",
+       "      <th>carbohydrates_100g</th>\n",
+       "      <th>sugars_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
+       "      <th>salt_100g</th>\n",
+       "      <th>sodium_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
+       "      <th>PNNS_pro_Animal_based</th>\n",
+       "      <th>PNNS_pro_Drinks</th>\n",
+       "      <th>PNNS_pro_NA</th>\n",
+       "      <th>PNNS_pro_Plant_based</th>\n",
+       "      <th>PNNS_pro_Processed</th>\n",
+       "      <th>PNNS_pro_Snacks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1520.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1510.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>10.714286</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-11.0</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.275</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6    4.0          0.0              15.0       2401.0      12.0   \n",
+       "9    6.0          NaN               4.0       1520.0      11.0   \n",
+       "11   7.0          0.0               4.0          4.0       1.0   \n",
+       "12   8.0          1.0               6.0       1510.0       2.0   \n",
+       "14   9.0          NaN             -11.0        293.0       0.5   \n",
+       "\n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6                10.50                13.0         9.00   36.000000   \n",
+       "9                 2.00                25.0         0.98    9.000000   \n",
+       "11                1.00                 1.0         1.00    1.000000   \n",
+       "12                0.50                 6.7         1.70   10.714286   \n",
+       "14                0.06                 2.0         0.24   88.000000   \n",
+       "\n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6            23.0      0.300         0.12   \n",
+       "9            22.0      0.950         0.38   \n",
+       "11            1.0      1.000         0.40   \n",
+       "12           76.0      1.500         0.60   \n",
+       "14           18.0      0.275         0.11   \n",
+       "\n",
+       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "6                                                 0.0       \n",
+       "9                                                 NaN       \n",
+       "11                                                0.0       \n",
+       "12                                                0.0       \n",
+       "14                                                NaN       \n",
+       "\n",
+       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
+       "6                     0.0              0.0          0.0                   0.0   \n",
+       "9                     0.0              0.0          0.0                   0.0   \n",
+       "11                    0.0              0.0          0.0                   1.0   \n",
+       "12                    0.0              0.0          1.0                   0.0   \n",
+       "14                    0.0              0.0          1.0                   0.0   \n",
+       "\n",
+       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "6                  0.0              1.0  \n",
+       "9                  1.0              0.0  \n",
+       "11                 0.0              0.0  \n",
+       "12                 0.0              0.0  \n",
+       "14                 0.0              0.0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2.3 Imputing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ": shape of df with only numeric features=(807, 19)\n",
+      ": shape of df with only numeric features=(1450, 19)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "code",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "additives_n",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "nutriscore_score",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "energy_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "saturated-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "carbohydrates_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sugars_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fiber_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "proteins_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "salt_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "sodium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Animal_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Drinks",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_NA",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Plant_based",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "PNNS_pro_Processed",
+         "rawType": "float64",
+         "type": "float"
         },
         {
          "name": "PNNS_pro_Snacks",
@@ -354,113 +3700,89 @@
          "type": "float"
         }
        ],
-       "ref": "558f6e86-a31f-4bd8-afcd-23b65f850648",
+       "ref": "29ba5422-883e-4206-a164-d179d8a81809",
        "rows": [
         [
-         "0",
-         "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Céréales et pommes de terre, Pains, Baguettes",
-         "Cereals and potatoes",
-         "Bread",
-         "xx:la-campaniere",
-         "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown",
-         "584019351.0",
-         "0.0",
+         "6",
          "4.0",
-         "1125.0",
-         "3.0",
+         "0.0",
+         "15.0",
+         "2401.0",
+         "12.0",
+         "10.5",
+         "13.0",
+         "9.0",
+         "36.0",
+         "23.0",
          "0.3",
-         "47.9",
-         "3.8",
-         "5.5",
-         "9.4",
-         "1.3",
-         "0.52",
+         "0.12",
          "0.0",
-         "Plant_based",
          "0.0",
          "0.0",
          "0.0",
-         "1.0",
          "0.0",
-         "0.0"
+         "0.0",
+         "1.0"
         ],
         [
-         "7",
-         "Produits de la mer, Poissons et dérivés, Poissons, Poissons gras, Saumons, Poissons fumés, Saumons fumés, Saumons fumés à la ficelle",
-         "Fish Meat Eggs",
-         "Fish and seafood",
-         null,
-         null,
-         "5869.0",
-         null,
-         "17.0",
-         "1059.0",
-         "17.0",
-         "2.6",
-         "0.5",
+         "9",
+         "6.0",
+         "1.8",
+         "4.0",
+         "1520.0",
+         "11.0",
+         "2.0",
+         "25.0",
+         "0.98",
+         "9.0",
+         "22.0",
+         "0.95",
+         "0.38",
+         "20.400223270165018",
          "0.0",
-         null,
-         "23.0",
-         "2.5",
-         "1.0",
-         null,
-         "Animal_based",
          "0.0",
          "0.0",
          "0.0",
          "1.0",
-         "0.0",
          "0.0"
         ],
         [
-         "42",
-         "Fresh papayas",
-         "Fruits and vegetables",
-         "Fruits",
-         "xx:curate",
-         "en:palm-oil-free,en:vegan,en:vegetarian",
-         "599990534.0",
+         "11",
+         "7.0",
+         "0.0",
+         "4.0",
+         "4.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "1.0",
+         "0.4",
          "0.0",
-         "-3.0",
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         null,
-         "100.0",
-         "Plant_based",
          "0.0",
          "0.0",
-         "1.0",
          "0.0",
+         "1.0",
          "0.0",
          "0.0"
         ],
         [
-         "45",
-         "Snacks, Snacks sucrés",
-         "unknown",
-         "unknown",
-         null,
-         null,
-         "600002458.0",
-         null,
-         "25.0",
-         "2464.0",
-         "47.0",
-         "29.0",
-         "34.0",
-         "31.0",
-         null,
-         "5.9",
-         "0.0",
+         "12",
+         "8.0",
+         "1.0",
+         "6.0",
+         "1510.0",
+         "2.0",
+         "0.5",
+         "6.7",
+         "1.7",
+         "10.714286",
+         "76.0",
+         "1.5",
+         "0.6",
          "0.0",
-         null,
-         "NA",
          "0.0",
          "0.0",
          "1.0",
@@ -469,26 +3791,20 @@
          "0.0"
         ],
         [
-         "47",
-         "Drink mix",
-         "unknown",
-         "unknown",
-         "tclinics-usa",
-         "en:may-contain-palm-oil,en:non-vegan,en:vegetarian-status-unknown",
-         "600020002.0",
-         "6.0",
-         "25.0",
-         "33472.0",
-         "0.0",
-         "0.0",
-         "300.0",
-         "0.0",
-         "200.0",
-         "1500.0",
-         "18.75000125",
-         "7.5000005",
-         "0.429687499999986",
-         "NA",
+         "14",
+         "9.0",
+         "0.6",
+         "-11.0",
+         "293.0",
+         "0.5",
+         "0.06",
+         "2.0",
+         "0.24",
+         "88.0",
+         "18.0",
+         "0.275",
+         "0.11",
+         "20.00029130415483",
          "0.0",
          "0.0",
          "1.0",
@@ -498,7 +3814,7 @@
         ]
        ],
        "shape": {
-        "columns": 25,
+        "columns": 19,
         "rows": 5
        }
       },
@@ -521,119 +3837,109 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>categories</th>\n",
-       "      <th>pnns_groups_1</th>\n",
-       "      <th>pnns_groups_2</th>\n",
-       "      <th>brands_tags</th>\n",
-       "      <th>ingredients_analysis_tags</th>\n",
        "      <th>code</th>\n",
        "      <th>additives_n</th>\n",
        "      <th>nutriscore_score</th>\n",
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
-       "      <th>...</th>\n",
+       "      <th>saturated-fat_100g</th>\n",
+       "      <th>carbohydrates_100g</th>\n",
+       "      <th>sugars_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
        "      <th>salt_100g</th>\n",
        "      <th>sodium_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
-       "      <th>PNNS_pro</th>\n",
        "      <th>PNNS_pro_Animal_based</th>\n",
        "      <th>PNNS_pro_Drinks</th>\n",
        "      <th>PNNS_pro_NA</th>\n",
        "      <th>PNNS_pro_Plant_based</th>\n",
        "      <th>PNNS_pro_Processed</th>\n",
-       "      <th>PNNS_pro_Snacks</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Aliments et boissons à base de végétaux, Alime...</td>\n",
-       "      <td>Cereals and potatoes</td>\n",
-       "      <td>Bread</td>\n",
-       "      <td>xx:la-campaniere</td>\n",
-       "      <td>en:palm-oil-free,en:vegan-status-unknown,en:ve...</td>\n",
-       "      <td>584019351.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>PNNS_pro_Snacks</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
        "      <td>4.0</td>\n",
-       "      <td>1125.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.300000</td>\n",
-       "      <td>0.52</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>2401.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>10.50</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>9.00</td>\n",
+       "      <td>36.000000</td>\n",
+       "      <td>23.0</td>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.12</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>Plant_based</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>Produits de la mer, Poissons et dérivés, Poiss...</td>\n",
-       "      <td>Fish Meat Eggs</td>\n",
-       "      <td>Fish and seafood</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5869.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1059.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Animal_based</td>\n",
+       "      <th>9</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1520.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>2.00</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.38</td>\n",
+       "      <td>20.400223</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>Fresh papayas</td>\n",
-       "      <td>Fruits and vegetables</td>\n",
-       "      <td>Fruits</td>\n",
-       "      <td>xx:curate</td>\n",
-       "      <td>en:palm-oil-free,en:vegan,en:vegetarian</td>\n",
-       "      <td>599990534.0</td>\n",
+       "      <th>11</th>\n",
+       "      <td>7.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>Plant_based</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.00</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.000</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>0.000000</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>Snacks, Snacks sucrés</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>600002458.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2464.0</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>...</td>\n",
+       "      <th>12</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>1510.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>6.7</td>\n",
+       "      <td>1.70</td>\n",
+       "      <td>10.714286</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>1.500</td>\n",
+       "      <td>0.60</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NA</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -642,22 +3948,20 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
-       "      <td>Drink mix</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>tclinics-usa</td>\n",
-       "      <td>en:may-contain-palm-oil,en:non-vegan,en:vegeta...</td>\n",
-       "      <td>600020002.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>33472.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>18.750001</td>\n",
-       "      <td>7.50</td>\n",
-       "      <td>0.429687</td>\n",
-       "      <td>NA</td>\n",
+       "      <th>14</th>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>-11.0</td>\n",
+       "      <td>293.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.24</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>0.275</td>\n",
+       "      <td>0.11</td>\n",
+       "      <td>20.000291</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
@@ -667,115 +3971,96 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                           categories          pnns_groups_1  \\\n",
-       "0   Aliments et boissons à base de végétaux, Alime...   Cereals and potatoes   \n",
-       "7   Produits de la mer, Poissons et dérivés, Poiss...         Fish Meat Eggs   \n",
-       "42                                      Fresh papayas  Fruits and vegetables   \n",
-       "45                              Snacks, Snacks sucrés                unknown   \n",
-       "47                                          Drink mix                unknown   \n",
-       "\n",
-       "       pnns_groups_2       brands_tags  \\\n",
-       "0              Bread  xx:la-campaniere   \n",
-       "7   Fish and seafood               NaN   \n",
-       "42            Fruits         xx:curate   \n",
-       "45           unknown               NaN   \n",
-       "47           unknown      tclinics-usa   \n",
-       "\n",
-       "                            ingredients_analysis_tags         code  \\\n",
-       "0   en:palm-oil-free,en:vegan-status-unknown,en:ve...  584019351.0   \n",
-       "7                                                 NaN       5869.0   \n",
-       "42            en:palm-oil-free,en:vegan,en:vegetarian  599990534.0   \n",
-       "45                                                NaN  600002458.0   \n",
-       "47  en:may-contain-palm-oil,en:non-vegan,en:vegeta...  600020002.0   \n",
+       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6    4.0          0.0              15.0       2401.0      12.0   \n",
+       "9    6.0          1.8               4.0       1520.0      11.0   \n",
+       "11   7.0          0.0               4.0          4.0       1.0   \n",
+       "12   8.0          1.0               6.0       1510.0       2.0   \n",
+       "14   9.0          0.6             -11.0        293.0       0.5   \n",
        "\n",
-       "    additives_n  nutriscore_score  energy_100g  fat_100g  ...  salt_100g  \\\n",
-       "0           0.0               4.0       1125.0       3.0  ...   1.300000   \n",
-       "7           NaN              17.0       1059.0      17.0  ...   2.500000   \n",
-       "42          0.0              -3.0          NaN       NaN  ...        NaN   \n",
-       "45          NaN              25.0       2464.0      47.0  ...   0.000000   \n",
-       "47          6.0              25.0      33472.0       0.0  ...  18.750001   \n",
+       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
+       "6                10.50                13.0         9.00   36.000000   \n",
+       "9                 2.00                25.0         0.98    9.000000   \n",
+       "11                1.00                 1.0         1.00    1.000000   \n",
+       "12                0.50                 6.7         1.70   10.714286   \n",
+       "14                0.06                 2.0         0.24   88.000000   \n",
        "\n",
-       "    sodium_100g  fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "0          0.52                                           0.000000       \n",
-       "7          1.00                                                NaN       \n",
-       "42          NaN                                         100.000000       \n",
-       "45         0.00                                                NaN       \n",
-       "47         7.50                                           0.429687       \n",
+       "    proteins_100g  salt_100g  sodium_100g  \\\n",
+       "6            23.0      0.300         0.12   \n",
+       "9            22.0      0.950         0.38   \n",
+       "11            1.0      1.000         0.40   \n",
+       "12           76.0      1.500         0.60   \n",
+       "14           18.0      0.275         0.11   \n",
        "\n",
-       "        PNNS_pro  PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  \\\n",
-       "0    Plant_based                    0.0              0.0          0.0   \n",
-       "7   Animal_based                    0.0              0.0          0.0   \n",
-       "42   Plant_based                    0.0              0.0          1.0   \n",
-       "45            NA                    0.0              0.0          1.0   \n",
-       "47            NA                    0.0              0.0          1.0   \n",
+       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "6                                            0.000000       \n",
+       "9                                           20.400223       \n",
+       "11                                           0.000000       \n",
+       "12                                           0.000000       \n",
+       "14                                          20.000291       \n",
        "\n",
-       "    PNNS_pro_Plant_based PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "0                    1.0                0.0              0.0  \n",
-       "7                    1.0                0.0              0.0  \n",
-       "42                   0.0                0.0              0.0  \n",
-       "45                   0.0                0.0              0.0  \n",
-       "47                   0.0                0.0              0.0  \n",
+       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
+       "6                     0.0              0.0          0.0                   0.0   \n",
+       "9                     0.0              0.0          0.0                   0.0   \n",
+       "11                    0.0              0.0          0.0                   1.0   \n",
+       "12                    0.0              0.0          1.0                   0.0   \n",
+       "14                    0.0              0.0          1.0                   0.0   \n",
        "\n",
-       "[5 rows x 25 columns]"
+       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "6                  0.0              1.0  \n",
+       "9                  1.0              0.0  \n",
+       "11                 0.0              0.0  \n",
+       "12                 0.0              0.0  \n",
+       "14                 0.0              0.0  "
       ]
      },
-     "execution_count": 28,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from scripts.encoding_func import one_hot_encode_column\n",
-    "\n",
-    "filtered_df = one_hot_encode_column(final_df, 'PNNS_pro')\n",
-    "filtered_df_test = one_hot_encode_column(final_df_test, 'PNNS_pro')\n",
-    "filtered_df.head()\n",
-    "filtered_df_test.head()\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filtered_df = filtered_df.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])\n",
-    "filtered_df_test = filtered_df_test.drop(columns=['pnns_groups_1', 'pnns_groups_2','brands_tags', 'categories', 'ingredients_analysis_tags', 'PNNS_pro'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "filtered_df.head()"
+    "from scripts.Imputing import knn_impute_numeric\n",
+    "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
+    "imputed_df_test = knn_impute_numeric(filtered_df_test, n_neighbors = 5)\n",
+    "imputed_df.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "##### 2.3 Imputing"
+    "##### 2.4 Scaling"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(807, 19)\n",
-      ": shape of df with only numeric features=(1450, 19)\n"
+      ": shape of df with only numeric features=(807, 18)\n",
+      ": shape of df with only numeric features=(1450, 18)\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "from scripts.Scaling import scaler_numeric\n",
+    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
+    "work_df_test = scaler_numeric(imputed_df_test, 'nutriscore_score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
     {
      "data": {
       "application/vnd.microsoft.datawrangler.viewer.v0+json": {
@@ -881,114 +4166,114 @@
          "type": "float"
         }
        ],
-       "ref": "a145b9fe-a807-455c-b868-d4d83b78a957",
+       "ref": "0215de47-8466-4117-b731-e37f849871d2",
        "rows": [
         [
          "6",
-         "4.0",
-         "0.0",
+         "-2.445750371869255e-06",
+         "-0.7499999999999999",
          "15.0",
-         "2401.0",
-         "12.0",
-         "10.5",
-         "13.0",
-         "9.0",
-         "36.0",
-         "23.0",
-         "0.3",
-         "0.12",
-         "0.0",
+         "1.461818181818182",
+         "0.021972656250000073",
+         "1.1003451776649746",
+         "-0.3534601599117728",
+         "-0.12803584060363116",
+         "2.7338144044616133",
+         "0.09493032908390153",
+         "-0.26118808727504383",
+         "-0.2611880872750438",
+         "-0.19735657983213245",
          "0.0",
          "0.0",
+         "-0.5",
          "0.0",
          "0.0",
-         "0.0",
-         "1.0"
+         "2.5"
         ],
         [
          "9",
-         "6.0",
-         "1.8",
+         "-2.426058339889631e-06",
+         "0.37500000000000006",
          "4.0",
-         "1520.0",
-         "11.0",
-         "2.0",
-         "25.0",
-         "0.98",
-         "9.0",
-         "22.0",
-         "0.95",
-         "0.38",
-         "20.400223270165018",
-         "0.0",
+         "0.18036363636363636",
+         "-0.039062499999999924",
+         "-0.28036548223350255",
+         "-0.02260821615660326",
+         "-0.6008016977128036",
+         "0.0038421831263237664",
+         "0.06528313074414463",
+         "-0.05414874980092376",
+         "-0.05414874980092372",
+         "0.3840135991997579",
          "0.0",
          "0.0",
+         "-0.5",
          "0.0",
          "1.0",
          "0.0"
         ],
         [
          "11",
-         "7.0",
-         "0.0",
-         "4.0",
+         "-2.4162123238998194e-06",
+         "-0.7499999999999999",
          "4.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "1.0",
-         "0.4",
+         "-2.0247272727272727",
+         "-0.6494140624999999",
+         "-0.4428020304568528",
+         "-0.6843121036669423",
+         "-0.5996227304880924",
+         "-0.8050384750470954",
+         "-0.5573080343907502",
+         "-0.03822264691829912",
+         "-0.03822264691829908",
+         "-0.19735657983213245",
          "0.0",
          "0.0",
-         "0.0",
-         "0.0",
-         "1.0",
+         "-0.5",
+         "5.0",
          "0.0",
          "0.0"
         ],
         [
          "12",
-         "8.0",
-         "1.0",
+         "-2.4063663079100075e-06",
+         "-0.12499999999999997",
          "6.0",
-         "1510.0",
-         "2.0",
-         "0.5",
-         "6.7",
-         "1.7",
-         "10.714286",
-         "76.0",
-         "1.5",
-         "0.6",
+         "0.1658181818181818",
+         "-0.5883789062499999",
+         "-0.5240203045685279",
+         "-0.5271574303832368",
+         "-0.5583588776232021",
+         "0.17717378162350847",
+         "1.666231841091017",
+         "0.12103838190794709",
+         "0.12103838190794709",
+         "-0.19735657983213245",
          "0.0",
          "0.0",
-         "0.0",
-         "1.0",
+         "2.0",
          "0.0",
          "0.0",
          "0.0"
         ],
         [
          "14",
-         "9.0",
-         "0.6",
+         "-2.3965202919201957e-06",
+         "-0.37499999999999994",
          "-11.0",
-         "293.0",
-         "0.5",
-         "0.06",
-         "2.0",
-         "0.24",
-         "88.0",
-         "18.0",
-         "0.275",
-         "0.11",
-         "20.00029130415483",
+         "-1.6043636363636364",
+         "-0.6799316406249999",
+         "-0.595492385786802",
+         "-0.6567411083540116",
+         "-0.6444234850271162",
+         "7.991538682588836",
+         "-0.053305662614882954",
+         "-0.26915113871635615",
+         "-0.2691511387163561",
+         "0.37261624753351613",
          "0.0",
          "0.0",
-         "1.0",
+         "2.0",
          "0.0",
          "0.0",
          "0.0"
@@ -1042,110 +4327,110 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.750</td>\n",
        "      <td>15.0</td>\n",
-       "      <td>2401.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>10.50</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>9.00</td>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>0.12</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>1.461818</td>\n",
+       "      <td>0.021973</td>\n",
+       "      <td>1.100345</td>\n",
+       "      <td>-0.353460</td>\n",
+       "      <td>-0.128036</td>\n",
+       "      <td>2.733814</td>\n",
+       "      <td>0.094930</td>\n",
+       "      <td>-0.261188</td>\n",
+       "      <td>-0.261188</td>\n",
+       "      <td>-0.197357</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>1.8</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>0.375</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>1520.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>2.00</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.98</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.950</td>\n",
-       "      <td>0.38</td>\n",
-       "      <td>20.400223</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>0.180364</td>\n",
+       "      <td>-0.039062</td>\n",
+       "      <td>-0.280365</td>\n",
+       "      <td>-0.022608</td>\n",
+       "      <td>-0.600802</td>\n",
+       "      <td>0.003842</td>\n",
+       "      <td>0.065283</td>\n",
+       "      <td>-0.054149</td>\n",
+       "      <td>-0.054149</td>\n",
+       "      <td>0.384014</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.750</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.40</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>-2.024727</td>\n",
+       "      <td>-0.649414</td>\n",
+       "      <td>-0.442802</td>\n",
+       "      <td>-0.684312</td>\n",
+       "      <td>-0.599623</td>\n",
+       "      <td>-0.805038</td>\n",
+       "      <td>-0.557308</td>\n",
+       "      <td>-0.038223</td>\n",
+       "      <td>-0.038223</td>\n",
+       "      <td>-0.197357</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.125</td>\n",
        "      <td>6.0</td>\n",
-       "      <td>1510.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.50</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>10.714286</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>1.500</td>\n",
-       "      <td>0.60</td>\n",
-       "      <td>0.000000</td>\n",
+       "      <td>0.165818</td>\n",
+       "      <td>-0.588379</td>\n",
+       "      <td>-0.524020</td>\n",
+       "      <td>-0.527157</td>\n",
+       "      <td>-0.558359</td>\n",
+       "      <td>0.177174</td>\n",
+       "      <td>1.666232</td>\n",
+       "      <td>0.121038</td>\n",
+       "      <td>0.121038</td>\n",
+       "      <td>-0.197357</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.6</td>\n",
+       "      <td>-0.000002</td>\n",
+       "      <td>-0.375</td>\n",
        "      <td>-11.0</td>\n",
-       "      <td>293.0</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>0.06</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.24</td>\n",
-       "      <td>88.000000</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.275</td>\n",
-       "      <td>0.11</td>\n",
-       "      <td>20.000291</td>\n",
+       "      <td>-1.604364</td>\n",
+       "      <td>-0.679932</td>\n",
+       "      <td>-0.595492</td>\n",
+       "      <td>-0.656741</td>\n",
+       "      <td>-0.644423</td>\n",
+       "      <td>7.991539</td>\n",
+       "      <td>-0.053306</td>\n",
+       "      <td>-0.269151</td>\n",
+       "      <td>-0.269151</td>\n",
+       "      <td>0.372616</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -1155,93 +4440,54 @@
        "</div>"
       ],
       "text/plain": [
-       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6    4.0          0.0              15.0       2401.0      12.0   \n",
-       "9    6.0          1.8               4.0       1520.0      11.0   \n",
-       "11   7.0          0.0               4.0          4.0       1.0   \n",
-       "12   8.0          1.0               6.0       1510.0       2.0   \n",
-       "14   9.0          0.6             -11.0        293.0       0.5   \n",
+       "        code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6  -0.000002       -0.750              15.0     1.461818  0.021973   \n",
+       "9  -0.000002        0.375               4.0     0.180364 -0.039062   \n",
+       "11 -0.000002       -0.750               4.0    -2.024727 -0.649414   \n",
+       "12 -0.000002       -0.125               6.0     0.165818 -0.588379   \n",
+       "14 -0.000002       -0.375             -11.0    -1.604364 -0.679932   \n",
        "\n",
        "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6                10.50                13.0         9.00   36.000000   \n",
-       "9                 2.00                25.0         0.98    9.000000   \n",
-       "11                1.00                 1.0         1.00    1.000000   \n",
-       "12                0.50                 6.7         1.70   10.714286   \n",
-       "14                0.06                 2.0         0.24   88.000000   \n",
+       "6             1.100345           -0.353460    -0.128036    2.733814   \n",
+       "9            -0.280365           -0.022608    -0.600802    0.003842   \n",
+       "11           -0.442802           -0.684312    -0.599623   -0.805038   \n",
+       "12           -0.524020           -0.527157    -0.558359    0.177174   \n",
+       "14           -0.595492           -0.656741    -0.644423    7.991539   \n",
        "\n",
        "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6            23.0      0.300         0.12   \n",
-       "9            22.0      0.950         0.38   \n",
-       "11            1.0      1.000         0.40   \n",
-       "12           76.0      1.500         0.60   \n",
-       "14           18.0      0.275         0.11   \n",
+       "6        0.094930  -0.261188    -0.261188   \n",
+       "9        0.065283  -0.054149    -0.054149   \n",
+       "11      -0.557308  -0.038223    -0.038223   \n",
+       "12       1.666232   0.121038     0.121038   \n",
+       "14      -0.053306  -0.269151    -0.269151   \n",
        "\n",
        "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                            0.000000       \n",
-       "9                                           20.400223       \n",
-       "11                                           0.000000       \n",
-       "12                                           0.000000       \n",
-       "14                                          20.000291       \n",
+       "6                                           -0.197357       \n",
+       "9                                            0.384014       \n",
+       "11                                          -0.197357       \n",
+       "12                                          -0.197357       \n",
+       "14                                           0.372616       \n",
        "\n",
        "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0          0.0                   0.0   \n",
-       "9                     0.0              0.0          0.0                   0.0   \n",
-       "11                    0.0              0.0          0.0                   1.0   \n",
-       "12                    0.0              0.0          1.0                   0.0   \n",
-       "14                    0.0              0.0          1.0                   0.0   \n",
+       "6                     0.0              0.0         -0.5                   0.0   \n",
+       "9                     0.0              0.0         -0.5                   0.0   \n",
+       "11                    0.0              0.0         -0.5                   5.0   \n",
+       "12                    0.0              0.0          2.0                   0.0   \n",
+       "14                    0.0              0.0          2.0                   0.0   \n",
        "\n",
        "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              1.0  \n",
+       "6                  0.0              2.5  \n",
        "9                  1.0              0.0  \n",
        "11                 0.0              0.0  \n",
        "12                 0.0              0.0  \n",
        "14                 0.0              0.0  "
       ]
      },
-     "execution_count": 30,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "from scripts.Imputing import knn_impute_numeric\n",
-    "imputed_df = knn_impute_numeric(filtered_df, n_neighbors=5)\n",
-    "imputed_df_test = knn_impute_numeric(filtered_df_test, n_neighbors = 5)\n",
-    "imputed_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### 2.4 Scaling"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ": shape of df with only numeric features=(807, 18)\n",
-      ": shape of df with only numeric features=(1450, 18)\n"
-     ]
-    }
-   ],
-   "source": [
-    "from scripts.Scaling import scaler_numeric\n",
-    "work_df = scaler_numeric(imputed_df, 'nutriscore_score')\n",
-    "work_df_test = scaler_numeric(imputed_df_test, 'nutriscore_score')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "work_df.head()"
    ]
@@ -1276,7 +4522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1288,128 +4534,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:528: FitFailedWarning: \n",
-      "540 fits failed out of a total of 1620.\n",
-      "The score on these train-test partitions for these parameters will be set to nan.\n",
-      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
-      "\n",
-      "Below are more details about the failures:\n",
-      "--------------------------------------------------------------------------------\n",
-      "206 fits failed with the following error:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
-      "    estimator._validate_params()\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
-      "    validate_parameter_constraints(\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
-      "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n",
-      "334 fits failed with the following error:\n",
-      "Traceback (most recent call last):\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 866, in _fit_and_score\n",
-      "    estimator.fit(X_train, y_train, **fit_params)\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 1382, in wrapper\n",
-      "    estimator._validate_params()\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\base.py\", line 436, in _validate_params\n",
-      "    validate_parameter_constraints(\n",
-      "  File \"c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 98, in validate_parameter_constraints\n",
-      "    raise InvalidParameterError(\n",
-      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestRegressor must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
-      "\n",
-      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
-      "c:\\Users\\jessi\\anaconda3\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\model_selection\\_search.py:1108: UserWarning: One or more of the test scores are non-finite: [         nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan -28.40078216 -27.41119183 -25.43973985\n",
-      " -30.92752695 -28.69548775 -24.98124643 -34.10000593 -28.11079336\n",
-      " -25.7229842  -32.69205128 -25.4949763  -25.28107748 -35.82649297\n",
-      " -29.12476986 -25.78841879 -42.7811845  -31.4564862  -27.04992778\n",
-      " -34.56585429 -29.66038082 -27.98264181 -34.56585429 -29.66038082\n",
-      " -27.98264181 -32.81587154 -29.45784358 -28.0036475  -28.40078216\n",
-      " -27.41119183 -25.43973985 -30.92752695 -28.69548775 -24.98124643\n",
-      " -34.10000593 -28.11079336 -25.7229842  -32.69205128 -25.4949763\n",
-      " -25.28107748 -35.82649297 -29.12476986 -25.78841879 -42.7811845\n",
-      " -31.4564862  -27.04992778 -34.56585429 -29.66038082 -27.98264181\n",
-      " -34.56585429 -29.66038082 -27.98264181 -32.81587154 -29.45784358\n",
-      " -28.0036475           nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan -32.6001312  -28.26068731\n",
-      " -25.19694896 -31.56943702 -27.8065814  -25.4765719  -35.33856308\n",
-      " -29.8607632  -27.39657916 -31.78543528 -27.9463427  -24.56787522\n",
-      " -31.10488548 -27.41086252 -25.98672186 -36.72497321 -30.40072803\n",
-      " -26.59289105 -38.18719755 -30.36983308 -28.15365161 -38.18719755\n",
-      " -30.36983308 -28.15365161 -36.31265589 -31.65254105 -27.91489056\n",
-      " -32.6001312  -28.26068731 -25.19694896 -31.56943702 -27.8065814\n",
-      " -25.4765719  -35.33856308 -29.8607632  -27.39657916 -31.78543528\n",
-      " -27.9463427  -24.56787522 -31.10488548 -27.41086252 -25.98672186\n",
-      " -36.72497321 -30.40072803 -26.59289105 -38.18719755 -30.36983308\n",
-      " -28.15365161 -38.18719755 -30.36983308 -28.15365161 -36.31265589\n",
-      " -31.65254105 -27.91489056          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan -28.40078216\n",
-      " -27.41119183 -25.34621528 -30.92752695 -28.75560499 -25.00224768\n",
-      " -34.75251947 -28.358578   -25.76613456 -33.00587297 -25.64588925\n",
-      " -25.31263651 -35.82649297 -29.12476986 -25.78841879 -42.7811845\n",
-      " -31.4564862  -27.04992778 -34.56585429 -29.66038082 -27.98264181\n",
-      " -34.56585429 -29.66038082 -27.98264181 -32.81587154 -29.45784358\n",
-      " -28.0036475  -28.40078216 -27.41119183 -25.34621528 -30.92752695\n",
-      " -28.75560499 -25.00224768 -34.75251947 -28.358578   -25.76613456\n",
-      " -33.00587297 -25.64588925 -25.31263651 -35.82649297 -29.12476986\n",
-      " -25.78841879 -42.7811845  -31.4564862  -27.04992778 -34.56585429\n",
-      " -29.66038082 -27.98264181 -34.56585429 -29.66038082 -27.98264181\n",
-      " -32.81587154 -29.45784358 -28.0036475           nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      "          nan          nan          nan          nan          nan\n",
-      " -28.40078216 -27.41119183 -25.43973985 -30.92752695 -28.69548775\n",
-      " -24.98124643 -34.10000593 -28.11079336 -25.7229842  -32.69205128\n",
-      " -25.4949763  -25.28107748 -35.82649297 -29.12476986 -25.78841879\n",
-      " -42.7811845  -31.4564862  -27.04992778 -34.56585429 -29.66038082\n",
-      " -27.98264181 -34.56585429 -29.66038082 -27.98264181 -32.81587154\n",
-      " -29.45784358 -28.0036475  -28.40078216 -27.41119183 -25.43973985\n",
-      " -30.92752695 -28.69548775 -24.98124643 -34.10000593 -28.11079336\n",
-      " -25.7229842  -32.69205128 -25.4949763  -25.28107748 -35.82649297\n",
-      " -29.12476986 -25.78841879 -42.7811845  -31.4564862  -27.04992778\n",
-      " -34.56585429 -29.66038082 -27.98264181 -34.56585429 -29.66038082\n",
-      " -27.98264181 -32.81587154 -29.45784358 -28.0036475 ]\n",
-      "  warnings.warn(\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Meilleurs paramètres trouvés :  {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2, 'n_estimators': 60}\n",
-      "MSE : 7.595541984511499\n",
-      "R² : 0.10416152266041212\n"
+      "n_iterations: 1\n",
+      "n_required_iterations: 5\n",
+      "n_possible_iterations: 1\n",
+      "min_resources_: 50\n",
+      "max_resources_: 100\n",
+      "aggressive_elimination: False\n",
+      "factor: 3\n",
+      "----------\n",
+      "iter: 0\n",
+      "n_candidates: 216\n",
+      "n_resources: 50\n",
+      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n",
+      "Meilleurs paramètres trouvés :  {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
+      "MSE : 7.504433114323556\n",
+      "R² : 0.12552387466001047\n"
      ]
     }
    ],
@@ -1420,12 +4566,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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SCPAEWuB7BE8C8g7wPcR3DTkmEHdbb711Sp9vF0kQzxho46488jmQNG21Bx6mZIIWeQ4jIdFvJj4PbfrjH/+Y8D1ok/VeeOjgwYBXCt4SFJaAIIMXAvaEOPjiiy+U5xHb4XmByIeYwk2DbLWZkHyEwoKQEYK7dLiT9sYbb6hEy6FApRf84cJdMfzRsScnYnCE7fY7qFhnB3dtkZiYLvg8+50uJBmiHVYSJgZiGMBgH+uufqrgzh7+2Fl3Ou3gj+pwYACNQSJCXuyg7xhEJuqLHbQb/YkfXHz99deDyjEi+TKVmv9WYjAGqonuDI+UdAUl7gJjQcI6klCR2Is7ulYoDe62YmCHBfaEFwOJnxjg4Q5pqucW3zskc+Nc2tuI85rN8xIftmW/E5sqCGeDFwfeiW9/+9vRaj8IzUJIH5KiLeClGSk4J7hhEH9O4r/T1jWL9fF3+bHOfk2PNrgWEa6V6LpDOBhEpTUwToZlYwjXVGyM/eG1wIJrE4ITAhgV5kbynb/qqquU5wLfeSSwo08IfcQNleHak+r3eLj+oGoaPBDDtR3nE/thgRCBkEeSOsSG1Vb8/sC7gQW/3xDD6NsFF1yQFXsRkq8wx4KQEYJ4b/zxwGAPAiEelE20SoDijitAtRQ71t0xxGXb/8DhjpgdCJhkHouhsMpZWqDaE7Aq4OCPHe6w4S5a/B1SvMagLRl4H2KdcdcS1WAsEE6Au5jDgffHfybinONLdVqgkpU99AyDZ4gtqy8WqExlL3mLP+p4jT/mqGg0FNiO8/9///d/KkQjHoQwjAR8T+LFYiIQ/hJ/TiwPgVXyMt4mGIRY4sraJ9VzC/th3WOPPRZdhwo2qGqTzfOCwZZ9SVSeeThwxxyDWHy/EN9u9RPY+wp74+7wSMG1CnGK75cF7qbHh+Mg7wODcAyC7eVI4UFCG+3X9GiD84BwG5wXe2ggfpcgTOEFGC4sD98F7INBcqL8HsvGOBfxJavx3YAIsJ+HVL/z9mMgDBCVthDahT7hNe72J/IM279zqX6PhwLhhDhGovcg5NOqxIRSzvEMd43iRgDmYMH3FOc2G/YiJF+hx4KQEYI/hPgjgDtS8ELYZ95+/fXX1UDOCvfYcsstlTsfgxMrfOOtt95SoSsIA8DdZQsIFSQE448qZh/GXTQM1BPdxR8O3LlFQiPi7+FZwd1ExPqiPVYfLrvsMnUXDX/g0BYMEPA+3D1EwipKsSYDggSufsRK4645BvVW/XZ73Hoyjw/CV5BEiZK9KO2IEpz2ZEY7COfBH1zsjz/AEGm4Ox+foIkQNcSAoz/wwiBMAaFDOPdD5YxYg3TET0OsoA/4LMRjY8CBu5H4Y48QpXTBwBwx1xCSaB88RPHlJAG+DxgU4448bAMhhYEOPtcSp/h+YHCDu+QIF0J8Oc45BjeWNyzVcwsvwE033aQSdlEWEwN+7GclI1t3bkfrvKQLrieEk8C++K6ib/Dw4dpCeU+0F6E8mYQR4fuEc4LrGcn+OCc4ZvwEavguoR04F7iecQ6tcrPwjGEehVyC69iaXwHXIoQYBDUGu4nm6ogHNkRpWcwGv80226hSrBDjuGmAkB/kB+C8wPuHO/UYiGOwjM/BbwX6jvfYv/M4HtqF6xQiLFn+hsW5556r8odwbcODgQXfL1wrsAs+D999hLrherIG+al+j4cC/cZn47cXn4n+4mYOPAhYj99giElcV7jxA+EITwlyL3DN4lrEuQcQDQgnwzGQhwKhifbhPVYBikztRUje4nRZKkLczoIFC1QpxmnTpqnygSgXuMsuuxg33nhjTLnL/v5+45JLLlHlOYuKiowpU6YYF1xwQcw+AGVCzz//fKOurk6VzESJz0WLFiUtN5uoNKlVChWlX7/zne+oNtXW1hqnn356TGlMi4ceekiVM0RZVCwo03jaaacZX3zxxbD9f+mll4xtt91W9R3lW1Ge0fr8oUC/zznnHFVOEmU3cc7eeOMNVQ7SXqbSKk953333qfOFEp/Y/+CDD44pcwvwPpR5RHnSnXbaSZXCxXm76aabYvazjvnggw8mbNv7779vHHHEEapsLEq04hjf/e53jeeee27QOY4vOxlfQtYqIYoSkmg3tll2jN/3vffeM44++mhjgw02UJ+Lvh5yyCGqPxb/+te/jP32209twznHviiv2tjYmPa5BV9++aU6l9hv3Lhx6n34PqBd8+bNS/u8ZAN8Nr5/ibj44otjSoiiDOg3vvEN1f6JEyca5513nvH0008nLDOaqAQobIF+2MH3CqWacf3hOkSJX5REjT8m+Oc//2lsvfXW6nyMGTPGOOaYY4wVK1YM+gxcV/EkaxPaA5uk+n21wPcHvxcVFRWq7Xvuuafx+uuvx+wz1O+G9Vk4Bsq54vqZMWOGcfzxx0e/gyj9CtvgNwJ9wn477rhjTHleq0wt+oDfHnye9b0bri977LGHKqfc0tKiXq9evVp9Hn4v8buJ8sB77723cdttt43oe5zsnFslhK+++mq1HfbEbyZ+2/C7jRLjAN91lJPFdw3XHx5xzeLvgMWtt96qrnfrOsE5PPfcc6PHyKa9CMk3PPjPaXFDCCHJQBw9PDrwANlLgCYCVZRQIjLdpHoSC+4Y4447SmbCM0GIG+H3mJDcwxwLQggpYBA/bgex6QjJQHlfDsaIW+D3mJD8gDkWhBBSwCCBH3NxIEcD8z4gDwdx5YhRJ8Qt8HtMSH5AYUEIIQUMKuogMRsDMCSrIkEWpW1RlIAQt8DvMSH5AXMsCCGEEEIIIRnDHAtCCCGEEEJIxlBYEEIIIYQQQgo7xyIcDqsZUjHhTCoT4BBCCCGEEEJSB1kTmLAVE7xiwlRthQVExZQpU5xuBiGEEEIIIVqzfPlyNcu8tsICngqro1VVVU43J69BlYzFixfLjBkzxOfzOd0cMkJoR32gLfWAdtQH2lIPaMfs09bWpm7kW+NubYWFFf4EUUFhMfyFVlFRoc4TLzT3QjvqA22pB7SjPtCWekA7jh6ppB0weZsQQgghhBCSMRQWhBBCCCGEkMKeIA8xX9XV1dLa2spQqGGAmVFFC9n8rKDlXmhHfaAt9YB21AfaUg9oR2fH2/RYFBDBYNDpJpAsQDvqA22pB7SjPtCWekA7OgeFRYEA9b5kyRL1SNwL7agPtKUe0I76QFvqAe3oLBQWhBBCCCGEkIyhsCCEEEIIIYRkDIVFATHcNOzEHdCO+kBb6gHtqA+0pR7Qjs7BqlCEEEIIIYSQhLAqFBkE9GNHR4d6JO6FdtQH2lIPaEd9oC31IFM7rlmzRi6++GJZvnx51ttWCFBYFAiojrBixQpWSXA5tKM+0JZ6QDvqA22pB5na8bTTTpN3331XfvzjH2e9bYUAhQUhhBBCyCgRChvyxuJ18ugHK9UjXo8mxx9/vJoYDktRUZFMnz5dzjvvPOnp6YnZz9pn3rx5Met7e3tl7NixatuLL74YXf/SSy/JXnvtJWPGjJGysjKZNWuWHHfccdLX16e2Y1/rmPHLqlWrRrXP+IxHHnkk4+M89NBD6liPP/64TJ06VW677TZxOy9G7NLS0pKTz/Pn5FMIIYQQQgqMpz5plEseny+NrQOD+obqErno0E3lgDkNo/a5BxxwgNx1113S39+v7r5DAGBwefXVV8fsN2XKFLXfN77xjei6hx9+WCoqKqS5uTm6bv78+eqYP//5z+VPf/qTlJaWysKFC9VAPBQKxRzziy++GBSHX19fL04DARQIBIbc58gjj1QL0EFUOAE9FgUCflBwQXF6e3dDO+oDbakHtKM+ZNuWEBWn/O29GFEBVrX2qPXYPloUFxfLhAkTlHA4/PDDZZ999pFnn3120H4QHPfff790d3dH1/3lL39R6+0888wz6nh/+MMfZM6cOTJjxgwlNG6//XYlMuJFBPa1L0NVafrkk0/kwAMPVGJm/Pjx8sMf/lDWrl0b3b7HHnvIGWecobwu8JbgeMiBsJg2bZp6/Pa3v61sh7bBjpdeeqlstdVWcscddyivTUlJidrvqaeekl133VVqamqUZ+aQQw6RxYsXR4+3dOlSdZwPPvgg5o7/c889J9ttt53y1uy8885KQNl59NFHZZtttlGfs+GGG8oll1wSMwM4jnHrrbeqz8MxNtlkE3njjTdk0aJFqo/l5eXquPa2pHpc9BH9tzxJjz32WLQve+65p3peW1ur9oVHazShsCgQcFHjC8kSbO6GdtQH2lIPaEd9yKYtEe4ET0WioCdrHbaPdliUNXB//fXXE96t33bbbdXAHJ4HsGzZMnn55ZfV4N4OBvONjY1qWzZBeA7Cq7beemt555131KB/9erV8t3vfjdmv3vuuUcNvN98800lbiAaLKH09ttvq0d4XtBGvIYdMYjGoB19+/e//x0VCp2dnfKLX/xCfR7EAuyNQflwORm/+c1v5Nprr1Xv8/v9cuKJJ0a3vfLKK/KjH/1IzjzzTOXdgYC4++675fLLL485xu9//3u1H9qy8cYbyw9+8AP52c9+JhdccIE6LhLOTz/99LSPC7GBc/bRRx/JQQcdJMccc4zyOEFYWraFEML5ueGGG2Q0YShUNgj2ifiKIBslX8GXFWXCUC6Md9bcC+2oD7SlHtCOhWfLQ298Vda09w55rN5gSNZ39Sf/LBHlydjusmel2O8btm3jKovl8Z/vKqnyxBNPKA8A7mwjZwKD55tuuinhvhggw0tx7LHHqkErBqbjxo2L2eeoo46Sp59+WnbffXclMhA6tffee6tBb3zY0+TJk2NeI1fh008/TfjZaBNExRVXXBFdh7ZgQLxgwQKZPXu2WrfFFlvIRRddpJ7jjjzeB1Gw7777RtsKDwTaBjtCsOAR4U/33ntvTH+sUCf752E7Bu7wxiQDg3n0H/zqV7+Sgw8+WOWtwJOAgT3WHRfx9EDYQETAy2K1G5xwwglR0XT++efLTjvtJL/73e9k//33V+sgILCPRarHhRfi6KOPVs9xLhGu9tZbbymvErw8licJ52i0obDIBh2rRbqbRYorRYqrzMU/dBxfroESR/JUZWWl+HzD/4iR/IR21AfaUg9ox8KzJUTFqrbY8KaRYoqP5AJkpCD8Ze7cueru/HXXXafusMcPqC0gKDB4/fLLL5WwwKA0HpwPeAQuu+wyef7555XnAANY5GxgANvQ0BBzlx3n0AIJ5Mn48MMP5YUXXlAiKB6EBNmFhR18XlNT05B2hLCAqIkXScgNufDCC1UfEHJleSrgrRlKWNjbYPUXbdhggw1UP1577bUYTwJyTyA8urq6VIhS/DEQ9gU233zzmHV4D+aNgGAbyXHh2cF7k52f0YbCIlsYYZGeVnMB/pKIyIDYqMxrbwYhhBBCUgPeg+EYzmNhUVtWlLLHIh0wuJw5c2b0jvyWW24pd955p5x00kmD9rXyDLANA1bkO7S3tyc87qRJk1SYFBbcOcfA/5ZbblF31i2Qz5DqnXHMN3HooYcOSioHdrESL07gUUqlnCzOQzz4PAgO5IdMnDhRHQeCwqpulQx7GyyPltUG9APn4Igjjhj0Piu3I9kxsn1c6zhOlU2msBgtgj3m0tkk4vGKBCpESqpNkeFP7weCEEIIIflBKiFJyJ3Y9ernVaJ2oiwKDB8nVJfIq+fvJT7v6N54RBjUr3/9a5VXgJj++GRrKxwKIVAIz0nV84ZkYAz+4RUZKUhKRg4A8jzgVRkpGFjHV6dKxLp161SuAUTFbrvtpta9+uqrkinoB447MyLmskU2jmvl1qRyfrIBs81y5c3obRNpXS7SNF+k6TOR1hUiPW0I6sxJE6BeodwZA+xuaEd9oC31gHbUh2zaEmIBJWXVceM/J/KI7aMtKuw5EhAMN998c8LtiMXHjNNIik4EkoZPOeUUVR0KIUrImYAIwSM8AHYQgoNQJPuCsrfJJqNDkjHyA5B0jWMjlwN5BukMhCFMkHOBz0J+RTI7QgzBQ4NSskjsRlgXBFemILQKuRyXXHKJOiefffaZqrb129/+1vHjwjuDc4G8G9gYXpDRhMLCCZQnY41I82KRVR+JrFss0rlWJDh0MlimdyyQDMXKJe6GdtQH2lIPaEd9yLYtMU/F3GO3UZ4JO3iN9aM5j0U88Aag2hAqKiXyMGDgWVdXl3Sehx122EENSE8++WTZbLPNVBIzJtbDpHRWQrPFRhttpDwZ9gVzaSQCoUjIIYCI2G+//VS+wVlnnaVCqdKxA6o1oUoU7IdKV3hMJCxwTAzM0R6EP5199tlyzTXXSKYg+RoD92eeeUa23357ldyO3BYM6p0+LsLXrCRw5HDYq06NBh4D2S0uBcktqN6AKg7xVQlySstyka6BmssZoXIzIkngCJ/K0g8cYu1wVwDVAfgH0L3QjvpAW+oB7agPo2VLhEW9taRZmtp7pL6yRHaYPiZnnopChNeks+Nt5ljkbW7GmoHcDIiMkqqMcjOgH1H9AG5A4l5oR32gLfWAdtSH0bIlRMROM8Zm9ZgkObwmnYXCwg25GVja8OtUbAoMeDQClVnzZhBCCCGEEJIpFBZuItRrejKy7M0ghBBCCCEkUygsdPJmwJMBkZHAm4EkJs4M635oR32gLfWAdtQH2lIPaEdnYfJ2viVvZwO7NwNioyi2IgUhhBBCCCHZHm8zSF9HLG9G2wqRNZ+JrJ4v4eZl0rhssYSDQadbRzKsdtHY2OjYjJoke9CWekA76gNtqQe0o7NQWBQCoV4xutZK6+plYqz62Jw3o6NJpL/H6ZaRNIGDEXcMXOxoJBFoSz2gHfWBttQD2tFZmGNRcBgDuRmyUsQXGAiZwiMrTRFCCCGEkBFAYVHohPrM/BCVI+IxczNUSdsq5mYQQgghhJCU4e3pAgHFEeoqS9RjcgyRvnaRtpWR3IxPzcT07hYELeausSQpqHJRV1fHahcaQFvqAe2oD7SlO7n77rulpqYm+vrSSy+V73znO0Pa8fjjj5fDDz8848/O1nF0gsKiQPDiB7OqRD2m7c1Yv0Rk1UciaxdFcjO6R7OpZAi8Xq/6w4dH4m5oSz2gHfVh1GwZDokseUXk43+Zj3g9imCwi0E1lqKiIpk+fbqcd9550tMTm1dp7TNv3ryY9b29vTJ27Fi17cUXX4yuf+mll2SvvfaSMWPGSFlZmcyaNUuOO+446evrU9uxr3XM+GXVqlWSK84991zV1mzacenSpaofH3zwQcz6G264QQkbMgBDoQqEcNiQlc1dMmlMmXi9I7kbE/FmYAEqNyOSl4FHry/bTSYJQJWLlStXyqRJkziQcTm0pR7QjvowKrac/5jIU+eLtH09sK5qosgBV4ts+i0ZLQ444AC56667pL+/X959910lADAwvvrqq2P2mzJlitrvG9/4RnTdww8/LBUVFdLc3DzQjfnz1TF//vOfy5/+9CcpLS2VhQsXykMPPSShUKxQ+uKLLwaVJK2vr5dcAdGzfv16qa2tHfVrEiVYSSz8FSwQUBuhs7dfPWYF5c1YF/FmfGx6M9pX05sxyqDKRWdnJ6tdaABtqQe0oz5k3ZYQFQ/8KFZUgLZGcz22jxLFxcUyYcIEJRwQqrPPPvvIs88+O2g/CI77779fursH/nb/5S9/UevtPPPMM+p4f/jDH2TOnDkyY8YMJTRuv/12JTLiRQT2tS+JBvgQcpMnT5a5c+fGrH///ffV/l999ZV6/cc//lE233xzKS8vV/059dRTpaOjI2nfL774Ytlvv/2idoTw+cUvfqHCpeCJgfcm3sZPPfWU7LrrrtF9DjnkEFm8eHF0O7w+YOutt1YCbY899kgYCtXb2ytnnHGGOgclJSXqmG+//XZ0u+XVee6552S77bZTImjnnXdWYkwXKCxIFoh4M9q/FlnzeSQ3Y1kkN2N0Xb6EEEJI3oG/ffBUJLydF1n31K9y8jfyk08+kddff10CgcCgbdtuu61MmzZNeR7AsmXL5OWXX5Yf/vCHMftBHGBuCGzLFhAPRx99tPzjH/+IWf/3v/9ddtllF5k6dWp0P3hJPv30U7nnnnvk+eefV+IgVa699loVrgTB9OqrrypPDLwydiAoIT7eeecdNejHZ37729+OzoXx1ltvqcf//e9/6jz8+9//TvhZ5513njqXaOd7770nM2fOlP333z/G+wN+85vfqHbh8/x+v5x44omiCwyFItnH8mZgUZWmys2QKVSbKoq9s0EIIYS4ilt3N/MNhyLYK9KNv4HJMMxCKdfMEvEXD/+ZFfUiP3sp5SY+8cQTKpwpGAyqu+gYKN90000J98WgFoPuY489Vg3ADzroIBk3blzMPkcddZQ8/fTTsvvuuyuRgdCpvffeW370ox8NCnuCF8IOBAJEQSKOOeYYNcCGoNlggw3UQB4elN/+9rfRfc4666zoc4igyy67TE4++WT585//nNK5uP766+WCCy6QI444Qr2+5ZZbVF/sHHnkkTGvcT5wDhACBg+NdT7gzUD/EwFxMnfuXHUODzzwQLUOHh14iu68806V+2Fx+eWXq3MJfvWrX8nBBx+scmDg5XA7FBYFAtIqJtSUqcfcezM6zAUeDa/fLGmLpbiCQiNN8MchmVuZuAvaUg9oxwK0JUQF/p5lgyHFx8jZc8891SAXg93rrrtO3RWPHzxbQFBgcPvll1+qQTG8A/H4fD6Vi4FBPTwGb775plxxxRUqZwN38xsaGqL7vvLKK1JZWRl9jQTyZGy11VayySabKK8F2oCk66amJiVkLOAluPLKK+Xzzz+XtrY2JZYwCO/q6lKhRPEg1Aj9hR0xUR48DDvuuGN0O7YhDMkeDoV8kQsvvFD1a+3atVFPBQQPhEUqIHSqv79feVvsfd9hhx3ks88+i9l3iy22iD63zh36DXHldvhLWCDgQqspDzhfRi8cFOlpEWlbYYZNIT+j+UuRjjUifV3Ots0tdqypcd6OJGNoSz2gHQvQlvAeVE4ceikdm9qHYr/hjoUFn5kGyEdAGM6WW26p7r5jwIy75omwcgpOOukkNWC37rYnAontCJOC9wNeCOwPD4Ad5CPgs63FCmlKBrwWVjgUHpG7gTZZ1ZjQNgzEEWKERPSbb75ZbbOqUcUD+0EIpXNNHnrooSpcCR4GnCssQ31GphTZxJbVTkvMuB16LAqoKtTSNR0ybVzFCKtCjabQaDUX4PGZngzLqxEYfDeikMEPD35o4Q7mHVJ3Q1vqAe1YgLZMJSQJuRPXzzETtRPmWXjM6lBnfTzqVRXRl1//+tcqh+AHP/jBoGRrKxwKIVDnn3++GpSnAqou4W47vCKZgDYh9Ami4V//+leMUME62AXhUpZNHnjggSGPB08EBAHeh6pNaCOEwje/+U21HR4PHHebbbZRr9etW6eSpyEqdtttN7UOuRh2rPyU+ApYdpDQHggE5LXXXouKKXgwkLxtD+fSHQqLAgE/a33BUPaqQo0WRmiw0FA5GpWm0EDoVAHfGbR+MFmBxv3QlnpAO+pDVm0JsYCSsqj+BBER89c38jfsgKtyVqodoUWI8cfd/l/+8peDtsNLsGbNmkH5Eha33nqrmsMBCc0YQMNTce+99yqvxY033hizL0J64ufMgAciWUgUhBwqI8FjgoH7t741UIYXHg8MzvEZ8Cpg0B7vIYkH9rMWcOaZZ8pVV12l5t3YeOONVZWplpaWGIGE9t12221KhCD8CWFZdlDlCYIM1aOQQ4JciPhSs/ASnXLKKeo8Y64PhDWhihZCttC3QoG3V0j+C43eNjPJbe0XZujUusVmjGtfJ35BnG4hIYQQMhjMU/Hde0WqBvIPFPBUYP0ozmMRD/IKTj/9dDXQTeRhsGYdT1Q5CiBPACVekTS92WabqcRjTKz3yCOPRJOQLTbaaCM1QLcv8BAMFw714YcfKuFi96gglAtCALkcyHVAxSjkW6TDOeeco8K3UEJ3p512Uvkf+BwLeEKQMI424jPOPvtsueaaawadP+SeQGBNnDhRDjvssISfddVVV6lcFnwePCKLFi1SieIQL4WCx3DxbRYk8UAxIjknmcrOCS3LzRmq85hQ2JCFja0yq6FafPkUCpUplkcjmgxeprVHA3dzkGSGOy+puqtJfkJb6gHtqA+jZkuERX31ukjHapGK8SJTd+aksqMIr0lnx9sMhSoQoCUmjy13oCpUjjwaWDApuMdry8+A4CjXSmjgzgrcsIzldj+0pR7QjvowaraEiJhuxu6T0YfXpLNQWBQIcHNWlCQv+aYNRnhAaIhdaES8Gi4XGsqOFRVON4NkAdpSD2hHfaAt9YB2dBbKuQIBoVALvm5VjwWFJTTaG0XWLRRZ9ZHI2kUi7atEejtcl6MBF++CBQuGrExB3AFtqQe0oz7QlnpAOzoLPRYFRNhlg+hRExp97eZieTSKIiFTKkejHH5UyWd0qXVNaEtdoB31gbbUA9rROSgsSGFjFxodWOGJSwbPf6FBCCGEEJIPUFgQEoMh0tdhLvFCw3qk0CCEEEIIGQSFRYGAalDT6yv1qwqVS6Gh8Jglbe2zg+dQaKDKxfTp01ntQgNoSz2gHfWBttQD2tFZKCwKCL+PF1nmGCL9neYiq+OEhuXRGN262Zioh+gBbakHtKM+0JZ6QDs6B0eaBQKKQWGCvEIrCpUzoYGJj5q/NGcGX7NApHWlSE+rOTFSlhPSMPEPE9PcD22pB7SjPtCWekA7OgslHSGj5dHobIp4NEoHksFz4NEghBBCCHECCgtCRl1odJmLEhpihk5FK09VUmgQQgghRAsoLAjJNVGhscZ87S+NTQb38bIkhBBCiPvwGIZ7Z01ra2uT6upqaW1tlaqqKuca0rJcpGut5DMwM/IrUBUK092TPMZXLOKPLOp5QMRfIuILwP+h4kZR7YJ2dDfqmqQtXQ/tqA+0pR7Qjs6Ot3lrtIAIhsIS8DNfP+8J9ZpLb/wGj4i3SIKGXwKlZabYiAqQABSjM+0lIyYYDEogEHC6GSRDaEd9oC31gHZ0Do4yCwR4K5Y0tbMqlKsxJBzslSUrvpZwxxqRthUizYtFmuaLNH4osnq+yLrFIq0rRLC9p00k2IvbN043nCQAd9SWLFnCyiUuh3bUB9pSD2hHZ6HHghAtMIb2dFheDeXhMMOqot4OQgghhJAsQGFBiPYYIsEecxlSdJTEPacbmRBCCCGpQ2FRQHgZg68F2bWjXXS0xW2j6BhtkFxI3A/tqA+0pR7Qjs7BqlAFUhWKkOziiatcFVfFihBCCCFawKpQZBDQj529QSkv9rP8movJHzvaPB3xeLwDORxKaEQ8HBQdg23Z2Snl5eW8Jl0M7agPtKUe0I7OQl9RgYBqUCvWdbIqlMtxhR2NsCk4elrN2cZbl4msWyTS9KlZvarpc5HmL0VaV4p0rhPpbRcJ9kmhgYolK1asYOUSl0M76gNtqQe0o7PQY0EIybHo6DaXhJ4O+4SA9udFTrSWEEIIIWlAYUEIyUPR0ZqC6CgWKSoV8fqcajEhhBBCbFBYFAiIMgz4feqRuJeCteNQogP5HBAY/lLzsajMFbkciP3FzLCMAXY3tKM+0JZ6QDs6C6tCZQNWhSIkv/D4IiLDEhol5nP+oSGEEELSglWhyCCgH1u7+qW6rIgq3sXQjilihET6OswlpkRuRGBYC7wcPr9ztmxtVT/WtKV7oR31gbbUA9qxgKtChUIh+d3vfifTp0+X0tJSmTFjhvz+979XXwqSXVBFaFVLV35XEyLDQjtmWiK3W6S7WaRtpVmpavXHIqs/FVm3WKStUaS7RSQ4aHryUQEVS1atWsXKJS6HdtQH2lIPaEdncdRjcfXVV8vcuXPlnnvukc0220zeeecdOeGEE5TKPOOMM5xsGiGkUAj1mYt95nF7KFU0d4OhVIQQQkjeCovXX39dDjvsMDn44IPV62nTpsl9990nb731lpPNIoQUOkOGUpXY8jbKHAulIoQQQvINR/8i7rzzznLbbbfJggULZPbs2fLhhx/Kq6++Kn/84x8T7t/b26sWezKJFVKFBSCezuv1KheYPaQq2Xqsw7Zk663j2teDGBdbOCzeyHvjQ1R8Xo86rn097nl6h1gfNgwxEq0PGwjmsPVJxOsZvN7rMftrX4/nZQG/OlYorpHYP1Hbk63Plz7Z1xdMnwxDSgN+9R5t+uQaO4XF6OsSwSLNA33Cz6glNopKxBsoV6Vw493w8b8deEQIKI6h2m7b3/q9SrY+1d+3Uf3dG2K9z+crmD5hKSsr06pPOtoplT5Z16TVLh36NNL1bu4TnmPWbTza2+nmPnkctlP8cfJWWPzqV79S4mDjjTdWjUfDL7/8cjnmmGMS7n/llVfKJZdcMmj94sWLpaKiQj1HGFVDQ4OsXr1aJe9Y1NXVqWXlypVqqneLCRMmSE1NjSxdulT6+gZm/508ebI6Jo5tP9HIB/H7/bJw4cKBBvS0yqy6IgmGwrKkqT26GoOU2ROrpbM3qGZLtkC50A3HV6okXMTLW5QXF8mUunJpbu+Vte090fXVZQFpqC2T1a3d0to10Ma6yhKpqyqRlc1d0tnbP9CnmjKpKQ/I0jUd0hcc+DJMHluuBlQLvm5Vg61on+orxe/zysLG2DKesxqqXdGnipIiWbyqrWD61N0XlMWr27Tqk6vt9PW6wX3yF8nC1R0iXr+It0h5NWbN3kSC4bAsWbJkoE9er1o6OjrUTLHRPgUCsuGGG6rfMMQKR/tUXi5TpkyR5uZmWbt2oBKdI797sNOsWRIMBgf1CTeK8HmF0idUScHxdeqTjnZKtU9r1qzRrk862mmoPmH/5cuXa9WnOgftZP99y+tys/fff7+ce+65cs0116gciw8++EDOOuss5bE47rjjUvJYWIaxyl85ovZaV4i3e11+eywMQ1o6emVsVUnMsa393XPXWMc74an3CZ+5rq1XaiuL1TF16FOituvZJ4RSFUs4EkIV9gZkfXuv1I0fH/0N0u0uVyH0Caxbt079cbfe6/Y+FbLHYv369TJmzBg1+NKhTyNd7+Y+4XlLS4u6Ju1VodzcJ4/DdsL5xHWRSrlZR4UFRAG8Fqeddlp03WWXXSZ/+9vf5PPPPx/2/ZzHInUw+MFdYdwJxuCMuBPaUUNbTqoTX3FZXLJ4idPNIymCP9y4+4c7fvgjTNwLbakHtGMBz2PR1dUVVUkW+BLEKyZCCNGWcL9ZkSqmKpU3thqVJTjifi8JIYSQfMJRYXHooYeqnIoNNthAhUK9//77KgzqxBNPdLJZhBDiLEZYpL/TXKKYoVSDSuD6ihxsKCGEEJInwuLGG29UE+Sdeuqp0tTUJBMnTpSf/exncuGFFzrZLC3xRJJWGTzjbmjHQrYlJvjrMRdZP7AayeFRr8ZAdSqSGxDHzBl+9YC21APa0VkczbHIFOZYEEJIAuyhVPBywKvhC0QWejgIIYRomGNBcgcq2qC85vjqUlURh7gT2lEfRtWWCUOpLDyxIgOP8eKDd/pSBjmBKA05fvz4QTmDxF3QlnpAOzoLhUWBALcUavbXV5c63RSSAbSjPjhnS0Mk1GsuyVBzb9iEx6Dn/NNhAac/7uLV19c73RSSIbSlHtCOzsK/DoQQQgZXqsIyME/g4FCrqMhIJDyK6PUghJAChMKCEEJI+qFW0URySRJulczrgbCrAEvnEkKIhlBYFAi4eVhXWcKbiC6HdtQHvW2JcKs+c0mG1z9MuJU7ksxReaauro4VaDSAttQD2tFZKCwKBC8utCqWoHQ7tKM+FLwtw0FzSRZu5ZIkcySHYhBD3A9tqQe0o7NQWBRQBZqVzV0yaUwZqwm5GNpRH2hLPZLMUYFm5cqVMmnSJFagcTm0pR7Qjs5CYVFAFWg6e/vVI3EvtKM+0Ja5SDL3xQmN7CeZowJNZ2eneiTuhrbUA9rRWSgsCCGE6IkREgmGUksyR84Hql0lXTyJ14cN3CI1F58vxx0khJD8gsKCEEJIgZJCkvlwQFh0toqs7kHizMjEyUj2IYSQPITCokDA37sJNWXqkbgX2lEfaEtN7YhSvFhGFc/oi5cCjE1HPP6ECRMYl+9yaEdnobAoEFB2raY84HQzSIbQjvpAW+qBM3Y0zDAvLKPJsALFEiG+NLb58la0KFvW1DjdDJIhtKOzUFgUUAWapWs6ZNq4ClagcTG0oz7QlnqgtR1H0/tiFxrxnpKk23w2b0uibZmFiaGa0NKlS2XatGm82+1iaEdnobAoEFAboS8YYgUal0M76gNtqQe0Y6aiJZjlA3tG5kXxeARFhPq62sXo7RTxW8n8kX05QHUNqAbV19fHqlAOQWFBCCGEEE2IhImFQiNLxO9uFVkXMpNn4knkSYnxotiFiC8S9mUt/tjXhGgKhQUhhBBCSM68LB6b8PAPiA1PEhFCUUJcBIVFgYCbL5PHlrMCjcuhHfWBttQD2lEfcmdLzH0CcRIcemb5hFCUDAfyKiZPnsz8CoegsCigKgkVJUVON4NkCO2oD7SlHtCO+uAOW1KUpGTHigqnm1GwUFgUCKGwIYtXtcmMCVXi460110I76gNtqQe0oz7ob8vCECWhUEgWL14sM2bMEJ/PPYJIFygsCogwKyRoAe2oD7SlHtCO+kBb5lCU4LkvIOIrEvEVR577s1ZyljgDhQUhhBBCCHFelKCilhIZEBsBEb/tuVrPYWu+QwsRQgghhBDnQdWtYLe5JBUegdjFb3+d7zky+kNhUSAgXHR6fSUrl7gc2lEfaEs9oB31gbZ0i/DoMZdEeLzi9RbJ9GqfeNtWiPhLTLGhPB8UHrmAwqKA8PtYek0HaEd9oC31gHbUB9pSD+HhR6pMV6c5eWEMngQhVnHeD5IRFBYFAiYUXdjYKrMaqsXHuzGuhXbUB9pSD2hHfaAtC8GOhpnnkTTXw5MkxIrCI1UoLAghhBBCCLELj75hhEd8iJWVdO4pbFVKYUEIIYQQQsiwpOLxKBqislVAe+FBYUEIIYQQQkhWhEefuQwpPIaobOVy4eExDPfOBtPW1ibV1dXS2toqVVVVzjWkZblI11rJZ2BmxB2i2gWmuyfuhHbUB9pSD2hHfaAt9cD1dvRaIVYRARIoFympds14m+UPCohgiDNR6gDtqA+0pR7QjvpAW+qBq+0Y7hfp6xDpXi/SsVqkY424CQqLAgHqfUlTu3ok7oV21AfaUg9oR32gLfWAdnQWCgtCCCGEEEJIxlBYEEIIIYQQQjKGwqKA8LoxiYkMgnbUB9pSD2hHfaAt9YB2dA5WhSqQqlCEEEIIIcRlBCpF6mY62gRWhSKDgH7s6OlXj8S90I76QFvqAe2oD7SlHtCOzkJhUSCgOsKKdZ2skuByaEd9oC31gHbUB9pSD2hHZ6GwIIQQQgghhGQMhQUhhBBCCCEkYygsCgTURwj4feqRuBfaUR9oSz2gHfWBttQD2tFZ/A5/PskRXq9HNhxf6XQzSIbQjvpAW+oB7agPtKUe0I7OQo9FgYDqCC2dfayS4HJoR32gLfWAdtQH2lIPaEdnobAoEFAdYVVLF6skuBzaUR9oSz2gHfWBttQD2tFZKCwIIYQQQgghGUNhQQghhBBCCMkYCosCAdURyouLWCXB5dCO+kBb6gHtqA+0pR7Qjs7CqlAFVCVhSl25080gGUI76gNtqQe0oz7QlnpAOzoLPRYFQtgwZG1bj3ok7oV21AfaUg9oR32gLfWAdnQWCosCAdfX2vYe9UjcC+2oD7SlHtCO+kBb6gHt6CwUFoQQQgghhJCMobAghBBCCCGEZAyFRYGA6gjVZQFWSXA5tKM+0JZ6QDvqA22pB7Sjs7AqVAFVSWioLXO6GSRDaEd9oC31gHbUB9pSD2hHZ6HHokAIhw1pXN+lHol7oR31gbbUA9pRH2hLPaAdnYXCokDA5dXa1aceiXuhHfWBttQD2lEfaEs9oB2dhcKCEEIIIYQQkjEUFoQQQgghhJCMobAoEDwekbrKEvVI3AvtqA+0pR7QjvpAW+oB7egsrApVIHg9HqmrKnG6GSRDaEd9oC31gHbUB9pSD2hHZ6HHokBAdYTlaztZJcHl0I76QFvqAe2oD7SlHtCOzkJhUSDg8urs7WeVBJdDO+oDbakHtKM+0JZ6QDs6C4UFIYQQQgghJGMoLAghhBBCCCEZQ2FRIHg9IhNqytQjcS+0oz7QlnpAO+oDbakHtKOzsCpUgeDxeKSmPOB0M0iG0I76QFvqAe2oD7SlHtCOzkKPRYGA6ghfrm5nlQSXQzvqA22pB7SjPtCWekA7OguFRYGAy6svGGKVBJdDO+oDbakHtKM+0JZ6QDs6C4UFIYQQQgghJGMoLAghhBBCCCEZQ2FRIKA6wuSx5ayS4HJoR32gLfWAdtQH2lIPaEdnYVWoAqqSUFFS5HQzSIbQjvpAW+oB7agPtKUe0I7OQo9FgRAKG7Lg61b1SNwL7agPtKUe0I76QFvqAe3oLBQWBUTY4EWmA7SjPtCWekA76gNtqQe0o3NQWBBCCCGEEELcLyxWrlwpxx57rIwdO1ZKS0tl8803l3feecfpZhFCCCGEEELckry9fv162WWXXWTPPfeUJ598UsaNGycLFy6U2tpaJ5ulJaiOML2+klUSXA7tqA+0pR7QjvpAW+oB7VjAwuLqq6+WKVOmyF133RVdN336dCebpDV+n+MOKpIFaEd9oC31gHbUB9pSD2hH53D0zD/22GOy3XbbyVFHHSX19fWy9dZby+233+5kk7QFxREWNraqR+JeaEd9oC31gHbUB9pSD2jHAvZYfPnllzJ37lz5xS9+Ib/+9a/l7bffljPOOEMCgYAcd9xxg/bv7e1Vi0VbW5t6DIVCarHqF3u9XgmHw2LYqgIkW4912JZsvXVc+3qA/aOEw+KNvDf+i+zzetRx7evhnfMOsR7VDIxE68OG2A/v8cDlN3g93H+euPUou2b1L74Em+UujG97svX50if7+kLpk9kWI2ab2/uUqO2F0Ce8z6pcokufdLTTcH0CWG8/vtv7pKOdUumTdU1i8YkefRq0vgD6ZP2uYr3o0KdwWDwYZ47WGHaI9T6fz/x9iztO3goLdAAeiyuuuEK9hsfik08+kVtuuSWhsLjyyivlkksuGbR+8eLFUlFRoZ5XV1dLQ0ODrF69WlpbW6P71NXVqQXJ4p2dndH1EyZMkJqaGlm6dKn09fVF10+ePFkdE8e2n2iEavn9fpULEqWnVWbVFUkwFJYlTe3R1bhYZ0+sls7eoKxYN/CZAb9PNhxfKa1d/bKqpSu6vry4SKbUlUtze6+sbe+Jrq8uC0hDbZmsbu2W1q6BNtZVlkhdVYmsbO6Szt7+gT7VlElNeUCWrumQvqD5ZcAXOhgyv3SLV7XFlGJDLCLchlD4dmY1VOd1n5SdxpariXAKpU/4ocDnLpK26I+b2/uko51S6ROuyfUd5ufr0icd7TRcn6aOq5De/rAsWjVwTbq9TzraKZU+4ZrE86ayHpk0Ro8+6Win4fpUW1GsHr9e3yXdfUH398nXI3XeMaM3hkWfZs2SYDAoS5YsGeiT1yuzZ89Wn4fjpIrHsEucHDN16lTZd9995Y477oiugwfjsssuUycvFY8FcjSam5ulqqrKOY9F6wrxdq/L6zsNUOKLV7XK7Ik1g85r3qhyTe+eZLNPauKfxlaZOaFatVmHPiVqeyH0Ce9btKpVNppYo7bp0Ccd7TRcn/BywdctMsN2Tbq9T4XsscA1iYFjkc+rRZ8GrS+APmE/iIQZ46vUdtf3KVAhnrqZjnosWlpaZMyYMeqGvTXezkuPBSpCffHFFzHrFixYoARHIoqLi9USDzqOJdFJiifd9fHHTbge78XVifUD3+EoMG4663GRR33sMW1MsDLF9XgKUWFdQIlI1JZ87lNMG72F0Sfko22UxI5u7ZOOdkqlT3ib3ZY69MlOofQJf3ST/ba6tU9Drde5T/ZrUpc+pdpGnfrkNUyvQrLxjuv65PWay2iNYYdZr9qeZP+EbREHOfvss2XevHkqFGrRokXyj3/8Q2677TY57bTTnGyWtsB1R9wP7agPtKUe0I76QFvqAe3oHI4Ki+23314efvhhue+++2TOnDny+9//Xq6//no55phjnGyWlsAFh3jAeNcdcRe0oz7QlnpAO+oDbakHtKOzOBoKBQ455BC1EEIIIYQQQtwLZxAhhBBCCCGEZAyFRQGhkoSI66Ed9YG21APaUR9oSz2gHZ3D0XKzmYJys5i3IpXyV6NKy3KRrrXOfT4hhBBCCNGPQKVI3UzXjLfpsSgQoB87evpj6hwT90E76gNtqQe0oz7QlnpAOzoLhUWBgOoImA2SVRLcDe2oD7SlHtCO+kBb6gHtWOBVoVxNOCTy1esiq+eL+ItEJmwh4k19EhFCCCGEEEJ0gcJipMx/TOSp80Xavh5YVz5OZOefi0z/ppMtI4QQQgghJOcwFGqkouKBH8WKCtC5RuTZC0WWvCz5BuojBPy+RDPHExdBO+oDbakHtKM+0JZ6QDs6C6tCjST86fo5g0WFnfJ6kaPvY1gUIYQQQggZOawKpTnIqRhKVIDOJpFVH0k+Af3Y0tnHKgkuh3bUB9pSD2hHfaAt9YB2dBYKi3TpWJ3afusWSz6B6girWrpYJcHl0I76QFvqAe2oD7SlHtCOzsLk7XSpGJ/afm/cJLL0VZGNDjCTuYvKRrtlhBBCCCGEOAaFRbpM3VmkaqJIWyMcbkPv2/iBubx6vcj03U2R0bCliIeOIkIIIYQQohcc4aYLErIPuDryIknNgRl7i1RPGXgd7BFZ+LTIE2eL3He0yNt3irSukFyClpYXF7FKgsuhHfWBttQD2lEfaEs9oB2dhVWhsjqPRb3IzqeboU84rU3zRRY8JbL4eZG+zsHHGD9HZPYBIjP2EAlU5LT5hBBCCCEkzwm4qyoUhUUuZt4O9op89ZrIgqdFVrwtYoRjt/sCItN2M0OlJm4zKmVqw4Yhze29MqayWLwe6ni3QjvqA22pB7SjPtCWeqCdHQPuEhbMscgECIDpu4nUThPpWpt8P3+xyIy9zKVrncjCZ01Pxvql5vZQn8ji58ylvE5k5r6mJ6N2ataaCvm4tr1HaiuKk0ZwkfyHdtQH2lIPaEd9oC31gHZ0FgqLXFM2VmTL74ts8T2RtQtMgbHoOZHeNnN751qRD+8zl3GbiMze3xQkJQ54ZAghhBBCCBkNYdHU1CT19fVJtweDQXnvvfdkhx12SOewhQncc+M2MpdvnCqybJ4pMvBohMx91nxmLm/cbFajgsiYsoOIl3qQEEIIIYTkF2mNUBsaGqSxsTEqLjbffHP573//K1OmmBWQ1q1bJzvttJOEQpGBMUkNX5EZUoWle73Iov+Z+RjrFpnbw/0iS14yl9LaSKjU/iJjZ6T8EfAGVpcF6BV0ObSjPtCWekA76gNtqQe0o4uERXye99KlS6W/v3/IfUiaQDhsfpS5QFhAYEBoQHAAPH78gLmMnWUKjJn7iJTWDHlYr9cjDbWcpM/t0I76QFvqAe2oD7SlHtCOms1j4dEhAz9fGDtTZKfTRI55UGT/K8wytt6ige3rFpozfP/tSJGnfyOy5BWRUKzQswiHDWlc36UeiXuhHfWBttQD2lEfaEs9oB2dhcH6bgA5FcixwNLTZs6LgXyMNZ+b25GTgXK2WIqrTA8GPBl1s81cjsgc4a1dfVJfXepsX0hG0I76QFvqAe2oD7SlHtCOLhIW8Ea0t7dLSUmJCnnC646ODlXfFliPZBRBdajNDjcXlKtFqBTK11rlblFd6tN/m0vtdFNgzNpXpGSM0y0nhBBCCCEak3aOxezZs2Neb7311jGvGQqVQzB/xo4/E9n+xyIr3zW9GEtfNefFAOuXiLx5i8hbt4l38vZSWbebyLh9RQLFTrecEEIIIYQUsrB44YUXRq8lJLOJ+lCGFktvu8iXL5qejNWfmNuNsHiWvymTlr8pxqe3mPNiwJNRv2k0VIq4A5irrrKEZtMA2lIPaEd9oC31gHZ0Fo/h4jJO6UwxPqq0LB965m0n27XwaZEFz4h0Ng3eXj3FnOEboVIVyecnIYQQQgghDhCoFKmb6ZrxdlrCAhPgYY6K4uKBUJrVq1fLLbfcIp2dnfKtb31Ldt11V8kVFBYpYoQlvPJ96f7kP1L29eviCfbE7eARmbSt6cXAXBr+EocaSoYDVS5WNnfJpDFlqqQecS+0pR7QjvpAW+qBdnYMuEtYpBUK9ZOf/EQCgYDceuut6jUSubfffnvp6elRk+ddd9118uijj8pBBx2UWQ9IdvF4xZi4jSz3zJBZe5wjvq9eNvMxGj+M7GCIrHzHXF4tE9lwD9OTMWFzhkrlGbgL0Nnbrx6Ju6Et9YB21AfaUg9oR2dJS1i89tprctNNN0Vf33vvvcqDsXDhQqVkzj//fLnmmmsoLPKZQJnIRgeaS1ujyMJnzHyM9q/N7f1dIl/811wqJ5pejNn7iVQ2ON1yQgghhBCiywR5K1eulFmzZkVfP/fcc3LkkUcqUQGOO+44+fTTT7PfSjI6VDWIbHucyPf/LnLon0Q2OkikyDZbJcTGu3eJ3He0yONninzxpCk8CCGEEEIIycRjgfkruru7o6/nzZunPBT27ZjXguQfCDOcUFOmHgeBcKeGLcxllzPMGbwRKrXyvYhTUcywKSyv3SAyfXfTkzFxKxVmRfLEjsRV0JZ6QDvqA22pB7Sjs6Q1Ktxqq63kr3/9q3r+yiuvqMTtvfbaK7p98eLFMnHixOy3kmQM5hepKQ8MP88IErdRJerga0V+8E9zjgxUj7JA4jcqTf3nF6Yn4+07RVpXjHr7SZp2JHkPbakHtKM+0JZ6QDs6S1pVoV566SU58MADVaJ2Y2OjHH300XLnnXdGt5966qmqOtQ999wjuYBVodKrkrB0TYdMG1eRfpUEfEWa5ptejMXPi/R1Dt5n/Bwz4XvGHiKBigQNCIms+kikq1mkbIzIhC3M+TdI7uxI8graUg9oR32gLfVAOzsGNK4Ktfvuu8u7774rzzzzjEyYMEGOOuqoQR6NHXbYYWStJqMK1GNfMDSyKglQ/eM3M5edThf56jUz4XvF26qUrQKT8WF5/U8i03Y1RQZK2EI8LHlZ5PUbRTrXDByzfJzIzj8Xmf7NbHWxIMjIjiSvoC31gHbUB9pSD2hHZ0lLWIBNNtlELYn46U9/mo02kXzGX2zO3I2la53IwmdNT8b6peb2UJ/p1cBSVicybmORr14dfByIjGcvFNn3UooLQgghhJBCExYvv/xySvt985scKBYEZWNFtvy+yBbfE1m7wBQYi54T6W0ztyM8LJGosPP6TSJTd2FYFCGEEEJIIeVYeL3eaDJMsrdhO+a2yAXMsUgd2KuzNyjlxf7RTWgK9Yssm2eKjK/eQLTj8O/Z8WSRmfuYQoXJVvlhRzLq0JZ6QDvqA22pB9rZMeCuHIu0hMXYsWOlsrJSjj/+ePnhD38odXV1Cfez5rUYbSgs8pz5j4m8+sfU9y+uFKmZKlI7XWTMNJHayFI6hoKDEEIIIYVHwF3CIq1QKFSCevjhh+Uvf/mL/OEPf1AzbJ900klywAEH6KEKNSYUNmTxqjaZMaFKfLmqklBjK1ObCr3tA0ng8YLDEhkQHepxakEKDkfsSEYF2lIPaEd9oC31gHZ0lrSERSAQkO9973tqWbZsmdx9991y+umnS29vr5p1+5JLLhG/P+18cJIjwqk7p7IDSsqi+pO9GlQ8EA2z9hdp+cpMAE+0LwTHqo/NJea9VTbBMU1kzHTT41Faq7XgyLkdyahBW+oB7agPtKUe0I7OMWIVsMEGG8iFF16oQqLgtbjqqqvknHPOkTFjxmS3hcS9ICEbJWVR/SkZ3zw3tipUX4fIeoiMJSLNS02x0QLBkSDUDEnimBsDS7zggMiwiw4VUlWbxc4RQgghhJCMhQU8FA899JAKiXrjjTfk4IMPlv/85z8UFWQwEA0oKTtoHot6kZ1PH1xqFpPrWXNmxHstlOCA2Fgy8LwrieBo/NBc7JRUx4VTWYKjJps9JoQQQggpSNJK3n7rrbfkrrvukvvvv1+mTZsmJ5xwghx77LGOCQomb6cOzNwXDEvAP1DZK6eM1szbSnAsHbxgjo1UKamJDaeycjiwPs9w3I4ka9CWekA76gNtqQfa2THgruTttMvNIgQK+RTbbrtt0v2+9a1vSS6gsEgdmDlsiCCPSYsLbTh62swQKns4FZ53N6d+DIROxYRTTY8IjtxUPUtEwdlRY2hLPaAd9YG21APt7BjQXFgMB+exyN8qCQsbW2VWQ3VhV0mA4IgPp8Lz7vVpCg5bOBVK49ZMEympGnWvDu2oD7SlHtCO+kBb6oF2dgy4S1iklWMRDg8/2VlXV1c6hyQkt2Dw37CFudjpaU0cUpVIcGAdlq/fi12P8rdq/g1bOBWeo/LVcCx5OUEeyjgz+T0+D4UQQgghJA/JWm1YJHTffPPNan6LVatWZeuwhOQGhDc1bGkudnpaBsKp7AvWx4Mwq5VY4gQHZhSPD6fCc0twQFQkqpwFkYH1SH6nuCCEEEKITsIC4uHiiy+WZ599Vs1pcd5558nhhx+uqkP99re/FZ/PJ2efffbotZaQXIME7olbmYud7pZIONXSgfK4SnC0Dj4GEsmxrHw3dn1ZnTnvRtP8odvw+k0iU3fJTrI7IYQQQsgokVaOxfnnny+33nqr7LPPPvL666/LmjVrVGWoefPmya9//Ws56qijlLjIFcyxKOBkpnwFIVIQGM1LBib9w3OUwM2EjQ9RJXiNkhoJl9SKt6xWPGW1Ir5AtlpOcgyvST2gHfWBttQD7ewY0DjH4sEHH5R7771XVX365JNPZIsttpBgMCgffvihHsbTnGDILL9GRhEkdmOZuPXAOmh3S3DEL6kKjs+fUAuushjpHig3Pw+eFeR4YE4Oqw0xS405Rwiv07yC16Qe0I76QFvqAe3oHGkJixUrVkTLzM6ZM0eKi4tV6BNFRf4D9b6kqd2skkBz5RZcH6jyhGXSNrGCA/kV/7to5Mfu6zSX1hXD7+stihMeQ4gR5Jx4s5aCRRLAa1IPaEd9oC31gHZ0lrRGDigji9yK6Jv9fqmoqBiNdhFSGIJj2q5m9Sd7Nah44I3Y5Uzl3Qh3NkvrutVS4+0UDxLIrQpVEBfDEe43P2eoz7JTXGWKoZIUxEhRqWgx4SIhhBBCciMsELd2/PHHK08F6OnpkZNPPlnKy8tj9vv3v/898hYRUkhgMIySsomqQlns9otoVSgjbMjqxlapiq/PHew1E8dRmarbJjjU87h1ECTG8KWjVZhWqqFa/pLBwqOkVgR5IBAmlkDBawgWTwYuapbmJYQQQtwvLDDjtp1jjz022+0ho4iXIWv5CQbDKCk7aLBcL7Lz6YMGywnt6C8Wqag3l+GAqIBg6IqIjKgISSJGgj3DHxP7tDeay3BAVAzrBbGtsyeoa1aal9ekHtCO+kBb6gHt6JKqUPkGq0IRrcjX8J7+7sSeD/sCgYJ2Z1r9KhH2BPW1C0RCfcn3hRg7+r78OG+EEEJIpuhcFYq4F+jHzt6glBf7mWyfr2AwbK8mlS92RP4ElqqG4fcNByMhWUnCsOI9I8j7yGaCemeTyL9/KjJmuum9QYgUxEZF5BFJ6Xny/ec1qQe0oz7QlnpAOzoLhUUBVUlYsa6TVRJcTt7bEZWkMNM4luGAs7SvwxQYPesHQrPg+VCPeL0+vQR10LzYXBLhKxIpGxcRGuMSiI9xpmckB3+M8t6WJCVoR32gLfWAdnQWCgtCiDNg8F5caS4yZfj9V7wt8t9zM/vMUL9I+9fmkoyk4sP2PEfigxBCCHETFBaEEHcwcZvhS/PC8/CdO80QrI5IaV0sHU2xz+EpyUR8YE4QJTTGDRYdFB+EEEIKFAqLAgHDm4Dfpx6JeyloO6ZSmhdVtCwvSM3U5Pv1d0VExgjFRzhz8eEprZOAzz96tszXYgCaUdDXpGbQlnpAOzoLq0JlA1aFIiR3JJzHInFp3ozIVHykwlDiw3quEs7TnPeDc30QQogeBNxVFYrCokCEBczc2tUv1WVFrJLgYmjHPLsbn1B84LFpdMVHfOiVXXwkm+vDwmVzfeQ7vCb1gbbUA+3sGHCXsGAoVAFVSVjV0iWVpayS4GZox9RL8+aEojIz5CrtsKs1YnQ0SW/rKinuXSeebIVdldeJrPli6DbDkzF1F4ZFZQlek/pAW+oB7egsFBaEEOKA+AiHDVna2GqWRAx1Z+b5SEV8WOB49xxiejkCFeYEhIMebc+L49dVmLOh63AnkBBCSFahsCCEEBd7PkYUdoXZ1LFkMl9JvNiIvk6yrjhuHcRJPpAvYXWEEKIBFBYFAu4tlhcXsUqCy6EdC9iWqYiPZfNEnvrV8MfCBIaYJR2TDuIxXawZ1rGMFAiLZF4S+7qoxyTBNgicTMhCknvBXJMFIMAKxpaaQzs6C5O3CyR5mxBSAGDwd9/3h5/r4+j7zEEhfv5DfaanQy2dA0tvgnWD9os8N8LiCP6SJOIkgQclJqSrXKTxI5EXr0x+bCa5D8AqY4Q4R4DJ2yQPCRuGNLf3ypjKYvEyNtq10I76MCq2THWuD+tOMz7XX2wu8GKMBIiTYHecILGJjkRCZNC6Lhwo/c8O9phL1zrJOs/9XqRhS3MmdiTH45yp535ziTw3vD7p7vdISWmJeK3t6tEXeZ9/8DpfZF3Mdtvr6HOfiAeLg9d6sipjEBlYr5EAy8o1WQCenXyHfyedhcKiQMDf/rXtPVJbUWz6CYkroR31YdRsiUEeBnu5mOsD4A83wrSw4C72SIDHAzkkdrHRO5y3JG5dJjkjyRLiV74z7G4wXVl2P3nwJyQVIpF10edJxEm8mEm6f6xoUiWMX7526Oa99iezQltRqfMiyOlrkp6dvIB/J52FwoIQQnQDgxiUlHXLnVMMYK0QppGicka6UvCSdIg0fynS9Jm4A4Sr9ZuLZFk8ZQOEAd9z6IAdlXDBErA92p8Psw3v9wdEvIHI4wiPl+6kkplSQJ4dQoaCwoIQQnQkX+b6yBW4w15SZS7D8fX7Ik+cPfx++18hUr+p6b2AcAkFI89D5mMoKKFQv3y9plUm1gTEZ2A99onsp/aPLBAG9m04hn1dzPYk6wbtH2kHXjuV52IHbQj1movTRD0vqQsVj7dIxvcY4llWKeJPQ/jgWnv1uqHb8/pNnD/GguFiWkNhUSDAG1hdFqBX0OXQjvpAWzoIBjIIURkuyX3KjsMOeDxhQ/zl3eKpLhXxOmhNJTLihMkgMWKJmgTiJCqCbNvXLxP54j/Df3bdLDORHoUAlHfF/mh7nkusPsDLk6LOgY+jdrTag7LQ9x4uUhwptTxI2MQtlmjBY8L9bc/tQifRdhXWlie/NDkIF+Nvq7NQWBQIXq9HGmpHNxKYjD60oz7Qlg6SbpK7G+yocinQ3uLsHROiY8Vbwwuww28Z/lwh8N0SM1HBEREdEDPBPttjIoGSQKhYIWLpHm8kJZazQV+7ueQcT2JREiNEkgmYuP0HiZ1Aavvi+dJXchIuljfXZIFCYVEgYJbf1a3dMr66VF10xJ3QjvpAWzpMlpLctbZjFgWYumOuBp1Fo57unlrI1mChEu7vk+a2DhlT6hGv8uKkKFTavhb56rXhP7ek1rydjmOEIsfLCZGy0rn2GqULyj+vXypSUmOGNBZX2R6rzep1hX5NuoC8ERZXXXWVXHDBBXLmmWfK9ddf73RztAOFHFu7+qQe7nriWmhHfaAt9Uhy196Oua4ylguQ2G2VWbZhhA1Z29cqtROq0wtrS3f+mIEPTOKRiXttFzUx+6SwbyrvyxdQ2e2dvyTfDs+HEhrVkcfKgeeWACmuEiNQKd1tHjECk0RKqzKfSJOkRV6c7bfffltuvfVW2WKLLZxuCiGEkEKi0JLcC6HKmFs8O0kETk5RIWpx4iYqROLFTgJRknDfBO+D6IJnJxPUcdaayxDgLG9oX4EJMeO9H0qURJ7HixQ8R4W6fMhLCSMc8R2Rxg9EKsaLTN057687x4VFR0eHHHPMMXL77bfLZZdd5nRzCCGEEBIPBZienh0VohbJgxhNUq3Ett2JImV1Ir1tIj2tkcd2kd5WkZ62yOu29MLIrNLT7Y2pvweizy5GrOeDXseJFH/x6Ca6V00UOeBqkU2/JfmK48LitNNOk4MPPlj22WefYYVFb2+vWuxTjINQKKQW4PF4xOv1SjgcFgNKPEKy9ViHbcnWW8e1rwfYP0o4LN7Ie8Nxk8f6vB51XPt6aGDvEOsxa6SRaH3YiJmbFr8HmFUyfj08uJ649Tjm2Ipi9Z5QXCMtj29825Otz5c+2dcXTp8MGVNerD5bwrr0SUc7Dd8ntAu2xOfq0icd7TRcn9Ae/Lbar0m390lHO6XSJ+uaNMcCI+jT1N3EmLKzyKqPxdO9TjxlY8XTsIWEUG/K9sEFaaf6zcUbqcSWyA+g3lY+TjxbHSOGxzt0n/BfsEc8vW3i7WuXcLclQCLCo7dN+jpaJBDqUPtIb7spUvo6xJNqWWbs19NiLmlgQKBFBYkpOjwl1WKUVIoRgEekUoziKvEUV4m3rFrCAayvinohon1d/JJ4nrsoui56/LZGkQd+JOHv3C2yyaHZGcMOsd7n86njxh8nb4XF/fffL++9954KhUqFK6+8Ui655JJB6xcvXiwVFebEStXV1dLQ0CCrV6+W1tbW6D51dXVqWblypXR2dkbXT5gwQWpqamTp0qXS1zcQazh58mR1TBzbfqKnT58ufr9fFi5cONCAnlaZVVckwVBYljQNVHzAxTp7YrV09gZlxbqBzwz4fbLh+Epp7eqXVS1d0fXlxUUypa5cTUWPWSMtUDYNFQ6QjIRY3mifKkukrqpEVjZ3SWfvgHqfUFMmNeUBWbqmQ/qCA1+GyWPLVZsWNLaafwStPtVXit/nlYWNA+cLzGqodkWfKkqKZPGqtoLpU3Nnr1p06pOOdkq1T/U1pdLR069Vn3S001B9KvL71PF16pOOdkq1TyHDyKxPnukiZdPNPnl9svhr/s1d0tQhFRudKJPeu1qJiJjBcuRx9aY/lglen7R29qXYpyppqJsgq9d3Sau3T6Q0tk/L13bG9qm6RGr8fbJsZaMSI77+NvH1tcvYQJ8UhzqlZd0aJVR8/e1qfXG4U4kUD3I/UsSDcC1MGonFvj6uzxbWNI4hf5mEiiqVIAmUVStxar0v9jim0Av/9zxZ7J0lEyZOynwMCzvNmiXBYFCWLFky0DavV2bPnq3GzDhOyufAsEucHLJ8+XLZbrvt5Nlnn43mVuyxxx6y1VZbJU3eTuSxmDJlijQ3N0tVVZVzHovWFeLtXpfXdxrwvHF9l/pRiDd4Qd49cWmfsO+K5i6ZWFsWrXbh9j4lansh9AnH+3p9l0wZW64+W4c+6Win4foEMOBrsF2Tbu9TwXosItfkpDFlapCsQ58GrXe6T0teFu+8m8RjC+8xysdJ+Buni2f6N7PSJ7y/cX23NNSUiseWhD/iPgX7Ta9HJBwL4gM3lI2oh6RdPD1t4ulrE0OFb2F7m3hGuepX6IePiXfDb+bEY9HS0iJjxoxRN+yt8XbeeSzeffddaWpqkm222Sa6Difg5ZdflptuukkJCHTITnFxsVriwX7x+1onKZ5018cfN+F6vDeS5ONLIElh3HTW4yJPJG2TlU1LdX1XX1BdTPiRSkSituR7n6JtLJQ+eUS6+4LqPfHbXdsnHe2UYp+6I9ekV6M+6WinodqOwUhXkmvSrX0aar3ufcI1if106lMqbcxZn2bsLjJ915hCAJ4JW4jPlpCcaZ9CYVGeCo+3LGE70+5TUUCkaKxIxdiY9Yn29lhPVLhWdyQ8KxKKZQ/Xin+MPoe3KrX7/b6uNdGxZ0Zj2GHWK3sk2T+vhMXee+8tH39sunosTjjhBNl4443l/PPPT6sThBBCCCHEBRRCIQCPR6SozFwqJ6RXBWrZPJFnfjP8vqgSlYc4JiwqKytlzpw5MevKy8tl7Nixg9YTQgghhBCiveja4BsqkT35vCgeszoUSs/mIYl9J0Q74OFDMlYSTx9xCbSjPtCWekA76gNtqQeut6M3Mi9KQiKdOuCqvJ3PwrHk7WyA5G1UgUolmWRUaVk+qAIAIYQQQggh2ZvHYpIpKnI8j0U6423H57EguQFVEFA2btq4iqSJSyT/oR31gbbUA9pRH2hLPdDGjtMjM96vXSTi83PmbZJfwC2FWtSudU8RBe2oD7SlHtCO+kBb6oFWdvT6RCZvJ1I3U9wCcywIIYQQQgghGUNhQQghhBBCCMkYCosCAWGGmHXbzeGGhHbUCdpSD2hHfaAt9YB2dBbmWBQImDmxoqTI6WaQDKEd9YG21APaUR9oSz2gHZ2FHosCIRQ2ZMHXreqRuBfaUR9oSz2gHfWBttQD2tFZKCyyQHtvUC3BPP8Sh907ZQmxQTvqA22pB7SjPtCWekA7OgdDobJAJ4RFa496HvB7pbTIp5aSgE+KGORHCCGEEEIKAAqLLNMXDKultbtfvS7yeaWkCGLDL6UBrwR8dBIRQgghhBD9oLAYZfpDYbW09wTVa7/XIyURj0ZpwCfF/twIDThOptdXskqCy6Ed9YG21APaUR9oSz2gHZ2FwiLHIA+jozeoFuDzeJTAsMQGhIZnlC4GP70lWkA76gNtqQe0oz7QlnpAOzoHz7zDhAxTaKzt6JXl67vky7UdsrKlR5o7+6S7LyTZyj9CXvnCxlb1SNwL7agPtKUe0I76QFvqAe3oLPRY5Bm4ELr6gtLVZ76G86K4yCdlyqvhlVK/X7yUg4QQQgghJM+gsMhzILh7+kNqiQoNPypOeVUIFYQGPX6EEEIIIcRpKCwyAJOvvLWkWRYubZOyYFDmjPOJb5SzhZTQCIbU0tJlVp5CXoYqb8sSt4QQQgghxCE8huHeWUTa2tqkurpaWltbpaqqKqef/dQnjXLJ4/OlMTJ/Bagr9cip25TIblOcnUoeJW5LoyVufVLk8wjMjDAraA5Md0/cCe2oD7SlHtCO+kBb6oF2dgxUitTNdM14m0E0IxQVp/ztvRhRAdZ2G3Lpa93yynLTk+AUKG/b1hOU1e09snRdpyxZ1ymr2syE8N5g2NG2kcwJhmhDXaAt9YB21AfaUg9oR+egsBhB+BM8FUO5eea+36P2yxeCIUMJjQWNrfJVc5d8uaZTiaL1Xf3S08+Lz03ga7WkqZ3VLjSAttQD2lEfaEs9oB2dhTkWaYKcinhPRTxrugy5+b0eOWhGQDas8Yo3z1xxVolbay4NuAsRNqUSwpGn4feN2lwahBBCCCFETygs0qSpfWhRYfH4on61VAY8smW9T7as98tW430ytQoT4OXXqB2qvrMvKJ22Ercl0RK3ptBgiVtCCCGEEDIUFBZpUl9Zktb+7X2GvLoiqBZQU2wKja3G+2Wrep9Mqsyd0EjVcwLvYXd/SC32uTSUNwNeDZa4dZR884CRkUNb6gHtqA+0pR7Qjs7BqlBpgtyJXa9+Xla19iTNs6gt8cj3NwnIR00h+WhNUNojnoBEjC31yFb1/qjYaKjI/xG7OZeGN1reFoLDzxK3hBBCCCEFXRWKwiKDqlAg0cm7cJfSaMlZCJElrWH5YHVQPoTQaApKl+m8SMj4Mo9sGfFmIHyqvjw7QgNm7g+Gpcg/Oh6SQGQuDdOrwbk0RgvYsbM3KOXF/rwLqSPpQVvqAe2oD7SlHmhnxwCFRcHOYzGuzCOnbD30PBYQGgvXh+WDpqB8uDoon6wJSY8ZcZSQiRVeU2RExMaY0pEJjbBhyNrWbqmrLs2JixBzaZRE59LwSoCxU1lBfX8aW2VWQ/WoT8ZIRhfaUg9oR32gLfVAOzu6TFgwx2KEHDCnQfbddEJk5u2lUhZsS2nmbWzfeKxPLd/fpFj6Q4Z80RxS3gx4NeavC0mfTWh83RFWy3+/NOfGmFJlCg2ETSF8qro4PwfsmEsDS3uP6Z5BqBQm6yv2m14NhFLpcCOBEEIIIYSYUFhkAETCTjPGyvSiZmlv7hrRMTAr9pxxfrUcs1mx9IUM+WxtyPRoNIXks3Uhsc9pt7wtrBZUnALTq73Km7F1vU82r/erKlT5SDBsKJHRLsGYylNKbMCzwYRwQgghhBBXQ2GRZwR8Zo4FFtATNGT+2pC8H8nRgHfDPukL8jeWtPbJIwvMwfrMWm+0tC3ESnmRKTTwv9/nVY/5QHzlKStPo0TlaviV2IBXg8QC+wUwz4jTDSEZQ1vqAe2oD7SlHtCOzsIciyywatlCaW9elZPP6uo35JM1psiAV2PR+nDS2SURlTV7jC+aCL7ZOJ+U+t1zqfk8nkjlKXg0zDAqzqdBCCGEkIIhwBwLMoqUFXlkh4lFagEdfYYqafvhalNofNkyEDcFwfH5upBa7v+sT6ApNhqL/AyIDb9sWudTHpJ8BTOEx0/cB3GBpPCSAq0+hfsArV39Ul1WpEe1iwKGttQD2lEfaEs9oB2dhcLC5VQEPLLzpCK1gNbesJo/w6w6FZKv2gaERtAQ+XRtSC1//7RPiryixAVEBsTGRmN8KucjX4FjpicYUot0mzkmfp8nkgxeGEnhEIurWrqksrRa8thUJAVoSz2gHfWBttQD2tFZKCw0A1WidpuCxRQazd3haMWp91b1yypbjnl/WNQ2LPd8IlLiiwgNlLYd75PZtcNXuXKaYMiQ9hCTwgkhhBBCnIbCIgvUVxZLZbhEuvvC0t0XlB57GSeHwbwXe071yu4b+GVtqyFGUbF8vAZCA4IiKKs6BxI0MJ/Ge6tDagFlflEJ4BAZyNGYUePNe6HBpHBCCCGEEGegsMgCmHCuPOCX8gBeBVRp1e6+kHT1BaWrP6TuqudLlYTqMq/sM80n+0wz16/qiEzWF/FqrO0eaCtmCH+rMagWkV6pKBLZot6cPwNejWnV3pxMtpcpfcGwWtoic2q4OSkcZ7u8uIjVLjSAttQD2lEfaEs9oB2dhVWhskHLcpGutUk394XC0tVr3kWH2EhWxclp8FVY2RGOJoJDbKzvSd7Y6mKPbAGREREbG1Qhv8GTdCZMzDK+rseQsSWYuyN/wqyiSeEBeDYKMymcEEIIIXlIwF1VoSgsciAs4rFCdSA2evpDKnxntIGZu3qDUlbsT7lKAt6zrA0ejZB8GJlHo60veWvHlAwIDXg0JlZ41Ge9srxf/vxeT4w3pK7UI6duUxLNBck3rKRwJTTyKCk8bBjS3N4rYyqLXeEtIsmhLfWAdtQH2lIPtLNjwF3CgqFQDoABK5YxZSLhsEh3MChdfSEVPtU7SvkZGNJ39vRLKYRFiu+BKJha7VPLYbMC6mJd0oJk8KASGx81BaXTLM6kaO4x5MVlQbWAcaUeaajwykdrBvIdLCAyLn2tWy7cRfJSXAyVFK7K3TqUFI7bAGvbe6S2othsFHEttKUe0I76QFvqAe3oLBQWDoPY/nKVn2GawsrPwPwN3XmSn2EB5T+j1qeWIzYyw5sWt4RVbgaEBibu6zbH4Yo13Yas6R4sKuzc/F6P7DTRp2YFd1tSOLwYxZGkcIRRBfK8D4QQQgghowmFRZ7h93qkssSvFgAPBrwZPXmYn4EcCczsjeW7m5iiaEGzWb4WYgPVp1DSdijWdRvyrYc6ZEqVVyZVeGVipVcmVgw8H1vqyVtXJmzTG5cUPlDm1l1J4YQQQgghmUJhkedYd8VFipR7D5PDmR6NkPSmkZ+BoXlpIPUwqJGKok3rMKO3X47etFieWdIn17zZM+z7ID4wY7h91nCLgE+koXyw4MDz+jJP3iSAWzOFd/QGpaN39JLC8e7qsgC9uxpAW+oB7agPtKUe0I7OQmHhInDjPpqfUW7mZ3QFg9IdqTg1VH4G8iUqy1Q93Jwxviy12/VI5Eb1qURRX30hUbOH22cQt4DemgDREREbSnRUeNRzrIfQyZuZwqU/K0nhXq9HGmrLRq/RJGfQlnpAO+oDbakHtKOzUFi4GITZVAT8agH9yM/oDUlXf1B5NRCaZK/w1NHdLxWlRSlXhcoUlJSFaLBXg4pnXJlH/npIhXre1GXIyvawfN1hLtbzxo5wwpAq6KgV7WG1SGPsNmiK8WWeAU9H5BELEsoDPk9eJIWjnfBqpJoUHg4bsrq1W8ZXl6ofT+JeaEs9oB31gbbUA9rRWSgsNAJhNkWlfqkqjc3PgMjo6O1Xs4KXQ1jkqD0IU0JJWVR/SsYpW5dEw5kaKswqUvEgSRy5GJhj4+uI2LA/702QHw5N1dhpSGNnSN6V2B3waXVlnoiHwxZmVWmKjlJ/7n6I0M74pHB4NZCvAQGIRwhI89Gj3CCrW3ukrLhI/H5s96rtyEMx35OzppMMgdxu7eqT+upSp5tCMoB21AfaUg9oR2ehsCiA/IzasiIJhoqltzcoY8oC0oOk4xzNn4FSsigpGz+PBTwVEBWplJqF8Kgvx+KVrcfHboMnBpPuNbZHxIZdfLSH1ezh8aAVa7oMWdOFiQBDCefjsHs47M/LA56ceDWCSayDkr+oGLaqvSdhUrsncr6w2IUJ9lXiw2s+QqPEr6cwIYQQQkgmUFgUCBgwFvk8MqY8oAaRIeRn9Aelpw+hUyHpG6X5MwDEw86T/KMy8zYGzwi3qiv1yub1g0VHa68RE1ZlCY+VHYa0J5nsD/NxNPeEVFWreGqKPTFeDvO56f2oDJiD+ZGSjdnJ0SOEwNnD4NLBEibwjnglIkRsAkQ9T7jeI34KE0IIIaSgobAoEDDgq6ssiQ78EMdfWexXC+gPmfNnJMrPyAYYiG45PrdfNwzya0qweGXTusHb23oNlb9hD6uyFiSTJ6Kl15CW3pDMXzdYdFQUScLqVfB4QJAMJTpSnZ0cRygvGb1wNkuYSJaEiXr0RNbFCROPFeIVESY+tb8U7DVJ3AntqA+0pR7Qjs7iMXBb16WkM8X4qNKyXKRrreiEPT8D82e49ksyQrr6jTgPx4D4GCoZPRlIe4mtXjXwfP66oPz+teRleS/cpTQvZycfDfB3ICo0Io8qbEuFbkU8JTZhguf444H34BHiTQka9dzp3hBCCCEZEqgUqZspbhlv02NRQFUSVjZ3yaQxZSlVSbDnZxi2BOOuXpS1zU1+hpOUFXlkZq1PLfH0BE1PR3z1KggPVLZKdG4wIzlmKceSLje+2yOzx3ilqtgrxV5D2rr6pbo8kLPqXrnEiMwHEgoZkQK9IwdnZ0B0RMTGICFiC+eKCBNL3Kjtoyhe0r0mSX5CO+oDbakHtKOzUFgUCBiwdfb2j0gQYPBUFvCpZWy5RPMzTG9GSPqxooAo8Xtkeo1PLfH0hQxZ1WmWyI0tnWuuH0mEEcKyjn28Uz1HldwyP8LY+qQi4JGKIo9KKMcjXpcXeVSuh7leBu1T7DMHxoUATjVEMRLezVfZJx3xErMO7zREmjt7pao8oCqBmQImdj8znGxUmk7y4LeV5Be0pR7Qjs5CYUHSJlF+RlckZApiA3ecCxXMj7FBlU8t8SB3oakzUjY34uH4qCkoi9LwYmASwfZ+LCMbLGNSwQGhMSA8LFESK0QkVqwEPI7N/zEaie5Oi5dwZKb2VW2JK3zZiXpRrIR5tdLuaYkNI7PEi3pPRPzgSVT4RHKQLOGj1kSEjDoOxQwhhJARQGFBMgbVpqpL/WoBPf1hM2xK5WgUXn5GMjATOCpIIbfC4sPVQfnlC13DvndOnTmTeEe/IW09YekKwWuUfo41in+ZCegjs0qRmpQxsfiwi5SBdaaQsV5nQ5ikmuiuY4iYOSVLbq4oy1KW98QUJhFREhEiMWFjkTdY+8TsH9lo31+93zqGJWys/exhaREPUIE42gghxNVQWBQIuFM5oaZMPY42agbpotj8DEtkwLuBO7UUG+nNTv5/e5WrO/KotYASwSUBnzqHyN3o7DOU4OiIPA68FunsN8vq2vfpjD6mP0TFDOgIzUpWNWs4Aj67x8QSI4nFSXmC7fO+DiaccBHnD+sxZ4pbxAUuxcrSQM4mrEwXy8JK0ETN7eyVaw89M7WIFX5mbrBEjPU7ZwmXqHcmsr/13pj9Ub3MmusljSplufxtJaMLbakHtKOzUFgUCKr0KoLuc/65A/kZIgOfHw6LhMUUGXiOATNSwo2wGeaC4Yu5zVADGzPcxEzKGngfHsXVpDs7OexYGglBU6Vni7B4JG4Kj5TAOezulxhRoh5t4qMD22NemyIFoVjwmKRLX0ikOYR5QkbHcP/3ZresaA9JRZFXSovMJPwyv0dK1aOox3K/R0r8zuea2G2pc7jY6IWeWWtGD/uEkwMTTGKdOeO9NfN9oMin8qvwnPO5uBen/k6S7EI7OguFRYGAQfjSNR0ybVxFXlRJsOYzMG8TZnYsCBOIEvTRGnQoMQLxAgGitkWeR0SKEi9qm3luLDHjBOnMTo42r+/oldqK4owHxhgo4bcX3oPx5em/H+cMM5vHC4+Y130DwiVenCSaFT1TcMy/fNQ37H44c4jcK40KDyTFe5QQKVWP2G6+tj+3tsXvizC1dMmmLeMpxHCx0SCVCScT2VEJEl+kZHKcILHP6eK3Sipzgsm8IN/+TpKRQTs6C4VFgYA/i32alomNipQs/ICEbAIkuiiPihkSojwrUQ+Kuc0MFcH6iEdlBKFeqc5OrgY6IUilgRh4p0DbKgOiErtHAoQJwrEswaFCtoYI6YInYkV7dr7BRkSEdAUNWZeFqwK5J6Z3xC5WbF4SJUJkQIxEvCb9fSGZ0BdS4s56T0mGlbsgKnQJF8sFmXp2El2T5jpD0s0woyBxFp3/ThYStKOzUFgQElfxCrHVmQ7bLSFieUiUVyQS6mVObG0XLmZIF5YdJ/uj4gVzOej8w4iBUlWxSFVxauc61UT3H80JSH2Z18w/6TekO4iwLTwi3weeEvN1l+01QsIyOdfIPWntNaS1F6/SPVJsnzCQVCIkTpwMhHVFtiXwqBT7DDXvyVDMfb9HiVi3h0Xp6NnJVJCYIsMzKHzLF6koRkFSmFiRg9FHtZgv1P/R9ebfq/j3Rv8S2dKt1L5xx7d/be2pWfibFv+NVmvs+ydpc8z+sSuSvjcUCquKe02ouIfcxLjtQ33O0G2I3z6wZtBhjOQrBp+L5J+LfpeU+aWhTlwDhQUhowD+YOMPdzYFCu6s4s5oT0+/1FeUiOGJDfFS3hYrB8Xh8C4nE91/sGlx2oNmlRQf8WCYYsMuQuzrTRFiiRNTtCCJPiJWIs8hNEYKBKblyZERzPI+HGu6DPnOw+1qPhZ1N1zdEcfgMyKsI84/M5HZ9traR82CHnlfZJ+B40TWRZ5H19uONbA+9jPNmdat59Z+A58ZfZ+9HUk/0/Z5tmPp6tnJpSAZ9MHRp7aBU5JmxA+YEh8nyTGTHEdS+iz7+uQDPPxudveHZX1X30Bfkx4ntb5Yn5f0vXED8Oi+toIJ9nH7gDiwDfaTCAOdfv/TAX8Pe4NhaesNDlvK2w0E8YfeRVBYFAj4jZw8tpxVEtwsUHwiht8rM8ZXSnmxP6VwGXv+iT33JGR7bXlMrLyTTEK68inRPe1Eatz5L4K7IMOGRuZ2sYsO03OCEsEDHpSuSNhXb8is7mUXL0rMRPbFtmyjkvLVXCgW+WTp0SMqMkTUeR+Kq+d1y+sr+lWYGjxClleoxBbSpvJ0/PhDWiStPWEpD3hVWJzThQFGQ5DoWAwgHvz+IRG/ubNvxDYshPOUKaN9jnCkmvJix8OFCxUKiwIBP5IVJe64+0ayZ8dM809ick7gMbGEiZVvYqvalUisOJno7vTcLlgQ6pUpYcubEucxSeRR+aotLK+tGF6J1JaYghUiUi3Kjqa3BI+Y70RHVF+HERQWEB7/+yp9VYdLTYWv2ULZ4B2KipFIKJu5LrJf5LlaF9nHysVB4TCn77rmW8jYaP6+QliMlEI5T5mQi3OUqR1JZngMu1/QZbS1tUl1dbW0trZKVVWVcw1pWS7StVbyGQwcFq9qkxkTqnj3xMW4yY72MC4rZMsUH/bwrYHtA/sOH8alw11B9LW5rUfGVJVkbeCI83Ls4x3Dhov99ZCKYc9XNLzOEh82ARK73tzP2sd6n7VfdJ39fRF7R8VM9P0D77W8aAOfbX3mwLEG1sfuZ7XV3nazdLX5vK3XGPIc5ROwUklSoWKvWDawjz1HZ8CzMrBfOtdKspAxiwt3KdVm0JzJNVlI52mk5OocjcZvq1OEwoYs6CgWo2qy1FeWyA7Txzjyty6d8TY9FgXEQO134mbcYsf4MK6RCpOo+LAESCSPZI+KOLESWY+k93wL48qVLbMZLqYqEA2ym7v/SKdbCOCXO5TI1GqfGZJmhbNFnw+Et7V0BSXk8SrPkn19NkLZrIkwcSzJ0vwvmFbIqkwWE9Zlq1oGr0mxT+TBz4cu33zDOz3K2xKdcd3+LbEmKoxfH/l9kASvE33DBrZ5hjx+zGfFHz+Fz8GvRkt7WNqNUMyA1H6sRH3EdTxc0YSb3u2RGTVmda/4z45pU5JzlrANMfsOzFo/6P3J7OAZyeckXjdc6Bh+p+GpyFVhCbf8nUzNu9OO7C+1rqG6RC46dFM5YE6D5Cv0WBSQx2JhY6vMaqh23Z1dMgDtmDox+SW2qlz5kl+Cz1jb2i111aVZv6uWKNwg38LFnCTbnp2h7GgmkkYESSSUzRQcAzk49vVWuNvA+kgxAdtzt08MSvQmkUixJ6kPRVXA9K5FizLEFIeILeaQeB2KDxjSHwxJWbFf/HHFHXxJC1YMLvgwuAjE0MUk4gtJ+JKsw/uGE2LJvDvWu+Yeu01OxQU9FoSQgke3/JLRmBelUBnNQgDxeO2FAbIAvnuoOhYtoRwvUuIKBVhek2hVs8hzu2clkypmhMQzqAxtGrT1YUlQv3ZE9Eu+4o0XSPYqfGLI2iTOHWu+nEseny/7bjohL3/TKSwKBHz3ptdXsiqUy6Ed3TOniRnrHx7IL7HlmqjXobCUFfnUwBPjuqhYyVKZYPzB2XI8f+JHuxAAvh1jKktyFiSmElN9CGfySE2WjomZxe2Cw/KazF8blHs+GX4m+72m+tXcMakOLuO3DV3HP26fuHKr6XzOsMeP5APZ7yYnLXNr29bcHZb3Vg9fFWDLeq/UFHuT9it5X4foS8LzaCQoZZv8ffH9SeRdiJ0DY3D7oo9x5XWtR0x4urJj+F+26oD52xWT02XLndKFcCSPbEDUp9457NnY2iNvLWmWnWaMlXyDf3UKCD9GSsT10I5uEibqv4RASOAPSyK3ePz8JQMJ8LFipRDmL3GDZ2fQ/A4uw+/1SGVApDIQ248t633yn8X9w4aMnbdjaV7eOU0XayI3dTshjfDEVEPrrt6jXIvz5FT4ofWbOVCQIXExh2DEw6x+JwUiJUlBCVvRiWhBiZh1CYpHDBI7gz8/FA2rTVDgItE6exEKeCNVifLhz2lT+9A5K05BYVEg4IKIxuYX5u+aFtCOhWHLTBPf7fklkmJYQkpyZNhjZPYZqQiiYcMrRqCqJtUM3KW1BpfWzMA4hdbEY9Ft1p1aVZo3LI3NXTKhtlQNRg0rcTQycZmVROpGsZfLkLF8ADay8mXS6VGhnaeRkI1zpCZxHPSTmDivSdmxNvv5a/lUXKK+skTyEQoLQgjRjEzzS0jq4A5mV3efTK4tGz7RO2yFkkSESlSwxK3DPpEQCSVs4sSOXbzYCw0MHMsmbqzjWceJ+Sx95o5xGp6n4eE5Sg14TTG3RzLvDn5lJlSbpWfzEQoLQgghJEeCL3kBVmcwvSp2ARIrdvDvezUlcuRmFfJuY7+s6QzLuDKvbDOhKHo3eEDYJPbORKWPPU4/LldiULx+smMlyZdI+L64ZID4uP/4NkD4qepAqrIQvE8DuQqx/UkMiyYMD89RZt4d6yyh5Gy+njMKC0IIIaRAgTYwQ/GGFzt7TS/S3vvU3xeU6XWp5ULYQ+LUY2T9zPqIJLEJF/u+lmCzdrF7leyqJT48zzqW9caoJ8rmyTLfF+vFim9jdHvkv/jk8OjxbN6taJskc1hYYuTeHXgqOI/FKMJ5LFJnqERR4h5oR32gLfWAdtQH2jI1LI9WvDCKht4NEkcDwie6r01ImRWSzC1WXlJ8TpO1D16E40MB48L6RpqEn4+EOPM2yWeCobAEMFsMcTW0oz7QlnpAO+oDbTk8GKvHjtcTzX2eW6LiQ02EakhPf0iKIhUUVcU8m4gZECzmevPRnotkFyxx+6SYm5QtICK2biiRKbMmiVugsCgQcGEsaWpnNSGXQzvqA22pB7SjPtCW7g/pw9xDmFHk6+ZO046jdHffLGUb52GxXos5mWp0m82LYu074JWxFWCweWPwfnthBjdBYUEIIYQQQki6lfcUo6tCjUCZuAn6+wghhBBCCMlDPHlQPS4dKCwKCDdOFEMGQzvqA22pB7SjPtCWekA7FqiwuPLKK2X77beXyspKqa+vl8MPP1y++OILJ5ukLYgznD1x9OINSW6gHfWBttQD2lEfaEs9oB0LWFi89NJLctppp8m8efPk2Weflf7+ftlvv/2ks7PTyWZpCRKHOnr6o5MHEXdCO+oDbakHtKM+0JZ6QDsWsLB46qmn5Pjjj5fNNttMttxyS7n77rtl2bJl8u677zrZLG2rXaxY12mWXCOuhXbUB9pSD2hHfaAt9YB2dJa8qgqFiTfAmDFjEm7v7e1Vi33CDhAKhdQikclQvF6vhMPhGLWabD3WYVuy9dZx7esB9o8SDos38t74LzJccVat5Ghb1HGSr7fKkA1aH44tPYYQQsQRxq+3Jvexr8ckK1b/8DymTxFvYXzbk63Plz7Z1xdKn6xa2vZtbu9TorYXQp/wPjXJk62eutv7pKOdhusTwHr78d3eJx3tlEqfrGsSC8qW6tCnQesLoE/W7yrWiw59CofFg3HmaI1hh1jv8/nM37e447hCWKAzZ511luyyyy4yZ86cpDkZl1xyyaD1ixcvloqKCvUcMwM2NDTI6tWro0IF1NXVqWXlypUxoVYTJkyQmpoaWbp0qfT19UXXT548WR0Tx7af6OnTp4vf75eFCxcONKCnVWbVFamJdVAD2wIXK+L8OnuDSj1bBPw+2XB8pbR29cuqlq7o+vLiIplSVy7N7b2ytr0nur66LCANtWWyurVbWrsG2lhXWSJ1VSWysrlLOnv7B/pUUyY15QFZuqZD+oLmlwFf6GDI/NItXtUWvfBUn+orxe/zysLGgfMFUAM6n/uk7DS2XCpKigqmT/ihwOcukrboj5vb+6SjnVLpE67J9R3m5+vSJx3tNFyfpo6rkN7+sCxaNXBNur1POtoplT7hmsTzprIemTRGjz7paKfh+lRbUawev17fJd19Qff3ydcjdd4xozeGRZ9mzZJgMChLliwZ6JPXK7Nnz1afh+OkisfIkyC0U045RZ588kl59dVX1QlJ1WMxZcoUaW5ujk4x7ojHonWFeLvX5fWdBjxftrZDXSjxBs8bVa7p3ZNs9gn7LlnTIVPrKtRn6dCnRG0vhD7heF+t7ZANlWAULfqko52G6xPAAGMD2zXp9j7paKdU+mRdk9PGVagBpQ59GrS+APqE9y9b2ykbjC0Xjy2B27V9ClSIp26mox6LlpYWFU2EG/bWeDuvhcXpp58ujz76qLz88stKTaUKhAU8FKl0dFRpWS7Stda5zyeEEEIIIfoRqBSpm+loE9IZbzuavA1NA1Hx8MMPy/PPP5+WqCDpoRRnZx+rJLgc2lEfaEs9oB31gbbUA9rRWRwVFig1+7e//U3+8Y9/qLksVq1apZbu7m4nm6UlcMEhvi/edUfcBe2oD7SlHtCO+kBb6gHtWMDCYu7cucqtsscee6iEa2v55z//Ka6ibIxIsYOhWIQQQgghhDiMo1WhtHFTBcpFxs4Q6esS6WwS6W5B75xuFSGEEEIIITkjb8rNakGgTCQwTaSyzxQYXetEjNgMe6fwRMqbDdRHIG6EdtQH2lIPaEd9oC31gHZ0lryoCjVS8qYqVDJCQbNaVOcakfBALWVCCCGEEEKGhVWhSBSfX6Rygkj9ZiLVU0R85qQtToBayWvbMAGQa3UkoR21grbUA9pRH2hLPaAdnYXCIhdg4pHyOpHxm4rUThMpKs95E3B9YXZHXmfuhnbUB9pSD2hHfaAt9YB2dBbmWOSa0lpz6W0X6WgS6W1zukWEEEIIIYRkDIWFUxRXmkt/tykwutezkhQhhBBCCHEtDIVymqJSkdqpIvWbipTXi3h8o/IxqI5QXRZglQSXQzvqA22pB7SjPtCWekA7Ogs9FvmCPyBSPclM9u60Kkn1Z+3wXq9HGmrLsnY84gy0oz7QlnpAO+oDbakHtKOz0GORb3h9IpXjRcajktQGIv6SrBw2HDakcX2XeiTuhXbUB9pSD2hHfaAt9YB2dBYKi3zF4xEpHytSv4lI7XSRQEVGh8Pl1drVxywOl0M76gNtqQe0oz7QlnpAOzoLQ6HcQGmNufR2mDN697Q63SJCCCGEEEJioLBwE8UV5tLfYwqMrmZWkiKEEEIIIXkBQ6HcSFGJSM0GZh5GxfiUKkkhsqquskQ9EvdCO+oDbakHtKM+0JZ6QDs6Cz0WbsZXJFI10RQXXevM+TCSVJLyejxSV5WdRHDiHLSjPtCWekA76gNtqQe0o7PQY6FLJamKetODUTNVxF86aBdUR1i+tpNVElwO7agPtKUe0I76QFvqAe3oLPRY6AT8fmVjzAUJ3vBg9HWoTbi8Onv7mZHhcmhHfaAt9YB21AfaUg9oR2ehsNCVkmpz6esU6Vgt0tXidIsIIYQQQojGMBRKdwLlImM2FBm3iUhRmYiHJieEEEIIIdmHHosCwRsokQnTNhJvRblI11qRzjUiRsjpZpE08XpEJtSUqUfibmhLPaAd9YG21APa0VkoLAoEj8cjNTU15ouqhoFKUpgPI9TndPNIOnYsDzjdDJIFaEs9oB31gbbUA9rRWRgXUyCEw2H58ssv1aPC6xWpGCdSv2nSSlIk/0CViy9Xt7PahQbQlnpAO+oDbakHtKOz0GNRIBiGIX19feoxeSWptkglqXanmkmGAdbrC4ZY7UIDaEs9oB31gbbUA9rRWSgsyAAlVebS12VWkkLJWl6ahBBCCCEkBSgsyGACZSJjposEe00PRneziBEJoSKEEEIIISQBFBYFgtfrlcmTJ6vHlPEXi9RMEalsGKgkFQ6OZjPJMKDKxeSx5ax2oQG0pR7QjvpAW+oB7egsFBYFVCWhoqJiZG/2+UUqJ4iU15veC3gxQr3ZbiJJ1Y4lRU43g2QB2lIPaEd9oC31gHZ0FlaFKhBCoZAsWLBAPY4YeDvK60TqNxGpnWZOuEdySihsyIKvW9UjcTe0pR7QjvpAW+oB7egs9FgUENFSs5mCSlKltebS2256MHrbsnNsMizh+MpexLXQlnpAO+oDbakHtKNzUFiQzCiuNJf+brOSVHcLK0kRQgghhBQgFBYkOxSVmuFRlX3mbN6Y1ZuVpAghhBBCCgYKiwIB1aCmT5+eXlWokeAPiFRPNitJoYpU51qRcP/ofmYBgSoX0+srWe1CA2hLPaAd9YG21APa0VkoLAoIvz+H5vb6WElqlPD7WHNBF2hLPaAd9YG21APa0Tl45gsocXvhwoXZS+BOt5LU+E1FaqebQqN0jEhxlVlVyhdANnhu2+RiUORiYWOreiTuhrbUA9pRH2hLPaAdnYUeC5I7SmvMJRHhkDn5XqjffLSW6Gtst20jhBBCCCF5BYUFyQ8QOoUFs30PB8rIDRIeSV5jYRI5IYQQQsioQ2FB3Afm0fAVmQuqUQ3HIG+Izfuh1tEbQgghhBCSKR7DcO8sIm1tbVJdXS2tra1SVVXldHPyGpgZ+RWoCoXp7kk63pAhhEiOvSHKjoZZ9YJ2dDe0pR7QjvpAW+qBdnYMVIrUzXTNeJseiwIiGAxKIIBkaZI9b0g4gegI2tYFY19ngWAoLAE/6y7oAG2pB7SjPtCWekA7OgeFRYEAb8WSJUtk1qxZ4vP5nG6OPqDqlRd5ISnkhgAlNIYIw8J2I2R6QtRiRLwipmMRd2GWNLXLrIZq8WlwI6aQoS31gHbUB9pSD2hHZ6GwICSX+PzmMhLgHQn2i7QtEhm3oenntQQIhEeMGDESrMtgX0IIIYSQYaCwIMRN3hGIElU9KyCSS8+TXWzYvSgJxYhkd9+It4YQQggh+Q2FRQGBxG3ifhyxI3JPPBAyDoTRWV6VpB6XcBKhkmSfVPbLkZjx6pBYSGhHjaAt9YB2dA5WhSKEkOHETEJRE87ufoQQQkg8rApF8hHox87OTikvL9ej/FqBQjvm0kOD8+vNnS3DiQRJIo+LkeZ+rr135DBx5y3mPBqxduzpk/LiIts1OdR7R2E7yQrKlr1BKS/28/fVxdCOzkJhUUBVoVasWMGqUC6HdtTYltEQN9rVTYRDIVmxcKHMmuzgNWnkUMjY855G9DzuONF1o/E8vTaGw4asaO6QWRNrxKeKYyRoY14QN1iOGTx7Rnn9SN+TZL11bu3nOurNtZ//uG3DVIVasa6TVaEcgsKCEEIIcTPxgzXepR0ZoZBI60KR8bOGLo4RraanXiR/ns1BO20aizGE8FB2/FJk7DSzemJCwTICMRMvNNN9X4FAYUEIIYQQknaoJMlLG3hD5iS3gbLcVk9MVwwZKYoSr7uG6u5qLRkxiDPErNuMN3Q3tKM+0JZ6QDvqA22pB3lrR09hCFJWhSKEEEIIIYRkPN7mxAYFAvRjS0uLeiTuhXbUB9pSD2hHfaAt9YB2dBYKiwKqQLNq1Sr1SNwL7agPtKUe0I76QFvqAe3oLBQWhBBCCCGEkIyhsCCEEEIIIYRkDIVFgYDqCJyt2f3QjvpAW+oB7agPtKUe0I7OwqpQhBBCCCGEkISwKhQZBJKY1q5dy2Qml0M76gNtqQe0oz7QlnpAOzoLhUWBAMcULjQXO6gI7agVtKUe0I76QFvqAe3oLBQWhBBCCCGEkIyhsCCEEEIIIYRkDIVFgYDqCEi8YZUEd0M76gNtqQe0oz7QlnpAOzoLq0IRQgghhBBCEsKqUGQQqI7Q2NjIKgkuh3bUB9pSD2hHfaAt9YB2dBYKiwIBjikoTRc7qAjtqBW0pR7QjvpAW+oB7egsFBaEEEIIIYSQjPGLi7HUKGK/yNCEQiHp6OhQ58rn8zndHDJCaEd9oC31gHbUB9pSD2jH7GONs1PxArlaWLS3t6vHKVOmON0UQgghhBBCtAXjbiRxa1sVCok5X3/9tVRWVrKsWApqEwJs+fLlrKDlYmhHfaAt9YB21AfaUg9ox+wDqQBRMXHiRPF6vfp6LNC5yZMnO90MV4GLjBea+6Ed9YG21APaUR9oSz2gHbPLcJ4KCyZvE0IIIYQQQjKGwoIQQgghhBCSMRQWBUJxcbFcdNFF6pG4F9pRH2hLPaAd9YG21APa0VlcnbxNCCGEEEIIyQ/osSCEEEIIIYRkDIUFIYQQQgghJGMoLAghhBBCCCEZQ2HhYl5++WU59NBD1YQlmCDwkUceidmO9JkLL7xQGhoapLS0VPbZZx9ZuHBhzD7Nzc1yzDHHqFrPNTU1ctJJJ0lHR0eOe1LYXHnllbL99turiR7r6+vl8MMPly+++CJmn56eHjnttNNk7NixUlFRIUceeaSsXr06Zp9ly5bJwQcfLGVlZeo45557rgSDwRz3prCZO3eubLHFFtH66TvttJM8+eST0e20ozu56qqr1G/sWWedFV1HW7qDiy++WNnOvmy88cbR7bSje1i5cqUce+yxylYY02y++ebyzjvvRLdzzJMfUFi4mM7OTtlyyy3l5ptvTrj9D3/4g/zpT3+SW265Rd58800pLy+X/fffX/2QWuAC+/TTT+XZZ5+VJ554QomVn/70pznsBXnppZfUH7Z58+YpO/T398t+++2n7Gtx9tlny+OPPy4PPvig2h8zzh9xxBHR7aFQSP3h6+vrk9dff13uueceufvuu9WPLMkdmLATg9B3331X/cHba6+95LDDDlPXGKAd3cfbb78tt956qxKMdmhL97DZZptJY2NjdHn11Vej22hHd7B+/XrZZZddpKioSN2smT9/vlx77bVSW1sb3YdjnjwBVaGI+4EpH3744ejrcDhsTJgwwbjmmmui61paWozi4mLjvvvuU6/nz5+v3vf2229H93nyyScNj8djrFy5Msc9IBZNTU3KLi+99FLUbkVFRcaDDz4Y3eezzz5T+7zxxhvq9X//+1/D6/Uaq1atiu4zd+5co6qqyujt7XWgF8SitrbWuOOOO2hHF9Le3m7MmjXLePbZZ43dd9/dOPPMM9V62tI9XHTRRcaWW26ZcBvt6B7OP/98Y9ddd026nWOe/IEeC01ZsmSJrFq1SrkC7dOx77jjjvLGG2+o13iEK3C77baL7oP9vV6vUvvEGVpbW9XjmDFj1CPufsOLYbclXPkbbLBBjC3hFh4/fnx0H9ypaWtri94tJ7kFdzrvv/9+5XlCSBTt6D7gScTdarvNAG3pLhAOg5DhDTfcUN2xRmgToB3dw2OPPabGKkcddZQKR9t6663l9ttvj27nmCd/oLDQFFxgwP5jaL22tuERF6gdv9+vBrTWPiS3hMNhFccNl++cOXPUOtgiEAioH8ShbJnI1tY2kjs+/vhjFauNyZlOPvlkefjhh2XTTTelHV0GROF7772ncqDioS3dAwaWCF166qmnVA4UBqC77babtLe3044u4ssvv1T2mzVrljz99NNyyimnyBlnnKFC0wDHPPmD3+kGEEJi75B+8sknMTHAxF1stNFG8sEHHyjP07/+9S857rjjVOw2cQ/Lly+XM888U8Vhl5SUON0ckgEHHnhg9DnyZCA0pk6dKg888IBK8CXuuekGT8MVV1yhXsNjgb+VyKfAbyzJH+ix0JQJEyaox/jqFnhtbcNjU1NTzHZUukDVBGsfkjtOP/10lUz2wgsvqCRgC9gCiYMtLS1D2jKRra1tJHfgDujMmTNl2223VXe7UWDhhhtuoB1dBEJk8Nu4zTbbqDuaWCAOkRiK57gLSlu6E3gnZs+eLYsWLeI16SJQ6QmeXzubbLJJNKyNY578gcJCU6ZPn64ulOeeey66DjGhiCNEvDfAI35Q8UfU4vnnn1d3BnBXh+QG5N5DVCBkBucftrODASoqYdhtiXK0+EG12xIhOPYfTdxtRUm9+B9jkltwPfX29tKOLmLvvfdWdoDnyVpwtxTx+dZz2tKdoLTo4sWL1UCV16R7QHhwfBn2BQsWKO8T4Jgnj3A6e5xkVrHk/fffVwtM+cc//lE9/+qrr9T2q666yqipqTEeffRR46OPPjIOO+wwY/r06UZ3d3f0GAcccICx9dZbG2+++abx6quvqgooRx99tIO9KjxOOeUUo7q62njxxReNxsbG6NLV1RXd5+STTzY22GAD4/nnnzfeeecdY6eddlKLRTAYNObMmWPst99+xgcffGA89dRTxrhx44wLLrjAoV4VJr/61a9UNa8lS5aoaw6vUXHkmWeeUdtpR/dirwoFaEt3cM4556jfVlyTr732mrHPPvsYdXV1qvoeoB3dwVtvvWX4/X7j8ssvNxYuXGj8/e9/N8rKyoy//e1v0X045skPKCxczAsvvKAERfxy3HHHRcuv/e53vzPGjx+vSq7tvffexhdffBFzjHXr1qmLqqKiQpXPO+GEE5RgIbkjkQ2x3HXXXdF98MN46qmnqtKl+DH99re/rcSHnaVLlxoHHnigUVpaqv5w4g9qf3+/Az0qXE488URj6tSpRiAQUIMPXHOWqAC0oz7CgrZ0B9/73veMhoYGdU1OmjRJvV60aFF0O+3oHh5//HEl8jCe2XjjjY3bbrstZjvHPPmBB/857TUhhBBCCCGEuBvmWBBCCCGEEEIyhsKCEEIIIYQQkjEUFoQQQgghhJCMobAghBBCCCGEZAyFBSGEEEIIISRjKCwIIYQQQgghGUNhQQghhBBCCMkYCgtCCMlD+vr65IorrpDPPvvM6aaQHNPT0yOXXXaZfPzxx043hRBC0oLCghDiOqZNmybXX3999LXH45FHHnlEPV+6dKl6/cEHH2T1M++++26pqanJ6BjHH3+8HH744Snte84556iB5cYbb5zy8ffYYw8566yzxAniz/uLL76oXre0tGTt/GXr+5LvXHjhhfL666/LD3/4QyUwCSHELVBYEEJyDgacQy0XX3zxkO9/++235ac//am4HQiNRH194IEH5NNPP5V77rlHnY98I5FAmjJlijQ2NsqcOXMk34HQgPDJR/Hy1ltvyZtvvimPPfaYfPe73x32WnADoyX2CSH5h9/pBhBCCg8MQC3++c9/qju0X3zxRXRdRUXFkO8fN26c6AwGlFjchM/nkwkTJjjdjLwmFAqpAbbXm/ye3g477CAvvfSSev7rX/86h60jhJDMoceCEJJzMAC1lurqajXYsl53dnbKMcccI+PHj1cCY/vtt5f//e9/Gd0d/uSTT+TAAw9Ux8NxEWKydu3aId+D0J0NNthAysrK5Nvf/rasW7du0D6PPvqobLPNNlJSUiIbbrihXHLJJRIMBmWk9Pb2yi9/+UuZNGmSlJeXy4477jjozvprr72mQp7QrtraWtl///1l/fr10e3hcFjOO+88GTNmjDqf8Xe8//jHP8rmm2+ujg8vw6mnniodHR0x/UbI0tNPPy2bbLKJOmcHHHBAVAziePCkoO+WhwltHMld6blz58qMGTMkEAjIRhttJH/9619jtuN4d9xxhzr/6O+sWbPUnfyhaGpqkkMPPVRKS0tl+vTp8ve//33YdixfvlwJOfQb5+2www5T/Yn30Pzf//2fNDQ0yNixY+W0006T/v5+tR32+Oqrr+Tss8+OnhP7uUSbN910UykuLpZly5Ypj9u+++4rdXV16vu/++67y3vvvTeo7/Hhff/+979lzz33VOdiyy23lDfeeCPmPa+++qrstttuqu+w7RlnnKGuJ/t1g9yNH/3oR8quU6dOVW1bs2aN6jPWbbHFFvLOO++kfVzkA5144olSWVmprpvbbrstuh12AFtvvbXqB84XIURPKCwIIXkFBrkHHXSQPPfcc/L++++rQS0GihiQjQTE+O+1115qUIMB01NPPSWrV68e0iOAUJSTTjpJTj/9dDVQxmAOAzI7r7zyihqgnXnmmTJ//ny59dZb1UDy8ssvl5GCz8Ng8f7775ePPvpIjjrqKNX/hQsXqu1oy957760GqdgPAz6cG9wJt8CgH6IBffjDH/4gl156qTz77LPR7bhb/qc//SkaavX8888rIWKnq6tLDaIx0H/55ZfVuYfgAXjEubPEBpadd9457b4+/PDD6twhlwTC72c/+5mccMIJ8sILL8TsB7GGz8P5wPcCorO5uTnpcSECIBRwnH/961/y5z//WYmNZEAcQJxhQAybQrhZYsqe34DjLV68WD3ivMHWWAAG/JMnT1bn2jon9nN59dVXK4GEc15fXy/t7e1y3HHHKfvNmzdPCSb0DeuH4je/+Y06//gezJ49W44++uiokEXb0OYjjzxSnSt4AnF8fKfsXHfddbLLLruoa+vggw9WIhvf42OPPVaJGwg9vDYMI63jXnvttbLddtup40KsnnLKKVEvJMK7AG4Q4NzgfBFCNMUghBAHueuuu4zq6uoh99lss82MG2+8Mfp66tSpxnXXXRd9jZ+yhx9+WD1fsmSJev3++++r17///e+N/fbbL+Z4y5cvV/t88cUXCT/v6KOPNg466KCYdd/73vdi2rn33nsbV1xxRcw+f/3rX42Ghoak/TjuuOOMww47LOG2r776yvD5fMbKlStj1uNzLrjggmi7dtlll6TH33333Y1dd901Zt32229vnH/++Unf8+CDDxpjx46NsQfOzaJFi6Lrbr75ZmP8+PFD9iP+vL/wwgvq9fr166PHtZ+/nXfe2fjJT34Sc4yjjjoq5rzj/b/97W+jrzs6OtS6J598MmFfYE9sf+utt6LrPvvsM7XO/n2Jt9lGG21khMPh6Lre3l6jtLTUePrpp6P9xXcuGAzGtBXfiWTfSfu5/OCDD4yhCIVCRmVlpfH4448P+Z2+4447ots//fRTtQ79AyeddJLx05/+NOa4r7zyiuH1eo3u7u5oG4899tjo9sbGRnWM3/3ud9F1b7zxhlqHbSM9Ls5lfX29MXfu3ITfDUKIvtBjQQjJO48F7soiDAdhJLh7jJKrI/VYfPjhh+ouM45jLValJdyNTQQ+D2FIdnbaaadBx8Udavtxf/KTn6g7srhLnS6oAAXPA+5E24+JeHurnZbHYigQymIHoTv2O/a4a4xjINwKd+lxxxphXvY2I9QGd66THSMb4BzjzrkdvI4vr2vvDzwxVVVVSduC9/r9ftl2222j62DroapRwY6LFi1S58I65wiHQslX+/djs802U3kk6Z4ThHnF2wQeM3xX4KlAKBT6hO/9cN9x+3Hw+cBqA/oBD4r9uwNPDELjlixZkvAYCAsECI2LX5fJca3Qxmx/Zwgh+Q+TtwkheQVEBUJ3EIozc+ZMFdf9ne98Z8RlNzFgQ7gQwlHisQZnIz0uwnSOOOKIQduQczGS42Hg+u6778YMYO3J7DgXw1FUVBTzGoM8DAKtWP1DDjlEhakgZAsDaIS1IOwL5xeCItkxrNCYXDNUf7IBzjuESKJcDHuRgJG2AzaLr+yFMCiIuRtuuEHlOSD3AsJ1uO+4vQ3WMa02oB8IJ0P+QzzIeRjqGNk+7mjYiRDiDigsCCF5BWLcESePhF1rYGNPpE0XJFc/9NBDKsEUd7NTAd4S5CjYQSx8/HERQw7xkw2QAwKPBe7yIlE2EbgrjNwTCJqRANGCwR7i4a3KRChtmy64C2/P6xgJOMewNQbZFniN/JGRAu8Ecg7QTyT9A9jImksjEbAj8gaQ+wDPwUhJ55ygn8j9QF4FQE7IcMUEhgP9QK5Ptr6P2Twuzg3I9DtDCMl/GApFCMkrEB6C5E6E/SAM4wc/+EFGdz5RvQfJvkh0RTUehLeg4hEShZMNdHB3Fkne8Jogcfqmm25Sr+2gRO69996rBvlIykUYDpKuf/vb346onQiBQmIyEmfRf4SZIOn1yiuvlP/85z9qnwsuuED1AcmxSKT9/PPPVWWlVAelGBwiWfnGG2+UL7/8UiVn33LLLWm3FSINn49BOz7bqo6UDueee64KsUH7cY5RrQr9tpLERwIqSyHRGHfYIQwhMH784x8P6enBOUd1JlRFQvI2zjuqXOE7sGLFirTOCRLdV65cOaw98B3Hucd3Bu1EG1LxRg3F+eefrybVswoO4Jyicld8krUTx4VoQ/+swgmtra0ZtYkQkr9QWBBC8goMMFFGFZWGEMKEeG7cNR0pEydOVHeIISL2228/FU+O2akRd59sPoFvfOMbcvvtt6tQFZT1fOaZZwYJBrTriSeeUNtwdxzvQcUdhLaMlLvuuksJC1RKwiAZJU4hJKyQE4gPfB4EF+Y7QPgMBnmpemLQF5xfhIVhIjuE/0C4pAvyA9A+VAFCuBDOb7qgbzi/EG/IX0BVLfQ/01KkOAZsjhKuCFPDRIoY2CYD4V8QBDjH2B+eFISGIcciHQ8G8m3gWUNuynDzrNx5552qRDC+18hxgYgZqo2pAG8W8nEWLFigPF7wgEH84lw4fVx8P1GJDDbG+yDiCCF64kEGt9ONIIQQQgghhLgbeiwIIYQQQgghGUNhQQghhBBCCMkYCgtCCCGEEEJIxlBYEEIIIYQQQjKGwoIQQgghhBCSMRQWhBBCCCGEkIyhsCCEEEIIIYRkDIUFIYQQQgghJGMoLAghhBBCCCEZQ2FBCCGEEEIIyRgKC0IIIYQQQkjGUFgQQgghhBBCJFP+H99r2tzcAHbsAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1435,7 +4581,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -1446,12 +4592,12 @@
     {
      "data": {
       "text/plain": [
-       "[np.float64(8.142660153833225),\n",
-       " np.float64(7.637385552703454),\n",
-       " np.float64(7.494284341019172)]"
+       "[np.float64(7.673473230326665),\n",
+       " np.float64(7.504433114323556),\n",
+       " np.float64(7.268204934351579)]"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1465,9 +4611,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x870 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x870 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import shap\n",
     "import matplotlib.pyplot as plt\n",
diff --git a/scripts/rf.py b/scripts/rf.py
index ea07ffa..9fcf004 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -1,4 +1,5 @@
-from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve
+from sklearn.experimental import enable_halving_search_cv  # noqa
+from sklearn.model_selection import train_test_split, GridSearchCV, learning_curve, HalvingGridSearchCV
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import root_mean_squared_error, mean_squared_error, r2_score
 import matplotlib.pyplot as plt
@@ -11,39 +12,42 @@ def random_forest_GS(X_train, y_train, X_test, y_test):
     # Définir le modèle
     rf = RandomForestRegressor(random_state=42)
 
-    # Grille des hyperparamètres à tester
     param_grid = {
-        "n_estimators": [5, 50, 100],              
-        "max_depth": [None, 10, 20, 50],         
-        "min_samples_split": [2, 5, 10],         
-        "min_samples_leaf": [1, 2, 4],           
-        "max_features": ["auto", "sqrt", "log2"] 
-    }
-
-    # Grid Search avec validation croisée
-    grid_search = GridSearchCV(
-        estimator=rf,
-        param_grid=param_grid,
-        cv=5,                 
-        n_jobs=-1,            
-        scoring="neg_mean_squared_error",  
-        verbose=2
-    )
-
-    # Entraînement
-    grid_search.fit(X_train, y_train)
+    "n_estimators": [50, 100, 200],              
+    "max_depth": [None, 10, 20, 50],          
+    "min_samples_split": [2, 5, 10],          
+    "min_samples_leaf": [1, 2, 4],            
+    "max_features": ["sqrt", "log2"]  
+}
+
+# Halving Grid Search
+    halving_grid_search = HalvingGridSearchCV(
+    estimator=rf,
+    param_grid=param_grid,
+    cv=5,
+    factor=3,                     # reduction factor (default=3)
+    resource='n_samples',      # progressively allocate more trees
+    max_resources=100,            # maximum number of trees
+    min_resources=50,              # minimum number of trees
+    scoring="neg_mean_squared_error",
+    n_jobs=-1,
+    verbose=2
+)
+
+# Fit
+    halving_grid_search.fit(X_train, y_train)
     
     # Prédictions
-    y_pred = grid_search.best_estimator_.predict(X_test)
+    y_pred = halving_grid_search.best_estimator_.predict(X_test)
 
     # Meilleurs paramètres
-    print("Meilleurs paramètres trouvés : ", grid_search.best_params_)
+    print("Meilleurs paramètres trouvés : ", halving_grid_search.best_params_)
     
     # Évaluation
     print("MSE :", root_mean_squared_error(y_test, y_pred))
     print("R² :", r2_score(y_test, y_pred))
 
-    return grid_search.best_estimator_, X_train, X_test, y_train, y_test, y_pred
+    return halving_grid_search.best_estimator_, X_train, X_test, y_train, y_test, y_pred
 
 
 
@@ -113,7 +117,7 @@ def plot_learning_curve_rmse(model, X, y, cv=5):
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import mean_squared_error
 
-def plot_rmse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[10,50,100]):
+def plot_rmse(best_rf, X_train, X_test, y_train, y_test, n_estimators_range=[50, 100, 200]):
     errors = []
 
     for n in n_estimators_range:

From 01cf69296474104cef091451e5669c7af25c064b Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Sat, 6 Sep 2025 23:43:24 +0200
Subject: [PATCH 11/12] h

---
 notebooks/project_starter.ipynb | 1758 +++++++++++++++++++++++--------
 scripts/rf.py                   |    2 +-
 2 files changed, 1324 insertions(+), 436 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index 0fe66a9..26a0d02 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,16 +46,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12412\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12360\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12412\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12360\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
       "  df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n"
      ]
     }
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1153,7 @@
          "type": "float"
         }
        ],
-       "ref": "af76bee0-c025-406d-81f4-4a1e1bd0b78c",
+       "ref": "964bcdbb-39c2-42c6-ac00-d3cb0eadf1eb",
        "rows": [
         [
          "0",
@@ -2275,7 +2275,67 @@
        "      <th>last_modified_by</th>\n",
        "      <th>last_updated_t</th>\n",
        "      <th>last_updated_datetime</th>\n",
+       "      <th>product_name</th>\n",
+       "      <th>abbreviated_product_name</th>\n",
+       "      <th>generic_name</th>\n",
+       "      <th>quantity</th>\n",
+       "      <th>packaging</th>\n",
+       "      <th>packaging_tags</th>\n",
+       "      <th>packaging_en</th>\n",
+       "      <th>packaging_text</th>\n",
+       "      <th>brands</th>\n",
+       "      <th>brands_tags</th>\n",
+       "      <th>brands_en</th>\n",
+       "      <th>categories</th>\n",
+       "      <th>categories_tags</th>\n",
+       "      <th>categories_en</th>\n",
+       "      <th>origins</th>\n",
+       "      <th>origins_tags</th>\n",
+       "      <th>origins_en</th>\n",
+       "      <th>manufacturing_places</th>\n",
+       "      <th>manufacturing_places_tags</th>\n",
+       "      <th>labels</th>\n",
+       "      <th>labels_tags</th>\n",
+       "      <th>labels_en</th>\n",
+       "      <th>emb_codes</th>\n",
+       "      <th>emb_codes_tags</th>\n",
+       "      <th>first_packaging_code_geo</th>\n",
+       "      <th>cities</th>\n",
+       "      <th>cities_tags</th>\n",
+       "      <th>purchase_places</th>\n",
+       "      <th>stores</th>\n",
+       "      <th>countries</th>\n",
        "      <th>...</th>\n",
+       "      <th>potassium_100g</th>\n",
+       "      <th>chloride_100g</th>\n",
+       "      <th>calcium_100g</th>\n",
+       "      <th>phosphorus_100g</th>\n",
+       "      <th>iron_100g</th>\n",
+       "      <th>magnesium_100g</th>\n",
+       "      <th>zinc_100g</th>\n",
+       "      <th>copper_100g</th>\n",
+       "      <th>manganese_100g</th>\n",
+       "      <th>fluoride_100g</th>\n",
+       "      <th>selenium_100g</th>\n",
+       "      <th>chromium_100g</th>\n",
+       "      <th>molybdenum_100g</th>\n",
+       "      <th>iodine_100g</th>\n",
+       "      <th>caffeine_100g</th>\n",
+       "      <th>taurine_100g</th>\n",
+       "      <th>methylsulfonylmethane_100g</th>\n",
+       "      <th>ph_100g</th>\n",
+       "      <th>fruits-vegetables-nuts_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-dried_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate_100g</th>\n",
+       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
+       "      <th>collagen-meat-protein-ratio_100g</th>\n",
+       "      <th>cocoa_100g</th>\n",
+       "      <th>chlorophyl_100g</th>\n",
+       "      <th>carbon-footprint_100g</th>\n",
+       "      <th>carbon-footprint-from-meat-or-fish_100g</th>\n",
+       "      <th>nutrition-score-fr_100g</th>\n",
+       "      <th>nutrition-score-uk_100g</th>\n",
+       "      <th>glycemic-index_100g</th>\n",
        "      <th>water-hardness_100g</th>\n",
        "      <th>choline_100g</th>\n",
        "      <th>phylloquinone_100g</th>\n",
@@ -2301,6 +2361,36 @@
        "      <td>NaN</td>\n",
        "      <td>1740205422</td>\n",
        "      <td>2025-02-22T06:23:42Z</td>\n",
+       "      <td>Limonade artisanale a la rose</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>en:fr</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2312,6 +2402,36 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2325,6 +2445,36 @@
        "      <td>bodysupport</td>\n",
        "      <td>1750061386</td>\n",
        "      <td>2025-06-16T08:09:46Z</td>\n",
+       "      <td>M&amp;amp;M white</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80 gram</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Fitpiggy</td>\n",
+       "      <td>xx:fitpiggy</td>\n",
+       "      <td>fitpiggy</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>en:fr</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2336,6 +2486,36 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2349,6 +2529,36 @@
        "      <td>teolemon</td>\n",
        "      <td>1751035658</td>\n",
        "      <td>2025-06-27T14:47:38Z</td>\n",
+       "      <td>Chocolate n3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>80 g</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Jeff de Bruges</td>\n",
+       "      <td>xx:jeff-de-bruges</td>\n",
+       "      <td>jeff-de-bruges</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Green Dot,Made in France</td>\n",
+       "      <td>en:green-dot,en:made-in-france</td>\n",
+       "      <td>Green Dot,Made in France</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>France</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2360,6 +2570,36 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2373,6 +2613,36 @@
        "      <td>NaN</td>\n",
        "      <td>1743653496</td>\n",
        "      <td>2025-04-03T04:11:36Z</td>\n",
+       "      <td>Paleta gran reserva - Sierra nevada-</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>750ml</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>AdvoCare</td>\n",
+       "      <td>xx:advocare</td>\n",
+       "      <td>advocare</td>\n",
+       "      <td>Bebidas y preparaciones de bebidas, Bebidas</td>\n",
+       "      <td>en:beverages-and-beverages-preparations,en:bev...</td>\n",
+       "      <td>Beverages and beverages preparations,Beverages</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Spanien, Germany</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2384,6 +2654,36 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.011335</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2397,10 +2697,42 @@
        "      <td>altroconsumo</td>\n",
        "      <td>1749171851</td>\n",
        "      <td>2025-06-06T01:04:11Z</td>\n",
+       "      <td>Filets de poulet blanc x2</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>240-400 g</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SoLo, selbstgemacht 2 Liter</td>\n",
+       "      <td>xx:solo,xx:selbstgemacht-2-liter</td>\n",
+       "      <td>solo,selbstgemacht-2-liter</td>\n",
+       "      <td>Protein powders</td>\n",
+       "      <td>en:dietary-supplements,en:bodybuilding-supplem...</td>\n",
+       "      <td>Dietary supplements,Bodybuilding supplements,P...</td>\n",
+       "      <td>île d’Orléans,Québec,Canada</td>\n",
+       "      <td>en:canada,en:quebec,fr:ile-d-orleans</td>\n",
+       "      <td>Canada,Québec,fr:ile-d-orleans</td>\n",
+       "      <td>Ancenis</td>\n",
+       "      <td>ancenis</td>\n",
+       "      <td>Organic, EU Organic, French meat, Bee Friendly...</td>\n",
+       "      <td>en:organic,en:eu-organic,en:french-meat,en:bee...</td>\n",
+       "      <td>Organic,EU Organic,French meat,Bee Friendly,Fr...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Brasilien, Germany</td>\n",
        "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
+       "      <td>0.0001</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2408,66 +2740,59 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 214 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   code                                                url  creator  \\\n",
-       "0    54  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
-       "1    63  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
-       "2   114  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
-       "3   105  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
-       "4     2  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
-       "\n",
-       "    created_t      created_datetime  last_modified_t last_modified_datetime  \\\n",
-       "0  1582569031  2020-02-24T18:30:31Z       1733085204   2024-12-01T20:33:24Z   \n",
-       "1  1673620307  2023-01-13T14:31:47Z       1750061386   2025-06-16T08:09:46Z   \n",
-       "2  1580066482  2020-01-26T19:21:22Z       1751035658   2025-06-27T14:47:38Z   \n",
-       "3  1572117743  2019-10-26T19:22:23Z       1738073570   2025-01-28T14:12:50Z   \n",
-       "4  1722606455  2024-08-02T13:47:35Z       1749171851   2025-06-06T01:04:11Z   \n",
-       "\n",
-       "  last_modified_by  last_updated_t last_updated_datetime  ...  \\\n",
-       "0              NaN      1740205422  2025-02-22T06:23:42Z  ...   \n",
-       "1      bodysupport      1750061386  2025-06-16T08:09:46Z  ...   \n",
-       "2         teolemon      1751035658  2025-06-27T14:47:38Z  ...   \n",
-       "3              NaN      1743653496  2025-04-03T04:11:36Z  ...   \n",
-       "4     altroconsumo      1749171851  2025-06-06T01:04:11Z  ...   \n",
-       "\n",
-       "  water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g  \\\n",
-       "0                 NaN          NaN                NaN              NaN   \n",
-       "1                 NaN          NaN                NaN              NaN   \n",
-       "2                 NaN          NaN                NaN              NaN   \n",
-       "3                 NaN          NaN                NaN              NaN   \n",
-       "4                 NaN          NaN                NaN              NaN   \n",
-       "\n",
-       "  inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g  \\\n",
-       "0           NaN            NaN           NaN          NaN          NaN   \n",
-       "1           NaN            NaN           NaN          NaN          NaN   \n",
-       "2           NaN            NaN           NaN          NaN          NaN   \n",
-       "3           NaN            NaN           NaN          NaN          NaN   \n",
-       "4           NaN            NaN           NaN          NaN          NaN   \n",
-       "\n",
-       "  carbohydrates-total_100g  \n",
-       "0                      NaN  \n",
-       "1                      NaN  \n",
-       "2                      NaN  \n",
-       "3                      NaN  \n",
-       "4                      NaN  \n",
-       "\n",
-       "[5 rows x 214 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_train.head()"
-   ]
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.052506</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 214 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   code  ... carbohydrates-total_100g\n",
+       "0    54  ...                      NaN\n",
+       "1    63  ...                      NaN\n",
+       "2   114  ...                      NaN\n",
+       "3   105  ...                      NaN\n",
+       "4     2  ...                      NaN\n",
+       "\n",
+       "[5 rows x 214 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.head()"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -2485,7 +2810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -2510,7 +2835,7 @@
        "True"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2540,7 +2865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -2554,27 +2879,27 @@
       "12                  unknown            NA\n",
       "14          Composite foods     Processed\n",
       "...                     ...           ...\n",
-      "4926   Cereals and potatoes   Plant_based\n",
-      "4935         Fish Meat Eggs  Animal_based\n",
-      "4936                unknown            NA\n",
-      "4943        Composite foods     Processed\n",
-      "4987          Sugary snacks        Snacks\n",
+      "4936   Cereals and potatoes   Plant_based\n",
+      "4945         Fish Meat Eggs  Animal_based\n",
+      "4946                unknown            NA\n",
+      "4953        Composite foods     Processed\n",
+      "4997          Sugary snacks        Snacks\n",
       "\n",
-      "[437 rows x 2 columns]\n",
+      "[439 rows x 2 columns]\n",
       "                pnns_groups_1      PNNS_pro\n",
-      "0        Cereals and potatoes   Plant_based\n",
-      "7              Fish Meat Eggs  Animal_based\n",
-      "42      Fruits and vegetables   Plant_based\n",
-      "45                    unknown            NA\n",
-      "47                    unknown            NA\n",
+      "10       Cereals and potatoes   Plant_based\n",
+      "17             Fish Meat Eggs  Animal_based\n",
+      "52      Fruits and vegetables   Plant_based\n",
+      "55                    unknown            NA\n",
+      "57                    unknown            NA\n",
       "...                       ...           ...\n",
-      "4952            Sugary snacks        Snacks\n",
-      "4966          Composite foods     Processed\n",
-      "4975  Milk and dairy products  Animal_based\n",
-      "4981            Sugary snacks        Snacks\n",
-      "4990            Sugary snacks        Snacks\n",
+      "4938          Composite foods     Processed\n",
+      "4952     Cereals and potatoes   Plant_based\n",
+      "4975            Sugary snacks        Snacks\n",
+      "4989          Composite foods     Processed\n",
+      "4998  Milk and dairy products  Animal_based\n",
       "\n",
-      "[772 rows x 2 columns]\n"
+      "[770 rows x 2 columns]\n"
      ]
     }
    ],
@@ -2601,14 +2926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -2746,10 +3064,10 @@
          "type": "float"
         }
        ],
-       "ref": "de68c5e6-3592-4420-ae4d-0c858fd51822",
+       "ref": "2451aee8-103a-49ce-8621-44a06881b9c4",
        "rows": [
         [
-         "0",
+         "10",
          "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Céréales et pommes de terre, Pains, Baguettes",
          "Cereals and potatoes",
          "Bread",
@@ -2772,12 +3090,12 @@
          "0.0",
          "0.0",
          "0.0",
-         "1.0",
          "0.0",
-         "0.0"
+         "0.0",
+         "1.0"
         ],
         [
-         "7",
+         "17",
          "Produits de la mer, Poissons et dérivés, Poissons, Poissons gras, Saumons, Poissons fumés, Saumons fumés, Saumons fumés à la ficelle",
          "Fish Meat Eggs",
          "Fish and seafood",
@@ -2800,12 +3118,12 @@
          "0.0",
          "0.0",
          "0.0",
-         "1.0",
          "0.0",
-         "0.0"
+         "0.0",
+         "1.0"
         ],
         [
-         "42",
+         "52",
          "Fresh papayas",
          "Fruits and vegetables",
          "Fruits",
@@ -2833,7 +3151,7 @@
          "0.0"
         ],
         [
-         "45",
+         "55",
          "Snacks, Snacks sucrés",
          "unknown",
          "unknown",
@@ -2861,7 +3179,7 @@
          "0.0"
         ],
         [
-         "47",
+         "57",
          "Drink mix",
          "unknown",
          "unknown",
@@ -2923,7 +3241,11 @@
        "      <th>nutriscore_score</th>\n",
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
-       "      <th>...</th>\n",
+       "      <th>saturated-fat_100g</th>\n",
+       "      <th>carbohydrates_100g</th>\n",
+       "      <th>sugars_100g</th>\n",
+       "      <th>fiber_100g</th>\n",
+       "      <th>proteins_100g</th>\n",
        "      <th>salt_100g</th>\n",
        "      <th>sodium_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
@@ -2938,7 +3260,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
+       "      <th>10</th>\n",
        "      <td>Aliments et boissons à base de végétaux, Alime...</td>\n",
        "      <td>Cereals and potatoes</td>\n",
        "      <td>Bread</td>\n",
@@ -2949,7 +3271,11 @@
        "      <td>4.0</td>\n",
        "      <td>1125.0</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>...</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>47.9</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>9.4</td>\n",
        "      <td>1.300000</td>\n",
        "      <td>0.52</td>\n",
        "      <td>0.000000</td>\n",
@@ -2957,12 +3283,12 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>7</th>\n",
+       "      <th>17</th>\n",
        "      <td>Produits de la mer, Poissons et dérivés, Poiss...</td>\n",
        "      <td>Fish Meat Eggs</td>\n",
        "      <td>Fish and seafood</td>\n",
@@ -2973,7 +3299,11 @@
        "      <td>17.0</td>\n",
        "      <td>1059.0</td>\n",
        "      <td>17.0</td>\n",
-       "      <td>...</td>\n",
+       "      <td>2.6</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>23.0</td>\n",
        "      <td>2.500000</td>\n",
        "      <td>1.00</td>\n",
        "      <td>NaN</td>\n",
@@ -2981,12 +3311,12 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>42</th>\n",
+       "      <th>52</th>\n",
        "      <td>Fresh papayas</td>\n",
        "      <td>Fruits and vegetables</td>\n",
        "      <td>Fruits</td>\n",
@@ -2997,7 +3327,11 @@
        "      <td>-3.0</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>100.000000</td>\n",
@@ -3010,7 +3344,7 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>45</th>\n",
+       "      <th>55</th>\n",
        "      <td>Snacks, Snacks sucrés</td>\n",
        "      <td>unknown</td>\n",
        "      <td>unknown</td>\n",
@@ -3021,7 +3355,11 @@
        "      <td>25.0</td>\n",
        "      <td>2464.0</td>\n",
        "      <td>47.0</td>\n",
-       "      <td>...</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>5.9</td>\n",
        "      <td>0.000000</td>\n",
        "      <td>0.00</td>\n",
        "      <td>NaN</td>\n",
@@ -3034,7 +3372,7 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>47</th>\n",
+       "      <th>57</th>\n",
        "      <td>Drink mix</td>\n",
        "      <td>unknown</td>\n",
        "      <td>unknown</td>\n",
@@ -3045,7 +3383,11 @@
        "      <td>25.0</td>\n",
        "      <td>33472.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>300.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>200.0</td>\n",
+       "      <td>1500.0</td>\n",
        "      <td>18.750001</td>\n",
        "      <td>7.50</td>\n",
        "      <td>0.429687</td>\n",
@@ -3059,63 +3401,20 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                           categories          pnns_groups_1  \\\n",
-       "0   Aliments et boissons à base de végétaux, Alime...   Cereals and potatoes   \n",
-       "7   Produits de la mer, Poissons et dérivés, Poiss...         Fish Meat Eggs   \n",
-       "42                                      Fresh papayas  Fruits and vegetables   \n",
-       "45                              Snacks, Snacks sucrés                unknown   \n",
-       "47                                          Drink mix                unknown   \n",
-       "\n",
-       "       pnns_groups_2       brands_tags  \\\n",
-       "0              Bread  xx:la-campaniere   \n",
-       "7   Fish and seafood               NaN   \n",
-       "42            Fruits         xx:curate   \n",
-       "45           unknown               NaN   \n",
-       "47           unknown      tclinics-usa   \n",
-       "\n",
-       "                            ingredients_analysis_tags         code  \\\n",
-       "0   en:palm-oil-free,en:vegan-status-unknown,en:ve...  584019351.0   \n",
-       "7                                                 NaN       5869.0   \n",
-       "42            en:palm-oil-free,en:vegan,en:vegetarian  599990534.0   \n",
-       "45                                                NaN  600002458.0   \n",
-       "47  en:may-contain-palm-oil,en:non-vegan,en:vegeta...  600020002.0   \n",
-       "\n",
-       "    additives_n  nutriscore_score  energy_100g  fat_100g  ...  salt_100g  \\\n",
-       "0           0.0               4.0       1125.0       3.0  ...   1.300000   \n",
-       "7           NaN              17.0       1059.0      17.0  ...   2.500000   \n",
-       "42          0.0              -3.0          NaN       NaN  ...        NaN   \n",
-       "45          NaN              25.0       2464.0      47.0  ...   0.000000   \n",
-       "47          6.0              25.0      33472.0       0.0  ...  18.750001   \n",
-       "\n",
-       "    sodium_100g  fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "0          0.52                                           0.000000       \n",
-       "7          1.00                                                NaN       \n",
-       "42          NaN                                         100.000000       \n",
-       "45         0.00                                                NaN       \n",
-       "47         7.50                                           0.429687       \n",
-       "\n",
-       "        PNNS_pro  PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  \\\n",
-       "0    Plant_based                    0.0              0.0          0.0   \n",
-       "7   Animal_based                    0.0              0.0          0.0   \n",
-       "42   Plant_based                    0.0              0.0          1.0   \n",
-       "45            NA                    0.0              0.0          1.0   \n",
-       "47            NA                    0.0              0.0          1.0   \n",
-       "\n",
-       "    PNNS_pro_Plant_based PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "0                    1.0                0.0              0.0  \n",
-       "7                    1.0                0.0              0.0  \n",
-       "42                   0.0                0.0              0.0  \n",
-       "45                   0.0                0.0              0.0  \n",
-       "47                   0.0                0.0              0.0  \n",
+       "                                           categories  ... PNNS_pro_Snacks\n",
+       "10  Aliments et boissons à base de végétaux, Alime...  ...             1.0\n",
+       "17  Produits de la mer, Poissons et dérivés, Poiss...  ...             1.0\n",
+       "52                                      Fresh papayas  ...             0.0\n",
+       "55                              Snacks, Snacks sucrés  ...             0.0\n",
+       "57                                          Drink mix  ...             0.0\n",
        "\n",
        "[5 rows x 25 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3131,7 +3430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3141,7 +3440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -3249,7 +3548,7 @@
          "type": "float"
         }
        ],
-       "ref": "85b8d5a1-18b0-4f31-bc19-b4d6b94d140f",
+       "ref": "1cf816e2-a31b-432c-875a-284f5e9597b7",
        "rows": [
         [
          "6",
@@ -3523,50 +3822,17 @@
        "</div>"
       ],
       "text/plain": [
-       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6    4.0          0.0              15.0       2401.0      12.0   \n",
-       "9    6.0          NaN               4.0       1520.0      11.0   \n",
-       "11   7.0          0.0               4.0          4.0       1.0   \n",
-       "12   8.0          1.0               6.0       1510.0       2.0   \n",
-       "14   9.0          NaN             -11.0        293.0       0.5   \n",
-       "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6                10.50                13.0         9.00   36.000000   \n",
-       "9                 2.00                25.0         0.98    9.000000   \n",
-       "11                1.00                 1.0         1.00    1.000000   \n",
-       "12                0.50                 6.7         1.70   10.714286   \n",
-       "14                0.06                 2.0         0.24   88.000000   \n",
-       "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6            23.0      0.300         0.12   \n",
-       "9            22.0      0.950         0.38   \n",
-       "11            1.0      1.000         0.40   \n",
-       "12           76.0      1.500         0.60   \n",
-       "14           18.0      0.275         0.11   \n",
+       "    code  additives_n  ...  PNNS_pro_Processed  PNNS_pro_Snacks\n",
+       "6    4.0          0.0  ...                 0.0              1.0\n",
+       "9    6.0          NaN  ...                 1.0              0.0\n",
+       "11   7.0          0.0  ...                 0.0              0.0\n",
+       "12   8.0          1.0  ...                 0.0              0.0\n",
+       "14   9.0          NaN  ...                 0.0              0.0\n",
        "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                                 0.0       \n",
-       "9                                                 NaN       \n",
-       "11                                                0.0       \n",
-       "12                                                0.0       \n",
-       "14                                                NaN       \n",
-       "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0          0.0                   0.0   \n",
-       "9                     0.0              0.0          0.0                   0.0   \n",
-       "11                    0.0              0.0          0.0                   1.0   \n",
-       "12                    0.0              0.0          1.0                   0.0   \n",
-       "14                    0.0              0.0          1.0                   0.0   \n",
-       "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              1.0  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
+       "[5 rows x 19 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3584,15 +3850,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(807, 19)\n",
-      ": shape of df with only numeric features=(1450, 19)\n"
+      ": shape of df with only numeric features=(811, 19)\n",
+      ": shape of df with only numeric features=(1446, 19)\n"
      ]
     },
     {
@@ -3700,7 +3966,7 @@
          "type": "float"
         }
        ],
-       "ref": "29ba5422-883e-4206-a164-d179d8a81809",
+       "ref": "ab43e568-f62d-4401-ba40-66c9e210e3fd",
        "rows": [
         [
          "6",
@@ -3974,50 +4240,17 @@
        "</div>"
       ],
       "text/plain": [
-       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6    4.0          0.0              15.0       2401.0      12.0   \n",
-       "9    6.0          1.8               4.0       1520.0      11.0   \n",
-       "11   7.0          0.0               4.0          4.0       1.0   \n",
-       "12   8.0          1.0               6.0       1510.0       2.0   \n",
-       "14   9.0          0.6             -11.0        293.0       0.5   \n",
-       "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6                10.50                13.0         9.00   36.000000   \n",
-       "9                 2.00                25.0         0.98    9.000000   \n",
-       "11                1.00                 1.0         1.00    1.000000   \n",
-       "12                0.50                 6.7         1.70   10.714286   \n",
-       "14                0.06                 2.0         0.24   88.000000   \n",
-       "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6            23.0      0.300         0.12   \n",
-       "9            22.0      0.950         0.38   \n",
-       "11            1.0      1.000         0.40   \n",
-       "12           76.0      1.500         0.60   \n",
-       "14           18.0      0.275         0.11   \n",
-       "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                            0.000000       \n",
-       "9                                           20.400223       \n",
-       "11                                           0.000000       \n",
-       "12                                           0.000000       \n",
-       "14                                          20.000291       \n",
-       "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0          0.0                   0.0   \n",
-       "9                     0.0              0.0          0.0                   0.0   \n",
-       "11                    0.0              0.0          0.0                   1.0   \n",
-       "12                    0.0              0.0          1.0                   0.0   \n",
-       "14                    0.0              0.0          1.0                   0.0   \n",
+       "    code  additives_n  ...  PNNS_pro_Processed  PNNS_pro_Snacks\n",
+       "6    4.0          0.0  ...                 0.0              1.0\n",
+       "9    6.0          1.8  ...                 1.0              0.0\n",
+       "11   7.0          0.0  ...                 0.0              0.0\n",
+       "12   8.0          1.0  ...                 0.0              0.0\n",
+       "14   9.0          0.6  ...                 0.0              0.0\n",
        "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              1.0  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
+       "[5 rows x 19 columns]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4038,15 +4271,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ": shape of df with only numeric features=(807, 18)\n",
-      ": shape of df with only numeric features=(1450, 18)\n"
+      ": shape of df with only numeric features=(811, 18)\n",
+      ": shape of df with only numeric features=(1446, 18)\n"
      ]
     }
    ],
@@ -4058,7 +4291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -4166,117 +4399,117 @@
          "type": "float"
         }
        ],
-       "ref": "0215de47-8466-4117-b731-e37f849871d2",
+       "ref": "65110a54-48f6-4568-9157-4113a041a8ce",
        "rows": [
         [
          "6",
-         "-2.445750371869255e-06",
+         "-2.6546675954127342e-06",
          "-0.7499999999999999",
          "15.0",
-         "1.461818181818182",
+         "1.4507904856857996",
          "0.021972656250000073",
-         "1.1003451776649746",
-         "-0.3534601599117728",
+         "1.2533604060913706",
+         "-0.36041608096710714",
          "-0.12803584060363116",
-         "2.7338144044616133",
-         "0.09493032908390153",
-         "-0.26118808727504383",
-         "-0.2611880872750438",
-         "-0.19735657983213245",
+         "2.95768999398436",
+         "0.09490219324244216",
+         "-0.2647296206618241",
+         "-0.2647296206618241",
+         "-0.19489463315452618",
          "0.0",
          "0.0",
+         "-1.0",
          "-0.5",
          "0.0",
-         "0.0",
-         "2.5"
+         "2.0"
         ],
         [
          "9",
-         "-2.426058339889631e-06",
+         "-2.6349595954467894e-06",
          "0.37500000000000006",
          "4.0",
-         "0.18036363636363636",
+         "0.1959834781370174",
          "-0.039062499999999924",
-         "-0.28036548223350255",
-         "-0.02260821615660326",
+         "-0.12735025380710657",
+         "-0.02305313466404274",
          "-0.6008016977128036",
-         "0.0038421831263237664",
-         "0.06528313074414463",
-         "-0.05414874980092376",
-         "-0.05414874980092372",
-         "0.3840135991997579",
+         "0.2506516944054542",
+         "0.0652637818612922",
+         "-0.05488297013720748",
+         "-0.054882970137207436",
+         "0.37922317870549505",
          "0.0",
          "0.0",
+         "-1.0",
          "-0.5",
-         "0.0",
-         "1.0",
-         "0.0"
+         "5.0",
+         "-0.5"
         ],
         [
          "11",
-         "-2.4162123238998194e-06",
+         "-2.625105595463817e-06",
          "-0.7499999999999999",
          "4.0",
-         "-2.0247272727272727",
+         "-1.9632530978493092",
          "-0.6494140624999999",
-         "-0.4428020304568528",
-         "-0.6843121036669423",
+         "-0.2897868020304568",
+         "-0.6977790272701715",
          "-0.5996227304880924",
-         "-0.8050384750470954",
-         "-0.5573080343907502",
-         "-0.03822264691829912",
-         "-0.03822264691829908",
-         "-0.19735657983213245",
+         "-0.5514337276919993",
+         "-0.5571428571428573",
+         "-0.03874092009685234",
+         "-0.038740920096852295",
+         "-0.19489463315452618",
          "0.0",
          "0.0",
-         "-0.5",
-         "5.0",
+         "-1.0",
+         "2.0",
          "0.0",
-         "0.0"
+         "-0.5"
         ],
         [
          "12",
-         "-2.4063663079100075e-06",
+         "-2.615251595480845e-06",
          "-0.12499999999999997",
          "6.0",
-         "0.1658181818181818",
+         "0.18174049280729226",
          "-0.5883789062499999",
-         "-0.5240203045685279",
-         "-0.5271574303832368",
+         "-0.37100507614213196",
+         "-0.5375316277762159",
          "-0.5583588776232021",
-         "0.17717378162350847",
-         "1.666231841091017",
-         "0.12103838190794709",
-         "0.12103838190794709",
-         "-0.19735657983213245",
-         "0.0",
+         "0.42252717064367357",
+         "1.665737996443391",
+         "0.12267958030669893",
+         "0.12267958030669894",
+         "-0.19489463315452618",
          "0.0",
-         "2.0",
          "0.0",
+         "4.0",
+         "-0.5",
          "0.0",
-         "0.0"
+         "-0.5"
         ],
         [
          "14",
-         "-2.3965202919201957e-06",
+         "-2.6053975954978728e-06",
          "-0.37499999999999994",
          "-11.0",
-         "-1.6043636363636364",
+         "-1.5516308218202537",
          "-0.6799316406249999",
-         "-0.595492385786802",
-         "-0.6567411083540116",
+         "-0.44247715736040605",
+         "-0.6696654484115828",
          "-0.6444234850271162",
-         "7.991538682588836",
-         "-0.053305662614882954",
-         "-0.26915113871635615",
-         "-0.2691511387163561",
-         "0.37261624753351613",
+         "8.171245237617807",
+         "-0.05328986366330771",
+         "-0.2728006456820017",
+         "-0.2728006456820017",
+         "0.36796800457441364",
          "0.0",
          "0.0",
-         "2.0",
-         "0.0",
+         "4.0",
+         "-0.5",
          "0.0",
-         "0.0"
+         "-0.5"
         ]
        ],
        "shape": {
@@ -4327,163 +4560,130 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>-0.000003</td>\n",
        "      <td>-0.750</td>\n",
        "      <td>15.0</td>\n",
-       "      <td>1.461818</td>\n",
+       "      <td>1.450790</td>\n",
        "      <td>0.021973</td>\n",
-       "      <td>1.100345</td>\n",
-       "      <td>-0.353460</td>\n",
+       "      <td>1.253360</td>\n",
+       "      <td>-0.360416</td>\n",
        "      <td>-0.128036</td>\n",
-       "      <td>2.733814</td>\n",
-       "      <td>0.094930</td>\n",
-       "      <td>-0.261188</td>\n",
-       "      <td>-0.261188</td>\n",
-       "      <td>-0.197357</td>\n",
+       "      <td>2.957690</td>\n",
+       "      <td>0.094902</td>\n",
+       "      <td>-0.264730</td>\n",
+       "      <td>-0.264730</td>\n",
+       "      <td>-0.194895</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
        "      <td>-0.5</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.5</td>\n",
+       "      <td>2.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>-0.000003</td>\n",
        "      <td>0.375</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>0.180364</td>\n",
+       "      <td>0.195983</td>\n",
        "      <td>-0.039062</td>\n",
-       "      <td>-0.280365</td>\n",
-       "      <td>-0.022608</td>\n",
+       "      <td>-0.127350</td>\n",
+       "      <td>-0.023053</td>\n",
        "      <td>-0.600802</td>\n",
-       "      <td>0.003842</td>\n",
-       "      <td>0.065283</td>\n",
-       "      <td>-0.054149</td>\n",
-       "      <td>-0.054149</td>\n",
-       "      <td>0.384014</td>\n",
+       "      <td>0.250652</td>\n",
+       "      <td>0.065264</td>\n",
+       "      <td>-0.054883</td>\n",
+       "      <td>-0.054883</td>\n",
+       "      <td>0.379223</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>-0.5</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>-0.000003</td>\n",
        "      <td>-0.750</td>\n",
        "      <td>4.0</td>\n",
-       "      <td>-2.024727</td>\n",
+       "      <td>-1.963253</td>\n",
        "      <td>-0.649414</td>\n",
-       "      <td>-0.442802</td>\n",
-       "      <td>-0.684312</td>\n",
+       "      <td>-0.289787</td>\n",
+       "      <td>-0.697779</td>\n",
        "      <td>-0.599623</td>\n",
-       "      <td>-0.805038</td>\n",
-       "      <td>-0.557308</td>\n",
-       "      <td>-0.038223</td>\n",
-       "      <td>-0.038223</td>\n",
-       "      <td>-0.197357</td>\n",
+       "      <td>-0.551434</td>\n",
+       "      <td>-0.557143</td>\n",
+       "      <td>-0.038741</td>\n",
+       "      <td>-0.038741</td>\n",
+       "      <td>-0.194895</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>-0.000003</td>\n",
        "      <td>-0.125</td>\n",
        "      <td>6.0</td>\n",
-       "      <td>0.165818</td>\n",
+       "      <td>0.181740</td>\n",
        "      <td>-0.588379</td>\n",
-       "      <td>-0.524020</td>\n",
-       "      <td>-0.527157</td>\n",
+       "      <td>-0.371005</td>\n",
+       "      <td>-0.537532</td>\n",
        "      <td>-0.558359</td>\n",
-       "      <td>0.177174</td>\n",
-       "      <td>1.666232</td>\n",
-       "      <td>0.121038</td>\n",
-       "      <td>0.121038</td>\n",
-       "      <td>-0.197357</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>0.422527</td>\n",
+       "      <td>1.665738</td>\n",
+       "      <td>0.122680</td>\n",
+       "      <td>0.122680</td>\n",
+       "      <td>-0.194895</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-0.5</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>-0.000002</td>\n",
+       "      <td>-0.000003</td>\n",
        "      <td>-0.375</td>\n",
        "      <td>-11.0</td>\n",
-       "      <td>-1.604364</td>\n",
+       "      <td>-1.551631</td>\n",
        "      <td>-0.679932</td>\n",
-       "      <td>-0.595492</td>\n",
-       "      <td>-0.656741</td>\n",
+       "      <td>-0.442477</td>\n",
+       "      <td>-0.669665</td>\n",
        "      <td>-0.644423</td>\n",
-       "      <td>7.991539</td>\n",
-       "      <td>-0.053306</td>\n",
-       "      <td>-0.269151</td>\n",
-       "      <td>-0.269151</td>\n",
-       "      <td>0.372616</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <td>8.171245</td>\n",
+       "      <td>-0.053290</td>\n",
+       "      <td>-0.272801</td>\n",
+       "      <td>-0.272801</td>\n",
+       "      <td>0.367968</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>-0.5</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>-0.5</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "        code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
-       "6  -0.000002       -0.750              15.0     1.461818  0.021973   \n",
-       "9  -0.000002        0.375               4.0     0.180364 -0.039062   \n",
-       "11 -0.000002       -0.750               4.0    -2.024727 -0.649414   \n",
-       "12 -0.000002       -0.125               6.0     0.165818 -0.588379   \n",
-       "14 -0.000002       -0.375             -11.0    -1.604364 -0.679932   \n",
-       "\n",
-       "    saturated-fat_100g  carbohydrates_100g  sugars_100g  fiber_100g  \\\n",
-       "6             1.100345           -0.353460    -0.128036    2.733814   \n",
-       "9            -0.280365           -0.022608    -0.600802    0.003842   \n",
-       "11           -0.442802           -0.684312    -0.599623   -0.805038   \n",
-       "12           -0.524020           -0.527157    -0.558359    0.177174   \n",
-       "14           -0.595492           -0.656741    -0.644423    7.991539   \n",
-       "\n",
-       "    proteins_100g  salt_100g  sodium_100g  \\\n",
-       "6        0.094930  -0.261188    -0.261188   \n",
-       "9        0.065283  -0.054149    -0.054149   \n",
-       "11      -0.557308  -0.038223    -0.038223   \n",
-       "12       1.666232   0.121038     0.121038   \n",
-       "14      -0.053306  -0.269151    -0.269151   \n",
-       "\n",
-       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
-       "6                                           -0.197357       \n",
-       "9                                            0.384014       \n",
-       "11                                          -0.197357       \n",
-       "12                                          -0.197357       \n",
-       "14                                           0.372616       \n",
-       "\n",
-       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
-       "6                     0.0              0.0         -0.5                   0.0   \n",
-       "9                     0.0              0.0         -0.5                   0.0   \n",
-       "11                    0.0              0.0         -0.5                   5.0   \n",
-       "12                    0.0              0.0          2.0                   0.0   \n",
-       "14                    0.0              0.0          2.0                   0.0   \n",
+       "        code  additives_n  ...  PNNS_pro_Processed  PNNS_pro_Snacks\n",
+       "6  -0.000003       -0.750  ...                 0.0              2.0\n",
+       "9  -0.000003        0.375  ...                 5.0             -0.5\n",
+       "11 -0.000003       -0.750  ...                 0.0             -0.5\n",
+       "12 -0.000003       -0.125  ...                 0.0             -0.5\n",
+       "14 -0.000003       -0.375  ...                 0.0             -0.5\n",
        "\n",
-       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
-       "6                  0.0              2.5  \n",
-       "9                  1.0              0.0  \n",
-       "11                 0.0              0.0  \n",
-       "12                 0.0              0.0  \n",
-       "14                 0.0              0.0  "
+       "[5 rows x 19 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4522,7 +4722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4534,7 +4734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -4553,9 +4753,9 @@
       "n_candidates: 216\n",
       "n_resources: 50\n",
       "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n",
-      "Meilleurs paramètres trouvés :  {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
-      "MSE : 7.504433114323556\n",
-      "R² : 0.12552387466001047\n"
+      "Meilleurs paramètres trouvés :  {'max_depth': 10, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5, 'n_estimators': 100}\n",
+      "MSE : 7.368583825283833\n",
+      "R² : 0.1591149468498232\n"
      ]
     }
    ],
@@ -4566,12 +4766,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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Qd4DzEOcackxg3G277bZpfb7dSILxjIE27sojnwNJ01Z74GFKZNAiz2E4xPvOxOehTb/73e/ivgdtst4LDx08GPBKwVuCwhIwyOCFgJ4wDr766ivlecTr8LzAyIcxhZsGuWozIU6AhgUhOQJ36XAn7c0331SJlslApRf8cOGuGH507MmJGBzhdfsdVKyzg7u2SEzMFHye/U4XkgzRDisJEwMxDGCwjXVXP11wZw8/dtadTjv4UU0FBtAYJCLkxQ76jkFkvL7YQbvRn9jBxcqVK4eUY0TyZTo1/63EYAxU490ZHi6ZGpS4C4wFCetIQkViL+7oWqE0uNuKgR0W6AkvBhI/McDDHdJ0jy3OOyRz41ja24jjmsvjEhu2Zb8Tmy4IZ4MXB96JY445JlztB6FZCOlDUrQFvDTDBccENwxij0nsOW1ds1gfe5cf6+zXdL7BtYhwrXjXHcLBYFRaA+NEWBrDcE1HY2wPrwUWXJswOGEAo8LccM7566+/XnkucM4jgR19Qugjbqikak+653Gq/qBqGjwQqdqO44ntsMAQgSGPJHUYG1Zb8f0D7wYWfH/DGEbfLrvsspzoRYhTYI4FITkC8d748cBgDwZCLCibaJUAxR1XgGopdqy7Y4jLtv/A4Y6YHRgwiTwWybDKWVqg2hOwKuDgxw532HAXLfYOKZ5j0JYIvA+xzrhriWowFggnwF3MVOD9sZ+JOOfYUp0WqGRlDz3D4BnGltUXC1Smspe8xY86nuPHHBWNkoHXcfz/3//7fypEIxaEMAwHnCexxmI8EP4Se0wsD4FV8jJWEwxCLOPK2ibdYwv9sO7xxx8Pr0MFG1S1yeVxwWDLvsQrz5wK3DHHIBbnF+LbrX4Ce1+hN+4ODxdcqzBOcX5Z4G56bDgO8j4wCMcg2F6OFB4ktNF+TecbHAeE2+C42EMD8b0EwxRegFRheTgXsA0GyfHyeyyNcSxiS1bj3IARYD8O6Z7z9n0gDBCVthDahT7hOe72x/MM28+5dM/jZCCcEPuI9x6EfFqVmFDKOZZU1yhuBGAOFpynOLa50IsQp0CPBSE5Aj+E+BHAHSl4Iewzb8+ZM0cN5Kxwj6233lq58zE4scI35s6dq0JXEAaAu8sWMFSQEIwfVcw+jLtoGKjHu4ufCty5RUIj4u/hWcHdRMT6oj1WH66++mp1Fw0/cGgLBgh4H+4eImEVpVgTAYMErn7ESuOuOQb1Vv12e9x6Io8PwleQRImSvSjtiBKc9mRGOwjnwQ8utscPMIw03J2PTdBEiBpiwNEfeGEQpoDQIRz7ZDkj1iAd8dMwVtAHfBbisTHgwN1I/NgjRClTMDBHzDUMSbQPHqLYcpIA5wMGxbgjD21gSGGgg8+1jFOcHxjc4C45woUQX45jjsGN5Q1L99jCC3D77berhF2UxcSAH9tZycjWndt8HZdMwfWEcBLoi3MVfYOHD9cWynuivQjlySaMCOcTjgmuZyT745hgn7ETqOFcQjtwLHA94xha5WbhGcM8CiMJrmNrfgVcizDEYFBjsBtvro5YoCFKy2I2+O22206VYoUxjpsGCPlBfgCOC7x/uFOPgTgGy/gcfFeg73iP/ZzH/tAuXKcwwhLlb1j89Kc/VflDuLbhwcCC8wvXCnTB5+HcR6gbridrkJ/ueZwM9Bufje9efCb6i5s58CBgPb6DYUziusKNHxiO8JQg9wLXLK5FHHsAowHhZNgH8lBgaKJ9eI9VgCJbvQhxDIUuS0VIsTFv3jxVinGjjTZS5QNRLnD33Xc3brvttqhyl4ODg8ZVV12lynOWlZUZkydPNi677LKobQDKhF5yySVGY2OjKpmJEp8LFixIWG42XmlSqxQqSr9+85vfVG0aNWqUcf7550eVxrR46KGHVDlDlEXFgjKN5513nvHVV1+l7P8rr7xibL/99qrvKN+K8ozW5ycD/f7xj3+sykmi7CaO2ZtvvqnKQdrLVFrlKf/1r3+p44USn9j+sMMOiypzC/A+lHlEedJdd91VlcLFcbv99tujtrP2+Z///Cdu2z744APj2GOPVWVjUaIV+zjhhBOMF154Ycgxji07GVtC1iohihKSaDdes3SM3fb99983TjzxRGPKlCnqc9HXww8/XPXH4r///a9x4IEHqtdwzLEtyqu2tLRkfGzBokWL1LHEdmPHjlXvw/mAdr311lsZH5dcgM/G+RePK6+8MqqEKMqA7rLLLqr9EyZMMH72s58ZzzzzTNwyo/FKgEIL9MMOziuUasb1h+sQJX5REjV2n+Df//63se2226rjMXr0aOOkk04yli9fPuQzcF3FkqhNaA80Sfd8tcD5g++Lmpoa1fZ99tnHmDNnTtQ2yb43rM/CPlDOFdfPjBkzjNNOOy18DqL0K7TBdwT6hO123nnnqPK8Vpla9AHfPfg867xL1Ze9995blVPesGGDer569Wr1efi+xPcmygPvt99+xt133z2s8zjRMbdKCN9www3qdeiJ70x8t+F7GyXGAc51lJPFuYbrD4+4ZvE7YHHXXXep6926TnAMf/rTn4b3kUu9CCk0LvxTaOOGEELSBXH08OjAA2QvARoPVFFCichMk+pJNLhjjDvuKJkJzwQhOsLzmJD8wxwLQgghUfHjdhCbjpAMlPflYIzoAs9jQgoDcywIIYSEQQI/5uJAjgbmfUAeDuLKEaNOiC7wPCakMNCwIIQQElVRB4nZGIAhWRUJsihti6IEhOgCz2NCCgNzLAghhBBCCCFZwxwLQgghhBBCSNbQsCCEEEIIIYRkTdHnWASDQTVrKiahSWdSHEIIIYQQQogJsiYwQSsmdMUEqSVtWMComDx5cqGbQQghhBBCiLYsW7ZMzSpf0oYFPBXWwairqyt0cxwJKmYsXLhQZsyYIR6Pp9DNIcOAGuoN9dMfaqg/1FBvqF/+6OjoUDfprTF1SRsWVvgTjAoaFokvxpqaGnV8eDHqCTXUG+qnP9RQf6ih3lC//JNOSgGTtwkhhBBCCCFZQ8OCEEIIIYQQkjVFP0Ee4sLq6+ulvb2doVAJwCmA6lnI9GflLD2hhnpD/fSHGuoPNdQb6ueMsTQ9FkTh9/sL3QSSJdRQb6if/lBD/aGGekP9Cg8NC6Is/MWLF6tHoifUUG+on/5QQ/2hhnpD/ZwBDQtCCCGEEEJI1tCwIIQQQgghhGQNDQuiSDVFO3E+1FBvqJ/+UEP9oYZ6Q/0KD6tCEUIIIYQQQuLCqlAkI2BbdnV1qUeiJ9RQb6if/lBD/aGGepNL/dasWSNXXnmlLFu2LCdtKyVoWBBVQWH58uWspKAx1FBvqJ/+UEP9oYZ6k0v9zjvvPHnvvffke9/7Xk7aVkrQsCCEEEIIySOBoCFvLlwnj324Qj3ieT457bTT1CRxWMrKymTatGnys5/9TPr6+qK2s7Z56623otb39/fLmDFj1Gsvv/xyeP0rr7wi++67r4wePVqqqqpk5syZcuqpp8rAwIB6Hdta+4xdVq1aldc+e71eef7557Pez0MPPaTa+8QTT8jUqVPl7rvvFt15OaTLhg0b8v5Z3rx/AiGEEEJIifL0py1y1ROfS0t7ZFDfXF8hVxyxuRw8uzlvn3vwwQfLvffeK4ODg+ruOwwADC5vuOGGqO0mT56stttll13C6x555BGpqamRtra28LrPP/9c7fOHP/yh/P73v5fKykqZP3++GogHAoGofX711VdDYvGbmpqk0MAA8vl8Sbc57rjj1AKKwagYaeixIOqLBhcaHomeUEO9oX76Qw31Jx8awqg45+/vRxkVYFV7n1qP1/NFeXm5jB8/XhkORx99tOy///7y3HPPDdkOBscDDzwgvb294XV/+ctf1Ho7zz77rNrfjTfeKLNnz5YZM2YoQ+Oee+5RRkasEYFt7Uuyik2ffvqpHHLIIcqYGTdunHz3u9+VtWvXhl/fe++95YILLlBeF3hLsD/kQFhstNFG6hFGDzwX1nNss80228if/vQn5bWpqKhQ659++mnZY489pKGhQXlmDj/8cFm4cGF4f0uWLFHnwYcffhh1x/+FF16QHXbYQXlrdtttN2VA2Xnsscdku+22U58zffp0ueqqq6JmA8c+7rrrLvV52Mdmm20mb775pixYsED1sbq6Wu3X3pZ094s+HnPMMWFP0uOPPx7uyz777KP+HjVqlNoWHq18QcOCqIsdJyrLtOkLNdQb6qc/1FB/cq0hwp3gqYgX9GStw+v5DouyBu5z5syJe7d+++23VwNxeB7A0qVL5dVXX1WDezsYzLe0tKjXcgnCcxBete2228q7776rBv2rV6+WE044IWq7v/71r2rg/fbbbyvj5te//nXYUHrnnXfUIzwvaKP1HGDQjr49/PDDYUOhu7tbfvSjH6nPg7EAzTEoT5Wf8Ytf/EJ++9vfqvfBgDnjjDPCr7322mtyyimnyIUXXqi8OzAg7rvvPrnmmmui9vGb3/xGbYe2zJo1S77zne/I97//fbnsssvUfpF8fv7552e8XxgbOGYff/yxHHrooXLSSScpjxMMS0tbGEI4PrfeeqsUZSgUTs6bbrpJuejQUbjeYFVb4OBeccUVyhrGibf77rvLHXfcoSwxrQiGXIRujzgRHGeUEEMpMd5t0xNqqDfUT3+oYeloeMRtr8uazv6U++v3B2R9z2DizxNRnowdrn5Oyr3Jxwdja8vliR/uIZnw5JNPKg8A7mwjZwKD59tvvz3uthggw0tx8sknq0ErBqZjx46N2ub444+XZ555Rvbaay9lZCB0ar/99lOD3tiwp0mTJkU9R67CZ599Fvez0SYYFddee214HdqCAfG8efNkk002Ueu22morNSYEGAfifTAKDjjggHBbkU8Cj4ddP4Q/3X///VH9sUKd7J+H1zFwhzcmERjMo//g0ksvlcMOO0zlrcCTgIH9pZdeGvb0wEiFEQEvi9VucPrpp4eNpksuuUR23XVX+dWvfiUHHXSQWgcDAttYpLtfeCFOPPFE9TeOJcLV5s6dq7xK8PJYniR4afJJQQ0LWIxbb721OqGPPfbYIa/DIsWBgZUKF5Z14CG85c7SgsFekXULRMprRSoaRCrqRTzOSW+BhY6kqtraWvF4nGn8kORQQ72hfvpDDUtHQxgVqzqiQ5uywTQ+EhsgwwXhL7gZi7HWzTffrO6wxw6oLWBQYPC6aNEiZVhg7BULjgk8AldffbW8+OKLynOAASxyNjCAbW5ujrrLjuNogQF/Ij766CN56aWXlBEUC0KC7IaFHXxea2tr1Lr169crHe36waiJNZKQG3L55ZerPiDkyvJUwFuTzLCwt8HqL9owZcoU1Y833ngjypOA3BMYHj09PSpEKXYfMILAlltuGbUO78HcETDYhrNfeHbw3tjjMxIUdHSLeDosie4c3HLLLfLLX/5SjjrqKLUOFicO+KOPPirf/va3RS8Mkf4Oc2l3OdbIIIQQQkhy70E6pPJYWIyqKkvLY5EpGFxuvPHG4TvyuJH75z//Wc4888wh21p5BngNA1aMzTo7O+Pud+LEiSpMCgvunGPgf+edd6o76xa4GZzunXHMPXHEEUcMSSoHdmMl1jiBVyKd0rI4DrHg82BwICJmwoQJaj8wKKzqVomwt8HyilhtQD+uuuqquDfK7TfD4+0j1/u19lOI0smOHdEuXrxY3TlAspEF3JM777yzSnRJZFjA3YfFAhafZd1ZVQtwsOESxAG3T6SSaD3WWQLFWx9bDcGKzwwLisegIe6QZy6IffS2m4u4xFNRK0ZFvQR9dWEjw2oLPs9+YmTa9nT6hPZbj2n3KcV63C1I1PaR6NOwdNK4T5aG2Ca2Lbr2KVXbi6lPqfTTsU+p1hdbn6z3Yr/2/ejcp2LUKVmfkulnb8uj5+6aVp/8gaDsedPLsrqjP26eBT5xfH2FvH7JviJGeudYun2yvkvsf8Mj8ZOf/ESNnzDYtra1fv8RSgPjAiE21ufZPyueHkgGxuAfRoh9nGV/fyo9EAaF/AeEPsGrEtsn7Mf+3Wide9aC162yurFtsNprb/u6detUrgGMCoTYY/3rr78+ZHurD9bn25/b+2e1Af348ssvlVEV21f7eWX/O7atlhb29yJp29qvOm9izr1kOll/W7k1MJys7TO5nmK30dKwsOodW24iCzxPVgv5uuuui7Ka7e40y80GAwUXApKDEE9p0djYqJYVK1Yo16EFYglheSOz3m7NIoYQ+8S+7QJAfFwccLUp/APKiJjZXK++aBa3Ru4CuF0u2WSCSHdHmyxf1yPi8Yl4K8RXXSvTZ8xU7bP3F18GuPiQkGOvmJBNn2CI4UsB/cC+0+pTCMQ5In4ThmC4T263uoOBz8NkNRY4sREbOBJ9GpZOGvcJXxzQEGUF8UVRDH0qRp0S9cnSD494fzH0qRh1StYnXHdoJyrr4HOLoU/FqFOyPiGBGa9j/5ahkW2fvrd9g1z90mplRMQaF3j+swOmi8ftknnzctsn3FRFmAywdILHAt8xGCchnMkqJbty5Ur1ORgYY8Zp7AfPrRuzlucCHgWE5SCnAccEoU6IIEHuBAwWvMc69xGCg/X2PmH/VolaO+ecc46qaIToFHhMcGwx4zUG+4hcQQ4uriuEOeGcsM49tBN9x/6gE/TDjWd8DipiYT+42w/sOqFNyDmwSslC79/97nfqb+uYWccRoVHYtzX/B467NZbEa9b+0AaE9Z9zzjlq+7POOksda8yrgdcuuuii8IAd+7KOgf27wtLJWgdd0E7kXCARG/s98MADVb/wGuYe+cEPfhB+v9VX69xDu6x+wzuDcxqeqz333FMdn0033TTt68nad1oYDgFNeeSRR8LP33jjDbVu5cqVUdsdf/zxxgknnJBwP319fUZ7e3t4WbZsmdpPW1ub4ff71RIIBNS2eLTWJVsfDAaTrrevs9ZjCa/raTf8y94zgsvNBX/bF2PF+0PWB7B+zTwj2LHa8Pf3DrvteetTivUgdr3VlkTr2Sf2iX1in9gn9qnY+vS/j5YbO1/7vDH1kifDyy7XPq/W56tPp5xyinHkkUcOWX/NNdcYY8eONbq6ulQbMT566KGH4rZ97dq16vUXXnhBrX/33XeNk046yZg2bZpRXl5ujBkzxthzzz2NRx99NLz/559/Xr0n3jJnzpyEffrqq6+Mo48+2mhoaDAqKyuNWbNmGRdddFG4LficCy64IEon9A/9tNr++OOPGxtvvLHh9XqNqVOnqvWXX365sfXWWw/R6ZlnnjE222wz1Y+tttpK9RFtfPjhh9X+Fy5cqJ6jz9j+xRdfVM9xTKx94DWsW7RoUUTr//3P2HXXXVUf6urqjB133NG48847w69bn2E9X7BggVr3wQcfhI+NdQzXrVsX1iN2vzvttNOQ/UJHu3719fXGn//85/A5dtVVVxnjx483XC6XOm6ZnHsYQ+MzMK5OhQv/iAOAJWWvCoUEItRI/uCDD1QNYgtk4+N5uqWyYHHDuoPFFlu1YMTo7xJZF22hZ4yvJpKT4U0+uUumwFLFnQtYxiyVqCfUUG+on/5QQ/3Jp4YoKTt3cZu0dvZJU22F7DRttPJUkNzBazB/ZDKWduyRhysG7lCUErN3DBn8KM1Vcgx0iXQsF2n9TGTNPJGuVjPEKgfAtoSL1yE2JhkG1FBvqJ/+UEP9yaeGMCJ2nTFGjtpmonqkUZF7eA06g4LmWCBmCxOXWCB2DROGwNpE6S7EpCEOEDGFVrlZZO/b57ooSQa7zaVjhUhZtUil5cnIvGoEIYQQQggh2hsWmGHQmmYcYBZEgElAUEcZlQmQgHL22WerCfIw/TpmZNRqDosRNTKqzHApGBo0MgghhBBCyAjimByLfFE0ORaZkoGRYVUOQMUtxiXqCTXUG+qnP9RQf6ih3lA/Z4ylaVgUq2Fhx1sZCpdqECmjt4cQQgghhJRQ8jbJIf5ekc4WkTVfiLR+KdLRIjLYG2Xlo0507KQ1RB+ood5QP/2hhvpDDfWG+jkDGhalaGR0rRJZ86VI6xfKyDAGepUVWuTOq6IG2lFDfaF++kMN9Yca6g31cwaOnXmbjAD+PtPICLaIYNbTzhqRqtEivqpCt4wQQgghhGgGDQtiEvSbc2P0rBHxlEdyMmhkEEIIIYSQNGAoFBGXS6SxtkI9KgL9Il2rRdZ+JbL6c5H2FSID3QVuJUk1c31jY6N6JPpB/fSHGuoPNdQXTFEwZsyYsH5XXnmlbLPNNknfc9ppp+VkXrRc7adYoGFBxI0v07oK9TgEGBndrSJr54ms/oxGhkNBaT18obLEnp5QP/2hhvqTVw2DAZHFr4l88l/zEc/zCAa7GGBjKSsrU5MMY26wvr6+qO2sbd56662o9f39/Wqgjtdefvnl8PpXXnlF9t13XzWRcVVVlZrAGHOPDQwMqNexrbXP2GXVqlWSbyz9fvKTn8gLL7yQ030vWbJE9QMTOdu59dZblWFDTBgKRSQYNGRFW49MHF0lbneSOzWBAdPIwOLxmbN9I1yqvGYkm0vigCoYK1askIkTJ3JgoyHUT3+oof7kTcPPHxd5+hKRjpWRdXUTRA6+QWTzIyVfHHzwwXLvvffK4OCgvPfee8oAwMD4hhtuiNpu8uTJartddtklvO6RRx6RmpoaaWtri3Tj88/VPn/4wx/K73//e6msrJT58+fLQw89JIFAtKH01VdfDSlL2tTUJPlm2bJlSj+0HctIgDKsJAK//YigfkJ3/6B6TBtlZKwx5+dY9alI+3Jzvg5SEFAFA7PUsxqGnlA//aGG+pMXDWFUPHhKtFEBUPYd6/F6nigvL5fx48crwwGhOvvvv78899xzQ7aDwfHAAw9Ib2+kDP1f/vIXtd7Os88+q/Z34403yuzZs2XGjBnK0LjnnnuUkRFrRGBb+xLPWIMxN2nSJLnjjjui1n/wwQdq+6+//lo9/93vfidbbrmlVFdXq/6ce+650tU1dMxh6RcbCgXD50c/+pE0NDQoTwy8N7E6P/3007LHHnuEtzn88MNl4cKF4dfh9QHbbrutMtD23nvvuKFQ/f39csEFF6hjUFFRofb5zjvvhF+3vDrwqOywww7K87PbbrspY6wYoGFBsic4GG1kbFgm0t+Jb+lCt4wQQggpDAh3gqci7m270LqnL817WBT49NNPZc6cOeLz+Ya8tv3228tGG22kPA9g6dKl8uqrr8p3v/vdqO1gHGCeCLyWK2A8nHjiifLPf/4zav0//vEP2X333WXq1Knh7eAl+eyzz+Svf/2rvPjii8o4SJff/va3KlwJBtPrr7+uPDHwysQaJTA+3n33XTXox2cec8wx4Xkx5s6dqx6ff/55dRwefvjhuJ/1s5/9TB1LtPP999+XjTfeWA466KAo7w/4xS9+odqFz/N6vXLGGWdIMcBQKJJ7I6Nnrbm4vWaoFCpM+WrMLHFCCCFEZ+7ay6yimAp/v0jvuiQbGCIdK0RuminiLU++r5omke+/klEzn3zySRUO5Pf71V10DJRvv/32uNtiUItB98knn6wG4IceeqiMHTs2apvjjz9ennnmGdlrr72UkYHQqf32209OOeWUIWFP8ELYgYEAoyAeJ510khpgw6CZMmWKGsjDg/LLX/4yvM1FF10U/htG0NVXXy0/+MEP5I9//GNax+KWW26Ryy67TI499lj1/M4771R9sXPcccdFPcfxwDFACBg8NNbxgDcD/Y9Hd3e38r7gGB5yyCFqHTw68BT9+c9/lp/+9Kfhba+55hp1LMGll14qhx12mMqBgZdDZ2hYEEFaxfiGKvWY8xK2UUaGlZNRSyMjx+AHI5GrmTgf6qc/1LCENIRR0RkT2pQNSY2P4bPPPvuoQS4GuzfffLO6Kx47eLaAQYHB7aJFi9SgGN6BWDwej8rFwKAeHoO3335brr32WpWzgbv5zc3N4W1fe+01qa2tDT9HAnkiELK02WabKa8F2oAE8dbWVmXIWMBLcN1118mXX34pHR0dyljCILynp0eFElnE0w+T5sHDsPPOO4fX4VggDMkeDoV8kcsvv1z1a+3atWFPBQweGBbpsHDhQpXTAm+Lve877bSTfPHFF1HbbrXVVuG/rWOHfsO40hl+AxIV69dQ7ctviT1lZKwTaVsoshrhUktF+toZLpVLDRsaWCZRU6if/lDDEtIQ3oPaCamXyjHpfTC2S7UvfGaGIB8BYThbb721uvuOATPumsfDyik488wz1YDdutseDyRHI0wK3g94IbA9PAB2kI+Az7YWK6QpEfBaWOFQeETuBtpkVWNC2zAQR4gREtH/8Ic/qNesalQW2VyDRxxxhApXgocBxwpLvM/IFWU2Y8tqs2XM6Aw9FkRVhVqypks2GluTvCpUro0MLC6P6clAuFR5HT0ZwwRfRvjyhYuYd0z1g/rpDzUsIQ3TDUlC7sQts81E7bh5Fi6zOtRFn4i4PZJP0J+f//znKofgO9/5zpBkayscCiFQl1xyifJOpMOoUaPU3XZ4RbIBbULoE4yG//73v1GGCtZBG4RLWbo8+OCDcfcDjwv0i63ahDbCUNhzzz3VOng8sN/ttttOPV+3bp1KnoZR8Y1vfEOtQy6GHSs/JbYClp0ZM2ao7d54442wMQUPBpK37eFcxQwNC6K+7gb8gcyqQuXswwMivW3mooyMulC4VB2+CQvRIi2BOxd3VViRRk+on/5QQ/3JuYYwFlBSFtWfYERE/cqGbqIdfH3ejQoLhBYhxh93+zHPQyzwEqxZs2ZIvoTFXXfdpeZwQEIzBtDwVNx///3Ka3HbbbdFbYuQntg5M+CBSBQSBWMAlZHgMcHA/cgjI2V44fHA4ByfAa8CBu2xHhKLRPpdeOGFcv3116t5N2bNmqWqTG3YsCHKQEL77r77bmWEIPwJYVl2UOUJBhmqRyGHBLkQsaVmq6ur5ZxzzlHHGXN9IKwJVbQQsoW+lQIcuRHnoIyM9SLrF4us/kSkbbH5vAhcg4QQQkoQzFNxwv0idZH8AwU8FVifx3ksYkFewfnnn68GuvE8DNbM4/EqRwHkCaDEK5Kmt9hiC5V4jIn1Hn300XASssWmm26qBuj2BR6CVOFQH330kTJc7B4VhHLBEEAuB3IdUDEK+RaZ8OMf/1iFb6GE7q677qryP/A5FvCEIGEcbcRnXHzxxXLTTTcNOX7IPYGBNWHCBDnqqKPiftb111+vclnwefCILFiwQCWKw3gpBVxGkd9eQZIPLEok7ySywvMO5ndAKVaHEggaMr+lXWY214tnJEKhMsXlNj0YVvI3PRlDwB0eJJ7hbky6LmziHKif/lBD/cmrhgiL+nqOSNdqkZpxIlN3GzFPRanAa9AZY2mGQhFVDWrSmOrcV4XKFUZQpG+DubiWmVWlYGDA0OAXc/huC1yzjO3WE+qnP9RQf/KqIX6rppmx+yQ/8Bp0BjQsiHJ/1lQkLgXnPCOj3VwQo2o3Mjze0tawpqbQzSDDhPrpDzXUH2qoN9TPGdCsIyoUat7KdvWoF4ZIf4dI+1KzhO26hSLd60QCfilFF/C8efOSVqsgzoX66Q811B9qqDfUzxmU7i1eEkVQ+1SbkJGhDA2XOdN3peXJ0MQbkyXFUP+6lKF++kMN9Yca6g31Kzw0LEgRYogMdJpL+zLTyLDCpbzxq10QQgghhJDsoGFBip+BLnPpWC5SVh3yZDTQyCCEEEIIySEsNzsSOLzcrJoUyB8Un9cdnla+JCirMg0MGBrecimGiZ1Qf7ykNCwSqJ/+UEP9oYZ6Q/3yB8vNkozxekowj3+wx1w6V4p4KyOejLIK0RFM3kP0hfrpDzXUH2qoN9Sv8JTgaJLEgmJQmCBPu6JQucTfK9LZIrLmC5HWL0Q6WkQGe0WnhDVMDMTENT2hfvpDDfWHGuoN9XMGNO0IicXfJ9K1yly8FZEZv31VhW4ZIYQQQohjoWFBSEojA8tqEU+5aWQgZMpXXeiWEUIIIYQ4ChoWhKRLoF+ku9VcPL6IJ6OcM30SQgghhLAq1EigQVUo5Fe4XcJKCsPBXRaZjK+8tnAaBoPidpdYZa8igfrpDzXUH2qoN9TPGWNpJm8ThT/AZKdhExwU6V4jsm6ByKpPRTYsE+nrwLfciDbD7/eP6OeR3EL99Ica6g811BvqV3hoWBDlrVjc2lnaVaFyaWT0rBVpWyiyGkbG0hExMnCXZvHixayGoSnUT3+oof5QQ72hfs6AORaE5IugX6Rnnbm4PJHE7/I6xJwVunWEEEIIITmFhgUhI4EREOltMxdlZNSFEr/rRNx0HBJCCCFEf2hYEIWbd9BH2MhYby4ut2lcKE9GfVZGBhLWiL5QP/2hhvpDDfWG+hUeVoUaCRxeFYo4BGVk1JqeDIRNuT2FbhEhhBBCSpwOVoUimQDbsqtvUD2SAmIERfraRTZ8LbLqE5F1C0V62kSCgfQ07OqihppC/fSHGuoPNdQb6ucMaFgQVQ1q+bpuVoVyFIZIf0e0kdG9TmSwTyQwtJweqmAsX76c1TA0hfrpDzXUH2qoN9TPGTDHghBdjAwsYVxmqBQm53PjMnaZ3o7OVSJl5aHXvKEF23hYiYoQQggheYWGBSFaYpjlbLEAuJsGe0S6VptTqMcjbGh4o40S67nH/txLQ4QQQgghGUHDguBet/i8HvVIilhDuyGS1k5DXg9lcMTxgNiNEGxDQ2TYuFwu8fl86pHoCTXUH2qoN9TPGbAq1EjAqlCkFLAMkUQekCFeEqZ4EUIIIcU0lqbHgqgKCu09g1JfVUZLX1McoSHm5whg6U+/vG4yD8gQL4m7uPVrb1df3LwG9YQa6g811Bvq5wxoWBAVnr9qQ4/UVtaLh9eilmipIcrrBgbMJSNDJEWeiPKceEKPehgjqGKyatUqqa2tFY+H85foCDXUH2qoN9TPGdCwIIQUpyFiAQNDGSUe29/uBOtDj/HWc8JCQgghJCk0LAghxQ1CtLAEB7PflyueQRIq/ZuxAaOLa4kQQghJDxoWRFUSqi4vY1UojaGGI+g1wQJST4iegohB4jJcUh3sElfbIhGPN4UXJWSsRP2tR8hXMYOY7urqasZ2aww11Bvq5wxYFWokYFUoQkhecSUwPFxxjJDQ4qs1jRhCCCEkCawKRTIiaBjS1tkvo2vLxU1LX0uoYanrZ0SqcqWNS6SsSqSiTqS8TsRXNYzPJfbE0ba2Nhk9erS46UHSEmqoN9TPGfDIE4HPam1nn3okekIN9aYw+mG29m6RzhaRtV+JrPpUZP3XIr3rRYJZx3mVHHD+r127Vj0SPaGGekP9nAE9FoQQQszk9t42c4E3w1dtejLKa+nNIIQQkhY0LAghhMRgiAx0mUsnfNtlpoFhhU2x9C4hhJA40LAgqpJQfZWPFYU0hhrqjeP1S+TNgKFRVlno1jkCVKLhjL96Qw31hvo5A1aFGglYFYoQUqx4fKY3wwqbojeDEEJKdizN5G0iwaAhLet71CPRE2qoN1rrh5nQe9aJrF8ssuoTkbULRLpaRQZ7pdQq0rS0tKhHoifUUG+onzOgYUEQTS3tPQPqkegJNdSb4tEPuRmdIh0rRNZ8KbL6M5ENS0V6N+BXX4oZOP9xN6/IgwCKGmqoN9TPGTDHghBCSH69GVhUbkZNJAG8rKLQrSOEEJJjaFgQQggZOW8GFlkRys0IJYBjFnBOaEUIIdpDw4IICig01laoR6In1FBvSlI/5c1Yay7wZtgTwDX0ZqASTWNjIyvSaAw11Bvq5wxoWBBx42Ks0++HnESghnpD/QyR/g5zAZ7yUMhUrTbeDLfbrQY1RF+ood5QP2fg/G9rkndQiWbZ2m49K9IQBTXUG+oXQ6BfpHuNSNsikdWfiKxbKNK1RsTfL04FlWiWLVvGijQaQw31hvo5A3osiKpE090/WAQVaUoXaqg31C8JRjDizeiwezOQm1HjGG8GKtF0d3ezIo3GUEO9oX7OgIYFIYQQ/bwZWFzuUKWpejNsylte6NYRQkhJQ8OCEEKI/t4M4K2ITgJnEichhIwoNCyIuF0i4xuq1CPRE2qoN9QvR/j7zGWIN6NOxOvLe+Lo+PHj1SPRE2qoN9TPGdCwIKo0W0N1fn90SX6hhnpD/UbKm2HNm1GTc2+G0rChIaf7JCMLNdQb6ucMaNYRVYlm0epOVqTRGGqoN9RvBFCejFaRdQtEVn1sVpzqXifiH8jJ7lGJZtGiRaxIozHUUG+onzOgx4KoSjQD/gAr0mgMNdQb6lcAb0Zfu7kAb6WZk5GFNwOVaAYGBliRRmOood5QP2fgaI9FIBCQX/3qVzJt2jSprKyUGTNmyG9+8xueNIQQQnKHv9fmzQjNm9G+XKRztUhPm0h/p8hgH26JFrqlhBDiaBztsbjhhhvkjjvukL/+9a+yxRZbyLvvviunn3661NfXywUXXFDo5hFCCCk2jEAoNyPB6y6PiMcn4ikzF3foEesDgyIBv4jHM8KNJoQQZ+Bow2LOnDly1FFHyWGHHaaeb7TRRvKvf/1L5s6dW+imFRWoRDNpTDUr0mgMNdQb6qeZ4QEPBxYbbsOQSZV+ca/5zKxIZRkfbm8cQyT0nOVwHQWqCU2aNIlVhTSF+jkDRxsWu+22m9x9990yb9482WSTTeSjjz6S119/XX73u98VumlFV0mhpqKs0M0gWUAN9Yb6FZuGhjmRH5ZkWN6OKOPDF/3cTe/HiGpYU1PoZpBhQv2cgaMNi0svvVQ6Ojpk1qxZ4vF4VM7FNddcIyeddFLC9/T396vFAu8HeC8W6+SDRYvKAfZ8jUTrsQ6vJVpv7de+HoQrE+AxaITvRsYWfvG4XWq/9vXY1J1kfdAwxIi3PmhEJYDihpjbNXQ92mL2yRC/qkjTIdPH1UmZx2X2KaaRidru1D7FW1/MfQqENNx4fJ1qZzH0KVXbi6lPqfTTsU+p1hdbn/DeJa2dMn1crdpXWn2SQQkGBpL3SYVeecXl8Ynb65OgyyOGZXy4feIu84kL64f7+5RiPX57Vdtt663fykTr0/1tzdtv7jD7hHULFixQeZ3Yphj6VIw6JepTLvRzWp/cDtEpdhttDYsHH3xQ/vGPf8g///lPlWPx4YcfykUXXSQTJkyQU089Ne57rrvuOrnqqquGrF+4cGHYkkWORnNzs6xevVra20NVQUSksbFRLStWrJDu7u7weky4gtrIS5YsURUHLOBywz6xb7sAOKm9Xq/Mnz/fXIFyhr3tMrO5XvyBoCxu7Qxvix+/TSbUS3e/X5avi3ymz+tRP1DtPYOyakNPeH11eZlMbqyWts5+WdvZF15fX+WT5lFVsrq9V9p7Im1srK2QxroKWdHWI939g5E+NVSpuvlL1nRJ32BA2jr71I/elMZqdddt4aoO9SMe7lNTrXg9bpnfEjlewKl9QoWdsE5jir9P0A4aTmmskfIyT1H0qRh1StQnSz8Y9wF/cfSpGHVK1ieXuASmQE+/X1au78lfn9Z2x+lTuSxZ0y0DfjUqUMbIpAnjpKamVhYuWSlBy0BxuYb+Pll9mjlT/H6/LF68OKKT262iBfB7uHz58kiffD6ZPn26+v1ctWpVpE/V1TJ58mRpa2uTtWvXRvo00r+5w+zT1KlTpbe3Vw1OrcGV7n0qRp0S9QljQ2xv10/3Pk12iE5dXV2SLi7DwSWWcEDhtTjvvPPC666++mr5+9//Ll9++WXaHgtLmLq6usJYewPdqtqIkz0WC1a1y8bj6+mx0LRPgZCGmzTX02OhYZ9S6adjn1KtL7Y+2b1OaXssRrpPbq+4Q7keQSvcKhSOBW8IngdVC/S/wzpcjwVCr1GBkh4L/fqUC/2c1ie3Q3TCWHr06NHKkLHG0lp6LHp6eoYk4UCUZJOflJeXqyUWvM860SwSJfhkuj52v0PW4322rExPnHw9iJvJevzAxHz/hz4qfjJgsvWe0P7wQ2/9IOLvuH1KkGvotD7FbWOR9wmfhXYkaouOfYpaX+R9SqWfjn1Ktb5o+xRnP87oUwAxDSKBPvW9H3d7JJ6HE8xN48PlLhOPlXyO9S5vpE9xfv9y9ds67N/cNNbHazsGWNb6fI0XRrpPydYXW59GQr9S1cmTQaU7RxsWRxxxhMqpmDJligqF+uCDD1Ti9hlnnFHophUV+N2By58VafSFGuoN9dOfotEQkwemTDyH+8Qr4i03DQ37o7dC24RzDLwQBsKqQnpC/ZyBow2L2267TU2Qd+6550pra6uKn/v+978vl19+eaGbVnQgjpjoDTXUG+qnP6WjoSESHBQZiOR6RIF8DhgYKryqPMYAcXb1M8SWE32hfoXH0TkWuQBxYUh0SScuLG/0d4msi07scRKIwUVyIhISE4UCEGdDDfWG+ukPNUwTNcdHebTRoQyO0LoCglAaJKwi8TaT0A/iDKifM8bSNO0IIYQQMnKhVnEmGDRxxXg3YgwQTihIiOOhYUEIIYQQB2CI+PvMJV6Kh0ooT+DtYFw9IY6AhgUhhBBCnE9gwFwipfgjoJJVvGRyGB2obkUIGRGYYzESODzHwqqvbtU9J/pBDfWG+ukPNXQwKpk8pnJVnGRyax4Bq74/0Qvqlz+YY0EyBrPI+rx0JesMNdQb6qc/1NChGAGRwR5zSZZM7vaJ33CLr6JapCxkfHCAqhWY7RozWZPCwW9Aou6yLW7tHDK7LNEHaqg31E9/qKHmyeR97RLsapXFC76S4LqFIq2fi7R8JLL6cxE8b18u0rVGbSeDfbg9XuiWkxjgrVi8eHHSSZRJ/qHHghBCCCFkCEZkssC0ksltSeWaThJISLbQsCCEEEIIyWkyuTdUrcpjhlsNWVwpnid4DyEOh4YFUbj5haU91FBvqJ/+UEP9yZmGQb+55BRVGSC54ZHoNfXeNA0YjUv3InGbFBZWhRoJHF4VihBCCCEkTJQxkoEBE7UNvTDFAqtCkYyAbdnd75fqci9LtGkKNdQb6qc/1FB/qGFMUrt6DOT5g1xmyV/7hIfhWdczm21d6dfdLdXV1dSvgNBnRFQVk+XrulnNRGOood5QP/2hhvpDDQuVII88lU6RnrUiHStE2haJrPkipirXCpHutSL9nSL+gYRVoZYvX86qUAWGHgtCCCGEEOLgqlwdCeYfCU14qLwbZbAuCtVYEoKGBSGEEEII0W/+ESwWcDV1t4us9psTHNpDq9Rs66jSxUCdfEPDgiDCUXxej3okekIN9Yb66Q811B9qWCT6oRrXYLe5JJx7JNbo4CzruYJVoUYCVoUihBBCCHEoSCL3RYdWhZPIfVLqdLAqFMkE2JbtPYNSX1XGSgqaQg31hvrpDzXUH2pYyvplmM9hGR0eDqNj4REhKixx1YYeqa2sFw+/S7WEGuoN9dMfaqg/1FBv8qZfvHyO2BnWmc8RhoYFIYQQQgghw51hnfkcYWhYEEIIIYQQkksCA5E5Okoon4OGBVGVFKrLy1gJQ2Oood5QP/2hhvpDDfVGH/2MzPM5yqq08XLQsCDidrtkcmN1oZtBsoAa6g310x9qqD/UUG+KQj8jQT5H0+amgaEBpZlZQqIIGoas7ehTj0RPqKHeUD/9oYb6Qw31hvo5AxoWRHANru3sU49ET6ih3lA//aGG+kMN9Yb6OQMaFoQQQgghhJCsoWFBCCGEEEIIyRoaFkRVUKiv8mlQSYEkghrqDfXTH2qoP9RQb6ifM2BVKKIqKTSPqip0M0gWUEO9oX76Qw31hxrqDfVzBvRYEAkGDWlZ36MeiZ5QQ72hfvpDDfWHGuoN9XMGNCwIpmqR9p4B9Uj0hBrqDfXTH2qoP9RQb6ifM6BhQQghhBBCCMkaGhaEEEIIIYSQrKFhQcTlEmmsrVCPRE+ood5QP/2hhvpDDfWG+jkDVoUi4na5pLGuotDNIFlADfWG+ukPNdQfaqg31M8Z0GNBVAWFZWu7WUlBY6ih3lA//aGG+kMN9Yb6OQMaFkRVUOjuH2QlBY2hhnpD/fSHGuoPNdQb6ucMaFgQQgghhBBCsoaGBSGEEEIIISRraFgQcbtExjdUqUeiJ9RQb6if/lBD/aGGekP9nAGrQhFxuVzSUO0rdDNIFlBDvaF++kMN9Yca6g31cwb0WBBVQWHR6k5WUtAYaqg31E9/qKH+UEO9oX7OgIYFURUUBvwBVlLQGGqoN9RPf6ih/lBDvaF+zoCGBSGEEEIIISRraFgQQgghhBBCsoaGBVEVFCaNqWYlBY2hhnpD/fSHGuoPNdQb6ucMWBWKqEoKNRVlhW4GyQJqqDfUT3+oof5QQ72hfs6AHgsigaAh81a2q0eiJ9RQb6if/lBD/aGGekP9nAENC6IIGrwQdYca6g310x9qqD/UUG+oX+GhYUEIIYQQQgjJGhoWhBBCCCGEkKyhYUFUBYVpTbWspKAx1FBvqJ/+UEP9oYZ6Q/2cAQ0LovB6eCroDjXUG+qnP9RQf6ih3lC/wkMFiKCAwvyWdvVI9IQa6g310x9qqD/UUG+onzOgYUEIIYQQQgjJGhoWhBBCCCGEkKyhYUEIIYQQQgjJGhoWRFVQmNlcz0oKGkMN9Yb66Q811B9qqDfUzxnQsCAKfyBY6CaQLKGGekP99Ica6g811BvqV3hoWBBVQWFxaycrKWgMNdQb6qc/1FB/qKHeUD9nQMOCEEIIIYQQkjU0LAghhBBCCCFZ481+FyQpwYDI13NEVn0sUjVaZPxWIm6POA23i9lOukMN9Yb66Q811B9qqDfUr/C4DMMo6mi0jo4Oqa+vl/b2dqmrqxvZD//8cZGnLxHpWBlZVz1WZLcfikzbc2TbQgghhBBC9KNpcxFvuRZjaYZC5dOoePCUaKMCdK8Ree5ykcWvilOAbdnVN6geiZ5QQ72hfvpDDfWHGuoN9XMGNCzyFf4ET4UkObnn3G5u5wBQQWH5um5WUtAYaqg31E9/qKH+UEO9oX7OgIZFPkBORaynIpbuVjPvghBCCCGEkCKAhkU+6Fqd3nZf/k+kqzXfrSGEEEIIISTvsCpUPqgZl952C54XWfCCSPNWIjP2FZm+l0hFg4w0qKHg83rUI9ETaqg31E9/qKH+UEO9oX7OwPEeixUrVsjJJ58sY8aMkcrKStlyyy3l3XffFUczdTeRugmh0zwVhkjLRyKv3yzyt+NEnrpEZN6zIgM9MlK43S6ZPq5WPRI9oYZ6Q/30hxrqDzXUG+rnDBztsVi/fr3svvvuss8++8hTTz0lY8eOlfnz58uoUaPE0WCeioNvMKtCKeMiTibRbheI9HeKLHxBZMNSc50REFn2trl4fCJTdhXZeD+RyTvntcwYKii09wxKfVWZuFgDWkuood5QP/2hhvpDDfWG+jkDRxsWN9xwg0yePFnuvffe8Lpp06aJFmx+pMgJ98eZx6JJZLfzI/NYbHeKyLoFIgtfNBcrPyMwILL4FXMpqxaZtofIjP1EJm4n4s6tbKigsGpDj9RW1ouH16KWUEO9oX76Qw31hxrqDfVzBo42LB5//HE56KCD5Pjjj5dXXnlFJk6cKOeee66cddZZoo1xMeswM48i0czbsKobZ5rLTmeJrP7M3H7RyyJ9G8xtBrtF5j1jLpWjRKbtZXoyxm0h4nJ8NBshhBBCCCkBHG1YLFq0SO644w750Y9+JD//+c/lnXfekQsuuEB8Pp+ceuqpcd/T39+vFvtsgSAQCKgFwEXmdrslGAxGTaSSaD3W4bVE66392tcDbK+YvItIVaNYYX/BmCLLHrdL7TdouESaZqvFtcv54m55XwwYGUteFxeMC9C7XuTzR9Vi1IwTY/o+YszYT1yjZ4jbg7YbUYFXsFswxX3serTF7JMhgSA+23y01uPvqD5ZbY+J6kq0PtInW1tCMZCJ1qMNRrz1w+hTvPXF3CdLQyP0ecXQp1RtL6Y+pdJPxz6lWl9sfbLei7YHgsXRp2LUKVmfIvoZRdOnYtQpUZ+S6qe7ToGgiKtAY9jQGDpdXIaDpyiEAbHDDjvInDlzwutgWMDAePPNN+O+58orr5SrrrpqyHq8p6amRv2Nacmbm5ulpaVFTU9u0djYqJZly5ZJd3doIC8i48ePl4aGBmXoDAwMhNdPmjRJ7XPevHlRAiBcy+v1qnwQhX9ApHedzGyuF38gKItbO8Pb4sTeZEK9mi0SE7uE++71qCSkDd0DsnrdBqlufU/qVr4mNa3vijsYaYOFv3ayeDfZX1rH7iZtnrGRPtVWSGNdhSxb2y3d/YORPjVUSUO1Txat7pT+wYB09A5KXWWZTG6slpqKMpm3sl2dzOE+NdWK1+OW+S2R4wWG2ye4Ky2qy83PXdvRJ2s7+8Lr66t80jyqSlrW90h7z0BGfRrwRy6CSWOKv0/oFjTcasoo8ZV5iqJPxahToj5Z+m03bYzarhj6VIw6JeuTS1xSVe6VhqoyWbG+pyj6VIw6JevTRmNrZH5Lh9q3FaKve5+KUadEfZo4ukq+XNGutLP0KxqdgrXhMPgRH8NiFoWuLtlxxx3V59XV1Ym2hsXUqVPlgAMOkD/96U/hdfBgXH311apaVLoeC+RptLW1hQ/GiFt7A90qjyInFuxAt7iXzhEDSd/L3xGXYbs1FsJo3ESM6fuJMWMfcdWM1csqL6K7J+wT+8Q+sU/sE/vEPrFPWfepcTMRr69gHguMpUePHq2/YfGd73xHWV6vvfZaeN3FF18sb7/9dpQXIxk4GLDu0jkYeaO/S2RdxPLLGcjBWPSKmfSNkrVDcKU1RwZO2rbOfhldW64uNKIf1FBvqJ/+UEP9oYZ6U9T6NW2e1+qguRxLOzrHAkbEbrvtJtdee62ccMIJMnfuXLn77rvVQsQ0FDY/ylwwg/eil8zE77XzoufIwPLG70Um7Siy8b4iU/cQ8VWFdwPTEu7QUTXl6U29QRwHNdQb6qc/1FB/qKHeUD9n4GjDAvFcjzzyiFx22WXy61//WsV93XLLLXLSSScVumnOo6ZJZKtvmcuGZeb8GDAy2pfZ5sh4y1wwRwYm8UP52sk7ibhN9xohhBBCCCEjYli0trZKU1NTwtf9fr+8//77stNOO0muOPzww9VCMqBhssj2p4lsd6o5RwYMDIRLdbdG5shAOVssZdXi2ugbUjVqZ5Fx38j5HBmEEEIIIaQ0yGgUaWWhW8bFlltuKf/3f/+nkqPBunXrZNddd82oLBXJI/Y5MnY+W2T1pyILXhwyR4Z7/tMyRZ4W4+NRZi4G58jQDleo+gW9v3pC/fSHGuoPNdQb6qehYRGb571kyRIZHBxMug1xCDASMDkfFsz8veJ9M1xq8Wsig2Z5M1dfZI4MqRlnJn1jGbNxpHYbcSSoHIGSekRPqJ/+UEP9oYZ6Q/2cQc7jXlC6ijgchDshtwLLHj+S4NdvSf9Xz0lFy9viCoQMxa7VIh/9y1wapohsvL9pZNRPKnTrSRxQGm91e6+Mq69UX65EL6if/lBD/aGGekP9nAED6ksdb7kY0/aUryu2lpn7esWz9A3Tk7H8XRFrjowNS0Xe/Yu5jN3UTPqesY9IdWQiPlJY4CfE5D9N9ZWFbgoZBtRPf6ih/lBDvaF+GhoW8EZ0dnZKRUWFCnnCc8zGh/q2wHokmuKrFtnkIHPp3SCy+BUz8XvVx5Ft1nxlLm/dEZojY7/QHBn1hWw5IYQQQgjRLcdik002iXq+7bbbRj1nKFSRUBkzRwaqSmGJO0fGrQnnyCCEEEIIIaVBRobFSy+9lL+WkMIWj6qtSJyfjTkytv62uSAsCgZGwjkyykWm7hqZI6OAM0WWEik1JI6G+ukPNdQfaqg31M8ZuIwiL+OUyTTkeaO/S2TdfCkqcNrEmyMjNrRqo2+Yid8Ttkk8R0YwYIZb9bSJVI02K1e5PXnvAiGEEEKI42navKA3ajMZS2dkWGACPMxRUV4e6dzq1avlzjvvlO7ubjnyyCNljz32ECdBwyK9Sgor2npk4uiq4VVSQJL3qk/NpG81R0b70G0qMUfG3qYnQ82REfqcxa+KzLlNpHtNZFskhe/2Q5Fpe2bRq9Iiaw1JQaF++kMN9Yca6k1R69ekj2GRUSjUWWedJT6fT+666y71HIncO+64o/T19anJ826++WZ57LHH5NBDD82uB2REgWXZ3T+oHoc9RwYSubHAIFjxnjkR35LIHBnSu17ks0fMxZojA3kcSAKPBUbGc5eLHPBrGhcjpSEpKNRPf6ih/lBDvaF+ziAjw+KNN96Q22+/Pfz8/vvvVx6M+fPnK0vmkksukZtuuomGhZT6HBk7m4v/RyJL3zI9GUvfFImdIyMVc24Xmbo7w6IIIYQQQorNsFixYoXMnDkz/PyFF16Q4447ThkV4NRTT5V77703960kegK3HUrRYhnoFlny+tA5MpKBvA3kXkyIVB4jhBBCCCHOxJ3Jxpi/ore3N/z8rbfekp133jnqdcxrQfQCoYjjG6rUY97nyDjkRpGTHxbZ9JD03rfoFZG+DXlsWHEwIhqSvEH99Ica6g811Bvqp6Fhsc0228jf/vY39fdrr72mErf33Xff8OsLFy6UCRMm5L6VJK9g7pGGat/IzUGC3IqZB6a37eePitx/jMij54q8f785OV863o4SY8Q1JDmF+ukPNdQfaqg31E/DUKjLL79cDjnkEHnwwQelpaVFTjvtNJW0bfHII4/I7rvvno92kjxXUliypks2GlszcpUUUFIW1Z/s1aASYoi0fm4u7/7FrDCFHI4pu4hM3F6kvFZKnYJoSHIG9dMfaqg/1FBvqJ+GhsVee+0l7733njz77LMyfvx4Of7444d4NHbaaadct5HkGVRQGPAHRraSAhKyUUEK1Z8Ssf3pIv4+MwF8/eLIelSYmve0uaAi1bjZIlOQML6LyOjpkVK2JURBNCQ5g/rpDzXUH2qoN9RPQ8MCbLbZZmqJx9lnn52LNpFSAaVkUVJ2yDwWTSK7nR8pNbvz981KUkvfFln2tlnOFgZHeA6Nj81l7j0i1Y2mgQFDY8L2Ir6qwvSNEEIIIaTEyMiwePXVV9Pabs89OfcASRMYDygpm2rmbcx9sfmR5hIYEGn52PRkwNBoXxbZrnutyJdPmgtK32JuDcvQqJ9Skt4MQgghhJCRIKOZt91udzgpJtHb8DrmtnAKnHk7NdCyu98v1eVePZOeOlZEvBkrPzANj3jUNkdyMyZsI+KtkGJBew1LHOqnP9RQf6ih3hS1fk36zLydkWExZswYqa2tVUnb3/3ud6WxsTHudta8Fk6AhkWJgRCplR+GvBlviXSuir+dx2caF5Y3o27iSLeUEEIIIaR0DYuBgQFV+ekvf/mLKjeLGbbPPPNMOfjggx1rHdKwSE0gaMjCVR0yY3ydeIqpkgJO7falIW/GW2b4VNAff9v6yRFvBsKnYHhoRNFqWCJQP/2hhvpDDfWmqPVr0sewyCjHwufzybe+9S21LF26VO677z45//zzpb+/X826fdVVV4nXm3E+OHEAwfTtS32Asdsw1Vy2OkFkoEdk5XsiS+eahoY9YRx5Glg+/a8ZIoUytsrQ2NnM79CAotSwhKB++kMN9Yca6g31KzzDtgKmTJmi5rVASBS8Ftdff738+Mc/ltGjR+e2hYTkClSI2ugb5oIvH5SwtRLAV30SmXgP4VRfv2EuYNQ005MxeSeR8VuaSeGEEEIIISSKYY2Q4KF46KGHVEjUm2++KYcddpj873//o1FB9PJmYM4LLNt8R6S/0yxjaxkamCvDAgYIlo/+JVJWLTJp+0huRtWYQvaCEEIIIcQxZJRjMXfuXLn33nvlgQcekI022khOP/10Ofnkkx1tUDDHIjU4BQb8QfF5I1W/Shp4LtbONw0MGBqtX4Sm3onDmJmRyfmaNhtaJneEoIZ6Q/30hxrqDzXUm6LWr0mfHIuMy80iBAr5FNtvv33C7Y488khxCjQsUoNTIGiIINep6C7GXNC3QWT5uyFvxlyR/o7425XXiUza0TQ0Ju0kUtkwYk2khnpD/fSHGuoPNdSbotavqYgNi1RwHgv9DAtUUpjf0i4zm+uLr5JCrgkGRNZ8GfFmrJ2XYEOXSNMs05OBJPCxm4i4Ul8/w4Ua6g310x9qqD/UUG+KWr8mfQyLjHIsgsFQcmsSenp6MtklIXqBUKdxW5jLDmeI9KwzvRgwNJa/IzLQHdrQMEOosLx3r0jlKNOLobwZO4qU1xa4I4QQQgghuSVn5W2Q0P2HP/xBbrzxRlm1KsGkZIQUG0je3vQQc8EcGas/C3kz3hZpWxjZDsng858xF3guYJhYCeCjZ5jJ5IQQQgghpWJYwHi48sor5bnnnlNzWvzsZz+To48+WlWH+uUvfykej0cuvvji/LWWECeDMrTNW5vLTmeLdLWGvBlvmTkaKGNrJYejvC2Wd+4RqWqMJIBP3E7EVx0/BGvVxyI9bSJVo0XGb1WwRHFCCCGEkKxzLC655BK56667ZP/995c5c+bImjVrVGWot956S37+85/L8ccfr4wLJ8EcixJPeHIKgQHTkLBmAd+wNLFxAqMBc2Zg7gxM7rfkNZE5t0VP6Fc9VmS3H4pM21M9pYZ6Q/30hxrqDzXUm6LWr6lIcyz+85//yP3336+qPn366aey1VZbid/vl48++qj4RCwx/AGzRBvJEx6fOZs3ll3PFelYGQmZWvm+aXgAhFPhOZa37xSpqBfpax+6PxgZz10ucsCvw8YFNdQb6qc/1FB/qKHeUL/Ck9HRX758ebjM7OzZs6W8vFyFPtGo0BtY+ItbO9UjGSHqJohscYzIIdeLnPqEyME3iGxxrEjthOjt4hkVdl69SVWnCq5dIEuXLZOggyqykfThNag/1FB/qKHeUD9nkJHHAmVkkVsRfrPXKzU1NfloFyGlA9ybyLHAgvCm9uVmuNT855KUsw2BGcOfvlQQgDgTrmAkhlc0mHkYqESlHkdHP1p/+2qYNE4IIYSQwhgWiF877bTTlKcC9PX1yQ9+8AOpro5ONn344Ydz10JCSgkM9BsmmwsG/y/+JrO3IzG8t81cUuEpG2p0xBof1mMBYzsJIYQQUoSGBWbctnPyySfnuj2kQLh559p5YFCfDhsfIEGPT3rWt0p1sENcMCpQPcpIERYVGBTpWm0uqUClqlTGBx6RE4IE9EKgeeUsXoP6Qw31hxrqDfXTrCqUjrAqFNEWDJT/9e3oalCxVDeJnPivoQNoeC4QJoVBtmVoWI/q7/WR530bcthol0hlQ3qekFyGYi1+NWXlLEIIIURLmoq0KhQpTmBbdvf7pbrcy0R8JwFjAQNjVH9KxG7nq+2GaKhyLerNRaYl/xxUourdEGN4JHgc7EnRaCNktKyPniAwbv/K0gvDqhol4q1IblTEO0ZxKmc5FV6D+kMN9Yca6g31cwY0LIiqoLB8XbfMbK4XD69FZ4EBMQbGQ+7GN5lGRWjAnJWGCF2qbjSXVAz2mkZDQk+I7REGSzKCGYRilVWbBsaQ0KsGkbn3JH/vnNtFpu7u6LCoEbsGNQ8XczL8HtUfaqg31M8Z0LAgxOnAeMDA2AkDwrJKc0G53GQgwnKgK7nhYT3CWwJPRzIGu0XasSzPvM3drSIPnWlWycJ8IkhaV4++oc+9PhF36DHe6/bn3jivFyq/JB0YLkYIISTPOPhXkBASBkbEhG1FG+CGLq81l1FTk28Lzwbm60gVhqVCsbqH1571S8wl3yAELanRUhbXaHG5fTK2zxDXyhozjjbF9kkNIpdnaO5KEYSLEUIIcT40LAjSbcXn9ahHoidaa4i7/FVjzCUV/j6RnlDiOZaVH4l8+l9xDEiaRxuxZDhTaRq9z9C4CRkeME6SFQAAr98iMnlnlhUu1WuQKKih3lA/Z8CqUCMBq0IRUtjKWd/6OzY2S+wGBkT8A+Zj1DIY/7k/0esDGWw/aOaUOBqXGeJWP8lc6iZG/q4Zx1wMQggpFE2sCkU0ArZle8+g1FeVsZKCppSshulWzkKIEEhWXWokvBnxjBP/gBiBAenq7pUab0BcMEBSGjlpGETW9gPdaYaQGSIdK8xl2dtDvUq1zRFDw2541DSZXpISp2SvwSKCGuoN9XMGNCyIqqSwakOP1FaykoKulLSGaVbOKjgYfOOOU5y7TsGgISv87WY1E3eOBVz5gciTF6feDt4K5LHEC+NCHkz7MnOJBeFWlqejzjI8QkZHVWPu5ipxOCV9DRYJ1FBvqJ8zoGFBCNEfJ1XOcho4Dqj+lCpc7IS/mcYPcldQfSu8rDANCngy4AWJBesSJcfDQ6Q8GxOHGh4oGVwiRgchhJQKNCwIIcWBbpWzHDjRosJKpG/eemgoV/faiMHRYRkd+Htl/BwSeD8wUWK8yRLLqqLzOMLLRJHyehodhBCiITQsiKqgUF1exkoKGkMN9Sbv+uUiXAzeDORTYJm43dAkeswXYhkadsMDRocRGLo/zOKOohbxClv4auInkWNBCWMHwmtQf6ih3lA/Z8CqUCMBq0IRQpxAIWbeRn4GZlePCq8KGR1dq0xPSCaU1w1NIm+YbIZZ+aqybCtnJieEOJAmVoUiGhE0DGnr7JfRteXiZviBllBDvRkx/QoRLoaKUhj8Y8FcGXZQwaqzJWJoKC9HaOlqjT8je3+HSOvn5hILZlePl0SOz8aM8XmcmZzXoP5QQ72hfs6AhgUR+KzWdvbJqJpy05dItIMa6k3J6odJ/BqmmEss/n6b0RFjeCRKRO/F5InrRVZ9MvQ1VKiKl0QOowPldbOcmbxkNSwiqKHeUD9nQMOCEEKI84Dbf9RG5hIvKRy5G8rgWBad24GqVvHoWWsuLR8NfS3VPBzwZKDqGMOiCCEkKTQsCCGE6AXK2I6ebi6xDPSYpXHjVa/q2xB/f6nyPOC5+MvBIpUNZo5HeY2ZRG797asVl69Wans9IoFxIhXWNnUivmoaJISQkoGGBVEew/oqHz2HGkMN9Yb65RAkcDfONJdYBrpsc3OEDI/Wz0zvRypQThcGRoIwLPg8JiZsU3XICKm1GRy2v9X6OAtK8joxVrxIk9x5HeoN9XMGNCyIuN0uaR6VZTUVUlCood5QvxECZWzHzjKXTGcmrxlnVrhC8jiSzjNhoNtckDOSCQjRsowMGCIVocfyNBYP4szzMMTKMsndyfA61Bvq5wxoWBAJBg1Z3d4r4+or1YVJ9IMa6g3102Bm8m//M3JXHonl/Z2mkYFy4v2dEuzrkK72Nql194kLr8E7ol7Hdra/Mymvi2372s1lOInxQ4yQOGFcMSFdplFSltioyDLJ3cnwOtQb6ucMaFgQVdCxvWdAmupTlGMkjoUa6g3102hmciuxHEt1Y3iVETRkZUu7zGyuF0+iQQ3K1gz22gyO0DLQKdIXerSvj9qmK7N+wauCRPZEyeypclhijQ8YHYtfLuokd16HekP9nAENC0IIIaVNLmYmTweEJiEHBEvt+MzzGlRIVSIjJOI9GfI3qmhlArbHksyLkzDJ/SCbB6Qm8pjo79h1Hl9mn0kIcRQ0LAghhBAYD7jb7tSkZLQD1aawJJ/4Nr73wvKM2MOyov5OsCBpPROQhzJcTwmAYWEZGcpTkoFRgsdEYVwjTZEmuBOSChoWRN1Ea6ytcGTxEZIe1FBvqJ9DyGJmckdriME2BrdYMgGhW4FQPsmyd0RevTH1e2qbQ96VLpHBnszbGhjI0jApH7a3xFVWnRsNizjB3cnGl6OvwRLCZRj45iheOjo6pL6+Xtrb26WuLtPbPDkCd4XWzS/MZxNCCCG5GAT+69upk9xP/FdkcAjvBeYVgZGhktljHlOtG45hki1pGSa1CV6rFln6ZvJ8Hc0T3HMGja/MaNrczOvSYCxNjwVRlRRWtPXIxNFVrKSgKdRQb6if/hS9hsNJcnd7I+Fbw6EQhgk8ND1Y1kleeOkakSVzRLxlIu4yEY/XfMSx8liPvpjnoUf79nHfF7udN/Ws8oUgT9XFiv4a1AQaFkRVUujuH1SPRE+ood5QP/0pCQ1HKsm9QIaJMdAl/d3tUh7sFVe+PCYoVTz/aRkxXJ7MDZSMDZgMtoOh88atyds85/ZhVRcriWtQA2hYEEIIIaQ4ktyzMExwx3uJvWRwPMPEKv0bz1BRs7kvE0dhBET8AdGK7laR568SGbuJSGWDSEWDSOWo0N+jRMoqnTkjPVHQsCCEEELIiCS5a0WmHpN0Z3Hf61KRxo1FAn6z6hYMmEDMI9bj76jnMY8J35fm69ZjJpM2jhRLXjWXeCBULGxoNIQNDldFg9T1l4v4J4hUj4q8xhLGIwoNCyK4MTO+oUo9Ej2hhnpD/fSHGupP1hqmO4v7zAOc5eFBYr4yOlIYMNkYOdZjV6vIyvezay8qh3WtNhcbyCaZgD8+itm+rNo0MOxeD/U8xhuCR8zB4iRtNISGBRGXyyUN1bTodYYa6g310x9qqD9ZazicBHcngPaoNpU7o7pY5WiR/a8U6W8X6d0g0rvefOzbEHmOv/va0/O2DHabS8eKNBroEqmoj290RIVkhQwTVAPLZ1hWMFSSt/VLkfpJIlN3c975EwMNC2LGla7pko3G1rCSgqZQQ72hfvpDDfUnJxqOdIK7bqRjfO1xkUjzVukNupHzEjI0gj3rpW3Nahnj7RGXZYTYjRHkwaTECBktG9LsjzfGCzIqfl6I9VomJWPjleStmyBy8A0imx8pToWGBVEVFAb8AVZS0BhqqDfUT3+oof7kTEOdEtwLQa6MLxxPK6QJ+gUNWVvZLqOs5PtYEKIV6/WI8oisj36O0sOpQMhX91pzSYeyyqi8kChjJOwVaRBZ85XIqzcNfX9Hi8iDp4iccL9jjQutDIvrr79eLrvsMrnwwgvllltuKXRzCCGEEEJKN8FdJ+ML5W+RA4MlHQZ7hxocCY2RDWYFrnT2iaWzRYYHzF6XyNOXisw6zJHGqjaGxTvvvCN33XWXbLVVGu4xQgghhBDiXJxufMG7gKWuOfW2hmGGWvXGGiH2/BDba/0dWTTMMPNFvp4jMu0b4jS0MCy6urrkpJNOknvuuUeuvvrqQjen6IDHcNKYalYz0RhqqDfUT3+oof5QQ70pqH5I4C6vNZeGKemFUCH5HIvd4MBj66ciKz9MvY+YqlhOQQvD4rzzzpPDDjtM9t9//5SGRX9/v1osOjpMqzAQCKjFqvzgdrslGAyKASszRKL1WIfXEq239mtfD7B96A+RoBE+2YMxAZyIBcR+7euxqTvJ+qBhKAN5yPqgERUfinPd7Rq6Hm1x2dZX+rzqc9ximH2KaWSitju5T7Hri71P0BDEtkXnPiVre7H1KZl+uvYp2fpi7FNNRZlqu30/uvepGHVK1qfqcvO30OpAMfSpGHVKtL4qkX5O65N4RCpGq8XVMC26Tys/FE8ahkWwukkM2/gzb2PY0Bi6aAyLBx54QN5//30VCpUO1113nVx11VVD1i9cuFBqamrU3/X19dLc3CyrV6+W9vb28DaNjY1qWbFihXR3d4fXjx8/XhoaGmTJkiUyMDAQXj9p0iS1T+zbLsC0adPE6/XK/PnzzRX+AZFeczZPfyAoi1s7w9vixN5kQr109/tl+brIZ/q8Hpk+rlbaewZl1Yae8Prq8jKZ3FgtbZ39srazL7y+vsonzaOqZHV7r7T3RNrYWFshjXUVsqKtR011H+5TQ5Uqq4cKGH2DAVnf1S+jasplSmO1+nFcuKpDfemE+9RUK16PW+a3RI4XcGqfkIAX1mlM8fcJX0bQcNtpY6S8zFMUfSpGnRL1ydJvx43Hqi/+YuhTMeqUrE8u/KfmQaiUlet7iqJPxahTsj5NHVsjny5dL2Ved3hgqHufilGnRH2aMLpKPv66Tf0GWvrp2acpMqNijHj71inDKBYD3zV1E2R1+XRpt8aZ+RzDhiKH0sVl2M0Xh7Fs2TLZYYcd5LnnngvnVuy9996yzTbbJEzejuexmDx5srS1tUldXV1hPBYD3SLrFjj2ToM/aMiCVe2y8fh6KfPgx5F3T3TrUyCk4SahahjF0KdUbS+mPqXST8c+pVpfbH3Cexet7pCNx9epfRVDn4pRp2R9wtN5KzfIjPGRqkK696kYdXIPRz/d+rT4VXG/cMUQwwJGhXrPCfdLcNbhIzOGDY2lR48erW7GW2NpLT0W7733nrS2tsp2220XXocD8Oqrr8rtt9+uDAiPJzojvry8XC2xYLvYba2DF0um62P3O2Q93mcL+vPEMUEhbibrcUHEM2UT1d5Ott4T2h9OeOsHMW6ptgRtd2Kf4raxyPuEz0I7ErVFxz5FrS/yPqXST8c+pVpftH2Ksx/t+1SMOsWsx6DR0i/2M3TtU7L1xdanpPrp1qcZe4m4h5bkhadCDr5elZqNPyLNwxg2yTbaGRb77beffPLJJ1HrTj/9dJk1a5ZccsklGXWUEEIIIYQQ7Uryeis583YuqK2tldmzZ0etq66uljFjxgxZT4YPDHDECSYwxIkGUEO9oX76Qw31hxrqTVHq5w6V5G3aPLNZuwtIIk8KKTGQfET0hhrqDfXTH2qoP9RQb6hf4dFOgZdffpmzbucYJAuhokFskhHRB2qoN9RPf6ih/lBDvaF+zkA7w4IQQgghhBDiPGhYEEIIIYQQQrKGhgUhhBBCCCEka2hYEFVBAbNEFlUlhRKDGuoN9dMfaqg/1FBvqJ8zoGFBFJh6nugNNdQb6qc/1FB/qKHeUL/CQ8OCqAoKi1s7WUlBY6ih3lA//aGG+kMN9Yb6OQMaFoQQQgghhJCsoWFBCCGEEEIIyRoaFkThdjHbSXeood5QP/2hhvpDDfWG+hUel2EYRR2N1tHRIfX19dLe3i51dXWFaUR/l8i6+YX5bEIIIYQQoi9Nm4t4y7UYS9NjQQS2ZVffoHokekIN9Yb66Q811B9qqDfUzxnQsCCqgsLydd2spKAx1FBvqJ/+UEP9oYZ6Q/2cAQ0LQgghhBBCSNbQsCCEEEIIIYRkDQ0LIqih4PN61CPRE2qoN9RPf6ih/lBDvaF+zsBb6AaQwuN2u2T6uNpCN4NkATXUG+qnP9RQf6ih3lA/Z0CPBVEVFDZ0D7CSgsZQQ72hfvpDDfWHGuoN9XMGNCyIqqCwakMPKyloDDXUG+qnP9RQf6ih3lA/Z0DDghBCCCGEEJI1zLEYAfr8AdnQ0SflXo+Ue93q0UOTjhBCCCGEFBE0LEaAQNCQzj6/dIo/vK7MAwPDXHxlprFR5i5MLQN8anV5GSspaAw11Bvqpz/UUH+ood5QP2dAw6JADAaCaunqj6zzuFxSXmZ5NdziCz2ORCWFyY3Vef8ckj+ood5QP/2hhvpDDfWG+jkDGhYOImAY0jPgl56ByDpY3hFjI2J0uHJokgcNQ9o6+2V0bbm4c7ljMmJQQ72hfvpDDfWHGuoN9XMGNCwcDoob9A0G1CIyaJsExvJoeKRC/T38vA1UZlvb2SejasrNnRPtoIZ6Q/30hxrqDzXUG+rnDGhYaGps9PuDanFq3gYhhBBCCCktaFgUed6G1+1S3gzL4Cgvc4uPJakIIYQQQkiOoWFR5PiDhvhj8jbgxPDZQqjKPC6pr/LRc6gx0I4a6gv10x9qqD/UUG+onzOgYVGCYFbKeHkbKzb0SlmO8jbIyFfDaB5VVehmkGFC/fSHGuoPNdQb6ucMaFgQMQxDOnsHpaayTPoS5W0ghCpkdDBvw3kEg4asbu+VcfWV6suV6AX10x9qqD/UUG+onzOgYUFUMnjvgF+qK4dOLJMobwMGhjXPBvM2nKFhe8+ANNVXFropZBhQP/2hhvpDDfWG+jkDGhZk2Hkb3QnyNtR8GyFjg6WkCSGEEEJKAxoWJK95G9YM4najw03nBiGEEEJI0UHDgigDoLpiaBhUTib38wfVIgnyNqwZxRFeRYYPPEONtRX0EGkK9dMfaqg/1FBvqJ8zoGFBxOVyKcNipEiUt+H1uFXpWw/+drvNdaH1eOSXRWLcLpc01lUUuhlkmFA//aGG+kMN9Yb6OQMaFkRVhWrvHpD6ap8yMgqWtxFEKFXibSxDwwOjwxMyOtxu8XjwGowPd8mWx0U1jBVtPTJxdBWrYWgI9dMfaqg/1FBvqJ8zoGFBVMjSgD+gHp18KZrGB1qJ0Kr4oP2Wh8M0PmBsWJ6PiCek2LwfOCrd/YPqkegH9dMfaqg/1FBvqJ8zoGGRZwJBQ+Z+3S7zvh6UMRUumT0Wk84V2ajWQRjhUKtwDnlc7GFWYcOD3g9CCCGEkGFDwyKPPP1pi1z1xOfS0t4XXtdY6ZJzt6uQb0weuZwGksT7oRLL4wPzDzORe1wx3g8rDAueEVfxeT8IIYQQQoYDDYs8GhXn/P39IS65tb2G/PqNXrl8d3GMcYFxcW2lz9FhUIULEQsZHgm8Hzhm4WRzK/EchofL9nwEvB9wgo1vqFKPRD+on/5QQ/2hhnpD/ZwBDYs8hT/BU5Eszu/md3qlzG3IqAqP1Je71FLhNSs0jTT4zMpyngrDwbAlntsq6g4BX3Qq7Mrm/VCJ6DbvR1kW34bQsKHaN+z3k8JC/fSHGuoPNdQb6ucMOJrMA3MXt0WFP8Wjc0DkV69Fb+PziNT7XFIXMjTMxR3+G+sbQo/W81zM/4CqUOu7+mVUTXnBqkIVO4i6ytT7YRkeZaEQrPASJ/wK1TCWrOmSjcbWsBqGhlA//aGG+kMN9Yb6OQMaFnmgtTO5UZGIgYDIml5DLelSUyZSX+EOGyR2wyPaIDENlKqyoV4Rddc9ECxYVSh4eD5dE5B1fUZJJ7in6/0AODqQEXW7TT0NWdPZL2VlXmWURF6LPLokYpS43CJuibwe3lZcnBm9wJXZiJ5QQ/2hhnpD/ZwBDYs80FSb3gQt+2/klUqvS9r7DenoN9SjtQTSvDK6BrEEZUWabfO6Rep8Qw2PMiMg4+sHwgaI/TWfJ3+D/NeWDcof3+9TuScWTHBPDY6WYcATov5Sj/5gUHoG/eL2Z6+X5T2B0eIOGSUwPMx15uuWUQNDBK+bBor5Zk+sUeNyiYdGCyGEEFLU0LDIAztNGy3N9RWyqr0voeU8tsolP9mpMu6deYQm9QyKbFBGRlAZHRviGB/W0tEfVAZGOiAap63PUMtQbFNh26jyis3QiDY8hoZpuaXGZw4o0zEqkMguGiS4l6r3xPwrt4SNEnhNYIxIrBFiGSYhowZ/hwwd/IEH8z0hoya0D3PbnDeXEEIIIWlCwyIPYAB0xRGbq6pQZpDKUM7ZtiJhuA8GSsg/qva5ZGJterd4MQiMNTzsBsmGkIFif30wcaXVKHr8WAxp6U4+OZ0FuhXPK2KFaTWEjI/fv5s8ZOyOD/pkt4nekgyLyhQcoYbqci0qe+EsCsDTgrlGcmy4xIaImeFd5kq1LvT6EK9LyNNieV7C21khY6HrMvxeyyDCPnLghcHnTxpTzWomGkMN9Yca6g31cwYuA7fHi5iOjg6pr6+X9vZ2qaurK/g8FvBUwKgo9J14yN7nN70ipsERjOMJifaUdA4YIx67eOBGZbLpGLNyVjhp3Zf/EC1CsjFoQs6VvBk09M4QQkgJ0bS5iLdci7E0DYsRSEx+9fOlMm/hQscmJiM+v62jT0bXVSQNYUJfYFzED8ey/g5GretTd6XzQ2UoRAtGhukhcUdVzLJ7TaznZUVqjKSrISku/TIxaCLPIyFmKuTMbYaeqbA0W3ga82EyA9+PC1d1yIzxdY77jifpQQ31pqj1a9LHsGAoVJ7Byb3T1HqZ5Hd2roCZBJy6Lw0VWNLfb58/Xm5IUOa1BeSFr1OUPkpBrx+LIatUiBZIbcUgXyRifLjDxoe9kpblEcllSd+RqJyVjobFjO7VxYajX2wSfy6xPCso2+ixDJFQ8r4yRNxWwr5pqISNEus9JWiclPo1WAxQQ72hfoWHhgXJKxVeTPznkqbqoYPAj1q7oqpBxYKcjIt3LFdzfnQMRAyU8GNoHR5VnnEG+SKmMZJekkl1WSRnxEpet3tE7EYIHmt9uTVGWDkrNTxG+cuFCQSMRFOvpO9RCc+/Yj5GGyFm2JcyWuA9kWijJd+z1hNCCMkdNCxIQcAgAoO+eFWhLC7YoUJ2m1SW1h2KLhgfSFC3hWrFGiDtw8wX6R7Ekn7yujW/iPKI2LwgUWFZ+NsXbYzEu7vOylmp4TFyLmGPSsAQfxYeFeURsbwkYcMjtD7GaIkX2hVvUklCCCG5h4YFUXcVR9dWjHhFIQz2MOiLvdOcaYI7Bg515RjIe9L+bHhMYCyEjZAEHhH7864MjBFrfpGVXWk3SWp9pjFiGRx4/vry5OFiqKylyvsqA6tMWvr94bKtFtbfdn0j68y4e/sGoXziqO3C65PuK/46+/6tTxv2/mP2C6PyD+/pX12sUNegLsAjqUIc0nVNxsEqWRxrnCQK7bK8J+HqYu7kifNYP62plhVpNIYa6g31cwZM3h4Bujs3yMqFn4hTwSlgzbodOyv3SKBLbDza2RUyRqKMkBijJLIuqMK4SOEZV+2S0RVulfCPSSkry1yRv73mjPQI2auKWV9ZFvm7wpve/CxOvgZ1udacjj3Ey0x2t9aHvCQ2L4qVKJ9sAkkmzDsDXIewXS3NiF4UtX5NTN4mGoEBzdr2XmmsryzIHVMMbLYe5/xTEe00w5kyG8iFc0HiGCHRnhFzrpF0Jzsk6bO625DV3dmXKINxURXKG4IxEjZAYIwoo8SlCgRU2P+OY7BYhoxVMnkkrkHmoeQnxMtKmodHxdIwGwM03qz3du+K3Rgx52kJVQAbhpeFRINB6fyWdpnZXK8KFhC9oH7OwPmjOUI0BgOEURVY0n8PJjtEDsjclX75f3OTh/mAfaZ4pbHKLb39g1JZjks6NFg1ousEWb5Ja516DK+LhHnZfZhx19n2E15t+6xE+4l+bsRtU2y7E+4/9AQTP362NnXeS5lb0p4QMhmY+wWVznJVgcnrNssmw9gocxtS4+sOGSERj0lCQ8bueQm/J75XhXko+pCvWe/jeVksT0qiGe9TeVnM+VdotBBCItCwIMRheEPGyP4blcl9n/QnrZyFfJRLdqlUP+xr24PSWF9a81jAI3TyE10pj9HfDq9Rf/cHzKpgfYOGqhCGcsW9g0a4dLH5PPJ3z6BpSOA9ahu1bWR7f5bGCt6PcDkYkibZWz8VHsvQMI0PPJ+3Pvl+b36nV1WAMrd3iQ+eFo9LyrF4xXz0mIZQ0YUYlEioWDwvS66wjpAyNnCOWAaHmVgVMVAk/pwqiSaJVCWPbR4Ya5JIaz1DxwhxHjQsCNG4chaS3LFdqdbuzuQYgSq3KI+AVObm8wcDEUOjJ8bosAwWy5AJ/x0yWMKGTMhgwftzMaEk9tEXMGS9epbeeQHj5po5qb1jOIwwMGBoICwMjz6PGdal1luPyiBxJd/WZrTA+LFvj+1G2oBhqNjwCc8khO8hdQ6P3PdR2BDBf6EqYKatYhkj0V6VsIcm7G0xX1A3ZAxD+v1B6R7wi9cN00WGGDXK0MH2NGpIDjBCNSnCUQPW33gM/Y3/y4NGOJfL6TB5ewRg8jbJ9YAntnJWqWuYzjFyMpZ+eBwIuExviTJCYgwW29/WerVNrOfF9ncuQsAKgd34UF6UkPERa4QMfT1kwMR4W6z3mLktkffA6EwUKmZx+e6VKc+jUr8Gi4FMNQyHl5mWii2RPzpsLH4FvOiKfGFDyNogZBzF2z68L5vBFDambPuzb2sZU9GV9SIeotg26p68bRiuULhuZMBu3YCzBuzhRyssNzyYj5wL1n6tbSKD/ujw4SHvM6L3rf4ORt4f2iRtpszaXsorqqRQMHmbZEwwaBSNy7/YwIAG5VJTzrxdwhqme4ycjKWfCmMqc8noHO33vZZBufSVxINmi8NnlMmYSrf0B0zPSb8fRo7p/cDfCCPDa/1+8xEGzUBo2yyqwCbE+jxRldXyd//L68Kd9uTb3PBWr7y/alCqytw2I8X0xEQ8MGYuDzxiFV532OBR64ssk7SYQ8Yy+R4Nh5eF78/qf5821nixDJ5YYyVSSjzGWLGFs4X3GfN6+HNs+7TfXEk+YA9tF349YiBAO5ybyCOicV84aFgQdYG2dfYVrCoUyb5yFjXUp7rYSOu3zTivCulJlYdy/vaRkLFMwA87ckWGGB4BQwaQ7I51IUPF2sYySvAcoWGx743dFo8wcvLhfVG5+ClAW55cmHxOmWTArrAbGsoYsXlf7F6YiqjHSBhZ7KPaX8hDM5L5L8UcMpbL61BX42to8Q17FRBnG074elhX4r+DTkDPX2GiLXbXcbgkonoBvkt8McCVad55iK1qRAjJfx5KpuA6LlN35UVq8vxzjsHaUAMm2mgJGyehv/ui1g3ddm1vUFp78vtNA48I8moQvmaS+8+DfMqDYgv9qrAZMfZ8F3tOTEWssRL3ubn9WysRMjY0F4fVxUrH+CIjSyBkoH48uFomjK6TnaaNdryBSsOixIiX6IY7jkhUqyjzqMdE1TvCCWxW8poVP6q2i7hMrapEQ5PkMm+vygUMuTqVwWEtQdtrygVq3jmNbGtub8VcwlBRz3N8PMnIED7f4iRn2stlWq/hPMA5YZ4P5nkRCJSu/rma5b7Q4Ac1nICfIz5a7ZefvNSTcrsLdqiQqXVum5cl4k2xyhBv6BkUl8cbFUIW3iZk1FjPsZ9cEhwB4yUV187plY1H9Sud4KXxqOs1NLN51HPbupjt7NtEHuOsD83t4YlZZ3+f+Z4U62yfjaj89l5DXL6gqs4Xbqc7+nOSeYZY2rn4vTqFMVA/V+ua6yvkiiM2l4NnN4tToWFRAOyD8ti79tGv2WIWYwf59lJ9+BtfjuFErMxK8+HiHuj3y6SGSsdd1OiD156pliV2o8NaLAMFv8M4FriEIwZM6HnQZqiE4nDNbcQxFKLMbOyA3zqPrfKS1jlsT26MGJuRUpN4tJeVjJr8K4elMCytoR20toxU6BgwgmGdzddCOotplOTbMM23fsWQh5IPcAzSCRU7dHpZ0mOF86Otw5DRdemVfMb2MEDshonlUemzGS+WtwXJ+LE5Lvb8F2wf5b0JPR9JEFb2ZZum1QKi6E76qvq+imP8uF2Y6DT5nq9/q1deXTqg8nBQ1tmqgmYVGfDFebReD2/njbwOA0hH8uXVKZZy668lMFBXtffJOX9/X+44eTvHGhesCjUCGIFBCbavFHf/BnGhLAApKtSdcVXpweY5CVWAGOI5sRkz1sAV/6k765bxMsIDfvuEWFGGbRxjNV8Dft20VgZHMKIzJjSzPCOm0RkyUpF/YFtHnEkuqkI5EbvxYoWFKY+JZYSo59HGSvxHzFwflGWdPImdhr0EdKwBYlVKS2W4hN8fx9Api3kvXst28O7U6836nUbOmFpC+WORBd/xZq4Xvt/t62FUB4Ixr6l9mX9HvRb+jMhr9m1Rxvz91YGEOWU4+uPrK+T1S/YdsRtDrArlMFyeMvGMnioSnCzS3y7S0ybS3+mYRChcTN39fqku97KSwjDAADtU8VzEk/3+EoZ/hUphhAf8MeUM+/oDUlPhVRUxSJ61HuYxxg+I3VsSDtkKBqVnwC8+jzvkEbOFcYUqnTjj26I4yUWoGLQa9AelzOt2zPcovh+snAopH5mQsZv2qZQtx3rVAAyLeS5bnkLrGki8zrw+7NuE1sW+1zLgjSTvta63qG3M9eHPjtqvoQZ1+C6PvCdBG6P2Za7rGjCkQ1UxGznQBqu0tEn+vylQ/SzKKLEZL4kere3LPIb887OBlBN2bugLSlBc4cG2OQA3y2cHYgbm6jWlnTkox3doxDiIMQJCz81BfsQosF7XAUNEWtr7ZO7iNtl1xhhxGjQsRnpUUjnKXAKYIWuDSG+byGDqL+t8fzEtX9ctM5vrlUuX6Bf+hS/KletNDYlzQTy3R/Dj6hqiX+uGHvMaTGC0WAZnOIQrbJSY4VqWgRK0ecMiHhQaJvkOFcPx3dDdX7QVadINGYNRoXIdhrzq/KOC62Rte6/ScDh35dM1vi7bpUKmj/KokLWBUOiaVSXNqoAW7xGeJWu7RI/YTg2w8zhIxr7V/vOUz4MJO3//XoqYMiKtnaknNS0ENCwKhadMpGasuQz2ivSuNz0ZwcFCt4wQ4mCDc7gx1ZYXxJ47FDs5lEXcSaJCZSftEz5FnkcbLuEJoOJMGhV659D9WH/b3y8ji84li3WvLlZKxtdeU5Ln6+QClTsZDBkc/jQNlyhDJ85rsY+Y6yYY2c6p4Z440ijHHFlcav4aj/W3tT5UAAAemfBr+N4N/Y114dfUd7HttfB2ErNP833xPrsstE/rs633fbnOL1fFqb4WS1NthTgRfoM6gbJKc6mbINLXYRoZfRtEBW8TQkiuQ/Y0ImqSLNssuvYZbCWesRPHaEpmMIV2GzW7rpUPZb1PTYRmFXAIrbS2KQWKpbpYKRhfarJNBEnkIAwuk/lskhkiWD+/LSB/TxEKBU6YVSbTGjwxg/bQgN02MMdz628Xkue7+qSpvlKFXanXQsaCTuxaUSaNlf0JDVQrxwKlZ50IDQunUVFnLsjHgHEBI6O/I68fiZPU50WABtEVaqg31C8xVoW70DP7K+IkUDnMa4hMHlOtcizCRollpIQS/cMGi8QzYCLGkFXswTKi8NxMLi3sHD/FXF1M3dn2mOb3cClV48s+n011kiO4c7NXnl40mNKrc8ZWmRtguD58QY/Ul+s987YniYFq9QolZ516zTm6KtR1110nDz/8sHz55ZdSWVkpu+22m9xwww2y6aabalUVKmuQj4EwKRgZ/sR3QgghhJQOqjxyyDiJNVKs9aZXxTJmbB4Yy0ixeYXsxpAyZMLV7ZyXo5NqSBU7rjTr2yV+XTLcPsXbVWLwx61+NQHjqEqXbDEGc0Rhpgzi1KpQOpTkbS7QPBaZjKUdbVgcfPDB8u1vf1t23HFH8fv98vOf/1w+/fRT+fzzz6W6urp0DAs7Az1mwjeMjKA/J7vEKdDeMyj1VWVaW/mlDDXUG+qnP6WgoSqnjcdQee2EA/GUg3BX8tdj3+8qHg1jy5Nb1eGseZSsQgxR8ywFo+dQKoYqcfEGzdl6dXCM+gYCUuHzFM01GAhNIugavVFBZ94umnKzTz/9dNTz++67T5qamuS9996TPffcU0oSX5W51E00Q6RU6dqOrPIxcGdq1YYeqa1kVShdoYZ6Q/30pxQ0tOauMYP2iq+TI6FhrsqTx5vs1T7hpzV/kn1unUgp89zPneSEkDr0o7N3QMp9xVOZzRMqKDFl1jgpr6gSHXC0YRELLCUwenTihJX+/n612K0sEAgE1ALUhGButwSDwai7LonWY52KmU2w3tqvfT3A9ums93g8obsXwSFtSbRefRmU1YjU14gEA+Lqa1cT8AX7OqO+JKy5DqwqMOG2hCZEs+5+WLX1rfX4O6rtoas0tupDovW4GKzJZsJtUccg8frYJMjw+pi2p9OneOuLuU+WhipG2yiOPqVqezH1KZV+OvYp1fpi65P1XnPukeLoUzHqlKxPwJo7Rpc+mbOAo7qQe9g6hUPqVEIP5o4Imu2xvCUubIc2mustAyW0ufgDofW2/Vt5RnbCx9h2rLZsiuSWhQskhPtqtj3q+Ib2HW+92kfIwEpn++Gut9oOz5vSQ314ZE4pF9oe+gvHx9zE9NN5PKF9Y04qa3uVYO5Wlp9hbWvZnip8zhAXjnkBx7Cx2xSFYYEOXnTRRbL77rvL7Nmzk+ZlXHXVVUPWL1y4UGpqatTfcOc0NzfL6tWrw8YKaGxsVMuKFSuku7s7vH78+PHS0NAgS5YskYGBSDWDSZMmqX1i33YBpk2bJl6vV+bPnx/VhpkzZ6qQrsWLF0cJuMkmm6jPW758eXi9z+eT6dOnq/atWrUqvB4hYJMnT5a2tjZZu3ZteL3Zp5myesUyaV+72ixha/ilsbZCGusqZEVbj3T3R0rZjm+okoZqnyxZ0yV9gwFp6+yXBdIhUxqrpaaiTBau6oi6OKc11aqktvkt7dF9aq5XXyqLWzsjfXK5ZJMJ9WrSPcyPEe6T1yPTx9UqVzPuCoX7VF4mkxurVRvW2uoy11f5pHlUlaxu75X2nshxT6dPA/7IRTBpTPH3Cb8B+KyBsUEpL/MURZ+KUadEfbL0C44XCfiLo0/FqFOyPlnhPT39flm5vqco+lSMOiXr09SxNdI/GJQFqzrCA/hS1KllfV+cPpXLsrXdcfu0aHWnBEN9wnfZhFFVUlnuVccRxoiVw9M8ukoNlL9e2xUe4OJh/KhqZXC1tvfYCja4ZOKYaukf8MvajtDNYhcm2XPLhDHV0t07KOu6rLa7pNLnkaa6Slnf3iv9vX41kSyorfTJ2PoKWdvRp7wZ1nU6uqZcxtRWqPmfcM1ahsm4hiqpr/bJ162dMuDHFH0mOF7Qad7K9qx16uobjKvThu6BuDqh7YsWLxJxews2hu3q6pJ0cXSOhZ1zzjlHnnrqKXn99dfVwcjEY2ENxK24sKLwWKTT9oEecfWtF3d/uwT9g0k9FvghxJeB12NWU+BdLr36FAxpOGl0Vehz9e9TqrYXU59S6adjn1KtL7Y+YZ+rNvTKhFGVUUkBOvepGHVK1ieAAR8G12hvMfSpGHVK1Kek+umuU+NmIl5fwcawGEsjWkj75G2L888/Xx577DF59dVXlSWVCUWXvD0cIHFfu5n0jXkytE/7IoQQQggpEZo2F/GOwIQkORhLh5xFzgQ2D4yKRx55RF588cWMjQpiM80rG0RGTxcZN1ukfrJIWaSqFqxhuNpi4yGJPlBDvaF++kMN9Yca6g31cwaONizOO+88+fvf/y7//Oc/pba2VuUaYOnt5VwOw8bjFaluFBm7icjYzURqxovh9qk4S16L+gLtqKG+UD/9oYb6Qw31hvo5A0cbFnfccYdyu+y9994qUcVa/v3vfxe6acVBWYVIXbNI02YilaNFqkaLuLKof0cIIYQQQkoWR1eF0iD9o3hA7B5CpBA21bfBnICvH9UMqAEhhBBCCNHcsCAjA6oCIClHzVSJagDwXGAJDJoGBpbBSAk04jxcoVKBkVo0RCeon/5QQ/2hhnpD/ZyBFlWhsoFVoXIE5sXALN8wMoKROtaEEEIIISSPsCoU0QnUKm5paRkyv0YUZZUi9RNFxs8WGT1DpHKUhGegIQUHNbdb1veoR6If1E9/qKH+UEO9oX7OgCNDonJZYIWm7byqqBMZtZHIuC1FGqaK+Grz3USSAiiHmVL5daon1E9/qKH+UEO9oX7OgDkWZPjY8zH8A6F8jDYRf1+hW0YIIYQQQkYYGhYkN2Cq+dpx5jLQYxoYKh/DX+iWEUIIIYSQEYCGBVHVoBobG82qULnAV2UudRNF+jvMpG88GklyOEhWQLrG2gr1SPSD+ukPNdQfaqg31M8Z0LAg4na7lWGRc3B1V9SbSzAg0ov5MdpEBrpy/1kljhvGYV1FoZtBhgn10x9qqD/UUG+onzNg8jZR1aCWLVuWvCpUtrg9ItVjRBpnmmXTaptFPIUrnVZsoArGsrXdrIahKdRPf6ih/lBDvaF+zoAeC6KqQXV3d4/cTOeoxVw73lwGuiPzYxiBkfn8IgTKdfcPshqGplA//aGG+kMN9Yb6OQMaFqSw+KrNpX6SGSLV32U+wuDg1wMhhBBCiDbQsCDOAPkY5bXmAhCWNdhNQ4MQQgghRBNoWBCVvD1+/Hj16BjQFhoaaeN2iYxvqFKPRD+on/5QQ/2hhnpD/ZwBDQuiysw2NDSIo6GhkVrDal+hm0GGCfXTH2qoP9RQb6ifM3DQLWpSKFANatGiRfmtCpUvQ6Ou2aw0NX4rkTEbi9SMF/HV4CtGSglUwVi0upPVMDSF+ukPNdQfaqg31M8Z0GNBVDWogYGBkasKlQ9K3KOBng34A0Xcw+KG+ukPNdQfaqg31M8Z0LAgxUmJGxqEEEIIISMNDQtSGtDQIIQQQgjJKzQsiKoGNWnSJGdVhco3RWZooArGpDHVrIahKdRPf6ih/lBDvaF+zoCGBVGVFGpqkPBcwmhuaCgNK8oK3QwyTKif/lBD/aGGekP9nEEJ3aImiQgEAjJv3jz1SPSsOhUIGjJvZbt6JPpB/fSHGuoPNdQb6ucM6LEgCq1KzRYCDTwaQZ2rehHqVwRQQ/2hhnpD/QoPDQtCitTQIIQQQggZSWhYEJIvQ8MyMGhoEEIIIaQEoGFBVDWoadOmlVZVqHyDY1lRZy4jYGigCsa0plpWw9AU6qc/1FB/qKHeUD9nQMOCKLxengq6GxpeDw1DnaF++kMN9Yca6g31KzxUgKjE7fnz5zOBuxCGhr3q1OgZw646hSIY81va1SPRD+qnP9RQf6ih3lA/Z8Db1ISUYOgUIYQQQkiuoWFBiE6Ghr9PJDAgEhgUCfojf9PoIIQQQkiBoWFBiE6GhoQMjVgG+kXa54mMniRiBE2DIzhoGh3KCAkZIoQQQgghecJlGMU9m0hHR4fU19dLe3u71NUlGJSVODgFkF+BqlAuF8spFK2G8HpYxobd6FBGiD/yN70fhdHPMKua8BrUE2qoP9RQb4pav6bNRbzlWoyl6bEgCr/fLz6fr9DNIPnUEF4Pd3nqL6eAP2R4DMT8bQu/ovcj5/gDQfF5WU9DZ6ih/lBDvaF+hYeGBVF3uhcvXiwzZ84Uj8dT6OaQQmvo8ZpLWWXibeDoDHs78OiP/zfCskhKcJdtcWunzGyuF0+R3WgrFaih/lBDvaF+zoCGBSEkc+Bm9vrMJRnBQPxkc3v4FQwQQgghhGgPDQtCSP5we0Tclel5P+KGX9n+pveDEEIIcTQ0LIgCSb9Eb7TV0PJ+CJbqFN6POMnmUeFXfm2Tz93FlmxYglBD/aGGekP9Cg+rQhFCigd8ncEAsYyLqK+32HW212LXZb2NjOBnDWebNEhrWyPLfaTxGYXaBzxkeJ/ylBX1zyQhxOk0sSoU0QjYlt3d3VJdXV18JdpKBGoYAn1H4rlmUD8NsIwMu7Fhe24YQVPDygpxhQ25xNubz2P/TrZt6DmNnPxeh/1+qS738jrUEOrnDPT7BSZ5qSi0fPlyVoXSGGqoN9RPAzBQcSXWJhgIyPI1K2XmzKb8apjIAElmjKR8fZjbmwfGPDZRf4eeZ/K3fSA4ZH9Zfk7cfQz9HHUdLloqM5s3Eg/yw4bdh5jPsXQz/0jxPJ1t0nye8nNy5B3Ntm/W3+HzLub8i1qX2IuIqlDL13WzKlSBoWFBCCGEaGXgYNSkaU6VkwkEzHATX41Iro1D3kHPPVFGMIqA+EU6Foo0ThNlWaQ0VCR9Iybua3HWSZJ9lYi3kYYFIYQQQgjR1MgOrzArEZaV594wzBXBZIZKoteCIu4y0QUaFkTFImLGZsYk6gs11Bvqpz/UUH+ood5ooZ+7+D2NrApFCCGEEEIIyXosXfymE0kJbMsNGzaoR6In1FBvqJ/+UEP9oYZ6Q/2cAQ0LoiphrFq1Sj0SPaGGekP99Ica6g811Bvq5wxoWBBCCCGEEEKyhoYFIYQQQgghJGtoWBBVQYEz/uoNNdQb6qc/1FB/qKHeUD9nwKpQhBBCCCGEkLiwKhTJCCQ6rV27lglPGkMN9Yb66Q811B9qqDfUzxnQsCCqNBsuxiJ3XhU11FBvqJ/+UEP9oYZ6Q/2cAQ0LQgghhBBCSNbQsCCEEEIIIYRkDQ0LoiooICmHlRT0hRrqDfXTH2qoP9RQb6ifM2BVKEIIIYQQQkhcWBWKZAQqKLS0tLCSgsZQQ72hfvpDDfWHGuoN9XMGNCyIqqAAK7TInVdFDTXUG+qnP9RQf6ih3lA/Z0DDghBCCCGEEJI1XilyLMsV8WEkPoFAQLq6utQx8ng8hW4OGQbUUG+on/5QQ/2hhnpD/fKHNYZOxxtU9IZFZ2enepw8eXKhm0IIIYQQQoi2Y2okcZd0VSgk8axcuVJqa2tZgiyJJQrDa9myZaycpSnUUG+on/5QQ/2hhnpD/fIHTAUYFRMmTBC3213aHgscgEmTJhW6GVqAC5EXo95QQ72hfvpDDfWHGuoN9csPqTwVFkzeJoQQQgghhGQNDQtCCCGEEEJI1tCwIFJeXi5XXHGFeiR6Qg31hvrpDzXUH2qoN9TPGRR98jYhhBBCCCEk/9BjQQghhBBCCMkaGhaEEEIIIYSQrKFhQQghhBBCCMkaGhZFzKuvvipHHHGEmtAEkwM++uijUa8jvebyyy+X5uZmqayslP3331/mz58ftU1bW5ucdNJJqiZ0Q0ODnHnmmdLV1TXCPSlNrrvuOtlxxx3V5I5NTU1y9NFHy1dffRW1TV9fn5x33nkyZswYqampkeOOO05Wr14dtc3SpUvlsMMOk6qqKrWfn/70p+L3+0e4N6XHHXfcIVtttVW4pvquu+4qTz31VPh1aqcf119/vfouveiii8LrqKNzufLKK5Ve9mXWrFnh16mdHqxYsUJOPvlkpRPGKltuuaW8++674dc5lnEWNCyKmO7ubtl6663lD3/4Q9zXb7zxRvn9738vd955p7z99ttSXV0tBx10kPqytcCF+Nlnn8lzzz0nTz75pDJWzj777BHsRenyyiuvqB+9t956Sx3/wcFBOfDAA5WuFhdffLE88cQT8p///Edtj1nmjz322PDrgUBA/SgODAzInDlz5K9//avcd9996kuY5BdMzImB6Hvvvad+BPfdd1856qij1PUEqJ1evPPOO3LXXXcpY9EOdXQ2W2yxhbS0tISX119/PfwatXM+69evl913313KysrUjZnPP/9cfvvb38qoUaPC23As4zBQFYoUP5D6kUceCT8PBoPG+PHjjZtuuim8bsOGDUZ5ebnxr3/9Sz3//PPP1fveeeed8DZPPfWU4XK5jBUrVoxwD0hra6vS45VXXgnrVVZWZvznP/8Jb/PFF1+obd588031/P/+7/8Mt9ttrFq1KrzNHXfcYdTV1Rn9/f0F6EVpM2rUKONPf/oTtdOMzs5OY+bMmcZzzz1n7LXXXsaFF16o1lNHZ3PFFVcYW2+9ddzXqJ0eXHLJJcYee+yR8HWOZZwHPRYlyuLFi2XVqlXKZWifrn3nnXeWN998Uz3HI1yGO+ywQ3gbbO92u9VdATKytLe3q8fRo0erR9wJhxfDriHc/FOmTInSEG7jcePGhbfBnZyOjo7wnXOSf3Dn84EHHlDeJoREUTu9gOcQd67tegHq6HwQEoNw4OnTp6u71ghtAtRODx5//HE1Bjn++ONVKNq2224r99xzT/h1jmWcBw2LEgUXIrB/YVrPrdfwiAvZjtfrVQNbaxsyMgSDQRXXDZfw7Nmz1Tpo4PP51BdmMg3jaWy9RvLLJ598omK3MWHTD37wA3nkkUdk8803p3YaAYPw/fffVzlPsVBHZ4PBJUKXnn76aZXzhEHoN77xDens7KR2mrBo0SKl3cyZM+WZZ56Rc845Ry644AIVlgY4lnEe3kI3gBCS3h3TTz/9NCo+mDifTTfdVD788EPlbfrvf/8rp556qorlJnqwbNkyufDCC1VcdkVFRaGbQzLkkEMOCf+N3BgYGlOnTpUHH3xQJfkSPW6qwdNw7bXXqufwWOC3EPkU+D4lzoMeixJl/Pjx6jG2AgaeW6/hsbW1Nep1VMNAdQVrG5J/zj//fJVs9tJLL6mEYAtogKTCDRs2JNUwnsbWayS/4I7oxhtvLNtvv726441iCrfeeiu10wSEy+A7cLvttlN3OLHAMESiKP7GXVHqqA/wTmyyySayYMECXoOagEpP8PLa2WyzzcIhbRzLOA8aFiXKtGnT1AX1wgsvhNchbhTxhogBB3jEly5+XC1efPFFdQcBd35IfkHOPYwKhM/guEMzOxisolKGXUOUo8UXrl1DhOPYv1Rx9xUl92K/rEn+wbXT399P7TRhv/32UxrA62QtuHuKWH3rb+qoDygvunDhQjVY5TWoBwj/jS2zPm/ePOV5AhzLOJBCZ4+T/FYy+eCDD9QCqX/3u9+pv7/++mv1+vXXX280NDQYjz32mPHxxx8bRx11lDFt2jSjt7c3vI+DDz7Y2HbbbY23337beP3111VllBNPPLGAvSodzjnnHKO+vt54+eWXjZaWlvDS09MT3uYHP/iBMWXKFOPFF1803n33XWPXXXdVi4Xf7zdmz55tHHjggcaHH35oPP3008bYsWONyy67rEC9Kh0uvfRSVcFr8eLF6vrCc1QhefbZZ9Xr1E5P7FWhAHV0Lj/+8Y/V9yeuwTfeeMPYf//9jcbGRlVhD1A75zN37lzD6/Ua11xzjTF//nzjH//4h1FVVWX8/e9/D2/DsYyzoGFRxLz00kvKoIhdTj311HCZtl/96lfGuHHjVGm2/fbbz/jqq6+i9rFu3Tp18dXU1KgSe6effroyWEj+iacdlnvvvTe8Db44zz33XFXGFF+2xxxzjDI+7CxZssQ45JBDjMrKSvWjih/bwcHBAvSotDjjjDOMqVOnGj6fTw1GcH1ZRgWgdsVhWFBH5/Ktb33LaG5uVtfgxIkT1fMFCxaEX6d2evDEE08oAw/jlFmzZhl333131OscyzgLF/4ptNeEEEIIIYQQojfMsSCEEEIIIYRkDQ0LQgghhBBCSNbQsCCEEEIIIYRkDQ0LQgghhBBCSNbQsCCEEEIIIYRkDQ0LQgghhBBCSNbQsCCEEEIIIYRkDQ0LQgjRgIGBAbn22mvliy++KHRTyAjT19cnV199tXzyySeFbgohhCSFhgUhRHs22mgjueWWW8LPXS6XPProo+rvJUuWqOcffvhhTj/zvvvuk4aGhqz2cdppp8nRRx+d1rY//vGP1cBy1qxZae9/7733losuukgKQexxf/nll9XzDRs25Oz45ep8cTqXX365zJkzR7773e8qA5MQQpwKDQtCSMHBgDPZcuWVVyZ9/zvvvCNnn3226A4MjXh9ffDBB+Wzzz6Tv/71r+p4OI14BtLkyZOlpaVFZs+eLU4HhgYMHycaL3PnzpW3335bHn/8cTnhhBNSXgs6kC9jnxBSeLyFbgAhhGAAavHvf/9b3aH96quvwutqamqSvn/s2LFSzGBAiUUnPB6PjB8/vtDNcDSBQEANsN3uxPf4dtppJ3nllVfU3z//+c9HsHWEEJI59FgQQgoOBqDWUl9frwZb1vPu7m456aSTZNy4ccrA2HHHHeX555/P6u7wp59+KocccojaH/aLEJO1a9cmfQ9Cd6ZMmSJVVVVyzDHHyLp164Zs89hjj8l2220nFRUVMn36dLnqqqvE7/fLcOnv75ef/OQnMnHiRKmurpadd955yJ31N954Q4U8oV2jRo2Sgw46SNavXx9+PRgMys9+9jMZPXq0Op6xd7x/97vfyZZbbqn2Dy/DueeeK11dXVH9RsjSM888I5tttpk6ZgcffHDYGMT+4ElB3y0PE9o4nLvSd9xxh8yYMUN8Pp9suumm8re//S3qdezvT3/6kzr+6O/MmTPVnfxktLa2yhFHHCGVlZUybdo0+cc//pGyHcuWLVOGHPqN43bUUUep/sR6aP7f//t/0tzcLGPGjJHzzjtPBgcH1evQ4+uvv5aLL744fEzsxxJt3nzzzaW8vFyWLl2qPG4HHHCANDY2qvN/r732kvfff39I32PD+x5++GHZZ5991LHYeuut5c0334x6z+uvvy7f+MY3VN+h7QUXXKCuJ/t1g9yNU045Rek6depU1bY1a9aoPmPdVlttJe+++27G+0U+0BlnnCG1tbXqurn77rvDr0MHsO2226p+4HgRQooDGhaEEEeDQe6hhx4qL7zwgnzwwQdqUIuBIgZkwwEx/vvuu68a1GDA9PTTT8vq1auTegQQinLmmWfK+eefrwbKGMxhQGbntddeUwO0Cy+8UD7//HO566671EDymmuukeGCz8Ng8YEHHpCPP/5Yjj/+eNX/+fPnq9fRlv32208NUrEdBnw4NrgTboFBP4wG9OHGG2+UX//61/Lcc8+FX8fd8t///vfhUKsXX3xRGSJ2enp61CAaA/1XX31VHXsYPACPOHaWsYFlt912y7ivjzzyiDp2yCWB4ff9739fTj/9dHnppZeitoOxhs/D8cB5AaOzra0t4X5hBMBQwH7++9//yh//+EdlbCQCxgGMMwyIoSkMN8uYsuc3YH8LFy5Ujzhu0BoLwIB/0qRJ6lhbx8R+LG+44QZlIOGYNzU1SWdnp5x66qlKv7feeksZTOgb1ifjF7/4hTr+OA822WQTOfHEE8OGLNqGNh933HHqWMETiP3jnLJz8803y+67766urcMOO0wZ2TiPTz75ZGXcwNDDc8MwMtrvb3/7W9lhhx3UfmGsnnPOOWEvJMK7AG4Q4NjgeBFCigSDEEIcxL333mvU19cn3WaLLbYwbrvttvDzqVOnGjfffHP4Ob7aHnnkEfX34sWL1fMPPvhAPf/Nb35jHHjggVH7W7Zsmdrmq6++ivt5J554onHooYdGrfvWt74V1c799tvPuPbaa6O2+dvf/mY0Nzcn7Mepp55qHHXUUXFf+/rrrw2Px2OsWLEiaj0+57LLLgu3a/fdd0+4/7322svYY489otbtuOOOxiWXXJLwPf/5z3+MMWPGROmBY7NgwYLwuj/84Q/GuHHjkvYj9ri/9NJL6vn69evD+7Ufv912280466yzovZx/PHHRx13vP+Xv/xl+HlXV5da99RTT8XtC/TE63Pnzg2v++KLL9Q6+/kSq9mmm25qBIPB8Lr+/n6jsrLSeOaZZ8L9xTnn9/uj2opzItE5aT+WH374oZGMQCBg1NbWGk888UTSc/pPf/pT+PXPPvtMrUP/wJlnnmmcffbZUft97bXXDLfbbfT29obbePLJJ4dfb2lpUfv41a9+FV735ptvqnV4bbj7xbFsamoy7rjjjrjnBiGkeKDHghDieI8F7soiDAdhJLh7jJKrw/VYfPTRR+ouM/ZjLValJdyNjQc+D2FIdnbdddch+8Udavt+zzrrLHVHFnepMwUVoOB5wJ1o+z4Rb2+10/JYJAOhLHYQumO/Y4+7xtgHwq1wlx53rBHmZW8zQm1w5zrRPnIBjjHunNvB89jyuvb+wBNTV1eXsC14r9frle233z68Dlonq0YFHRcsWKCOhXXMEQ6Fkq/282OLLbZQeSSZHhOEecVqAo8ZzhV4KhAKhT7hvE91jtv3g88HVhvQD3hQ7OcOPDEIjVu8eHHcfSAsECA0LnZdNvu1Qhtzfc4QQpwHk7cJIY4GRgVCdxCKs/HGG6u47m9+85vDLruJARvChRCOEos1OBvufhGmc+yxxw55DTkXw9kfBq7vvfde1ADWnsyOY5GKsrKyqOcY5GEQaMXqH3744SpMBSFbGEAjrAVhXzi+MCgS7cMKjRlpkvUnF+C4wxCJl4thLxIw3HZAs9jKXgiDgjF36623qjwH5F7AcE11jtvbYO3TagP6gXAy5D/EgpyHZPvI9X7zoRMhxJnQsCCEOBrEuCNOHgm71sDGnkibKUiufuihh1SCKe5mpwO8JchRsINY+Nj9IoYcxk8uQA4IPBa4y4tE2XjgrjByT2DQDAcYLRjsIR7eqkyE0raZgrvw9ryO4YBjDK0xyLbAc+SPDBd4J5BzgH4i6R9AI2sujXhAR+QNIPcBnoPhkskxQT+R+4G8CoCckFTFBFKBfiDXJ1fnYy73i2MDsj1nCCHOg6FQhBBHg/AQJHci7AdhGN/5zneyuvOJ6j1I9kWiK6rxILwFFY+QKJxooIO7s0jyhtcEidO33367em4HJXLvv/9+NchHUi7CcJB0/ctf/nJY7UQIFBKTkTiL/iPMBEmv1113nfzvf/9T21x22WWqD0iORSLtl19+qSorpTsoxeAQycq33XabLFq0SCVn33nnnRm3FUYaPh+Ddny2VR0pE37605+qEBu0H8cY1arQbytJfDigshQSjXGHHYYhDIzvfe97ST09OOaozoSqSEjexnFHlSucA8uXL8/omCDRfcWKFSn1wDmOY49zBu1EG9LxRiXjkksuUZPqWQUHcExRuSs2yboQ+4XRhv5ZhRPa29uzahMhxDnQsCCEOBoMMFFGFZWGEMKEeG7cNR0uEyZMUHeIYUQceOCBKp4cs1Mj7j7RfAK77LKL3HPPPSpUBWU9n3322SEGA9r15JNPqtdwdxzvQcUdhLYMl3vvvVcZFqiUhEEySpzCkLBCTmB84PNgcGG+A4TPYJCXricGfcHxRVgYJrJD+A8Ml0xBfgDahypACBfC8c0U9A3HF8Yb8hdQVQv9z7YUKfYBzVHCFWFqmEgRA9tEIPwLBgGOMbaHJwWhYcixyMSDgXwbeNaQm5JqnpU///nPqkQwzmvkuMCISdbGdIA3C/k48+bNUx4veMBg/OJYFHq/OD9RiQwa430w4gghxYELGdyFbgQhhBBCCCFEb+ixIIQQQgghhGQNDQtCCCGEEEJI1tCwIIQQQgghhGQNDQtCCCGEEEJI1tCwIIQQQgghhGQNDQtCCCGEEEJI1tCwIIQQQgghhGQNDQtCCCGEEEJI1tCwIIQQQgghhGQNDQtCCCGEEEJI1tCwIIQQQgghhGQNDQtCCCGEEEKIZMv/B413jusfc2yQAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -4581,7 +4781,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x500 with 1 Axes>"
       ]
@@ -4592,12 +4792,12 @@
     {
      "data": {
       "text/plain": [
-       "[np.float64(7.673473230326665),\n",
-       " np.float64(7.504433114323556),\n",
-       " np.float64(7.268204934351579)]"
+       "[np.float64(7.613610313527229),\n",
+       " np.float64(7.368583825283833),\n",
+       " np.float64(7.366031019544516)]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4611,12 +4811,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x870 with 2 Axes>"
       ]
@@ -4626,7 +4826,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 800x870 with 1 Axes>"
       ]
@@ -4651,6 +4851,694 @@
     "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns, plot_type=\"bar\")\n",
     "\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Feature selection for RF"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.feature_selection import SequentialFeatureSelector as SFS\n",
+    "from sklearn.ensemble import RandomForestRegressor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Scores for each number of features:\n",
+      "Number of Features: 1, Score: 0.7254\n",
+      "Number of Features: 2, Score: 0.9303\n",
+      "Number of Features: 3, Score: 0.9526\n",
+      "Number of Features: 4, Score: 0.9597\n",
+      "Number of Features: 5, Score: 0.9658\n",
+      "Number of Features: 6, Score: 0.9652\n",
+      "Number of Features: 7, Score: 0.9658\n",
+      "Number of Features: 8, Score: 0.9652\n",
+      "Number of Features: 9, Score: 0.9655\n",
+      "Number of Features: 10, Score: 0.9654\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# scores dict\n",
+    "scores = {}\n",
+    "\n",
+    "# Iterate from 1 to 10 features\n",
+    "for i in range(1, 11):\n",
+    "    # Initialize the model and SFS object with the current number of features\n",
+    "    rf_feature = RandomForestRegressor(random_state=42)\n",
+    "    sfs_feature = SFS(rf_feature, n_features_to_select=i, cv=5, direction='forward')\n",
+    "    \n",
+    "    # Fit the SFS instance\n",
+    "    sfs_feature.fit(X, y)\n",
+    "    \n",
+    "    # Get the selected features\n",
+    "    selected_features = X.columns[sfs_feature.get_support()]\n",
+    "    \n",
+    "    # Train the final model with the selected features\n",
+    "    final_model = rf_feature.fit(X[selected_features], y)\n",
+    "    \n",
+    "    # Get the R-squared score on the training data\n",
+    "    current_score = final_model.score(X[selected_features], y)\n",
+    "    scores[i] = current_score\n",
+    "\n",
+    "# Print the scores\n",
+    "print(\"Scores for each number of features:\")\n",
+    "for num_features, score in scores.items():\n",
+    "    print(f\"Number of Features: {num_features}, Score: {score:.4f}\")\n",
+    "\n",
+    "# Plot the scores\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.plot(list(scores.keys()), list(scores.values()), marker='o', linestyle='-')\n",
+    "plt.title('Score vs. Number of Features (Iterative Approach)')\n",
+    "plt.xlabel('Number of Features')\n",
+    "plt.ylabel('Score (R-squared)')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-4 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: #000;\n",
+       "  --sklearn-color-text-muted: #666;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "  align-items: start;\n",
+       "  justify-content: space-between;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
+       "  font-size: 0.6rem;\n",
+       "  font-weight: lighter;\n",
+       "  color: var(--sklearn-color-text-muted);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 0.5em;\n",
+       "  text-align: center;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SequentialFeatureSelector(estimator=RandomForestRegressor(max_depth=10,\n",
+       "                                                          max_features=&#x27;sqrt&#x27;,\n",
+       "                                                          random_state=42),\n",
+       "                          n_features_to_select=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SequentialFeatureSelector</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.feature_selection.SequentialFeatureSelector.html\">?<span>Documentation for SequentialFeatureSelector</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>SequentialFeatureSelector(estimator=RandomForestRegressor(max_depth=10,\n",
+       "                                                          max_features=&#x27;sqrt&#x27;,\n",
+       "                                                          random_state=42),\n",
+       "                          n_features_to_select=5)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>estimator: RandomForestRegressor</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=10, max_features=&#x27;sqrt&#x27;, random_state=42)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(max_depth=10, max_features=&#x27;sqrt&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "SequentialFeatureSelector(estimator=RandomForestRegressor(max_depth=10,\n",
+       "                                                          max_features='sqrt',\n",
+       "                                                          random_state=42),\n",
+       "                          n_features_to_select=5)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "rf_feature = RandomForestRegressor(random_state=42, max_depth= 10, max_features= 'sqrt', min_samples_leaf= 1, min_samples_split=2, n_estimators=100)\n",
+    "sfs_feature = SFS(rf_feature, n_features_to_select=5)\n",
+    "sfs_feature.fit(X,y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['saturated-fat_100g', 'sugars_100g', 'salt_100g',\n",
+      "       'fruits-vegetables-nuts-estimate-from-ingredients_100g',\n",
+      "       'PNNS_pro_Snacks'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "selected_features = sfs_feature.get_support()\n",
+    "# Use the mask to get the names of the selected columns\n",
+    "selected_feature_names = X.columns[selected_features]\n",
+    "print(selected_feature_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test_s = X_test[['saturated-fat_100g', 'sugars_100g', 'salt_100g',\n",
+    "       'fruits-vegetables-nuts-estimate-from-ingredients_100g',\n",
+    "       'PNNS_pro_Snacks']]\n",
+    "\n",
+    "X_s = X[['saturated-fat_100g', 'sugars_100g', 'salt_100g',\n",
+    "       'fruits-vegetables-nuts-estimate-from-ingredients_100g',\n",
+    "       'PNNS_pro_Snacks']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "n_iterations: 1\n",
+      "n_required_iterations: 5\n",
+      "n_possible_iterations: 1\n",
+      "min_resources_: 50\n",
+      "max_resources_: 100\n",
+      "aggressive_elimination: False\n",
+      "factor: 3\n",
+      "----------\n",
+      "iter: 0\n",
+      "n_candidates: 216\n",
+      "n_resources: 50\n",
+      "Fitting 5 folds for each of 216 candidates, totalling 1080 fits\n",
+      "Meilleurs paramètres trouvés :  {'max_depth': None, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 2, 'n_estimators': 100}\n",
+      "MSE : 6.299131081097346\n",
+      "R² : 0.3854887079648106\n"
+     ]
+    }
+   ],
+   "source": [
+    "best_rf, X_train, X_test, y_train, y_test, y_pred = random_forest_GS(X_s, y, X_test_s, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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uPGkn0WHelc3oO8HRvjsceLIduiya20nOuSz0tU+vP14wGAZ/n+ri3yh3ngs6YrhZJ8MoUtEOvZVR+MOOPhfonIP76HDJKxGr0/Twl7Y+Zpp5mMn+to+DZ46nzMEbk6Eno+Z2iOxnbodI/13jrD8Jtt79juUkOPpbTE6/4xxmGaLJyDu5/L+vj5nqY6Zx6Ae49h9ZvPpvqV+9ki39AGM55+UViwSGA8caRoyIsn4kDZz75Th9e0TEd4cDThbT3mUOuN3Bvyzg3WjzzrR128OZZRk5CQ683SNDJ8HhOhtGc4tJ6K0mYd5V5skFEVHm4XfspVWKSbHUPVK9SrGEf4OZhQUReU60I2mM6Hopi3siIqIo8VaoBL4Vyg3YiUsfM9W5tHzZm1OC7lsNN+LGzKdbJfy7Q4mKx6k+ZqqPmepjpu7LNJZzXu+OA0hZBjN4ky5mmjkoFnq1qxX2sUQfccNJeJzqY6b6mKk+ZurdTFlYUFyhwl6/fr35TDqYqY7N+46az6G1A65UcKjZzONxqo+Z6mOm+piptzNlHwsi8pzdh47Lh1PXmq/7dawvZQrlsnXEDSIiIjdgYUFEnjNo8mo5dDxFzi9XUG5sUF58vlOOGXGDiIgoUfFWKIo7dDYiXcw049bsPCRf/b7RfN27fW3/MKnMVB8z1cdM9TFTfczUu5lyVKgM4qhQRM7U9bP58svyHdK6dkn5pEsjuzeHiIgooXFUKEoYqFsPHTpkPpMOZpq5GbdRVOB2p2fa1fYvZ6b6mKk+ZqqPmepjpt7OlIUFxRVGMNi8ebMjRjJwCmaaMadO+aTPuOXm69svqSjVSuYPeIyZamOm+pipPmaqj5l6O1MWFkTkCf/7c4v8tWW/5M+VXR5tXd3uzSEiInIdFhZE5HrHTqbKWxNWmq8fbFFViufPZfcmERERuQ4LC4orTD2fM2dOW6agdytmGrv//rZetu4/JmUL5ZZ7L6t81uPMVB8z1cdM9TFTfczU25lyVKgM4qhQRM6w69BxafHWNDNvxYBbLpAbLipv9yYRERE5BkeFooSBunXfvn2OGMnAKZhpbAb9cmYyvOsuKBf2OcxUHzPVx0z1MVN9zNTbmbKwoLjCCAbbt293xEgGTsFMY5sMb8TctMnwnm1fxz8ZXihmqo+Z6mOm+pipPmbq7UxZWBCRa70xfrmknvJJ69qlpEnVYnZvDhERkauxsCAiV5q1dpf8snzn6cnwatm9OURERK7HwoLiCiMY5MuXzxEjGTgFM83cZHjhMFN9zFQfM9XHTPUxU29nylGhMoijQhElrlELN0vPkX+ayfCmP9lCinHeCiIiogzhqFCUMNDRaNeuXY7ocOQUzDSKyfAmpk2G91DLqlEVFcxUHzPVx0z1MVN9zNTbmbKwoLjCBTH8Z+CFMT3MNH2fzlwv2/Yfk3KF88g9zc6eDC8cZqqPmepjpvqYqT5m6u1MWVgQkasmwxs8ba35+sm2NSV3jmS7N4mIiMgzWFgQkWsM/GWVmQyvXrlCcu0FZe3eHCIiIk9hYUFxhREM0OHHCSMZOAUzDW/NzoPy9dxN5utnO9SOOBleOMxUHzPVx0z1MVN9zNTbmXJUqAziqFBEiaXrZ/PMvBVt6pSSj+9saPfmEBERuQJHhaKEgREMtm3b5oiRDJyCmepPhsdM9TFTfcxUHzPVx0y9nSkLC4orXBBDhcsLY3qY6dmT4b0+Nm0yvE6NK0rVEulPhhcOM9XHTPUxU33MVB8z9XamLCyIyNF+WLRFlm49IAVyZZdHr6hu9+YQERF5FgsLInKsoycCJ8Orxhm2iYiIbMTCguIKIxgUL17cESMZOAUzPeO/v52ZDO/uZudleD3MVB8z1cdM9TFTfczU25lmt3sDyN2SkpLMfwbSw0zT/HPwuHw4dY3KZHjMVB8z1cdM9TFTfczU25nyigXFFUYw2LRpkyNGMnAKZnpmMrzDJ1KlfvnMT4bHTPUxU33MVB8z1cdMvZ0pCwuKK4xgcPjwYUeMZOAUzFRk9Y6D8s28tMnwerePbTK8cJipPmaqj5nqY6b6mKm3M2VhQUSO88b4FZJ6ymcmw7u0SjG7N4eIiIhYWBCR08xas0smr9gp2ZOySa8MTIZHRERE8cHCguLe4ah06dLmM+nwcqZmMrxxZybDq5KByfDC8XKm8cJM9TFTfcxUHzP1dqYcFYriCkOjFS5c2O7NcBUvZzr6jzOT4XVXnAzPy5nGCzPVx0z1MVN9zNTbmSZ+6UOOhhEM1q1b54iRDJzCq5liMry3f06bDO/hVrqT4Xk103hipvqYqT5mqo+ZejtTFhYUVxjB4MSJE44YycApvJrppzPX+SfDu6tpxifDC8ermcYTM9XHTPUxU33M1NuZsrAgIkdMhjd42lrz9VNXZW4yPCIiIooPFhZE5KjJ8K6pn7nJ8IiIiCg+WFhQXGEEg/LlyztiJAOn8FqmmAzv67kbzdfPKkyGF47XMs0KzFQfM9XHTPUxU29nylGhKO4jGeTPrzMkKHkz077jV8gpn8iVdUpJ4zhNhue1TLMCM9XHTPUxU33M1NuZJn7pQ46Wmpoqq1atMp9Jh5cy/W3NLplyejK8Z+I4GZ6XMs0qzFQfM9XHTPUxU29nysKC4s4Jw6M5jRcyTcVkeGPTJsPrfGkltcnwvJxpVmOm+pipPmaqj5l6N1PbC4stW7ZI586dpVixYpInTx6pV6+ezJ8/P92fmTZtmjRo0EBy5col1apVk+HDhwc9/tJLL5nLRoEftWoFv9vZokWLs57TrVu3uLSRiDI2Gd6ybfqT4REREZEL+1js3btXmjVrJi1btpTx48dLiRIlZPXq1VKkSJGIP7N+/Xrp0KGDKQK++uormTx5snTt2lXKlCkjbdu29T+vbt268ssvv/i/z5797Kbed9998sorr/i/z5s3r2r7iCgTk+FNPDMZXtF8Oe3eJCIiIkrkwuLNN9+UChUqyLBhw/zLKleunO7PDBkyxDynf//+5vvatWvLzJkzZcCAAUGFBQqJ0qVLp7suFBLneg5lDkYwwP5ywkgGTuGFTDEZ3vYD8ZkMz6uZZjVmqo+Z6mOm+piptzO1dQvHjBkjDRs2lJtuuklKliwpF110kXz88cfp/szs2bOldevWQctQUGB5IFz5KFu2rFSpUkU6deokGzemDVcZCFc8ihcvLueff7706tVLjhw5otQyChTuahFljpsz3XnwmC2T4bk5U7swU33MVB8z1cdMvZuprVu5bt06GTx4sPTs2VN69+4t8+bNk+7du0vOnDmlS5cuYX9m+/btUqpUqaBl+P7AgQNy9OhR00+jcePGpt9FzZo1Zdu2bfLyyy/Lv/71L1myZIkUKFDA/Mztt98ulSpVMsXH4sWL5emnn5aVK1fKqFGjwr7u8ePHzYcFrwfooW/10kc/DVST6GATOO16pOVYhsciLQ/t/W9VqqEdeCItT05ONusNXG5tS6Tl0W57tG1CG9asWSM1atQw/ync0KbQ5VndJizD6BBVq1Y1P+uGNgVu44CfgyfDy4o2Wccp+mzlyJFDvU0ZXZ7I++lcy/H92rVrzXEa+C6bk9tk9346efKk/zjF71M3tMnu/YR24DjF36hQTm2T3fspJSXFvLmL4xQ/74Y22b2fUgPOpbD9Wd0mxxQW2HhcsejTp4/5HlcscPKP250iFRbRaNeunf/r+vXrm0IDRcTIkSPl3nvvNcvvv/9+/3PQYRx9NK644gr/H8JQffv2NQVKKDzfGlu4UKFCZj07duyQ/fv3+5+DqyL4QEf1w4cP+5fjNqzChQvLhg0b5MSJE/7lmAQF68S6Aw8GXAbDHxP8hw1UvXp18x8Z/U8sODhwAOL1Nm/e7F+Oog1XcbB9KNIs+fLlM7el7dmzR3bt2uVfntk2YfuxTlwNKliwoCvaZPd+QjGMIhq/ZKxfPk5vk7WfFqzZKv83f5NZ/uClpcxkeNu2bY97m6zjFJniDQkee5lvk9VXbuvWreZ4dUOb7N5PuPJuHae5c+d2RZvs3k9oA7YP3NKmRNhP1nGKdbilTXbup1On/0ZZb4RldZuwrmhl8wWWJVkMJ/tt2rSRTz75xL8MVzBee+01s1PCufzyy82IUAMHDvQvQx+Nxx57LCiIUI0aNTK3UKFACAc7BDt3woQJQX010rtiYe0YnDADq3JesciKNrn5isU9w+fLtFX/mMnwhnRukGVt4hULXrFwwn7iFQtesXDCfsJxyisWp1x1xeLQoUOm8MB5tnXOm5BXLDAiFG4/CoQTJhQckTRp0kTGjRsXtGzSpElmeSQIBL847rjjjojPWbRokfmMai0cDG2Lj1DYmdbJnSXwj2hmloeuNyPLcUDEslxr28OdSMS67YncpnNtYzzbhF8w1nI3HXuz1u4xRYU1GZ61bVnVJvyc9UdQq01uO/Yyso1Wrm5rUyzLNdtk5Wm9lhvaFO3yeLWJf5/022Qdp4GPO71NsWxjUhzaZJ1LxbrtWm1yxBUL9Klo2rSpucXo5ptvlrlz55ohYIcOHWo6XAM6VePqxeeff26+x+UfdLZ++OGH5Z577pEpU6aYfhljx471X2l44okn5JprrjEFCi7Dv/jii6ZwWLZsmRnSFkXGiBEjpH379mb+DPSx6NGjh7kkNX369Ki2HVcsoq3evMyqlAOLC8ocN2aKyfCufm+mLN92wIwC9dK1dbP09d2Yqd2YqT5mqo+Z6mOm7ss0lnNeW0eFwu1Jo0ePlq+//toUC6+++qq5xckqKgCdrwNHdMJ9ZygicJXiggsuMMPO4laqwNuXcI/ZbbfdZu6VRsGC4mHOnDmmqADcK4Y5Lq688kozcd7jjz8uHTt2lB9//DGLE/AG6/5V0uO2TEct3GyKigK57ZsMz22ZJgJmqo+Z6mOm+pipdzO19YqFk/GKRXRw2w7utUQnqkiX+sjbmWIyvBZvT5UdB45Lr3a15IHmZw+eEG9uyzQRMFN9zFQfM9XHTN2XqWOuWBARfTJjnSkqMBlelyyYDI+IiIjig4UFEdk7Gd70tMnwnm5XK8smwyMiIiJ9LCwo7jI7wgC5N9MBk1bLkROpckGFwnJN/fAjsmUVt2SaSJipPmaqj5nqY6bezZR9LDKIfSyIMmfVjoNy1cBf5ZRP5NtuTaTReUXt3iQiIiIKwT4WlDBQt2IeEdavetySad9xy01RcVXd0rYXFW7JNJEwU33MVB8z1cdMvZ0pCwuKK4y7jOF/Q2eJJG9nOnP1Lpm6Mm0yPPStsJsbMk00zFQfM9XHTPUxU29nysKCiLJ8MrzXxi4zX3e+tJJULp7P7k0iIiIiBSwsiCjLJ8Nbsf2gmQzvUZsmwyMiIiJ9LCworjD1PGY6t2MKerdycqZHTqTI2z+vNF8/0qqaFMmXUxKBkzNNVMxUHzPVx0z1MVNvZ8pRoTKIo0IRxe7dyavlnUmrpHyRPPJLz+act4KIiCjBcVQoShioW/ft2+eIkQycwqmZYjK8IdZkeFcl1mR4Ts00kTFTfcxUHzPVx0y9nSkLC4orjGCwfft2R4xk4BROzXTApFVmMrwLKxSWq22eDM8tmSYyZqqPmepjpvqYqbczZWFBRHG3cvtB+b95m8zXz3ao7Yj7RImIiCg2LCyIKO76jk+cyfCIiIgoPlhYUFzhnel8+fLxHWoPZzpj9T8y7fRkeM8kwGR4bsjUCZipPmaqj5nqY6bezpSjQmUQR4Uiim4yvA7vzjDzVtzd7Dx58Zq6dm8SERERxYCjQlHCQEejXbt2OaLDkVM4KdPvT0+GVzB3duneKnEnw3NSpk7BTPUxU33MVB8z9XamLCwornBBDP8ZeGHMe5liMrz+/snwqifMZHhOztRJmKk+ZqqPmepjpt7OlIUFEcXFx7+ulx0HjpvJ8O5sWsnuzSEiIqI4Y2FBROp2HjgmH/16ZjK8XNkTZzI8IiIiig8WFhRXGMEAHX6cMJKBUzgh0wG/JO5keE7N1GmYqT5mqo+Z6mOm3s6Uo0JlEEeFIoo8GV67Qb+aeSu+69ZEGnLeCiIiIsfiqFCUMDCCwbZt2xwxkoFTJHqmfcalTYbX7vzSjikqEj1TJ2Km+pipPmaqj5l6O1MWFhRXuCCGCpcXxryR6a+r/pHpq/6RHMnZTN8Kp0jkTJ2KmepjpvqYqT5m6u1MWVgQkdpkeLhaAXdcep6cVzyf3ZtEREREWYiFBRGp+H7BmcnwHmlVze7NISIioizGwoLiCiMYFC9e3BEjGThFImaKyfDedshkeE7J1OmYqT5mqo+Z6mOm3s40u90bQO6WlJRk/jOQuzMd+us62XnwuFQo6szJ8BIxU6djpvqYqT5mqo+ZejtTXrGguMIIBps2bXLESAZOkWiZmsnwpq9z9GR4iZapGzBTfcxUHzPVx0y9nSkLC4orjGBw+PBhR4xk4BSJluk7k1bJ0ZOpclHFwtKhXuJPhueETN2AmepjpvqYqT5m6u1MWVgQUYat2H5ARs7fZL5+rkNtR9z/SURERPHBwoKIMqzPuBVmMrz29UrLxZWcMRkeERERxQcLC4p7h6PSpUubz+SuTDER3q8OnAwvkTN1E2aqj5nqY6b6mKm3M+WoUBRXuDWmcOHCdm+GqyRCppgMr+/pyfDubHKeVCrm7MnwEiFTt2Gm+pipPmaqj5l6O9PEL33I0TCCwbp16xwxkoFTJEKm3y3Y5KrJ8BIhU7dhpvqYqT5mqo+ZejtTFhYUVxjB4MSJE44YycAp7M708PEU6f/zKvN19yuqS+G8zpoMLxEzdSNmqo+Z6mOm+piptzNlYUFEMfl4RtpkeBWL5pU7mjhvMjwiIiKKDxYWRBS1HS6YDI+IiIjig4UFxRVGMChfvrwjRjJwCjszfefnM5PhYYhZt+Bxqo+Z6mOm+pipPmbq7Uw5KhTFfSSD/Pnz270ZrmJXpsu3HZCRC9w5GR6PU33MVB8z1cdM9TFTb2ea+KUPOVpqaqqsWrXKfCZnZ9pn3HJBv7EO9cq4bjI8Hqf6mKk+ZqqPmepjpt7OlIUFxZ0ThkdzmqzOFJPhzVi9y0yG99RVNcWNeJzqY6b6mKk+ZqqPmXo3UxYWRHTOyfD6jHXPZHhEREQUHywsiOick+Gt3HFQCuXJ4YrJ8IiIiCg+WFhQXGEEg8qVKztiJAOnyMpMMRne26cnw0NR4YbJ8MLhcaqPmepjpvqYqT5m6u1ME38LyfGyZ+fgY07NdOiv6+Qfj0yGx+NUHzPVx0z1MVN9zNS7mbKwoLh3Nlq9erVjOh05QVZlisnwUFjAM+3cPRkej1N9zFQfM9XHTPUxU29nysKCiMLq//NKMxleg4qFpd357pkMj4iIiOKDhQURhZ0M79sFm83Xz3ao46rJ8IiIiCg+WFgQUeTJ8OpjMrwidm8OEREROYDthcWWLVukc+fOUqxYMcmTJ4/Uq1dP5s+fn+7PTJs2TRo0aCC5cuWSatWqyfDhw4Mef+mll8w7rIEftWrVCnrOsWPH5OGHHzavi2nSO3bsKDt27IhLG70MIxhUr17dESMZOEW8M522cqd/Mryn2wb/v3ErHqf6mKk+ZqqPmepjpt7O1NYt3Lt3rzRr1kxy5Mgh48ePl2XLlkn//v2lSJHI75CuX79eOnToIC1btpRFixbJY489Jl27dpWJEycGPa9u3bqybds2/8fMmTODHu/Ro4f8+OOP8u2338r06dNl69atcuONN8atrV6WkpJi9ya4TrwyTUk9Za5WQJcm50nFYnnFK3ic6mOm+pipPmaqj5l6N1Nbx6568803pUKFCjJs2DD/MozTm54hQ4aY56AAgdq1a5uiYcCAAdK2bdugYblKlw7f4XT//v3y6aefyogRI6RVq1ZmGbYB65ozZ45ceumlSi0kjGCAYhCVdnKye0cVckum3y3YLKt2HDKT4f3HQ5Ph8TjVx0z1MVN9zFQfM/V2prYWFmPGjDHFwE033WSuGpQrV04eeughue+++yL+zOzZs6V169ZBy7AOXLkIhGG5ypYtK7lz55YmTZpI3759pWLFiuaxBQsWyMmTJ4PWg1ul8DjWH66wOH78uPmwHDhwwHxOTU01H4BbrnCZCgeADzeonxZpOZbhsUjLrfUGLofQ4cYiLcfBh/UGLre2JdLyaLc92jahDYHPcUObQpdndZvAylazTZgMr/+k05PhtawqBXIl+1/D7fvJOk7x2S1timZ5PNtk/Wy4bXFqm+zeT4HHqVvaZPd+Qjv490m/TdZx6qY22bmfUgPOpUK3MSva5JjCYt26dTJ48GDp2bOn9O7dW+bNmyfdu3eXnDlzSpcuXcL+zPbt26VUqVJBy/A9TvSPHj1q+mk0btzY9LuoWbOmuQ3q5Zdfln/961+yZMkSKVCggFkHXqNw4cJnrQePhYPCBOsJtXbtWtNHAwoVKiRlypQxfTVwVcRSvHhx84H+JIcPH/YvxxUVbMOGDRvkxIkT/uXly5c368S6Aw8GXKnBlRgUTYFQweISGapZCw6OGjVqmNfbvDltdB9Au6tUqWK2L7Ct+fLlM1eP9uzZI7t27fIvz2ybsP1Y55EjR6RgwYKuaJPd+wkFM471NWvW+H/5aLTpiz/2mMnwKhXLK01KpARtv9v3k3WcIlP83nBDm+zeT9YtrbjNFMerG9pk937auHGj/zjFm2ZuaJPd+wltsG4xcUubEmE/Wccp1uGWNtm5n06d/htlFWxZ3SasK1rZfIFlSRbDhjZs2FBmzZrlX4bCAgUGrhyEg/Duvvtu6dWrl3/ZuHHjTL8LnLyisAi1b98+qVSpkrzzzjty7733mlugsI7AKxBwySWXmL4buEUrmisW1o7BCTOwKg9/xQIFJDrZ4z+JG9oUujyr24Rl+IWNXzzWJdHMtmn7/mNyxYBf5djJU/JhpwbStk5JT+0n6zjFL2H0+XJDmxLhigX++OE4tV7f6W2yez/hSrt1nOL3qRvaZPd+sk7S8DcqlFPbZPd+wnGKE2Ucp/h5N7TJ7v2UGnAuhe3P6jYdOnTIFB4oOKxz3oS8YoHKqE6dOkHL0M/h+++/j/gzqPpCR2/C92houKICUCWiIMHJmLUOVIwoOAKvWmA9kfplYAQqfITCzgy93y3wj2hmlke6jy6W5TggYlmute3WcrxG4IhcbmhTNNsY7zbhXfVYtvFcywdNWWOKCgwti8nwIl36dOt+Cj1O3dCmaJfHq034WfzejcSJbcrocq024c240OPU6W2yez/hdSL9Pk1v2xO5Tefaxni3CW/OhB6nTm+T3fspOYpzqXi3yRGjQmFEqJUrVwYtW7Vqlbm6EAn6S0yePDlo2aRJk8zySFBpoXpGIQMXX3yxOfAD14PtwGXm9NZDsUPVi/xtvDDmOtqZLtsaOBle7Zjvp3QDHqf6mKk+ZqqPmepjpt7O1NbCAkO+YhSmPn36mKsJuEVp6NChZn4JC255uvPOO/3fd+vWzVwOeuqpp2TFihXy4YcfysiRI826LE888YTpDI572XCb1Q033GCqtdtuu808jss5uCUKfTumTp1qOnPj1igUFRwRShcuqeGev9DLdpQYmeKXVOBkeA0qenMyPB6n+pipPmaqj5nqY6beztTWW6EaNWoko0ePNsXDK6+8Yu7FHThwoHTq1Mn/HHS+xpUEC54zduxYU0gMGjTIdHz55JNPgoaaRfgoInbv3i0lSpSQyy67zBQw+NqC4WlxuQcT46HvBH4eRQqRl0xf9Y/MXLNLciYnyTNXeWMyPCIiInJhYQFXX321+YgkdFZtaNGihfzxxx8Rf+abb7455+tiRI0PPvjAfBB5UdBkeE0rSYWi3pkMj4iIiPQl/tzg5GhWh0Mv3ref6Jl+e3oyvMJ5c8h/WlYXL+Nxqo+Z6mOm+pipPmbq7UxtHW7WyTDcbLRDbxElGkyG1/ytabLr0HF54eo6cs9l6c94T0RERN50IIZzXl6xoLhC3YphfVm/JlamH01fa4oKTIbX+dLIo7B5BY9TfcxUHzPVx0z1MVNvZ8rCguIKIxhgxkcnjGTglUwxGd7QGevM1+iwnTM7fw3wONXHTPUxU33MVB8z9XamPKMg8pj+P680k+E1rFRErjo//ISQRERERFkyKtR3331n5o7AMLCYwTrQwoULM7JKIsoCmAzvu4XengyPiIiIEuSKxbvvvmsmkytVqpQZ8vWSSy6RYsWKmUnr2rVrF5+tJMfCiWu+fPl4ApsAmQZOhnd1/TJykUcnwwuHx6k+ZqqPmepjpvqYqbczjXlUqFq1asmLL75oJqArUKCA/Pnnn1KlShV54YUXZM+ePfL++++LF3BUKHKaqSt3yt3D5pnJ8CY/3pzzVhAREZG9o0Lh9qemTZuar/PkySMHDx40X99xxx3y9ddfx7o6cjl0NNq1a5cjOhy5OVMzGd7YtMnw7mp2HouKEDxO9TFTfcxUHzPVx0y9nWnMhUXp0qXNlQmoWLGizJkzx3y9fv16RwyDRVkLxwT+M/DYsDfTkfM3y+qdaZPhPdyiWly3z4l4nOpjpvqYqT5mqo+ZejvTmAuLVq1ayZgxY8zX6GvRo0cPadOmjdxyyy1yww03xGMbiSgTDh1PkXcmrTJfd29VXQrlzWH3JhEREZELxTwq1NChQ/2XYh5++GHTcXvWrFly7bXXygMPPBCPbSSiTBh6ejK88zgZHhERESVSYbF582apUKGC//tbb73VfODyzKZNm8ztUUQWjGCADj9OGMnAjZlu23/0zGR47TgZXiQ8TvUxU33MVB8z1cdMvZ1pzKNCJScny7Zt26RkyZJBy3fv3m2WpaamihdwVChygie+/VO+W7BZGp1XREY+0MQRv5SIiIjII6NCoQ4Jd3Jy6NAhyZ07d6yrI5fDbXMoRJ0wkoHbMl26db98f3oyvN7tORleenic6mOm+pipPmaqj5l6O9Oob4Xq2bOn+YyTk+eff17y5j0zXCWuUvz+++9y4YUXxmcrybFQiKLCDb3CRfHNNHAyvGsuKMvJ8M6Bx6k+ZqqPmepjpvqYqbczjbqwwCzbVuP++usvyZkzp/8xfH3BBRfIE088EZ+tJKKYTFv5j/y2ZreZDO+ptjXt3hwiIiLygKgLi6lTp/qHmB00aBD7FRAlKDMZ3ri0yfDu5mR4RERElKijQg0bNiw+W0KuhFvnihcvzvv7szDTwMnwHmrJyfCiweNUHzPVx0z1MVN9zNTbmcZcWMD8+fNl5MiRsnHjRjlx4kTQY6NGjdLaNnKBpKQk85+BsibTtMnwVpqvH72iuhTKw8nwosHjVB8z1cdM9TFTfczU25nGPCrUN998I02bNpXly5fL6NGj5eTJk7J06VKZMmWKGYqKKBBGMMD8Jk4YycANmX5kJsM7YSbD69SYk+FFi8epPmaqj5nqY6b6mKm3M425sOjTp48MGDBAfvzxR9NpG/0tVqxYITfffDMnx6OzoLP/4cOHzWeKb6aYDO9j/2R4tTkZXgx4nOpjpvqYqT5mqo+ZejvTmM881q5dKx06dDBfo7BAQ3HPV48ePWTo0KHx2EYiisLbE1fJsZOnzGR4beuWsntziIiIyGNiLiyKFCkiBw8eNF+XK1dOlixZYr7et2+fHDlyRH8LieiclmzZL6P+SJsM79kOdRzRwYuIiIjcJebO25dffrlMmjRJ6tWrJzfddJM8+uijpn8Fll1xxRXx2UpydIej0qVLm88Un0wDJ8O79oKycmGFwnZvouPwONXHTPUxU33MVB8z9Xam2Xwx3rC1Z88eOXbsmJQtW9Z0IunXr5/MmjVLqlevLs8995y5ouEFBw4cMJ3VMRMi5/QgO01ZsUPuGT7fTIY3+fHmnLeCiIiIbDnnjbn0KVq0qCkqzA8nJckzzzwjY8aMkf79+3umqKDoofhct26dI0YycGKmaZPhrTDLORlexvE41cdM9TFTfcxUHzP1dqbZo61UosV37ykQLohhrhMnjGTgxEz/b/5mWbPzkBThZHiZwuNUHzPVx0z1MVN9zNTbmUZVWBQuXDjqzqCpqamZ3SYiisLBYykyYNIq8zUnwyMiIiJHFBZTp071f71hwwZz+9Ndd90lTZo0Mctmz54tn332mfTt2zd+W0pEQYbOWGcmw6tcPJ/czsnwiIiIyGmdtzHyU9euXeW2224LWj5ixAgzj8W0adPEC9h5O7ZJXfLly8chUBUzXbttj3T4cK4cTzklH91xsbStW9ruzXI0Hqf6mKk+ZqqPmepjpu7LNK6dt3F1omHDhmctx7K5c+fGujpyOfwHyJ8/P3+5KEKWg2duNkXFJecVlSvrcDK8zOJxqo+Z6mOm+pipPmbq7UxjLiwqVKggH3/88VnLP/nkE/MYUWifm1WrVrHvjaLFm/bKqIVpk+H17lDbEb9oEh2PU33MVB8z1cdM9TFTb2ca8wR5AwYMkI4dO8r48eOlcePGZhmuVKxevVq+//77eGwjOZwThkdzCjMZ3vgVgvsXr6lfhpPhKeJxqo+Z6mOm+pipPmbq3UxjvmLRvn17U0Rce+21ZrI8fFxzzTWmksJjRBQ/U1fulDnr9kiOpGzyxJXV7d4cIiIiooxfsYDy5cvL66+/npEfJaIMCpwM7/o6haR8EU6GR0RERA6+YkEUC8zOXrlyZfOZMuebeZv8k+E9dfUFzFQRj1N9zFQfM9XHTPUxU29nmvhbSI6XPXuGLoxRgIPHTsrAX85Mhle0QB67N8l1eJzqY6b6mKk+ZqqPmXo3UxYWFPfORuiT45ROR4lqyPS1/snwbm1Unpkq43Gqj5nqY6b6mKk+ZurtTFlYECW4rfuOyicz1puvn2lXS3Ik878tERERJZ6Yz1BatWol+/btCzsrHx4jIl1v/7wybTK8ypwMj4iIiFxUWEybNk1OnDhx1vJjx47JjBkztLaLiERkyZb9MmrhFvP1s+05GR4RERElrqh7gixevNj/9bJly2T79u3+7zET4IQJE6RcuXL6W0iOhhEMqlev7oiRDBJxMrzXxi4zX193YVm54PRkeMxUHzPVx0z1MVN9zFQfM/V2plEXFhdeeKF5txQf4W55ypMnj7z33nva20cukJKSIjlz5rR7Mxxnyoq0yfByZk+SJ9vWDHqMmepjpvqYqT5mqo+Z6mOm3s006tJn/fr1snbtWvMu6ty5c8331seWLVtMH4t77rknvltLjoMRDHCMOGEkg8SbDG+5+fqeZpWDJsNjpvqYqT5mqo+Z6mOm+piptzON+opFpUqVzGcnNIrI6b6et0nW/nPYTIb3UMuqdm8OERER0TnFfLPWZ599JmPHjvV//9RTT0nhwoWladOm8vfff8e6OiIKNxnepLTJ8B5rXUMK5s5h9yYRERER6RcWffr0Mf0pYPbs2fL+++9Lv379pHjx4tKjR49YV0ce4ITORok2Gd7uwyekSvF8cnvjimGfw0z1MVN9zFQfM9XHTPUxU+9mms2HThMxyJs3r6xYsUIqVqwoTz/9tGzbtk0+//xzWbp0qbRo0UL++ecf8QL0KSlUqJDs379fChYsaPfmkIsmw2v59jQzb8XQOy6WK+uWtnuTiIiIyMMOxHDOG3P5kz9/ftm9e7f5+ueff5Y2bdqYr3Pnzi1Hjx7N6DaTS6FuPXTokPlM5/b2xDOT4bWJMBkeM9XHTPUxU33MVB8z1cdMvZ1pzIUFComuXbuaj1WrVkn79u3NclyxOO+882LeAIwo1blzZylWrJi5xapevXoyf/78c07S16BBA8mVK5dUq1ZNhg8fHvG5b7zxhhki97HHHgtajqsr1vC51ke3bt1i3n5KHzr7b968mZ3+o50M74+0yfCe6xB5Mjxmqo+Z6mOm+pipPmaqj5l6O9OYC4sPPvhAmjRpYm55+v77701BAAsWLJDbbrstpnXt3btXmjVrJjly5JDx48ebiff69+8vRYoUifgzGG6rQ4cO0rJlS1m0aJEpGFDkTJw48aznzps3Tz766COpX79+2HXdd9995lYu6wN9RYjsngzv+gvLSv3yaZPhERERETlF1MPNWjACFDpsh3r55ZdjfvE333xTKlSoIMOGDfMvq1y5cro/M2TIEPMcFCBQu3ZtmTlzpgwYMEDatm3rfx4uGXXq1Ek+/vhjee211yL2Fyldmvewk/0mLz8zGd4TIZPhEREREbmysIAZM2aYKwHr1q2Tb7/9VsqVKydffPGFOeG/7LLLol7PmDFjTDFw0003yfTp0816HnroIXMlIRKMRNW6deugZVhH6K1ODz/8sLmygedGKiy++uor+fLLL01xcc0118jzzz9vio1wjh8/bj4CO7JAamqq+QDcuoJe+7hUFXgfXKTlWIbHIi231hu4HEIvhUVanpycbNYbuNzalkjLo932aNuEz9mznznM3NCm0OWZbdMpySZ9xp+eDK9pJSlTMJd5TqQ24QNX+TLTVi8ce7G0yTpOrdzd0KZolsezTfgas8Tic+B2OrlNibCfrOPUTW0K3ZasbBO+xu9TN7UpcLkdbcJy6zh1S5vs3k+nAs6lQrcxK9oU18ICtz/dcccd5mrAwoUL/Sfb6CmOoWjHjRsX9bpQmAwePFh69uwpvXv3Nrcude/e3fwx6tKlS9if2b59u5QqFdypFd/jRB+dx9FP45tvvjHbhvVFcvvtt5tJ/8qWLSuLFy82I1ytXLlSRo0aFfb5ffv2DXtVBrORo0M7oMd8mTJlZMeOHSYPC4bixQf6kxw+fNi/HAUNrgBt2LBBTpw44V9evnx5s06sO/BgQOGGA2v16tVB21C9enUz1TtuE7Pg4KhRo4Z5PdyXZ0G2VapUMduHLC358uUzV4/27Nkju3bt8i/XahP2jdvapLWfft+dU9b9c1gK5UqSNuV95ufO1Sa0Aa+bqG1y6n7Cc9zWJjv3E9azadMmV7UpEfYT1ue2Ntm9n7BN6DfqpjbZtZ/27dtn1mX9jXJDmxJlPwF+PqvbhHXFbbjZiy66yMxXceedd0qBAgXkzz//NBv+xx9/SLt27YI2/lywoQ0bNpRZs2b5l6GwQEGAKxPhILy7775bevXq5V+GYgZXJ44cOWJCwjonTZrk71uBjtoXXnihDBw4MOK2TJkyRa644gpZs2aNVK1aNaorFtaOsYbeYlV+dpvwgazwHwqPuaFNocsz0yZMhtfqnRmy5/AJeema2nLHpZXO2SZ84Bc3/v9Z7yQkUpucuJ+s4xT/l7HtbmhTNMvj2SY4ePCgOU5Dt8WpbbJ7P2E91nGK57qhTXbvJ3zGcYq/UdFue6K3ye79hNe0hiW1/mY5vU127ydfwLmUtZ6sbBO6F0Q73GzMVyzwrv7ll19+1nK8IE52YoHKqE6dOkHL0GcCV0UiQdWHaioQvkdDcbUCnch37txpRo2yYIf9+uuvpm8IigPrxCFQ48aNzedIhQVGoMJHKKwrdH3WAREq1uXhtjPW5TggYlmute3WcmSP/YHjI9ZtT9Q2RbON0S4fOmO1KSqqlMgnnS49T5KTk87ZJmRqHfM89qJfnt62Bx6nVrHm9DZFuzxebUKmeKMJhUUs60nkNmV0uVab8GEdp9ZrOb1Ndu+n9H6fprftidymc21jvNsE1nEa+LiT22T3fkoN+BuFddjRpmjFXFjgxB4n36FDy6IDNa5cxAIjQqFQCYRLkbhFKRKMSBV6uxWuTmA54KrDX3/9FfQ4rnDUqlXL3O4UaedhhCmr2CHKClv2HZVPZ6ZdzuzVrrbkCCkqiIiIiJwk5sICHasfffRR+e9//2uqoK1bt5rblp544gnT+TkWuKWqadOmpm/GzTffLHPnzpWhQ4eaDwtuecK9apjdGzDXBK48PPXUU3LPPfeYW5hGjhwpY8eONY/j3bHzzz8/6HVwPxmGxbWW4162ESNGmDk4sBx9LLAtuBITaWhaonhNhte4clFpXbuk3ZtDRERElLWFxTPPPGPuv8KVAfRpwMk4bhFCYfHII4/EtK5GjRrJ6NGjTfHwyiuvmM4q6AeBjuEWzC+xceNG//d4DooIFAKDBg0yHV8++eSToKFmo+nb8csvv5jXQmcX9JXo2LGjPPfcczFtP50bik8UdrGOKuB2f23eL6NPT4b3bDqT4YXDTPUxU33MVB8z1cdM9TFTb2cac+ftwF7puCUKHTrQT8IaGckr0Ikm2o4sRIHwX+62j+eYeStuuKicDLjlQrs3iYiIiCjT57wx39SN248wggLe9UdBcckll5iiAu/84zGiQLi6hZG6Qkcw8LJfMjkZHjPVx0z1MVN9zFQfM9XHTL2dacyFxWeffWbmJAiFZVY/CKLAd+fxnyGDF8Zc52TqKel7ejK8ey+rLOUK54l5HcxUHzPVx0z1MVN9zFQfM/V2ptljuQxizUuAKxa5c+f2P4ZhsDBSU8mS7IBKlJ5v5m40k+EVy5dTHmpx9rDGRERERE4VdWFhTcqBD0xSFwrLw81MTURpDhw7KQN+SZtF87HW1aVA7hx2bxIRERFR1hcWU6dONVcrWrVqZSawK1q0qP8x9LfA3BNly5bV2zJyBRScgZOOedngaWv9k+HdeknFDK+HmepjpvqYqT5mqo+Z6mOm3s405lGh/v77b6lYsaIjGhdPHBWKYp0Mr+Xb0+REyin55M6G0rpOKbs3iYiIiMjeUaFwZcLrRQVFDyMYYC4SJ4xkEE9vTVhhiopLqxSVKzI5GR4z1cdM9TFTfcxUHzPVx0y9nWnMhQVRLHBBDBWuE0YyiJfFm/fJD4u2mq+fbV8n04U5M9XHTPUxU33MVB8z1cdMvZ0pCwuiOMIvgdfHpg0ve+NF5aRe+UJ2bxIRERGR/YUFTpI2btwox44di8/WELlwMrzf1++RXNmT5PEMTIZHRERE5NrColq1arJp06b4bRG5Cm77KV68uCf75ZjJ8MZlbjK8cLycabwwU33MVB8z1cdM9TFTb2caU2GRlJQk1atXl927d8dvi8hVcMzgPwM+e83XmAxvV9pkeA8qTobn5UzjhZnqY6b6mKk+ZqqPmXo705i38I033pAnn3xSlixZEp8tIlfBCAa4wuWEkQy0J8MbaE2G16aG6mR4Xs00npipPmaqj5nqY6b6mKm3M416gjzLnXfeKUeOHJELLrjATIyXJ0/w7R179uzR3D5yONw+d/jwYUeMZKDpw6lpk+FVxWR4jSqorturmcYTM9XHTPUxU33MVB8z9XamMRcWAwcOjM+WELnE5r1H5L+/rTdf92pXW3IkJ/6lSyIiIqIsLyy6dOmS6RclcrO3J640k+E1qVIs05PhEREREbm2sMBws+mpWLFiZraHXAYdjUqXLu2IDkca/twUMBleh9pxGcHBa5lmBWaqj5nqY6b6mKk+ZurtTGMuLM4777x0T5ZSU1Mzu03kIjhWChcuLJ6ZDG/cmcnwzi8Xn8nwvJRpVmGm+pipPmaqj5nqY6bezjTm0uePP/6QhQsX+j9+//13GTJkiNSoUUO+/fbb+GwlORZGMFi3bp0jRjLIrEnLdsjc05PhPRHHyfC8lGlWYab6mKk+ZqqPmepjpt7ONOYrFhgNKlTDhg2lbNmy8tZbb8mNN96otW3kknfxT5w44YiRDDI7Gd4b41eYr7v+q7KUVZoMz8uZZiVmqo+Z6mOm+pipPmbq7UzVbtaqWbOmzJs3T2t1RI4y4ve0yfCK588p3ZrrTYZHRERE5BQxX7E4cOBA0PeonrZt2yYvvfSSmZWbyGvSJsNbZb5+rLXuZHhEREREri0s0HkktPM2iosKFSrIN998o7lt5AIYwaB8+fKOGMkgM5Ph7T1yMi6T4Xk106zGTPUxU33MVB8z1cdMvZ1pzIXF1KlTg75HI0uUKCHVqlWT7NljXh25HIrQ/Pnzi1tt2nNmMrze7WtL9iyYDM/tmdqBmepjpvqYqT5mqo+ZejvTmCuB5s2bx2dLyJUw/PDatWulatWqkpycLG7z9s9pk+E1rVpMWtXKmsnw3J6pHZipPmaqj5nqY6b6mKm3M4357dXPPvtMxo4d6//+qaeeMrdHNW3aVP7++2/t7SMXcMLwaBmdDO9/i7YK7gzE1Yp4TIbntUztxEz1MVN9zFQfM9XHTL2bacyFRZ8+fSRPnrShNGfPni3vv/++9OvXT4oXLy49evSIxzYSJeZkeGPTJsO7IY6T4RERERE5Rcy3Qm3atMn0p4AffvhB/v3vf8v9998vzZo1kxYtWsRjG4kSzs+YDG/D6cnwrozfZHhEREREThHzFQt0Htm9e7f5+ueff5Y2bdqYr3Pnzi1Hjx7V30JyNHTur1y5siNGMsjIZHj3/atKXCfD80qmdmOm+pipPmaqj5nqY6bezjTmKxYoJLp27SoXXXSRrFq1Stq3b2+WL126VM4777x4bCM5nNtGC/tqzt+y3poMr4U9k+G5LdNEwEz1MVN9zFQfM9XHTL2bacylzwcffCBNmjSRf/75R77//nspVqyYWb5gwQK57bbb4rGN5PDORqtXr3ZMp6Nz2X/0pAyavNo/GV7+XFn/H91tmSYCZqqPmepjpvqYqT5m6u1MMzRBHjpsh3r55Ze1tokoYX04bY2ZDK9ayfxZMhkeERERkVMk/s1aRAk0Gd6wmRvM173b18qSyfCIiIiInIJnRkRRemviSjmRmjYZXsuaWTMZHhEREZFTZPNhQH6K2YEDB6RQoUKyf/9+KViwoN2bk7BweOGeQIxkkJUTyGlbtGmfXP/Bb2YyvJ8euUzqlrVv3gq3ZJpImKk+ZqqPmepjpvqYqfsyjeWcl1csKO5SUlLE6f+h+5yeDO/Gi8rbWlS4JdNExEz1MVN9zFQfM9XHTL2badSFxc6dO8/Z4Llz52psE7kIKuz169c7YiSDSCYuTZsML3eOJHmibQ27N8cVmSYaZqqPmepjpvqYqT5m6u1Moy4sypQpE1Rc1KtXz8zCbcGkeRiGlshNTqRgMrzl/snwyhTK2snwiIiIiJwi6sIitCvGhg0b5OTJk+k+h8jpRvz+t2zYfcRMhvdAc3smwyMiIiJyAtU+FuykQ+E4YQr6c02G16ONPZPhuS3TRMZM9TFTfcxUHzPVx0y9m2ninCmRKyUnJ0uNGvb3S8iID6emTYZXvWR+uaVh4kyG5+RMExUz1cdM9TFTfcxUHzP1dqZJsVyNOHjwoBlyCsNN4ftDhw6Z760PolC4PQ7HidNukzOT4f1mTYZXO6Emw3NqpomMmepjpvqYqT5mqo+ZejvTmPpYoFoqUqSIFC1a1DTwoosuMt/jo2bNmvHdUnIkjGCwefNmR4xkEKjf6cnwmlUrJi1qlpBE4tRMExkz1cdM9TFTfcxUHzP1dqZR3wo1derU+G4JUYL4Y+Ne+fHPrWYyPFytYN8hIiIiIsXConnz5tE+lcjZk+GNSxtetmODxJgMj4iIiMhVhQUmwEtNTZVcuXL5l+3YsUOGDBkihw8flmuvvVYuu+yyeG0nORTe7c+ZM6dj3vWfuHS7zNuw10yG9/iVidlRymmZOgEz1cdM9TFTfcxUHzP1dqbZfFH2BLn77rtNoz766CPzPTpy161bV44dO2Ymz1u2bJn873//k/bt24sXoLN6oUKFTEf2ggUL2r05pDQZ3pUDppt5Kx5pVU0ev5L9hoiIiMjbDsRwzht15+3ffvtNOnbs6P/+888/N1cwVq9eLX/++af07NlT3nrrrcxtObkO6tZ9+/Y5YiSDr/yT4eVK6MnwnJSpUzBTfcxUHzPVx0z1MVNvZxp1YbFlyxapXr26//vJkyebQgMVDHTp0kWWLl0an60kx8IIBtu3b0/4kQwCJ8PrmWCT4Tk1UydhpvqYqT5mqo+Z6mOm3s406sIid+7ccvToUf/3c+bMkcaNGwc9jiFoiZzog6lrZN/pyfBublje7s0hIiIicpyoC4sLL7xQvvjiC/P1jBkzTMftVq1a+R9fu3atlC1bNuYNwJWQzp07S7FixSRPnjxSr149mT9/fro/M23aNGnQoIHpSF6tWjUZPnx4xOe+8cYbprPLY489FrQcfUMefvhh87r58+c3V1/QJvIeTIY33JoMr0NiTYZHRERE5BRRn0G98MILMmjQIKlataq0bdtW7rrrLtNp2zJ69Ghp1qxZTC++d+9e8zM5cuSQ8ePHmw7g/fv3NxPuRbJ+/Xrp0KGDtGzZUhYtWmQKhq5du8rEiRPPeu68efNMZ/P69euf9ViPHj3kxx9/lG+//VamT58uW7dulRtvvDGm7adzQ1GXL1++hB7JwJoM77JqxaVFjcSaDM+pmToNM9XHTPUxU33MVB8z9XamUY8KBcuXL5eff/5ZSpcuLTfddJMkJZ2pS4YOHSqXXHKJubIRrWeeecZ0CscVkGg9/fTTMnbsWFmyZIl/2a233mo6tUyYMMG/DLdl4arGhx9+KK+99prZroEDB5rH0Ku9RIkSMmLECPn3v/9tlq1YsUJq164ts2fPlksvvfSc28FRodwzGd4NH84yk+H99MhlnLeCiIiIKIPnvDH1UMWJNz7Cuf/++yVWY8aMMVc/UKTgqkG5cuXkoYcekvvuuy/iz+DEv3Xr1kHLsI7QW51wmxOubOC5KCwCLViwQE6ePBm0nlq1aknFihUjFhbHjx83H4EhA0bGwgegkkSxhc41gfVapOVYhsciLbfWG7gcQjvvRFqenJxs1hu43NqWSMuj3fZo24TPuDKFW86wPYnUJmzLa2OXmWU3XlROapcu4H/NRN5PsHv3bilcuLB/nTz2Mtcm6zjF1dLs2bO7ok3RLI9nm6xRTHCcBr7L5uQ22b2fMJ+UdZximRvaZPd+wtc4TvE3KvR9Vqe2ye79hON0z5495ji1tsPpbbJ7P50KOJey1p+VbYpF1IXFr7/+GtXzLr/88qhffN26dTJ48GAzVG3v3r3NrUvdu3c382VglKlw0Cu+VKlSQcvwPU700bkc/TS++eYbWbhwoVlfpHXgNfAHL3Q9eCycvn37yssvv3zWcvQtQR8NQDWH28PQVwNVnaV48eLmA/1JMJmgBVd+sA0bNmyQEydO+JeXL1/erBPrDjwYKleubE56MMRvIIzWhf/IuE3MgoOjRo0a5vU2b97sX452V6lSxWxfYFtxia1ChQrml8GuXbv8yzPbJmw/1onb0VDlJlKbRs5aKQv+3ie5krPJDVWzm+c5YT+hL9OmTZvkn3/+8f/y4bGXuTZZx2nRokWlZs2armiT3fsJJxX4Q4j1Bw784eQ22b2fNm7c6D9OMWCKG9pk935CG7B9yNQtbbJ7P+GNL6wfmWIdbmiT3fvp1Om/UbgzyI42YV3qt0JZVRVE+pFwVVd6sKENGzaUWbNm+ZehsEBBgCsH4SA8TNbXq1cv/7Jx48aZqxNHjhwxIWGdkyZN8vetaNGiRdCtULgFCusIvAIB2GHou/Hmm29GdcXC2jHWZSFW5We3CW1Ys2aN2W/4T5IobUo5JdJmwHT5e/cR+U/LqtKjdXXH7CcsW7VqlenvhJ/V2E9ef0fIOk4xGAT6fLmhTdEsj2eb8D3+WOI4tV7f6W2yez/hSrt1nOL3qRvaZPd+QjtwnOJvVCintsnu/YTjFCfEOE7x825ok937KTXgXArbn9VtQvcC9Vuh8O5TgQIFTKftO+64w1QwmYXKqE6dOkHLcKvV999/H/FnUPWFjt6E79FQXK3AbU47d+40/Sss2CG44vL++++b4gDrQMVoXaYPXA8eCwcjUOEjFHamdXJnCfwjmpnloevNyHIcELEs19r2cCcSsW57PNv05az1pqjAZHjdWqT98oulTRnddo024Xi2lvPYi375ubYdP2f9EXRLm6JZnhVtimU9TmlTLMs122Tlab2WG9oU7fJ4tSnR/j5pLrerTdZxmpm/rYnWpli2MSkObbLOpWLddq02RSvqn962bZt5Jx9XEjAk7L333muuNOCEHlWM9RELjAi1cuXKoGV4J7ZSpUoRf6ZJkyZmcr5AuDqB5XDFFVfIX3/9ZUaMsj5wBaNTp07ma4R78cUXm3clA9eD7cBlZms9pAMHNI6LWO/Ri6f9R07Ku1PSLi8+fmViT4bnlEydjpnqY6b6mKk+ZqqPmXo705hGhbLgBBxzR3z22WfmCgD6Q6D/gdXpMVq45alp06bmZ2+++WaZO3eu6biNEaZQCABuecK9ap9//rn5HveVnX/++aZz9j333CNTpkwxt09hpCh04g4n9FYoePDBB80tVGgHiqNHHnnELA+8LSs9HBXKuV4fu0w+nrFeapTKL+O6/4vzVhAREREpnPNm6IwKoydhXotffvnF3O+FSeisUZJi0ahRIzP/xddff22KhVdffdWc/FtFhXWlBIVMYIcWFBG4SnHBBReYeS8++eSTiEVFJAMGDJCrr77aTIyHDue4BWrUqFExt4HSh3v1sA9D7we0y8bdR+SzWX+br3u1d+ZkeImWqRswU33MVB8z1cdM9TFTb2ca8xULXKFAH4j//ve/5rYodJrGlYOrrrpKvIRXLKKD/gDoxIXRGSLdQ5iV/jNiofy0eJv8q3px+fyeSxxxWTHRM3UDZqqPmepjpvqYqT5m6r5M4zKPBW5TGjZsmBnK9bzzzjOjKo0cOdIMJ0bkBAs37jVFBWqJXu1qO7KoICIiIkpUURcWmDQOt0ChPwM6P8PMmTPPet61116ru4VECnBh7vWxy83X/25QXuqU5VUmIiIiIk0x9bZGXwf0g4gk1nksyP1wTGBoYruvDkxYsl0W/L1X8uRIlsevrClOliiZugkz1cdM9TFTfcxUHzP1dqZRFxZO6DBCiQfjIWvMeZIZJ1JOyRsTVpiv77u8ipQulFucLBEydRtmqo+Z6mOm+pipPmbq7UxVh8Q5evSo5urIBVCQbtq0ydbC9Is5f/snw3vg8iridImQqdswU33MVB8z1cdM9TFTb2eqUlhgpCgM+4qhYIlC+zYcPnw4aHr4LJ8Mb/KZyfDyOWwyvETM1I2YqT5mqo+Z6mOm+piptzNNiqV4wGR1mMUak9r98MMPZjlGikJBgfknevToEc9tJYrZ+1NXy/6jJ6VmqQJyc8MKdm8OERERkWtF/fYtJsT76KOPpHXr1mZ26ptuuskMOTtnzhx55513zPccr5gSSfBkeLUkOSnxOz0RERERub6w+Pbbb+Xzzz83w8kuWbJE6tevLykpKfLnn386opc62dfhCLOa43NWe3PiCjmRespMhte8RglxCzszdStmqo+Z6mOm+pipPmbq7Uyjnnk7Z86csn79eilXrpz5Pk+ePGbSvHr16okXcebtxIahZTsOnmUmwxvX/V9Suwz3EREREVE8z3mjLn0wPwWKC0v27Nklf/78MW8ceQtGMFi3bl2WjmSQNhneMvP1TReXd11RYUembsdM9TFTfcxUHzPVx0y9nWn2WE7W7rrrLsmVK5f5/tixY9KtWzfJly9f0PNGjRqlv5XkWDhuTpw4kaUjGYxfsl0WbtznisnwEiVTt2Om+pipPmaqj5nqY6bezjTqwqJLly5B33fu3Dke20OU+cnwxqdNhnf/5VWkVEFnT4ZHRERE5BRRFxYYVpYo0WEyvI17jkiJArlMYUFEREREWSPxu5eTo2EEg/Lly2fJSAb7jpw4MxleG3dMhmd3pl7BTPUxU33MVB8z1cdMvZ2pO8+8KGFgKOKs6uT//pQ1/snwbnLxZHhZmalXMFN9zFQfM9XHTPUxU29nmvilDzkaRhNbtWqV+Rz3yfBmbzBf9+5Q29WT4WVVpl7CTPUxU33MVB8z1cdMvZ0pCwuKu6wYHu3NCSvkZKrPdZPhReKEIeechpnqY6b6mKk+ZqqPmXo3UxYW5IrJ8Mb+tc1Mhte7fW27N4eIiIjIk1hYkKMFToZ388UVXDcZHhEREZFTsLCguMIIBpUrV47bSAbj/jozGV7PK2uIF8Q7Uy9ipvqYqT5mqo+Z6mOm3s408beQHC979vgMPnY8JdX0rfDiZHjxytTLmKk+ZqqPmepjpvqYqXczZWFBce9stHr16rh0OvpidtpkeCUL5JIHmntnMrx4ZupVzFQfM9XHTPUxU33M1NuZsrAgR8JkeO9NWWO+fvzKGpI3pzMqeSIiIiK3YmFBjvTe6cnwapUuIP++2L2T4RERERE5BQsLcpy/dx+Wz63J8Nq7ezI8IiIiIqfI5sN4nRSzAwcOSKFChWT//v1SsCCHOI0EhxfuCcRIBpiSXsPDXy0081ZcXqOEfH7PJeI18cjU65ipPmaqj5nqY6b6mKn7Mo3lnJdXLCjuUlJS1Na14O89pqjARYre7WuJV2lmSmmYqT5mqo+Z6mOm+pipdzNlYUFxhQp7/fr1KiMZoGJ/bexy8/XNDStIrdLevFKkmSmlYab6mKk+ZqqPmepjpt7OlIUFOQYmw/vDmgyvjTcmwyMiIiJyChYW5AiYDO+NCWlXKzBnRUkPTYZHRERE5AQsLCjuNKagx2R4m/YcNZPhYZZtr9PIlIIxU33MVB8z1cdM9TFT72bKUaEyiKNCZe1keJf3myoHjqVIv4715eZGnLeCiIiIKCtwVChKGKhbDx06ZD5n1LuT15iiApPhdby4vHidRqYUjJnqY6b6mKk+ZqqPmXo7UxYWFFcYwWDz5s0ZHslgw67D8sUcToanmSmdjZnqY6b6mKk+ZqqPmXo7UxYWlND6TVwhJ1N90rxGCTMhHhERERElJhYWlLDmb9hjhphNmwyvtt2bQ0RERETpYGFBcYWp53PmzBnzFPShk+HVLF0gTlvonUwpMmaqj5nqY6b6mKk+ZurtTDkqVAZxVKj4+mnxVvnPiD8kb85kmfZEC85bQURERGQDjgpFCQN16759+2IayQCT4b05YYX5+oHLq7KoUMiU0sdM9TFTfcxUHzPVx0y9nSkLC4orjGCwffv2mEYy+HxW2mR4pQrmkvsurxzX7fNKppQ+ZqqPmepjpvqYqT5m6u1MWVhQQtl7+IS8N2W1+frxK2tK3pzZ7d4kIiIiIooCCwtKKO9NCZgMrwEnwyMiIiJyChYWFFcYwSBfvnxRjWQQOBnesx04GZ5GphQdZqqPmepjpvqYqT5m6u1MOSpUBnFUKH0PfrlAxi/ZLi1qlpDhd19i9+YQERERed4BjgpFiQIdjXbt2nXODkeYDA9FBS5S9GrHyfA0MqXoMVN9zFQfM9XHTPUxU29nysKC4goXxPCfIb0LY4GT4d3SiJPhaWRKsWGm+pipPmaqj5nqY6bezpSFBdnup8XbZNGmfWYyvB5tati9OURERESUASwsyFaBk+F1a15VShbgZHhERERETsTCguIKIxigw0+kkQwwGd7mvWmT4XX9FyfD08iUYsdM9TFTfcxUHzPVx0y9nanthcWWLVukc+fOUqxYMcmTJ4/Uq1dP5s+fn+7PTJs2TRo0aCC5cuWSatWqyfDhw4MeHzx4sNSvX9/0XMdHkyZNZPz48UHPadGihdlBgR/dunWLSxu9LCkpScqUKWM+h+JkePqZUsYwU33MVB8z1cdM9TFTb2dq6xbu3btXmjVrJjly5DAn/suWLZP+/ftLkSJFIv7M+vXrpUOHDtKyZUtZtGiRPPbYY9K1a1eZOHGi/znly5eXN954QxYsWGCKlFatWsl1110nS5cuDVrXfffdJ9u2bfN/9OvXL67t9SKMYIBsw41k8O6U1WYyvNplCnIyPKVMKWOYqT5mqo+Z6mOm+piptzO19S3iN998UypUqCDDhg3zL6tcOf3bYYYMGWKegwIEateuLTNnzpQBAwZI27ZtzbJrrrkm6Gdef/11cxVjzpw5UrduXf/yvHnzSunSpZVbRYEwggHGPS5ZsmTQ8vWYDG/23+brZ9tzMjyNTCnjmKk+ZqqPmepjpvqYqbcztbWwGDNmjCkGbrrpJpk+fbqUK1dOHnroIXMlIZLZs2dL69atg5ZhHbhyEU5qaqp8++23cvjwYXNLVKCvvvpKvvzyS1NcoBh5/vnnTbERzvHjx81H4GQh1vrxAbidCpepUFEGDgkWaTmW4bFIy631Bi6H0Io10vLk5GSz3sDl1rZEWh7ttkfbJrQh8DlWm94Yv1xSTvmkRY0S0qxasajbmghtsns/gZWtW9pk936yjlN8dkubolkezzZZPxtuW5zaJrv3U+Bx6pY22b2f0I7Qv09Ob1PgcrvaZB2nbmqTnfspNeBcKnQbs6JNjiks1q1bZ64k9OzZU3r37i3z5s2T7t27S86cOaVLly5hf2b79u1SqlSpoGX4Hif6R48eNf004K+//jKFxLFjxyR//vwyevRoqVOnjv9nbr/9dqlUqZKULVtWFi9eLE8//bSsXLlSRo0aFfZ1+/btKy+//PJZy9euXWvWD+hYg3vgduzYYSpLS/Hixc0H+pOgwLGgoClcuLBs2LBBTpw4EXQrF9aJdQceDLhSkz17dlm9Oq1fgqV69eqSkpJibhOz4OCoUaOGeb3Nmzf7lyPbKlWqmO1DlhZMFY+rR3v27DFjJVsy2yZsP9Z55MgR098FbVq87bBMXLrDTIb3RJuq5jlOapPd+wnHLI71NWvW+H/5OL1Ndu8n6zhFpjVr1nRFm+zeT9YtrVu3bjXHqxvaZPd+2rhxo/84zZ07tyvaZPd+QhuwfeCWNiXCfrKOU6zDLW2ycz+dOv03yirYsrpNWFe0svlsnG0DG9qwYUOZNWuWfxkKCxQYuDIRDsK7++67pVevXv5l48aNM/0ucPJqFRbYcfgljHC+++47+eSTT8xVkcDiItCUKVPkiiuuMP8RqlatGtUVC2vHWNObsyo/u034jL406JyP7cF/8o5D5sifm/fLrY3KS98b6zuuTXbvJ9i9e7f5JWWt0+ltsns/WccpTobxy9wNbYpmeTzbhK/37dtnjtPAd7yc3Ca79xN+f1rHKZa5oU127yd8jeMUf6NCT4ec2ia79xOOU5wb4Ti1tsPpbbJ7P50KOJey1p+VbTp06JApPHBObZ3zJuQVC1RGoSf66DPx/fffR/wZVH2opgLhezTUKiqsogUjRsHFF19sipVBgwbJRx99FHa9jRs3Np8jFRYYgQofobAz8RHugAgV6/LQ9WZkOQ6IWJZrbbu1HK8ReE/guKU7TVGRL2ey9Lyypv+Ew0ltimYb492mEiVKuK5Ndu6n0OPUDW2Kdnk824R3uiJxapsyslyrTRjoJPQ4dXqb7N5PeJ1Iv0/T2/ZEbtO5tjHebcKbM+H6Aji5TXbvp+SQv1F2tMkRo0JhRCjcfhRo1apV5halSHB70+TJk4OWTZo06az+E6FQgQVecQiFEaasYof0IPdNmzaZz8dOpsqb4zkZnmampIOZ6mOm+pipPmaqj5l6O1Nbr1j06NFDmjZtKn369JGbb75Z5s6dK0OHDjUfFtzyhHvVPv/8c/M95pp4//335amnnpJ77rnH3MI0cuRIGTt2bNDPtGvXTipWrCgHDx6UESNGmLkvrCFpcS8blrVv395cVkIfC2zL5Zdfbua/ID24nIb7/vD589kbZMs+azK8KnZvmisyJR3MVB8z1cdM9TFTfczU25naWlg0atTIdKpGIfDKK6+YzioDBw6UTp06+Z+DcXvRV8KC56CIQCGAW5vQ8QX9J6yhZmHnzp1y5513mp/FPWEoFlBUtGnTxn+b1C+//GJeCzsKfSU6duwozz33XBYn4B17zGR4a8zXT1xZU/LkDH/Zj4iIiIicyfapjq+++mrzEUnorNrWrNl//PFHxJ/59NNP031NFBLoyE1Z5/2pa+Xg6cnwbuRkeERERESuk/hzg5OjoRPQ8ZyF5Kvf0646PdeBk+FpZIpBDDLbwYrOYKb6mKk+ZqqPmepjpt7O1PYrFuRuGI1g8G9bzWR4LWtiMrzIo8RQ9JliCE/Sw0z1MVN9zFQfM9XHTL2daeKXPuRoc9bukglLt5vJ8Hq1r2335rgCRoXA5JJOGB3CKZipPmaqj5nqY6b6mKm3M2VhQXFz6pRP+oxLG172loYVpEapAnZvkitgVAhMAOmE0SGcgpnqY6b6mKk+ZqqPmXo7UxYWFDc//bVNFm/ZL3myZ5NHr0ibrJCIiIiI3ImFBcVF4GR4N9UrIiUKnD1rORERERG5BwsLiovPZqVNhle6YG555Mo6jhjJwCmQJeZvYaZ6mKk+ZqqPmepjpvqYqbcz5ahQFJfJ8N6fenoyvLY1pUSRQnZvkutGh8ifP7/dm+EqzFQfM9XHTPUxU33M1NuZJn7pQ47z7uTVZjK8OmUKynX1S8uqVaskNTXV7s1yDWTJTHUxU33MVB8z1cdM9TFTb2fKwoJUrfvnkHw552//ZHhJSdkcMTya0zBTfcxUHzPVx0z1MVN9zNS7mbKwIFVvTlhhJsNrVaukNOVkeERERESewcKC1Py+brdMXLojbTK8drXs3hwiIiIiykIsLEhxMrzl5utbL6ko1U9PhocRDCpXruyIkQycgpnqY6b6mKk+ZqqPmepjpt7ONPG3kBzhx8Vb5c/N+yVfzmTp0bpG0GPZs3PwMW3MVB8z1cdM9TFTfcxUHzP1bqYsLEhlMrx+E1aarx9sUTVoMjx0Nlq9erVjOh05ATPVx0z1MVN9zFQfM9XHTL2dKQsLyrThAZPh3XtZFbs3h4iIiIhswMKCMj0Z3gdT0ibDe7JtTcmTM9nuTSIiIiIiG7CwoMxPhnc8bTK8Gy4qZ/fmEBEREZFNsvl8Pp9dL+5kBw4ckEKFCsn+/fulYMGC4tXJ8K4c8KuZt2JE18Zh563A4YV7AjGSAaakp8xjpvqYqT5mqo+Z6mOm+pip+zKN5ZyXVywow94YnzYZ3hXnmAwvJSUlS7fLC5ipPmaqj5nqY6b6mKk+ZurdTFlYUIYnw/t52Q5JTsomvdpHngwPFfb69esdMZKBUzBTfcxUHzPVx0z1MVN9zNTbmbKwoAxNhve6NRleowpSrWTaZHhERERE5F0sLChDk+Et3rxf8ufKLj3aBE+GR0RERETexMKCMjUZXvH8ZybDi8QJU9A7DTPVx0z1MVN9zFQfM9XHTL2bKUeFyiCvjgo1eNpaeXPCCilTKLdMebwF560gIiIicrEDHBWK4mH3oePy4dS0yfCeuDK6yfBQtx46dMh8Jh3MVB8z1cdM9TFTfcxUHzP1dqYsLCjmyfDqlo1+MjyMYLB582ZHjGTgFMxUHzPVx0z1MVN9zFQfM/V2piwsKCpr/zkkX/2+0Xz9bPvakpTESW+IiIiI6AwWFqQ6GR4REREReRMLCzqnOet2y6QoJsMLB1PP58yZ05Yp6N2KmepjpvqYqT5mqo+Z6mOm3s6Uo0JlkFdGhcJkeNd/+JuZt6LzpRXltevr2b1JRERERJRFOCoUqRnz55nJ8B5rHftkeKhb9+3b54iRDJyCmepjpvqYqT5mqo+Z6mOm3s6UhQWlOxneWxNjmwwvFEYw2L59uyNGMnAKZqqPmepjpvqYqT5mqo+ZejtTFhYU0bDfNsiWfUfNZHj3XlbZ7s0hIiIiogTGwoLOORnek21rSu4cnGGbiIiIiCJjYUFhDTo9Gd755QrK9RdGNxleOBjBIF++fI4YycApmKk+ZqqPmepjpvqYqT5m6u1MOSpUBrl5VChMhnflgF8l9ZRPRtzXWJpW5bwVRERERF50gKNCUWb0HbfCFBWta5fMdFGBjka7du1yRIcjp2Cm+pipPmaqj5nqY6b6mKm3M2VhQUFmr90tvyxPmwzvmXa1M70+XBDDfwZeGNPDTPUxU33MVB8z1cdM9TFTb2fKwoKCJsPrM265+fr2SypKtZL57d4kIiIiInIIFhYUNBneX1vSJsN7tHV1uzeHiIiIiByEhQX5J8PrN2FFpibDCwcjGKDDjxNGMnAKZqqPmepjpvqYqT5mqo+ZejtTjgqVQW4bFerDaWuk34SVUrZQbpnyRAvOW0FEREREwlGhKAOT4a01Xz95le5keBjBYNu2bY4YycApmKk+ZqqPmepjpvqYqT5m6u1MWViQDPxltRw6niL1yhWS6y7I+GR44eCCGCpcXhjTw0z1MVN9zFQfM9XHTPUxU29nysLC49bsPCQj5m40X/duX1uSkhL//j0iIiIiSjwsLDzujfHWZHilpEnVYnZvDhERERE5FAsLDwueDK9WXF4DIxgUL17cESMZOAUz1cdM9TFTfcxUHzPVx0y9nWl2uzeA7JsM7/Vxy8zXnRrHbzK8pKQk85+B9DBTfcxUHzPVx0z1MVN9zNTbmfKKhUf9788tsmTLgbTJ8K6I32R4GMFg06ZNjhjJwCmYqT5mqo+Z6mOm+pipPmbq7UxtLyy2bNkinTt3lmLFikmePHmkXr16Mn/+/HR/Ztq0adKgQQPJlSuXVKtWTYYPHx70+ODBg6V+/fpmrF18NGnSRMaPHx/0nGPHjsnDDz9sXjd//vzSsWNH2bFjh3hlMry3Jqw0Xz/UsqoUU5oMLxyMYHD48GFHjGTgFMxUHzPVx0z1MVN9zFQfM/V2prYWFnv37pVmzZpJjhw5zIn/smXLpH///lKkSJGIP7N+/Xrp0KGDtGzZUhYtWiSPPfaYdO3aVSZOnOh/Tvny5eWNN96QBQsWmCKlVatWct1118nSpUv9z+nRo4f8+OOP8u2338r06dNl69atcuONN4oXfDpzvWzdf0zKFc4j9zSrbPfmEBEREZEL2NrH4s0335QKFSrIsGHD/MsqV07/RHfIkCHmOShAoHbt2jJz5kwZMGCAtG3b1iy75pprgn7m9ddfN1cx5syZI3Xr1jVjAX/66acyYsQIU3QAtgHrwnMuvfRScatdh47L4GmnJ8NrqzsZHhERERF5l62FxZgxY0wxcNNNN5mrBuXKlZOHHnpI7rvvvog/M3v2bGndunXQMqwDVy7CSU1NNVclcAkJt0QBrmScPHkyaD21atWSihUrmvWHKyyOHz9uPgKnN7fWjw9Ab310sME9cIGXqyItxzI8Fmm5td7A5RB6j12k5cnJyWa9gcsHTlp1ejK8gtLh/FIZ3vZo24SPkiVL+kcyiEebrG2JtFy7TaHL7WhTqVKlzGNOOvYSeT9Zx6l1zLqhTdEsj2eboHTp0uZz4HY6uU1276fA4xTPcUOb7N5P+Izfp3jMLW0KXG5Hm8A6TvH6bmiT3fvJF3AuFbqNWdEmxxQW69atM1cSevbsKb1795Z58+ZJ9+7dJWfOnNKlS5ewP7N9+3bzSyAQvseJ/tGjR00/Dfjrr79MIYG+FOhDMXr0aKlTp45/HXiNwoULn7UePBZO37595eWXXz5r+dq1a836oVChQlKmTBnTVwNXRSzoyY8P9CdBgRP4RxfbsGHDBjlx4kTQrVxYJ9YdeDDgSk327Nll9erVQdtQvXp1SUlJMbeJWXBw1KhRw7ze5s2bzbKN+07IiLmbzNePNq8oa9eu8T8/X7585urRnj17ZNeuXf7lWm1C3vFok7XuKlWqmO0L3H/xblO89lM0bcIvoTVr1riqTYmwn3bu3Om6Ntm9n9Dh0G1tsns/4Th1W5vs3k84eXJbm+zaT7jNHW3CceqWNiXKfipUqJB5Yzyr24R1RSubz8aeINjQhg0byqxZs/zLUFigwMCVg3AQ3t133y29evXyLxs3bpzpd3HkyBF/YYEdt3HjRhPOd999J5988om5KoLiArdAYR2BVyDgkksuMX03cItWNFcsrB2DDuKQ6FX5/V8skMkr/pE2dUrJ0DsuzpKqHJ///vtv858E2+PFdxq02wT4pYIrbNY6nd4mu/eTdZxWqlTJ/DJ3Q5uiWR7PNuFr/A7GcRr4jpeT22T3fsJJknWcYpkb2mT3fsLXOE7xNyr0dMipbbJ7P+E4xQk0jlNrO5zeJrv306mAcylr/VnZpkOHDpnCA+fU1jlvQl6xQGVkXUWwoJ/D999/H/FnUPWFjt6E79FQq6iwihaMGAUXX3yxKVYGDRokH330kVkHCo99+/YFXbXAeqxL96EwAhU+QmFn4iPcAREq1uWh683IchwQWD5r7S5TVFiT4VnLM7uN0SzHL5mMbHuk5ZG2PSvbdK5tjGeb8AsI71hge5xw7DllP+E4tX7Ju6VN0SyPV5twnOL3bKzrSeQ2ZXS5VpuskzZ8DnxTwcltSoT9hN+nOJFyU5vS28Z4twnLreM08HEntykR9lPK6XOpjOwPjTY5YlQojAi1cmXasKeWVatWmSo3EtzeNHny5KBlkyZN8vefiAQVmHXFAYUGRqIKXA+2A+9anGs9Tp0Mr8+45f7J8KqWiM9keERERETkXbZescCQr02bNpU+ffrIzTffLHPnzpWhQ4eaDwtuecK9ap9//rn5vlu3bvL+++/LU089Jffcc49MmTJFRo4cKWPHjg36mXbt2plL8AcPHjS3PmHuC2tIWlzOuffee03fjqJFi5qrHY888ogpKtw4ItQPi9ImwysQ58nwiIiIiMi7bC0sGjVqZDpVoxB45ZVXzL1jAwcOlE6dOvmfs23bNnMlwYLnoIhAUYJbm9DxBf0nrKFmAR2G7rzzTvOzKCIwWR6KijZt2vifg+FpcbkHE+PhSgZ+/sMPPxRXToY30ZoMr1pcJ8MLBxljH2X20hqdwUz1MVN9zFQfM9XHTPUxU29namvnbSdD5+1oO7LY6YOpa0xhgcnwJj/enPNWEBEREVFcznkTv/ShDPvn4HH5cGrakKRPXWXPZHjowIl+M6EjHlDGMVN9zFQfM9XHTPUxU33M1NuZsrBwsUGTV8nhE6lSv3whuaZ+Wdu2I3RYNMo8ZqqPmepjpvqYqT5mqo+ZejdTFhYutWbnQfn69GR4z7avLUlJsc2cSEREREQUCxYWLtV33ApJPeWTK+uUksZVitm9OURERETkcuy87cLO27PW7JLbP/ldsidlk597XC5VbJy3AocXJsnChIWBs+9SxjFTfcxUHzPVx0z1MVN9zNR9mbLztodhMrzXAybDs7OosGTPbuuoxq7ETPUxU33MVB8z1cdM9TFT72bKwsJlRv+xRZZuTZsMr3sCTIaHzkarV692TKcjJ2Cm+pipPmaqj5nqY6b6mKm3M2Vh4SJHT6TK2z+nTYb3cKusnwyPiIiIiLyLhYWL/Pe39bJt/zEzGd5dTc+ze3OIiIiIyENYWLhEIkyGR0RERETexVGhXDIq1LOj/5Kvft8oF5QvJKMfapYw81bg8MI9gUlJSRwdQgkz1cdM9TFTfcxUHzPVx0zdlylHhfKY1TsOyjfz0ibD652Ak+GlpKTYvQmuw0z1MVN9zFQfM9XHTPUxU+9mysLCBfqOT9zJ8FBhr1+/3hEjGTgFM9XHTPUxU33MVB8z1cdMvZ0pCwuH+23NLpmyYqeZDO+ZdrXs3hwiIiIi8ihnzLZBQXB1Yu76PbLjwDEZMGmVWdb50koJMRkeEREREXkTCwuHmbBkm7z84zIzrKwFPSrqlrW/A3kk6GxEupipPmaqj5nqY6b6mKk+ZurdTDkqlINGhUJR8eCXCyXcDkNxMbhzA7nq/DJZsi1ERERE5H4HOCqUO29/wpWK9KpAPI7nJRLUrYcOHTKfSQcz1cdM9TFTfcxUHzPVx0y9nSkLC4dAn4rA259C4VDD43heIsEIBps3b3bESAZOwUz1MVN9zFQfM9XHTPUxU29nysLCIXYePKb6PCIiIiIiTSwsHKJkgdyqzyMiIiIi0sTCwiEuqVxUyhTKbTpph4PleBzPSySYej5nzpy2TEHvVsxUHzPVx0z1MVN9zFQfM/V2phwVyoGjQkHgTrMOM44KRURERESaOCqUS6FoQPFQulDw7U74PlGLCtSt+/btc8RIBk7BTPUxU33MVB8z1cdM9TFTb2fKCfIcBsVDmzqlzehP6KiNPhW4/Sk5KTEvj2EEg+3bt0uBAgUkOTnZ7s1xBWaqj5nqY6b6mKk+ZqqPmXo7UxYWDoQioknVYnZvBhERERGRH2+FIiIiIiKiTGNhQXGFEQzy5cvniJEMnIKZ6mOm+pipPmaqj5nqY6bezpSjQjloVCgiIiIioqzEUaEooToc7dq1yxHT0DsFM9XHTPUxU33MVB8z1cdMvZ0pCwuKK1wQw38GXhjTw0z1MVN9zFQfM9XHTPUxU29nysKCiIiIiIgyjYUFERERERFlGgsLiiuMYIAOP04YycApmKk+ZqqPmepjpvqYqT5m6u1MOSpUBnFUKCIiIiJyuwMcFYoSBUYw2LZtmyNGMnAKZqqPmepjpvqYqT5mqo+ZejtTFhYUV7gghgqXF8b0MFN9zFQfM9XHTPUxU33M1NuZsrAgIiIiIqJMy575VXiTVTXivjOKLDU1VQ4dOmRySk5OtntzXIGZ6mOm+pipPmaqj5nqY6buy9Q6143migkLiww6ePCg+VyhQgW7N4WIiIiIKO7nvujEnR6OCpVB6ECzdetWKVCggCOG/7ILqlwUX5s2beLoWUqYqT5mqo+Z6mOm+pipPmbqvkxRKqCoKFu2rCQlpd+LglcsMgjBli9f3u7NcAz8R+AvGF3MVB8z1cdM9TFTfcxUHzN1V6bnulJhYedtIiIiIiLKNBYWRERERESUaSwsKK5y5colL774ovlMOpipPmaqj5nqY6b6mKk+ZurtTNl5m4iIiIiIMo1XLIiIiIiIKNNYWBARERERUaaxsCAiIiIiokxjYUEqtmzZIp07d5ZixYpJnjx5pF69ejJ//nz/4+jK88ILL0iZMmXM461bt5bVq1fbus2JLDU1VZ5//nmpXLmyyatq1ary6quvmhwtzPTcfv31V7nmmmvMpD6YyPKHH34IejyaDPfs2SOdOnUyY4cXLlxY7r33Xjl06JB4VXqZnjx5Up5++mnz/z9fvnzmOXfeeaeZTDQQM43tOA3UrVs385yBAwcGLWemseW5fPlyufbaa83Y/DhWGzVqJBs3bvQ/fuzYMXn44YfN37T8+fNLx44dZceOHeJV58oUx9p//vMfM78XfpfWqVNHhgwZEvQcZhqsb9++5rjDRMslS5aU66+/XlauXBlzZjhuO3ToIHnz5jXrefLJJyUlJUXswsKCMm3v3r3SrFkzyZEjh4wfP16WLVsm/fv3lyJFivif069fP3n33XfNL5rff//d/CJv27at+U9DZ3vzzTdl8ODB8v7775s/gPgeGb733nv+5zDTczt8+LBccMEF8sEHH4R9PJoMcbK2dOlSmTRpkvz000/mD+z9998vXpVepkeOHJGFCxeaohifR40aZf5Q4gQuEDON7Ti1jB49WubMmWNO7kIx0+jzXLt2rVx22WVSq1YtmTZtmixevNgcs7lz5/Y/p0ePHvLjjz/Kt99+K9OnTzfF8Y033iheda5Me/bsKRMmTJAvv/zS/M167LHHTKExZswY/3OYaTBkgKIB/6fx/xZvzFx55ZUm62gzw5uQKCpOnDghs2bNks8++0yGDx9u3jCzDUaFIsqMp59+2nfZZZdFfPzUqVO+0qVL+9566y3/sn379vly5crl+/rrr7NoK52lQ4cOvnvuuSdo2Y033ujr1KmT+ZqZxg6/7kaPHu3/PpoMly1bZn5u3rx5/ueMHz/ely1bNt+WLVt8XheaaThz5841z/v777/N98w0Y5lu3rzZV65cOd+SJUt8lSpV8g0YMMD/GDONLc9bbrnF17lz54g/g98DOXLk8H377bf+ZcuXLzfrmj17ts/rwmVat25d3yuvvBK0rEGDBr5nn33WfM1Mz23nzp0mj+nTp0ed2bhx43xJSUm+7du3+58zePBgX8GCBX3Hjx/32YFXLCjT8I5Ew4YN5aabbjKX4S666CL5+OOP/Y+vX79etm/fbm4zseDyc+PGjWX27Nk2bXVia9q0qUyePFlWrVplvv/zzz9l5syZ0q5dO/M9M828aDLEZ9xWguPbgucnJSWZKxx0bvv37ze3TiBHYKaxO3XqlNxxxx3mFoe6deue9TgzjS3LsWPHSo0aNczVSfzNwv/5wFt7FixYYN49DvzdgKsbFStW5O/XdP5m4VwAt0Wj9pg6dar5+4V34IGZRve7EooWLRp1ZviMW09LlSrlfw6O6wMHDpgrmHZgYUGZtm7dOnPbTvXq1WXixIny4IMPSvfu3c0lOcDJGwQe+Nb31mMU7JlnnpFbb73V/BLBLWYo1nBpGbc7ADPNvGgyxGeceATKnj27+cXPnM8Nt5Shz8Vtt91m7v0HZho73AqJjPB7NRxmGr2dO3ea/gBvvPGGXHXVVfLzzz/LDTfcYG4vwa0mgMxy5szpL4Yt/P0aGW7TRb8K9LFAdsgWt01dfvnl5nFmeu6CF3/jcVv5+eefH3Vm+Bzub5j1mB2y2/Kq5Lr/EHinrE+fPuZ7nAQvWbLE3LfepUsXuzfPkUaOHClfffWVjBgxwrxDuWjRIvNLB/dWM1NyArzTdvPNN5t3L/HGA2UM3rUcNGiQ6bOCKz+U+b9XcN1115n71+HCCy8096fjb1bz5s1t3kLnFhboK4CrFpUqVTJ9fNB/AH+zAt9xp/CQFc6bcGeC0/GKBWUaRtTBOxWBateu7R9ho3Tp0uZz6EgG+N56jILhlgfrqgUuc+I2CPwRxCgSwEwzL5oM8RnvcAbCaBsYgYc5n7uo+Pvvv02nROtqBTDT2MyYMcPkhdsfcBUCH8j18ccfl/POO888h5lGr3jx4ibDc/3NQmfYffv2BT2Hv1/DO3r0qPTu3VveeecdM3JU/fr1TcftW265Rd5++23zHGYaGbLCgAu4fQxXfCzRZIbP4f6GWY/ZgYUFZRou3YUOkYZ7K/GuBWDIVBzg6DNgwf1/uPe3SZMmWb69ToDRdXB/dKDk5GT/u23MNPOiyRCf8Usd7xpbpkyZYvYD7sumyEUFhu395ZdfzDCJgZhpbPCmAkYtwlVL6wPvAuPNB9x6Csw0eri1BEN8pvc36+KLLza3oAb+bsDzUXjw92v4//P4SO9vFjM9G67moqjAaG/4/4q/SYGiyQyf//rrr6A3Fqw3c0KL5yxjS5dxchWM+pI9e3bf66+/7lu9erXvq6++8uXNm9f35Zdf+p/zxhtv+AoXLuz73//+51u8eLHvuuuu81WuXNl39OhRW7c9UXXp0sWMAPPTTz/51q9f7xs1apSvePHivqeeesr/HGZ6bgcPHvT98ccf5gO/7t555x3ztTVCUTQZXnXVVb6LLrrI9/vvv/tmzpzpq169uu+2227zeVV6mZ44ccJ37bXX+sqXL+9btGiRb9u2bf6PwBFKmGlsx2mo0FGhgJlGnyd+n2K0naFDh5q/We+9954vOTnZN2PGDP86unXr5qtYsaJvypQpvvnz5/uaNGliPrzqXJk2b97cjAw1depU37p163zDhg3z5c6d2/fhhx/618FMgz344IO+QoUK+aZNmxb0u/LIkSNRZ5aSkuI7//zzfVdeeaX5nTthwgRfiRIlfL169fLZhYUFqfjxxx/NwY2hOmvVqmV+YQfC0J7PP/+8r1SpUuY5V1xxhW/lypW2bW+iO3DggO/RRx81v1Dwy7lKlSpm2L7AkzNmem74I4c/gqEfKNyizXD37t3mBC1//vxmCL+7777b/JH1qvQyRREc7jF84OcszDS24zSawoKZxpbnp59+6qtWrZr5/XrBBRf4fvjhh6B14M2Fhx56yFekSBHzRtkNN9xgTvq86lyZIpu77rrLV7ZsWZNpzZo1ff379ze/Yy3MNFik35UoymLJbMOGDb527dr58uTJY96AfPzxx30nT5702SUb/rHnWgkREREREbkF+1gQEREREVGmsbAgIiIiIqJMY2FBRERERESZxsKCiIiIiIgyjYUFERERERFlGgsLIiIiIiLKNBYWRERERESUaSwsiIiIiIgo01hYEBHZZPjw4VK4cGHbXn/Dhg2SLVs2WbRokfq6sd4ffvhBEsGJEyekWrVqMmvWLElU06ZNM5nt27dP3GLChAly4YUXyqlTp+zeFCLKIiwsiMiz7rrrLnMy98YbbwQtxwkxlnuNVWi4zZAhQ6Ry5crStGlTSQQtWrSQxx57LGgZtm3btm1SqFAh1xQwV111leTIkUO++uqruL8WESUGFhZE5Gm5c+eWN998U/bu3StOgHff3bp9J0+eFG0+n0/ef/99uffeeyWR5cyZU0qXLu2Ywg65pqSkRFW8v/vuu1myTURkPxYWRORprVu3Nid0ffv2Tfd533//vdStW1dy5col5513nvTv3z/ocSx77bXX5M4775T8+fNLpUqVZMyYMfLPP//IddddZ5bVr19f5s+ff9a6cYWkevXqpshp27atbNq0yf/YSy+9ZG4n+eSTT8y77ngO4B3nrl27SokSJaRgwYLSqlUr+fPPP9Ntw9y5c+Wiiy4y62jYsKH88ccf6T5/9+7dctttt0m5cuUkb968Uq9ePfn666/Pevf9P//5j3kHvnjx4mb7LXgHvl27dpInTx6pUqWKfPfdd2ddHfm///s/ad68udkm651ttLV27dpmWa1ateTDDz8MKlzwemXKlDGPI+f09t2CBQtk7dq10qFDh7Nee9SoUdKyZUvTtgsuuEBmz54t0Zo5c6b861//Mm2rUKGCdO/eXQ4fPux/HNts7dNSpUrJv//9b/+J9vTp02XQoEFmG/CB7Qm9kmDdJvfTTz9JzZo1zTZiHUeOHJHPPvvMHG9FihQxr5uamup/3S+++MLs2wIFCpjj+vbbb5edO3f62432An4Wr4ftgePHj5t1lSxZ0mzzZZddJvPmzfOv19q+8ePHy8UXX2z+HyADHHNYJ14PxyEeCzzGr7nmGvM99gEReYCPiMijunTp4rvuuut8o0aN8uXOndu3adMms3z06NG+wF+P8+fP9yUlJfleeeUV38qVK33Dhg3z5cmTx3y2VKpUyVe0aFHfkCFDfKtWrfI9+OCDvoIFC/quuuoq38iRI83PXX/99b7atWv7Tp06ZX4GP58jRw5fw4YNfbNmzTKvc8kll/iaNm3qX++LL77oy5cvn1nPwoULfX/++adZ3rp1a98111zjmzdvnnm9xx9/3FesWDHf7t27w7b14MGDvhIlSvhuv/1235IlS3w//vijr0qVKqadf/zxh3nO+vXrg9q9efNm31tvvWUeX7t2re/dd9/1JScn+37//Xf/c5o3b+7Lnz+/78knn/StWLHCfADWg+35+OOPTdufe+4587PLli0Leq3zzjvP9/333/vWrVvn27p1q+/LL7/0lSlTxr8Mn5Hr8OHDzc9heypUqOD79ddffRs2bPDNmDHDN2LEiIj7+J133vHVqlUraJn12lj+008/me3797//bfbhyZMnz3ncrFmzxuyTAQMGmOx/++0330UXXeS76667zOPYJ2grtgvbiP02aNAg89i+fft8TZo08d13332+bdu2mY+UlBTf1KlTzTbt3bs36Nho06aN+fnp06ebPK+88krfzTff7Fu6dKnZhzlz5vR98803/m379NNPfePGjTP7a/bs2ea12rVrZx7D6yBPvA7ajNfG9kD37t19ZcuWNT+LdeP/RpEiRfzHk7V99evX9/38888mAzxWt25dX+fOnX3Lly83WeBYX7RoUVBepUqVCvq/QkTuxcKCiHxeLyzg0ksv9d1zzz1hCwucjOMELxBOpOvUqeP/HielOMGy4KQN63j++ef9y3Cih2V4DHCyhe/nzJnjfw5O0LDMOnlHYYETzJ07d/qfg5NpFC3Hjh0L2qaqVav6Pvroo7BtxXKcmB49etS/bPDgwUGFRTQ6dOhgipjAwgIn1aGw3m7dugUta9y4sSm4Ak/uBw4ceFYbQguFV1991ZwgwyOPPOJr1aqVvzg7l0cffdQ8P5D12p988ol/GU6msQz5n8u9997ru//++4OWYZ+g+ES+OHnH/jlw4EDYn0dm2K5A4QoLfI8TeMsDDzzgy5s3rykSLW3btjXLI0GRg/VYPxP6OnDo0CFzjH311Vf+ZSdOnDCFRr9+/YJ+7ocffghaf4ECBfxFXyQ4Pl566aV0n0NE7sBboYiIREw/C9xisnz58rMew7JmzZoFLcP3q1evDroNBbc6WXD7C+D2odBl1q0pkD17dmnUqJH/e9z6g1tgArcDt/vglicLbj85dOiQFCtWzNxiZX2sX78+4i0nWB+2z7qVCpo0aZJuJmjbq6++atpQtGhR8xoTJ06UjRs3Bj0Pt7+EE7p+fB+aL27bseBWImw/+kMEtgu3mFntwq07GMUKtwfh1p2ff/453TYcPXo0qM2BAvcXbq0K3TeRIH/cqhS4jbgFDKMfYR+0adPG7DPc/nXHHXeYW7xwC1OscPtT1apVg44f3AKF1wtcFrjNuPULtx9VrFjR3J6E28wgdJ8FQrbo3xJ4jKPT9SWXXJLu/oKePXuaW/JwSyEGQQh3/OF2sYy0n4ich4UFEZGIXH755ebksFevXhleB07GLFYn3HDLYh1+M1++fEHfo6jAiTBOsAM/Vq5cKU8++aRoeeutt0xfgKefflqmTp1qXgMZhXbQDt2+WAT+LNoFH3/8cVC7lixZInPmzDGPNWjQwJy8o+BB0XDzzTf7+y+Eg34fkTrmZ3TfYDsfeOCBoG1EsYFCE4UATugXLlxo+qNgP73wwgumD0esIzEFbp+1jeGWWduMwgz7B30dUMygj8To0aNVO/2H7mv0AVq6dKnpwzJlyhSpU6eO/zUte/bsCSqMici9WFgQEZ2Gd1x//PHHszrxoiPxb7/9FrQM39eoUUOSk5Mz9ZoYWSewsyuKA5yA4jUjwcn19u3bzdUOzM8Q+IET6XCwvsWLF8uxY8f8y6yT9UjQRnQ879y5szkxxjvwq1atirptoevH9+m1C+++ly1bVtatW3dWu9Bx3YIT51tuucUUIOj8jY71OHkNB53VV6xYYUYx0oL8ly1bdtY24gOjOwH2Dd7F79evn8kdHadx4g14TuCVLi1oJzrc4zhGx3Jc/Qq9AmNtX+DroxjC8sBjHFcwUJigUDgX/D/o0aOHuXp04403yrBhw/yP4XjDVQzsByJyPxYWRESn4ZafTp06nTU85uOPPy6TJ08275LjxBq3TGEI0yeeeCLTr4l3oB955BH5/fffzW0suNXn0ksvNbehRIITVtxWdP3115uTOZy0YvK3Z599NuyoU4DRgfDu9n333WdOiseNGydvv/12utuGUY0mTZpk1o1bYvAu/Y4dO6Ju27fffiv//e9/TWYvvviiGZUKIzql5+WXXzajPGEf4Of++usvc6L6zjvvmMfxGVcCcBKNx/EaGP0o0kSDGLEIVxjwrroWXMFBJmgLrlbgSsX//vc/f9swkhO2H4/9/fff8vnnn5urCrh9C3A7E/Y39tuuXbvUJpDD7U8oEN577z1TnGFUMhyzgXCLFo4DbCNGLEM2uArx4IMPmqtdmNQOxweOE9y+lN4wvbhihDZjxCi0E4UJipHA4hHFJEaQOtdtd0TkDiwsiIgCvPLKK2ed6OEd6pEjR8o333wj559/vrm1Bc+zhurMDNxHjxNVnPjjHnfcP4934dODE0MUBrh96+677zbvGN96663m5M7qxxEK68XVGJyo491jFCHoV5Ke5557zrQdt9dgWFmcwKOYiRaKBGSGvgw4uUZBcK53wHG/PoabRTGBQg99BNCfwbpigduMcBUA9/qjbwpOzpFFUlL4P2foh3LDDTeoTtKG9mDIWBQ2uDKAPHFM4GoLoMjBULYYAhgn2ZigD23HcMWAghRXupAFbhFKr/9DLLAuZIViC+vGlYvQ4hFDB2O/PPPMM+ZYsYohPLdjx46mTwj2+Zo1a0x/GgxLGwnagCskGGIZxyBuS8Pwwli/Be1GsY7jnIjcLxt6cNu9EURERPGCW5HQoRq35AR2fKb4wtUYXKXBVbTAW9mIyL1YWBARkevhnXyMXhU4ShfFlzUxHvrDEJE3sLAgIiIKgNt5ZsyYEfax3r17mw8iIjobCwsiIqIAW7ZsMR2Tw8F8HvggIqKzsbAgIiIiIqJM46hQRERERESUaSwsiIiIiIgo01hYEBERERFRprGwICIiIiKiTGNhQUREREREmcbCgoiIiIiIMo2FBRERERERZRoLCyIiIiIiksz6fyP3112NePlmAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[np.float64(6.027862903633642),\n",
+       " np.float64(6.059228790372735),\n",
+       " np.float64(6.059690135889395)]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plot_learning_curve_rmse(best_rf, X, y)\n",
+    "plot_rmse(best_rf, X_train, X_test, y_train, y_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x350 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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LLcKxxx4bnn766RiUJB988EE455xzwmuvvRaDxhVWWCEccMABMQApdb5IUkswsJCkKpHrTyGbgvTWW28dC7MUJF944YWaeW688cZYu77RRhvFwuMXX3wRbrvttrDzzjuHa665Jsw888xxvhNOOCHWUk822WRhp512qvn7avsPZB199NGxsMm6kdefCq9NtU5PPfVUOPjgg8NMM80Uttlmm5gSRNoPgQ1BC/snIbXnkEMOiQEJqT4UlilUs4xK8JmsD+tJEMPPmGKKKeJ30pIIKihY8xl//fVXDKhOOumk8PPPP8caf2y88cbhjz/+iOtz4IEHxm1DNqBqSm+99Vb8Tp+KYgsttFA8f+inwXoTYHz33Xdh7bXXLjkvCFJSYEE6FfuyUCiEfv36hZ49e8bAY5999mmWbZGkShlYSFKVqIGndYIWg3LohFvcB4BAhI7DdIwmdx70FbjoootiQZmfm8Kkk04aWymKWw6aYp1orTnxxBPDggsuGOdJn8FoTRTSSfFJIz4RcA0cODAGHldeeWVNYZ55KRBXgvVlHZ5//vkYWBSvD+tP60QW20MrDK1K2267bVxHCvhzzjlnDCxWXnnlmpaX5k7dotBfLE0jmEidtsvNm1o70rzg2BIkXXbZZbHFCbRsHH744eGdd95plu2RpErYeVuSGlFw//jjj+t0wM1KBXhqlUeNGhV++eWXWPs+yyyzxBGCmhMF6+KgoqnWiU7UP/74YxwtKi0jfS2//PI184BCLqlipAOloCLtP4KLppANlAh6WI/ffvstLLPMMrHwTapUa6DlBF26dKnzu9TfJc1T37xpWpqHYI3WiQUWWKAmqEhooZKk1mSLhSRViVQa8t6pdWd0IGrnV1xxxZgfP8EE/7e+hjQX+j8wElFxB1/+pjmllKZiTbFOn3zySfyeUpJKIfAAnZFB4FKMfhBZFJhJXcqacMIJYxBSH/p30NfioYceikFMMYKM1sC6459//qnzOwKg7Dz1zZumpXnYRxy7Uvt01llnbdJtkKRqGVhIUpVIpSFHnppj8vtJ06GzNB2SSVOhYM3QopNMMknsv0CBj4Ih/R3OPPPMekcSqhQF8XJSITSLjs1NsU60doBO6nPPPXfJeUql9DQktWxkVdIJ+cgjj4z9Neg3Qodu+pAQ3HFsSO/677//QmtI/VpSmlNWmpbSnNL+KjVvSoHKdgCXpHGVgYUkNQIFWPL9+aKwTf+Fq666Kjz++OOxgEhNOh2gs6MqpfcVFKe8ULiv73P4m2KpNaBS9C1oinVKrSGkIC299NL1fmZqBfnss8/KtnwkU045Za3hVysJUHgfBUEFx+CII46o9TuCvWL17eemRqoSXn/99VqjU4GO7qzLvPPOWxOEEDgwvViaNt9888XvpK6x70vt09ZK+5KkxD4WklRlS0HxC9YoJKY3KVNIT+lQqXY/YWSjlCaURUGxXMoOBXkKlynHHsxb7UvTmmqdll122dipm47RpQIe1pO+DakwzDC8rCt9HxL6Ztxyyy11+h0QqGS/ePN0Y7aJjtO8b6JYeg9ES6RHMWLW/PPPHx5++OFaLRH8zLQll1yyplUDa665Zhyl64knnqh1rjF0MG8JT/1XGFWLgQMYderVV1+t9ZnXXntts2+XJNXHFgtJqgK1/muttVbsT0EwQQ0y71q4+eab4+hHTCeHnhYM3hLNS9QoGPK+gREjRsR3FRSnMTGkKKlUjLJE3wMCFZZD4Z6/Z/hYRjmiZp6ghkIz740oFRCUQ8G0qdaJ0bAGDBgQO2CTvkQhmvWixpyWEV4MR6sIhWDercBoRbzfgpp7phFo0BJDelYepHXRSZt3cxCY0ErAuzduvfXW2FpSHPgwkhXOO++8OLQrrTRzzDFHHC2qUvfcc0/8DBAsjRkzJo7OhPQuj4S3Z3PcGBqWUZtAoEB61v77719ruewfhsk96qijYidsWmt4twXDzDKNbU323HPP8Oyzz4Z99903HktaO2i5ScFbS7bMSFKWgYUkVYF+CVtuuWVMteGLQIOaZwrdvDMhpe9QeCW1Z+jQobFmfZFFFonveTj99NNrCqZJ//79YyH4pptuigV0auApfFOIpwBMLTfvoGAoVwrMFFRZZjWjSxE8NNU60WrB8LF8UainQzFBFZ9BoTj7bghe2MZnUfimkzWtHekFeXvvvXfu48HQtwRMTz75ZCz0E+Sw7oyKVTwcMKMo8a4HAg/ec0EwxQvyqgksCLboV5NFh3jQxyMbWKT9S3DGFwV+hr3lPR/F/VMYNevyyy+P28Kxps8LAd0pp5wS1lhjjVrz0j+Gfckbua+//vqaF+Qdeuih8W3dbf0t65Larg6F4jZkSZLU5jC8L+/tIGDbYYcdKv67DgPHNut6Se1BYYB19bCPhSRJbUy2zw2oI2TwADTUqV6SmovhlSSp3aKPRKlO6MXoS0P/kHEFL0GkAzhpXKRNkQrGm8n79OlTM4KUJLU0AwtJUrtFB3Y6WDeE/iXTTz99GFf07t07BhP33ntv7CvCurEd1aRASVJTs4+FJKndYuhZ+iY0hI7f42unaPtYSPnZx+L/ci9IktotRrOyT4IkNQ07b0uSJEnKzcBCkiRJUm6mQkmS1I4N7j4kvtyxc+fOrb0qkto4WywkSZIk5WZgIUmSJCk3AwtJkiRJuRlYSJIkScrNwEKSJElSbgYWkiRJknIzsJAkSZKUm4GFJEmSpNwMLCRJkiTlZmAhSZIkKTcDC0mSJEm5GVhIkiRJys3AQpIkSVJuHQqFQiH/YiRJUlvUYeDY1l4FjYcKAzq19iqoFdhiIUmSJCk3AwtJkiRJuRlYSJIkScrNwEKSJElSbgYWkiRJknIzsJAkSZKUm4GFJEmSpNwMLCRJkiTlZmAhSRrv7LbbbmH99ddv7dWQpHbF1yJKktqFu+66K/z+++9hq622atTf//DDD2HYsGHh3XffDe+880745ZdfwnrrrReOO+64sn9z9913h+uuuy589tlnYZJJJgkrrrhi2HvvvcPkk09eZ94333wzXHjhhfF7hw4dwsILLxznnWeeeRq1vpLU0myxkCS1m8Di+uuvb/Tff/rpp2Ho0KHh448/DvPPP3+D81977bUx6Jh00knDQQcdFDbeeOPw4IMPht133z2MHj261rxvvPFGbGX58ssv4+/5+fPPPw+77rpr+PDDDxu9zpLUkmyxkCSpAvPNN1946KGHYmsDrRWrr7562Xn5/UUXXRQDEL537NgxTuf/Bx54YAxwdtppp5r5zzjjjNC5c+dw6aWXhqmnnjpO69OnT9hss83C2WefHQYNGtQCWyhJ+RhYSJJa3d9//x2uuOKK8MADD4Rvv/02FrKnmWaasNxyy4X99tsvzkNt/3333Rfef//98NNPP4WJJ5449OrVK+yxxx5hrrnmqnf59Lf4+uuv489LLLFEzfSLL7641v/rQyoTX5V47LHHwl9//RW22GKLmqACK620UphhhhnidqTAYuTIkeHtt98Offv2rQkqwM+rrbZabGkhDWuqqaaq+d3DDz8cLrvssphiRaCzwQYbhEUWWSTstdde4dhjj7V/iaRWYWAhSWp1p512WrjzzjvDuuuuG7beeuvw77//xgL3Cy+8UDPPjTfeGHr06BE22mijWMj+4osvwm233RZ23nnncM0114SZZ5657PJJRbrgggtiSwItBslss83WLNvz1ltvxe/0kyi20EILxQDqzz//jMFRQ/OyX+jXscIKK9QEWEceeWSYccYZY6oUgQt9OZ588slm2RZJqpSBhSSp1VHDT+vE8ccfX3ae888/P0w00US1phGI0BmbDtKHHXZY2b9deeWV4zy0jKyzzjqhudHCgJ49e9b5HdMKhUL4/vvvwyyzzNLgvPjuu+/i97Fjx8bUKFoprrzyytC9e/c4fdNNNw1bbrlls26TJDXEztuSpFZHB2c6RdfXUTkFFRTKR40aFVsfKGBTOGckpXEJaVDo0qVLnd917dq11jzVzEvLBQEJo1GloAK0fNA5XJJaky0WkqRWR3oSfQP69esX+yDQ74GhWemTMMEEE9QUqukT8dJLL9UZVYm/GZdMOOGE8fs///xT83NCq0l2nuy8xYrnZdQoEEwVKzVNklqSgYUkqdWRqkRfgqeffjq8/PLL4fnnnw933HFHWHTRReO7HX788cc4BCudp+lTMeuss8bCNu97OPPMM+sEGq0tdbSmdWGmmWaq9Tumsd4pzSk7b7E0LdupW5LGVQYWkqRxAh2z6f/AF+lO9Km46qqrwuOPPx4L2HR2Puuss+qM4vTrr7+WTCMqRmG+pSywwAKxY/nrr79eJ7DgnRW0LpC+lOYF82644YZ15mW955133vj/6aefPn5nNKhipaZJUkuyj4UkqVUxAhRvxM6iMJ3eOE3gkNKhCDiyKLzTmlEJCvK//fZbnWU0h969e8f+EYxkxfYlTzzxRExnWmuttWqmEXjwfguGkM22WvAz05ZccsmaVg3epcHPjALFtiQEXbfeemuzb5ck1ccWC0lSq6JQTEGb/hQEE3TI/uqrr8LNN98cOygznb4GtGAcc8wxYfPNNw/dunULr732WhgxYkQcdjVbeC9nwQUXjEOynn766XFoV4IVCu1TTDFFxevKuyOyfR8++OCDmmmLLbZY/ALbsOeee4Zzzjkn9O/fP6y55poxUGBYXNK4GMmqeDhc3sexyy67xHdfYNiwYeG///4L+++/f818nTp1iv8/6qijwvbbbx/fX8Fws7zrghYfgpaWbJmRpKwOhZaoupEkqYwxY8aEwYMHx34VFIwJNKiVJ+Vpxx13rHk/BX0veAM1hXmCAl4It88++8RAgZffUbhO6I9RPI2RlZj3qaeeiiNKUWiv5gV5qG9e3imx++6715rG5zPMLWlK9A/hXRSsc6lghlQo3tLNCFcEBwQ/e++9d00aVNbw4cNrXpDHsggweEngwQcfHE499dT41u5KdRg4tuJ5pUoVBlh33R4ZWEiSNB6gNYQWkqFDh8YX61XKwELNwcCifbKPhSRJbayFpzj1i1aem266KaZDlWrhkKSWYDgpSWq3KJDzVR/6MNBnYlxButi+++4b1lhjjThKFG/uvueee+J03j7euXPn1l5FSe2UgYUkqd26+uqrw6WXXlrvPNNNN12tvhqtbbLJJosd0e+7777w888/x8BnzjnnjP0xqulbIUlNzT4WkqR264svvqh5m3U5DBvbq1evML6yj4Wag30s2iePuiSp3WKoWr4kSfnZeVuSJElSbgYWkiRJknIzsJAkSZKUm30sJElqxwZ3HxLfcO4wtZLyssVCkiRJUm4GFpIkSZJyM7CQJEmSlJuBhSRJkqTcDCwkSZIk5WZgIUmSJCk3AwtJkiRJuRlYSJIkScrNwEKSJElSbgYWkiRJknIzsJAkSZKUm4GFJEmSpNw6FAqFQv7FSJKktqjDwLGtvQrtXmFAp9ZeBalJ2GIhSZIkKTcDC0mSJEm5GVhIkiRJys3AQpIkSVJuBhaSJEmScjOwkCRJkpSbgYUkSZKk3AwsJEmSJLV8YPHll1+Ggw46KKy++uphiSWWCMcdd1xoKl999VVc5uDBg5tsmarrrrvuivv5xRdfrGj+3XbbLay//vrNvl5qPK4ZjinX0Pjul19+Ccccc0xYa6214jZzfrYl49v1xH2E48B9pb5pkqTxX9Wvejz++OPDBx98EHbaaacw5ZRThhlnnDE0p99//z1cd911YfHFF48PqvaMh/VLL70Uttpqq9CtW7fWXp3xpkA+zzzzhJVXXjmM6x577LHw3nvvhd133z2Mq7hWOTebs+B89tlnh4ceeijeg2aYYYYwxRRTNNtnqW0ggOFZwb2xOd1yyy3hlVdeCe+8804YOXJk+O+//+qtoPn000/D+eefH15++eUwZsyYMO+888brd8kll6wz76hRo8KFF14YHn300fDrr7/GZ+vmm28eNtlkk9ChQ4dm3S5JapXA4p9//ok3VW522267bWhq0003XXj66adDx44da6bxsLj00kvjz+09sCCoYF9QaDOwaBrsz/XWW6/NBBZ33313ycBi5513DjvssEPo0qVLaE3XX399vI6bM7B47rnnwjLLLBN23XXX0BYNGjQoFAqFMD5bbLHF4r28U6eq664aHVh8/fXXzR5YXHHFFbHQT2XEX3/9Fb799tuy837xxRfxuuR5tt1224VJJ5003HbbbWHvvfcO5513Xlh66aVr5iXo6N+/f6w42GKLLcJss80WRowYEf73v/+FH3/8cZyuTJCkrKru+j/99FN8IHbv3r3Bef/4448wySSTVLP4WCvTtWvXqv5GUogFuJYqxLU2Clo9evSoaN7G3IeaW+fOnVt7FZp9v0wwwQTj5b2cFs5pp502bt/+++9fb2BxwQUXxIqxq6++OgYiWHfddWPF3GmnnRZbP1JLxO233x7efvvtMGDAgNCvX784baONNgoHH3xwGDp0aOjbt28M2CVpvOljQV8KanZTLS+tBymHNvWLePDBB8M222wTll9++XDGGWfUm09cqj9F8TSamLmhFn9mdnnU4FIbRI3zCiusEDbYYINw1FFHhZ9//rne7Tn88MNjrSf52qWar/mcM888s9Z0to8aqJVWWilu4/bbbx+GDx9e5+///fffcNlll8X9tdxyy8UHBX9bLg/+hx9+CKeeemp86LBO5I6ffPLJMZDL7v/UcsM+Sfsi7avvv/8+pohQY7fKKqvEz91ss81iDRvrUwrT+XvWc9lll43r+cADD4RKff755+Hoo48Oa665Zlxvjsu5554bRo8eXWu+b775JqbQpc/p06dPTGPh2FUinUNs4xFHHBG3j/1Pzd9nn31WcV8DlpHy8dO5BtYj7c9sq9hTTz0V519ttdXi53F8eNAXf2Z9Kj1nGvosfpf2V3ZdUw57qe1O0z7++ON4LnOcWPaee+4Zz3E88sgjYeutt47T2T+33npryW044IAD4jpx/FhH+lmREpnFZ1FrTNpHdh2z65QKTyyDZW288cbh8ssvD2PHjm1wX6btoXIje8zSPkh9vp5//vm4z1dcccW43tkWH8477hP8jp+ZVu48ef/992MtMvNyznJ9sZ5///13OOecc8Laa68drzNaTj755JNQqVL3xGrOcbBPOT969+4dvw488MDY/y17jicN7Zdqjgn7i3sM2835cNFFF5Wcr1wfC47dzTffXPOcYF2ojS9OJ8o+C5588sl4j+czOYe5x2Q/k23mnOPcy553aZkfffRROPTQQ+PxYvtYBp/JNVet6aefPgYVDeEe+MQTT8QU3hRUYOKJJw4bbrhhvHe+9dZbNdPvv//+MOGEE8ZgIot9zbZyDTb2+EtSS6q4ipOHzdxzzx3OOuus+NDjCzPPPHP8/vjjj4dhw4bFfFC+mqI2jOZgbpjFn8nNGffcc098YC666KJhjz32iDVk1CDRBE+hfPLJJy+7bB6K5GlTkKbpOYvlpnkScl+HDBkSH258Fg8XcmEPO+ywcMghh8RaqOT000+PtVE83HiAErxQQ8VDqRiF7h133DE2hRMUkVdL7i5/z4OR2i6a0Nn/1DLymeyTySabLP79XHPNFb9TyON3BFgsg4fRM888E2vNeOAceeSRdT6b3F8egJtuumn8P4UA5iPlraFUFnKM2Q+kZLFuU089dSyI3XDDDeG1114Ll1xySaxBZz322muvWGDiczhfyCX+8MMPY1pdClYbwnpSgFtooYXi8tgmPosCLuddNn2uEpwbJ5xwQuwEzPlT/EAn7Yz9PMccc8TjwzEgAKRwxvGZZZZZGvyMSs+ZSj6LQjCFMvYZ650svPDCDa4H18hEE00Ul825eM0114R99tknrhMpGRwXWiHvuOOOcMopp4TZZ5899OrVq+bvb7zxxthCwD6aaqqpYooHKR0UUllWugewXlyrnJusb3Zfg4IchaGZZpopXhd85htvvBELj5w7XCP1WXXVVePfFh+z7D6gkEywROEte27ddNNNcfmzzjpr2GWXXeI0ghMK1BTkOYezvvvuu3ieEVDwuaRfXXvttfE8I1AjuCBIJC2Ga5TzkAJzJYXOvOc4x5D5aLnhXst9kvOC41kc1De0X6o5Jpy7nLfcx9iHrA/3jGoK6Bw77rkEMdxjuO/dd999cXu5b1JIzuJezn5lO6lQ4TnD/ua+k84x9g/3OfYL11HCfmEagTRYBq0NTOP+9eabb8YgszlwP+Y+yrEstuCCC9YcE36mn8a7774b+18Ut/IssMACsVWDeZPGHH9JGucCCx7eFCooOMw555xhnXXWidNTbSS1QjwEuck1FTqHU1Au/sxs7RkBDLVm2TQQbrANoeaK5RNEZAMLCm886Pg8bvTgpk8BkYIZD8CEGn4eauRME4SwLuwHggKWT81aKmgwilap/F8ephS+KbRMM800NdOZn89jOrVr7H/WKQUPxUEKOc0UDLOd/Pg8WhSYzjI4flk8oDhmFGRBAZNtomaWAhU1aOVQiGR5V111Va0gcqmllooFFfYhBQdqcqlxpSBLQayxWFf69WSXQYGVgjEFcPZ3NShocz5R0KEDcPG5RQGGBz7HNts5OBVKG1LNOVPJZ9EiRK0mBYjidW0I5znXUDo3KPgPHDgwnnsUWClsYY011ojrRCCRDSwIQNlfWczH+UVnbQIlsF5ci2xD8TpSED/xxBNjQSp7vVIwIjjmnEu13OUwH1/ljhko9LMfs/nrv/32WzxPCLhpwcue77TW0PrA+Z7tt0TwRH4712Gal4I3hVpq2Qka0/4k6GJ/EnxUex425hy/8sorYwUK+5Na+LR+3G9Yv1JK7Zdqjgmtm2wjgQefnyo2mDel7jSEexf3heJAjr/nOqFVjZa97D2M9eZ8TPc7Po/7NedtCiy4H3Iesj2lrmMqmWgR5hi3FCpSQIVLsTQtzcP5ybqXmpc+U+zrNG9jj78ktbn3WFDz05RBRSUoINCBjhqzajtDUtvGTZmaoJQWkmqPaUXI1urxMORhR2GKh3/2iwchLQnU8oFm+/SwzNZeEhRQOMyi5p51ZxnUVGWXy4OUghCFlUoQBKQHMrWA1KSyHAojFFqzNV4JD6NUyAI/8+DmQcd+KIfWBmrkSNnis7LrTYGUQuizzz5bs8y0X7OpXdViXxYXYNLIKqQVNLW03tTyVpKmU6yacybvZzWEgli2sJaCBtYjBRWpEEvrCK0kWSmo4BrjnGUb0rzU+laC85gaVoLNtIz0RUpMmicvWlWzhee0XGpyOX+Kz3em/fnnn3U+m0JeCiqy+419UG5/5j0PKz3HuccQ1JPSk1XfgBrl9kulx4QafgqztBqkoCJ7z6jEvffeGwNpAoHsZ/HZBGtUUhXvw+JKFPY7gQ7rzXFrSDredITmc1oKz6Vy/WnSAAtpnvrmTfOneRp7/CWppTRZb8+UDtGSqOUit5Z0BmoNqbXngUjNVKpF5+FT/ABiXm7iBA+kctBqkWqV+Zmgg0JzQq07BYqUMlQKD7psC06pVBmm8YBLCGgo9NOiwFcp1MxWggIptbE8vCkYFgdaBAvFSAsploJD0jDKSfnkpEuUe+dICiLocEjNIuvGPqWAQ2GJQhvN/AmpP1kcn2wH3Z49e9ZJE0i/J4hqaqQpUdtJrTU19ossskhNjndK7eFhX1xYoSBDkFfNOVPJZ+VRPCR0GnyhVGoetfYE1sWtLxdffHEMDotTLSo9P9M5k03jKrc/6rtmG3MfSucyKV7F0rTi873cvim1zWl/pvOQ2v3iPl6cE9mgppRKz3HuMVw7xWlXtBSVGy2u1H6p5pik/VPqvlZphRL3OwJqWsbK4b6R/YxS51d2n6S02HLo40BwT8oWwf78888fAyyeEaXOh6aSWnupeClGilR2nvrmTfNnW48bc/wlqc0FFuXSZsqNv12uQ3E1eFiSO02awAsvvBCDjJNOOikWdunoTIGKpuHU6TmhkEStF60IFHR54NBJk+Zoao158BSnDbEdpCSUy6EmP76xaDkp19eg0pFVSFsgPSB1jKZASmoDhUIKq005vGVaFqkh5VI/siOHsW+p6aR15tVXX41BFMeFDpn77rtvnCcbyIEgkX4aSX2569ltq2+892rOOWplSfMi9YhaW76TTsS5RcoBqWn00aFTetaxxx5b0z+l0nOmks/Ko9znl5ue3Z8EGXQGJVCnTwXBaGodI3Wl0pzutMz99tsvXnPlCtao75ptSH3pe9Wo73xraL+lmv0sru+GXiZa6TneGKX2SzXHpCnwedyXuEeXU3wfbYp9wjVKbT6VOlxbVCaRpkh/jOL+dU0l7Tf66hRL09I83Cu5z5eal6CCVh3uh5LUFjT7+JTcNCncFquvRjyroRcD0UxMGlbqhEfhlWEA6ZvASCDUVmXzxZF9iPLApxBHLjG15tSoFRfy6djIQ4m0kYZq51JNJ/0KimuKi0d34fdsH60NxWkK1e4LWip4+JBLnFWc1pKVTQErrsWsryY61X7y0K9kvdO2kubBFwEcfS4oTBOcUNNG/ndWJUMal5L+jhaabK0zn8nxreaFjrRcZUeKIv2L9WXEHAr8BFXF650KRtWcM5V8FlrjJVnkxdN6wDVSXLCnxrj4vRnl1jGdM6RVNXTONHTNVisdc/L16QNU7fleLfq0FJ8XTVlApxUwvZwtW/Cmtp/hTStVzTFJ+6fUCFWVjojFNUGqEx2aG2ppqFZD1waVSHxRmcE+oh8LHb5pLWyO64rP4tpI6Y5ZKX2Q1hNwDOnPxzssCCSy1xQjRxFAzTfffE1+/CVpnO5jUQ7N2hTWs7nY3BDpbFeJlN9dKpWn1FCxqcN1Sh2gUMFDM/uVLbRSU06hjhQovkhXKB6ZJHUIpLBQqtY7pQuAXGHQKZrtzPZLSP0OEmqqSd2ilaTUA4gHSjalIj2MS+0LHjDFNXjUJte3nxltJZvKw890PKc5nRSCchg+kQI089LJtRiBUtr/LLO43wC1cykNK21L8THKPkirkdIoinPm2Q/Z45Hdp6VSqUqdW6m2Pq0zrVrF651auqo5Zyr5rOy10BypX+WkgkvxucWoUNltyK5jqfOTIIwAMr1grBhpZdwnKrlmq8Xfs1606KXPAD8zjXOguP9THpzfxevflGk39I0hSC4eGrrajrvVHBOuRwaXuPPOO2udr+meUQkCRq5BCvSllDqfKsUx5LwrPk/ZruLrnvsbgRLbR4VDc2B9eBaQPsjoWglBOu+sIKjLpoKS9sj6FA/3zH2L51M2faypjr8ktckWC4aEpOmZkYKorSZP+uGHH644LYXCNzVdjONNgYMHIYUEbq70i+AhwdCTPPSorSGXlhqoSkfOYXnks7NO1BaRwlBqyD/SQUjNYSQc+gdQA8nNnU6NDImYggYK3GwzBS9SgFJHRVK2KJAzf7aGjBF1GP2H4QN58DIPD0JadBgHne1Ib11NwxSSXkP6FDVbfB61YwzfyEOJ93NQK8tDmn1R34vE2LfU3KXUHeYn9YX3gNSXUsL6k5fNMI5bbrll3GcUnHgwEmgQKDH+PsulJYh3cjBkJ4V+HrjsA9Kh2J5S/TzyYNv5HNKIKFTQasHwtwRu2U6nCetAKh2FK1oX2DYe8qRrkJpAoZAaQgogpD5R0MoOQ1xONedMpZ9FTS8j5NAXgxY6Ut1Y/6asbS9G4EsqHSMxUbvL9cb+pDWG67H4OmYdObaMMkRLDfuTa5VrlpQU+kOloUO5rrlmaTmjZYR331SS7lQt1pmUO4ZO5e3kqUWS4Wap+WWUoob6P4xLuGYZIYz9SY021xApPq+//no8xyutga/mmFC45d0X3F/4fIatZRqBBveY4n45pXANcE/gHKYVm4I368u5z7pz7yjX16whXAd0amakM1IHCYjpy8V+onDOUOWcr1wzpMwyFHdDI9+Vwj05BQqpNZh3FqXzLJtaxT2QFF2+cw8gnZDnAiM8MRJZ9jjxzOD+S0or7+Pg2uEewTEgBTHb+tpUx1+S2mRgQaGHYQoZnpE8aR5CFJZ5iNXXsTWLYfVIxaD2l8IrhS8KK/w9BTAK1BQiWTYFc8Zar6aAQkEjjeZUrtBIIZGma1oirr/++tgaQFBCwZ4HcxbBAoVIHpKksVDQZRoPAQqV2cCFwiyBF0MI0oGX/h4EDARK6cVcCekhpBCxvRRGKdQRkBBYkC/Mg4v9wXL4ex5WrDMBTiksiz4PBD00o1OLxnKL+zuUwn4m3Yy3wvKwpdaSz+fYUHhIo9kwbCUPdWrueBiyzmwzHe9J9WlqFHY4VzjnqI0mkKU2mgI+D+hiHBcKnGxHqp0lsOAc5UFPKxatRmwbwRPzEsRVotJzptLPYr1IlyDIJhAmAKVPR3MGFhTGCGS59thHFNjoXE7gRiGOQlAW5xrXIucUBVRqkCl8UoilhpzznC/Oc7aVlgg+g2Ff0ztZmgMvi6Q1Kdt/g/QqzhOC/7aEwiOFWQqn7FsKkrQwcn8l1aeaN15Xc0wIDDj+fDbXE+cy904qdig8V4LzlXszBWyCeToskzpGS3N2WOZqsa5UxnBdcC/i2mB/sF+4Zri/E9Rzf6CQTrps9t1DlaLSpPjFnnwOuPdlAwuCNFIZCczTtrKdXE/FqWfcp3hG8kVLBNcQx4AKueL1bMrjL0lNrUOhKXv1ql7U+FGDRcG/2he6SVJ9aBml8M87ImiFUfuS5/h3GNj0w1yrOoUBzV7PK40ffSzao+yY4wmdcUkfoSbfoEJSU99jaHVApQMqqO3y+EsaVxkiNwOayhmlifx0hlckX5mmf/J7U38JSWoshogl9YbUGtJ+aAkl3Yf+BW0ttas1lXrnSCmVvkelpXj8JY2rDCyaATf7xx57LOb4kytLzjx5xeTcp1GrJKmx6H9Fnxw699LZnz5V9Fmiz5UtopUr9c6RUip9j0pL8fhLGlfZx0KS1C5RKGcAi4Yw3G6eIY/HdfaxaH32sdD4wjNZktQupXeOSJKahp23JUmSJOVmYCFJkiQpNwMLSZIkSbnZx0KSpHZscPchYccddxynhtSV1DbZYiFJkiQpNwMLSZIkSbkZWEiSJEnKzcBCkiRJUm4GFpIkSZJyM7CQJEmSlJuBhSRJkqTcDCwkSZIk5WZgIUmSJCk3AwtJkiRJuRlYSJIkScrNwEKSJElSbgYWkiRJknLrUCgUCvkXI0mS2qIOA8e29iqM0woDOrX2Kkhthi0WkiRJknIzsJAkSZKUm4GFJEmSpNwMLCRJkiTlZmAhSZIkKTcDC0mSJEm5GVhIkiRJys3AQpIkSVJuBhaSJI0jBg8eHJZYYonw1VdftfaqSFLVfJ2kJDWjF198Meyxxx61pk000URhlllmCeuuu27YfPPNQ8eOHcNdd90Vjj/++Pj7Cy64ICyzzDK1/oaCZt++fcNmm20WDj300Jrp66+/fvj666/DIossEi6//PI6n3/ccceFu+++OwwfPjxMNtlkNdPff//9cMUVV4S33347fPfdd3GdevbsGRZaaKGwySabhHnnnTeML3744YdwzTXXhBEjRoRvvvkmdOjQIUwxxRRxG/v06RNWXXXV1l5FSRovGFhIUgtYc801w/LLLx8KhUL4/vvvY2H/zDPPDB9//HE48sgja81LYLH00kvHAnClXnvttfDYY4+FlVdeucF5n3zyyTBgwIAYaBDczDTTTOH3338Pn3/+eXj66afDzDPPPN4EFgRd22+/ffjjjz/CWmutFTbddNM4feTIkeGll16KAZ2BhSQ1DQMLSWoBFNTXWWedmv9TwKX14fbbb6/VojH//PPHVoQHHnggFoQrMd1004W//vorXHjhhWHFFVeMLSD1IXDp2rVruOqqq8I000xT63f//fdf+PXXX0NLGzt2bPj333/jejWlq6++Ovz0009h4MCBJYMuWjMkSU3DPhaS1AomnXTSmHZEC8aXX35ZM32LLbYIU089dbjooovCmDFjKloWaUw777xzbP2gBr4h1NaTilUcVGCCCSYIk08+eZVbE2K/ANKunnvuubDDDjvE1hlaaSjQ//nnnyX7EXz00UfhrLPOigHXcsstF9544434+19++SWcdtppsTWFlDC+83+mV4ttxVJLLVXy91NNNVWt/5Natttuu4VPP/007LfffmGllVYKvXv3DoccckidIISWp7PPPjtstdVWYZVVVonbQLBIihlBUjGO55VXXhnnZ/+w3G233TYMGzas3m1gWaecckpYcskl498ntHptt912MWBaYYUVwgYbbBCOOuqo8PPPP1e1jySpqdhiIUmtgIDiiy++iD+TkvTZZ5/Fn6mxp2B70kknhVtuuSX069evouXRL+L6668Pl1xySWzpmHDCCcvOO+OMM8YghPQp+mY0lXfffTc8/PDDYcMNN4zBAP1LbrjhhhhADBo0KAYtWUcffXTc3q233jqmfVHIHzVqVNhpp51iQECfElp63nvvvXDzzTeHF154IRasJ5lkkorXiW3FbbfdFgv0laSXETDsvvvuscC+7777hg8++CDceuutMZ2K7UiY/uijj8b5+BxaXZ555pnYIkSwmE1xI6jYe++9Y/oVwdLaa68dunTpEj788MO4DALKUmiJYjmkqNEHJ7V63XPPPTGQW3TRRWOLF/vx22+/jfPRQtOY4FCS8jKwkKQWQAGRGncCCmq+qaWmAzWtFvRpoJCfrTW/9tprY2dsfq6kIN25c+ew5557xhprCvO0GpRD4HL44YfHVo4555wzLLzwwmGBBRaINeLTTz99o7eRQnI25Yjae/7P+jz00EOxBaO41Yb0rU6d/v+jiII7fT3ooM7fJ3PPPXc4/fTTY/oW21kpgpZ77703tixcd911sSBOuhnf55tvvpJ/Q1Bz6qmnxo7dCUHRTTfdFFsyZp111jhtscUWC3fccUetYIXghYCJ6QQnqUWEzyao2HHHHcNee+1VJ/2sFFLSDjjggLhfzznnnFod+ulPw3lBy1Z2/xUPFCBJLclUKElqAaT/rL766rGwuuWWW4Y777wzptlQ8C5GHwkKn6S00EegUhTcqeGnVr++fhKsx6WXXhpWW221WMtNbfyJJ54YWwgOPPDARqfSkF5V3I8hBTgUhItRCM8WitN81LZvtNFGtaZvvPHGcTq1+9WgJYGWnBSk3H///TH9ihQkWoPeeeedOn/D6FjZoAKkbmVTq0CrUAoqaJFgnxM8LrvssjFYoK9Mwud279497LLLLnU+r7glJ3U6J/Cj5YNWqOJRwgjKCFafeuqpGKxK0rjAFgtJagEUlCnQUxClTwStFD169Cg7PwV00pRouUgjGTWEZZNuw9eQIUNibXc5vXr1il8USmkhIG2JdKMnnngi1riTzlOt2Wabrc40auy7detWqx9Jwj4oxrC6tCQUBxz8n/lJt6oWrTC0gPBFa9Grr74aU4kYHWv//fcPN954Y61jMcMMM9RZRvp9NmAj9Yn+FLSIEHAUF/B/++23mp/Zx/PMM0/FndMJ8Fg+rT2M2lWMlo+XX345ju7FutF6Qr8NAqJqUsUkqSnZYiFJLYBCMUPI0omY9Kf6gopkn332CaNHj46tC5WiZpvPIG2HdzZUEozQ0kAfDQrJFKqfffbZ2JLR3OrrB9JcCHQI8EiNoi/Kjz/+GPslNNSCkGSDB5Zx8cUXx4Dh2GOPDeeee25M5eK4Fc9bLVqf/v7775gOVypVivOJY0yK1HrrrRePNf1yCEJT3x1JamkGFpI0jqJFgZGDGJKWGu9K0eGY1Bzy76tBbTp9GVIH5mp98skndabRQsA7Mkq1ApTCfHRkp7Y+i/+zDypdTiUWXHDB+J0XBDYGLRW0FNAfg8I9LQYEj6VaDAje6J/xzz//VLRsUsg4joz8RNBSapQpOn8zGhQtU6TMEWRw3GjlkqTWYGAhSeMw0ppAJ+dK0c9ijTXWCPfdd1/s+FuMN1CXqk2nb8Xrr78e+3iUSr9pCAFBcV+KNDwqAVIlmI/1IJjK4v9MZ1jXapDiRV+EYrQCkAqF2WefPTQGLRvF+5EWJjpqF6N1hNSoUm9HL9eywVCypERxHOmUnw22Sg29m15q2BrvIZEk2MdCksZh9FugNpxRhqrByEmPPPJIyT4J9DWYYoopYm03y6f/An0gqIEnNWjXXXetKFWrGCNM0T+D4WZJ1aFQz/Cz1OoT6FSCt2TzN4wAxTCzpBnxne2n1p/CdjWuueaaOOIWLw6k4E2nZ7aRfUPHbTplsx8ag87vdHxnhC3Sz1gu7xEpte/osE8gQ2BBp25aNmghYthfArJygSMd3Bnxi/1BYEHrCMeLzv30XWF0K95HQqsQn01qW/ZFjJLUkgwsJGkcx7CljCpEzn01oyHRb4LOv8VIraFfAe+FIJjgBXYUhil4U0NOgbkx+HvScigkU+AmJWjzzTePheD6+i1kUfCn8M0oWnQkZ/SsKaecMm4L+6HajsmMrDR8+PDwyiuvxL4j1ObTeZ6Aio7brF+l61aMfcX6MJTu448/Hgv4dNJnONv+/fvXmpfggA7xBDq8VZ19RCoTARhDCteHEa0IJnhJHi/q42WB9KXgc9nPbBPHjyCM36cRrCSppXUoOE6dJCknCrO0rPDSNrUtHQbW7s+i2goDrIOVKmUfC0mSJEm5GYZLkkqis3Sp0YiyJp544vjVklinSl7iR3oQKUiSpJZhYCFJKomO0rwBuj509KbvQ0viHRu8JbwhvGPC/gaS1HLsYyFJKok3VDfUYZz3StBRvCWxTqxbQ3iDd/fu3Vtkndoy+1jUzz4WUuUMLCRJascMLOpnYCFVzs7bkiRJknIzsJAkSZKUm+17kiS1Y4O7Dwk77rijI2hJys0WC0mSJEm5GVhIkiRJys3AQpIkSVJuBhaSJEmScjOwkCRJkpSbgYUkSZKk3AwsJEmSJOVmYCFJkiQpNwMLSZIkSbkZWEiSJEnKzcBCkiRJUm4GFpIkSZJyM7CQJEmSlJuBhSRJkqTcDCwkSZIk5WZgIUmSJCk3AwtJkiRJuRlYSJIkScrNwEKSJElSbgYWkiRJknIzsJAkSZKUm4GFJEmSpNwMLCRJkiTlZmAhSZIkKTcDC0mSJEm5GVhIkiRJys3AQpIkSVJuBhaSJEmScuuUfxGSJKktKhQKYfTo0eG3334LnTt3bu3VkTQO69atW+jQoUO983QocFeRJEntzg8//BB69uzZ2qshqQ349ddfQ/fu3eudxxYLSZLaqa5du4ZevXqFe+65J0w66aStvTpt1qhRo8K6667rfszJ/Thu70NaLBpiYCFJUjtFWkPHjh1jLaQFucabYIIJ3I9NwP3Y9vehnbclSZIk5WZgIUmSJCk3AwtJktqpLl26hF133TV+V+O5H5uG+7Ht70NHhZIkSZKUmy0WkiRJknIzsJAkSZKUm8PNSpLUznz66afh9NNPD6+//nqYZJJJwjrrrBP69+/v27erNHLkyHD11VeHN998M3z00UdhlllmCTfeeGNrr1abMnz48HDvvfeGd999N74BfuaZZw5bbLFF6Nu3b4Nvedb/99RTT4WrrroqfPzxx+GPP/4IU089dejdu3fYbbfdWnTYWQMLSZLaEQpve+yxRyzAnXHGGeG7774LZ599dvjrr7/CoYce2tqr16YQTDz99NNhgQUWCP/991/8UnWuvfbaMN1004X9998/TD755OG5554LJ598cvj2229joViVX9echwRlPXr0iOfmJZdcEr8PGjQotBQ7b0uS1I4MHTo0DBkyJNx9992xAIJbb701nHbaaXFaz549W3sV2wwCCV5IhuOOOy68/fbbtlhU6ZdffgmTTTZZrWkEFg8++GB49NFHa/avqnfbbbfFfXnfffe12HXt0ZIkqR0ZMWJEWGqppWqCCvTp0ycWkp999tlWXbe2xkJvfsVBBeaZZ56YzjN69OhWWafxRY//d42PGTOmxT7TK0KSpHbWv2LWWWetNa1bt25hqqmmir+TWturr74a+wjQ/0fV+ffff8Pff/8d+6xcdtllYaWVVgrTTz99aCn2sZAkqZ3lYhNIFGMav5NaO6ggDYo+F6re+uuvH/tNYbnlloupUC3JwEKSJEmtjg7bhx9+eFhiiSVCv379Wnt12qRzzz03ppAxOtTll18eDjjggNh5u2PHji3y+QYWkiS1I927dw+jRo2qM/3333+Pv5NaA+ffvvvuG/sFMBSy/VcaZ6655orfF1544TD//POHrbbaKnaCX3311UNLMLCQJKkdoX9FcV8KAo0ffvihTt8LqSUw1DGpT5yHjFrWku9dGN+DjE6dOoUvvviixT7TcFCSpHaEvOvnn38+1hBnX1JGDfEyyyzTquum9mfs2LEx/Ylg9/zzz4+dttU0eHEj+3eGGWYILcUWC0mS2pFNNtkkDBs2LBx00EFhp512ih09ycveeOONfYdFI2raeeMxvv766zhEKkEaFl988fjCN9WP96c8+eSTscWC/ffGG2/UGna2S5curbp+bcXBBx8c5ptvvthK0bVr1/D+++/Ht8Lz/5VXXrnF1sMX5EmS1M588skn8a3br732WhzSc9111w39+/cPnTt3bu1Va1O++uqr0Ldv35K/u/jii2MnZDU8ihFBWSl33nlniw6V2pZdccUVcTStL7/8Mr6ThreZr7rqqmGbbbZp0dQyAwtJkiRJudnHQpIkSVJuBhaSJEmScjOwkCRJkpSbgYUkSZKk3AwsJEmSJOVmYCFJkiQpNwMLSZIkSbkZWEiSJEnKzcBCktRo3333XejRo0e49NJLa03fYYcdwqyzztpq6zU+OO6440KHDh3Cp59+2mJv7i3+vNGjR8c3Hx9//PFNdm4o/zF67LHHWntV1MbvD1c007lkYCFJarSjjjoq9OzZM+y4444Vzf/NN9+EAQMGhAUXXDB069YtdO/ePcw111yhX79+4dZbb60178orrxwmnXTSBh+sL774Ysnf//zzz2GiiSaK81x99dVll0MAxDzpq0uXLnHaLrvsEkaOHBnaM/bfYYcdFs4444zw9ddfN+u5ofbt1Vdfjdd0SwXSah4GFpKkRvniiy/CkCFDwj777BM6derU4PyfffZZWGSRRcKgQYPCMsssE/73v/+FU089Nay33nrh3XffDUOHDm3S9bv22mvD33//HWabbba4nvWZccYZY/DB17nnnhuWXnrp+Dd8/+GHH0J7tvPOO8eA66yzzmq2c0OV2XbbbWMr0korrRTGx8CCljEDi7bNq12S1CiDBw+OBc4tt9yyovkHDhwY02Nuv/32sMEGG5RszWhKl19+eVhllVXiZ+2///7h448/DrPPPnvJeUnZ2WabbWr+v+eee4app546XHDBBTHgOfjgg0N7Nckkk4SNN944pk6cdNJJoWvXrk1+brS2f//9NwahE088cRiXdezYMX5J4ypbLCSphaSc1ocffjiccMIJYZZZZompJtSKP/vss3Gexx9/PKywwgqxMDfddNOFE088seSySP/ZaKONwlRTTRULevPMM084+eSTw9ixY2vN9/zzz8f+DnPPPXcsNJF+tPzyy4fbbrutzjKZj/X79ddfawrWE044YZz/ueeeqzP/TTfdFJZYYok4XyU++OCD+H211VYr+ftpp502NJWXX3451oBuv/32Yauttoq15g21WhRbc8014/cPP/yw7Dz33Xdf3GfnnXdeyd8vu+yyMR1ozJgxVR+PUtIxKoXp/L7YsGHD4jnFZ/GZnG8333xzqMbaa68dW24effTRiuYvd278999/8Tylxp3jTdrZzDPPHM+3H3/8sWa+X375JZ57BDSlHH744XF7OcYJ5+2hhx4a5pxzznhNsN8JbAgoS12Hw4cPj9fXHHPMET/rxhtvjL9/8MEHwxZbbBGDUK7PySabLKyxxhrx2izllltuiS1xLINtodadZfMZfFYWwcspp5wSFlhggTg/y15//fXDK6+80ui8+Ka6r5D+R/oh186qq64a0xCnmGKKeA1RIZD1+++/x1Q3PiPdg9jvpM39+eefdZZdKBRiXxvmZ7l8LbTQQuGYY46JvycFKqXMURmQ0hJLnc/FXn/99XgvnHLKKeM+nX/++cPpp58eg8U897f60i/ffvvtWFnBvuSa4p723nvvxXlI6VxsscXiMWCfXnLJJSWXddlll9XMR8UG59hTTz1VZz6uGVp2aXllfUkjpTW2HFIW2T7ORa4v+kjttttudY5htSrdzxxsSVILGDp0aIHb7hJLLFFYdNFFC2eeeWbh1FNPLUw11VSFbt26FW677bbCFFNMUTjssMMKF154YWHllVeO81999dW1lnP33XcXunTpUph//vkLp5xySuHiiy8ubL/99oUJJpigsOmmm9aal2UtvfTShSOPPLJwySWXxM+bd95543KvvfbaWvOyDKYz/3rrrVc4//zzC8cdd1yhe/fuhSmnnLLw22+/1cz7zTffxHn33XffktvKsmaZZZZa0/r37x//5qyzzir8999/De6v3r17FyaZZJLC999/X/Lr4IMPjst74YUX6vwtnzXppJMWRo0aFf+/0UYbFWacccbCv//+W2de1nOBBRaoM531ZPlHHHFE2XUcO3ZsYdpppy0svvjidX73/vvv19lH1RyPY489Nk7/5JNP6hyjUpjO77P4HKavtdZahbPPPrtw7rnn1pxXF1xwQcnzM/t5yaeffhp/d+ihhxYaUt+5MXr06EKPHj0KO+20U2HgwIGFiy66KP7cuXPnwoILLlj4+++/a+bdbLPN4nn+448/1loGx3CmmWYqLLzwwjXTfvnll3g9cMz53MGDB8dzd+qpp47XF+tfvJ2LLLJI/BuOwaBBgwojRoyIv99yyy0Lq6++etz/l156aVwO507Hjh0LTzzxRK11ueGGGwodOnQozDnnnPFaPP300+O5xPnAZ/BZyT///BP3Pdu08847x2ucz5599tkLE000UcnzuFha90cffbTJ7ytcB3PMMUdhsskmi+vHPuE728d++uOPP2rmfeeddwrTTDNNvM7OOeeceC5xvJh3jTXWqLPeW2+9dc29hf3EenCc5ptvvvj71157rbDbbrvVXG+sG1/pmJTDPpt44onjOcXfnXfeeYU+ffrE5Wy11VaNvr+Vk65J9jX7kc87+uij4zqw/6666qp4Pzj++OPj8nv16hXnf/LJJ2st55BDDonTl1pqqXifYf4ZZpih0KlTp8I999xTa9799tsvzrvSSivF65drmu3lWBdfr5999llh+umnj8eea5V7DJ/FecA5ynVS37nUFPvZwEKSWki6kfNAyBag7rjjjjidh0q2cME8PKSWWWaZWgUzHugrrrhiYcyYMSULwtkHRSpYZ1FAmHvuuWse6sUP3j333LPW9BtvvDFOJ4BJHnnkkTiNB12lgcVHH30UH+L8HQVDHkgUdl988cWygQXzNvRVXCBjH1E4yhayb7/99jjvvffeW+dzWE8K9ylg+fjjjwtDhgyJD1GOyRtvvFGoz4ABA+Ky33rrrVrTjzrqqDj9pZdeatTxyBtY8LlMO/zww+vMu8EGG8TCRrYwVV9gAfYFBbKG1HduEFD++eefdaZfdtll8W+GDRtWK4BmGgXcrOHDh8fpFKATCqkTTjhh4dVXX601LwEF25ndL2k72efZwnJ9x4hgicLn2muvXTON649CHMHLTz/9VDP9999/L8w222x1Aot0fd5///21lv3rr7/G64HzPU9gkee+kq4D5ueazErrTbCSXQaBUrF0zj/33HM10zimTNtmm23qBPbZ/1dT0E2WW265GPARmGTPMYIclsW50pj7WznpmuQ6yFaOcK4zvVu3boXPP/+8Zvp3331X6Nq1a6Ffv3410959990YgC2//PK1jteXX34Z7zkcByossvOuuuqqNdPStc304uu1b9++hZ49exZGjhxZa705/uwn1r8x+7ua/WwqlCS1MJqpaaJOVlxxxfidNAHSRxLmWWqppWpSiPDQQw+Fb7/9NqYNkC5Cekr6WmeddWpSORJSHxJSFEg34TupDu+880747bff6qzfAQccUOv/zIvsenz//ffxO6kSlSK15LXXXgt77bVX/P91110XP4ttXnjhhcNLL71U529ocmebS33RkbUUUhHYN6RwJOwbUmPKpUPReZzf88V67rTTTjHF44477oipB/VJn3PVVVfVTKOcf80118S/Jd0hz/FoLNIlSNtg/bLnCV99+/aN6SzPPPNMxcvjWFeSTlHfucH6kPoBUijSOZzOsWxKCqlo00wzTa39Cv5PatvWW29ds6/ZVtKrZphhhlrbyf5moIDsNZG9Dkv1qcgeo1GjRsVjRL8Grs/s+nG+fvXVVzHFZvLJJ6+ZTprPHnvsUWe5nA/zzjtvWHzxxWut4z///BP69OkT02DomN0a95WEUdr69+9faxr/Z3o2XY9ldO7cOf5M+iUjsLEtq6++epyW3U8pbYc+VhNMULvYWfz/anAujhgxIp7L3D+y59iRRx4Zfy6VYljJ/a0h++67b62UxLSv+/btG2aaaaaa6dxPSFPNLpt7CufsIYccUut4kbLEfZ1BLlJqXJr3wAMPrNW3hnsK50wWKV533313XAfum9lzjJQsUtVKXQdNvZ/tvC1JLay4A3EqlJBDW4zfZXPPKXyCgm85BB7ZhwK50DygShUKKdhRaKhv/cipRXY90kP1/1aUV44HHB2i+SIXmMIUIzHdddddcXSot956q1aBlIdpKqwUK5WPnDpt80BnpKds/whymMn950FL0FC8Xul9CykvmQdxJVLwQAGK/HkKS0888UQc3YYc5KzGHI/G4lzh+FCYreRcaQjLKte/I6uhc4O+DGeeeWYsPKW+JwkF1CQFD4xG9f7778d+KX/88UcMHDmWBB0pkOHcpNDEcS+lVAGW5ZXy0UcfxQLTAw88EI9HqW3DJ598Er9TcCxWahrHg8Ch3DqCczNbMG2p+0p2GdnCLug/wfTivioXXnhhuPjii+M1Sz+AcseRQjV9EdLxaipp/9Nfpdh8880Xj3nxOld6f2vqff3ZZ59VtN5pGutNMJjWv9Q1TB+HbKBAHw+OA/c/vipZ7+bYzwYWktTCyo3qUsloL6mwxnsFevXqVXIeCsVpXgpgFGj222+/+KCikyCfw0hHtBgUFwjqW49sQTEVjn766afQWBQ2Nttss/hFAZL1uffee2uNztSYhyAdjFnXcgVHao7peFlcS10ugKnEdtttF5f5yCOPxOVQq85+zG5LY49HVrmCfXGn/fR5zE8H83LHtFRhoRwKi/UViis5NwgK6BhNjTnD+lKIpnaV1ou11lqrzvazXwks2J+MSMXf04qQbY1K5yX7nc7blSrVWsGyafkggOF40sGYTu8UnuhAy/FtLNaT5dU3bG8l+7c57ivVYhsOOuigeD5Te889h4Dkyy+/jC04DZ3HramS+1tjl9GxCZbdWOkzuOdkr4+s1FrYnAwsJKkN4WVylRaEGcWD1CNGXil+czIjkuSRCqTVpA/Uh3QVCtYUTPKggJ5GoGHEnWK0FpAOVRxY5MXIUwxJSwGYUWYYdYlUBYKnpjweqTWHQnu2ZadUzSznyv333x9Hh6FmMQ9aXwheGkoLa+jcoHWKQILgL1uwJxWtFEZb4otgkJGM2L8cV9IysoVxppFGlic4BCMrkd7EOVL8Yj/Onaz0Zvk0GlBWqWkcD1pXSL3JkwLUnDiPSM3KtlowkhXTs7XmHEe2n6A1uy2cb8UI8Gmho3WsvlaLSlrDslLrAC0mxTifCG4aU0Pf3Gb/f+vEejMiWRajTWXnSd/ZnnLzJrSwsg85fnmvgzz7edw8syVJJZF3zjCJvFyuVI0wqRbkzmdrz4pry958882Khzcth8IcBcg0nGUlGCKzVA45DyZSoVLzfmOxHIbepFaYt2Zvuummdb4YfvSNN94IL7zwQmhK7A+GZKVGnZQoCrnFtYZNcTxSKwzDmWaRWlQs9UE54ogj6g4JWWUaVDrOvXv3znVusA8o/GRrtNkftEaUw34klYTAkxYDWjwIThIKtrR4MZRvuWF0Kx1qs9wxIuWkeEhSWpwIHDnnsqk/tHqQIlSM1hfe1VKuxaKa49FcOG9Jccri/0zfcMMN6xzH7H4i8OS+VCz1haFPQXFLRvbv6ZtSTSso98Hlllsu3ju4hrLLpHUJDI86runbt2/cd7Q6Z1MBSQ2lYoThghdddNFa83LOZK9hhgQuvgeQ0kVfMu5Bpa499kvq/1SNavezLRaS1IbQUkGtLQ958rjpa0FNFbng1B7xUKGQynj01FJTwCPPnw7CzE+uOi8vo/BdqrN0NUhhohaZB2K2Zr4cOm8+/fTTcdx++iSQBkRBi/cAsC6MX7/uuus2en0o/I0cOTK+KbqcTTbZJI5FTw7ykksuGZoSBeA777wzpoiwbdmCGJrieBAYESgwLj3Hm1YLaolLvR2c7WNb+SJtjuNFygrHi88i7YzazUowL/1SOEZ5zg2CO443tfYUtClY8cLEUu8+yBZMKZTSiZiCaak0D96Nwbm1+eabxy9awKh1JyBh3ekwXfxOiVJ41wPv1+AY0kpDPx3elUENPceIoDTbB4RzmvUjtYvzjml8DoU80vKytfCkvzHoAC1bBEjsA/rTfP7557GlJLXktCZqxWlNowDJPuM8ofWG1gpSnrLHkXeJEEzzrhECDwK/1KG7+FwgGOS+RSsWhWX6HXDu048lFVY5XwkSOZYEatzrqC2n83k5pNMR7NJ5mkEhOHZ0YGa5tCKWe2dOa5pnnnniOcB9gLQ79g2VQbzvgqCUiokU4LLf2S76pHG+cP8iSOb/tOQVv//koosuiucwy+X6IkDhmqHFiVYjpnE/qFZV+7nBMaYkSU2ivuH9Sr2DoL7hRRkClbHhGe6SdwAw5OWyyy5bOOGEE2qN+89wm7zbgnHNGSt/ySWXLNx6661N8o4EhkdkKEveR1DJcLPPPPNM4cADD4xjwLO+/C3DKzLsJUOH/vXXXyXfY1FO2oY0lCbbyf9ff/31Qn0YZpTPTcOelnuPRbUYOpL3BbAOu+yyS8l5qjkepabh2WefjcM/MowlQ6DuuuuuhZ9//rnsOcSwrbxbYPLJJ4/vUOCdDLzXgndIZJUbbpbhVzkODKtbqfrODcbWZ2hd1p9hT1l/ztly6w+G9+T3c801V9nPZOhYzn/eh8HQs7zTgmGEORbss0qH2WRIzTXXXDMOWcwyOA95f0W564PhShdaaKG4bxk2lncjcEyLh89NQ9QyNCnXAO8F4Iv3CzD08gMPPFDvPi237k11X+E6YFsZynSVVVaJ68Y+YJhYhtvNYuhT3kfBey/Y7plnnjm+V+btt9+Oy80Oa5qGleVdFwyJy3nPfmWfsa+yrrjiinhucE+r73zIYohhhk9O5zfH/LTTTqs1PGu5bW5oPxUrd03y/1LbDfZp8b0wXQe854LrgGFqeXdK8XtS0r476aST4j5m+7hXXXPNNWXXhSGzuVa5Vlg29zquCYZkzg6JXe3wvpXu5w78kz/+kiS1RwyrSUsBOeXZ2ko6cJL6RK2v2gZq2ulXQE176j+QaisZJSmN7pP33GgPSE0bMGBAHM6X1pO2gGPOV/at3lK17GMhSWq0E044IQ7TSG6wxj/0iSFvntSNaoKK9nJukEpW3H+FdJZBgwbFdKjsO0yk9sA+FpKkRqNjHy9m0viJ4SnpJ9EY7eHcIHedfgb9+vWL/QHYV1deeWVs9SHfvfidENL4zsBCkiSpERgBi1QnOtzSqZbO23TyppWHTuRSe2MfC0mSJEm52cdCkiRJUm4GFpIkSZJyM7CQJEmSlJuBhSRJkqTcDCwkSZIk5WZgIUmSJCk3AwtJkiRJuRlYSJIkScrNwEKSJElSyOv/AES3nY7iqdRvAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "model = best_rf  # ou grid_search.best_estimator_ si tu gardes grid_search\n",
+    "\n",
+    "# Initialize SHAP explainer\n",
+    "explainer = shap.TreeExplainer(model)\n",
+    "\n",
+    "# Compute SHAP values\n",
+    "shap_values = explainer(X_train) \n",
+    "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns)\n",
+    "shap.summary_plot(shap_values.values, X_train, feature_names=X_train.columns, plot_type=\"bar\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/scripts/rf.py b/scripts/rf.py
index 9fcf004..9c41091 100644
--- a/scripts/rf.py
+++ b/scripts/rf.py
@@ -44,7 +44,7 @@ def random_forest_GS(X_train, y_train, X_test, y_test):
     print("Meilleurs paramètres trouvés : ", halving_grid_search.best_params_)
     
     # Évaluation
-    print("MSE :", root_mean_squared_error(y_test, y_pred))
+    print("RMSE :", root_mean_squared_error(y_test, y_pred))
     print("R² :", r2_score(y_test, y_pred))
 
     return halving_grid_search.best_estimator_, X_train, X_test, y_train, y_test, y_pred

From 70ff06fc91268e08815c206eeb7bbd8525afa6ed Mon Sep 17 00:00:00 2001
From: Jess <jessicasilva2505@yahoo.fr>
Date: Sun, 7 Sep 2025 21:00:54 +0200
Subject: [PATCH 12/12] up date

---
 notebooks/project_starter.ipynb | 1191 ++++++++++++++-----------------
 1 file changed, 538 insertions(+), 653 deletions(-)

diff --git a/notebooks/project_starter.ipynb b/notebooks/project_starter.ipynb
index 26a0d02..cbab5b2 100644
--- a/notebooks/project_starter.ipynb
+++ b/notebooks/project_starter.ipynb
@@ -23,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,31 +46,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12360\\3034605521.py:2: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
-      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_12360\\3034605521.py:4: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n"
+      "C:\\Users\\jessi\\AppData\\Local\\Temp\\ipykernel_29772\\3776307945.py:2: DtypeWarning: Columns (11,17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
+      "  df_train = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n"
      ]
     }
    ],
    "source": [
     "path = \"https://static.openfoodfacts.org/data/en.openfoodfacts.org.products.csv.gz\"\n",
-    "df_train = pd.read_csv(path, nrows=5000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
+    "df_train = pd.read_csv(path, nrows=10000, sep='\\t',encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True)\n",
     "\n",
-    "df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 5001), nrows=5000)    # skip rows 1–5000, keep header (row 0)\n",
+    "df_test = pd.read_csv(path, sep='\\t', encoding=\"utf-8\", na_values=[\"\", \" \", \"NA\", \"N/A\", \"null\"], na_filter=True, skiprows=range(1, 10001), nrows=10000)    # skip rows 1–5000, keep header (row 0)\n",
     "               # read 5000 rows (5001 → 10000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -1153,7 +1151,7 @@
          "type": "float"
         }
        ],
-       "ref": "964bcdbb-39c2-42c6-ac00-d3cb0eadf1eb",
+       "ref": "6aedc9c9-16c6-46ad-ace1-b92ed1bf0e90",
        "rows": [
         [
          "0",
@@ -2275,67 +2273,7 @@
        "      <th>last_modified_by</th>\n",
        "      <th>last_updated_t</th>\n",
        "      <th>last_updated_datetime</th>\n",
-       "      <th>product_name</th>\n",
-       "      <th>abbreviated_product_name</th>\n",
-       "      <th>generic_name</th>\n",
-       "      <th>quantity</th>\n",
-       "      <th>packaging</th>\n",
-       "      <th>packaging_tags</th>\n",
-       "      <th>packaging_en</th>\n",
-       "      <th>packaging_text</th>\n",
-       "      <th>brands</th>\n",
-       "      <th>brands_tags</th>\n",
-       "      <th>brands_en</th>\n",
-       "      <th>categories</th>\n",
-       "      <th>categories_tags</th>\n",
-       "      <th>categories_en</th>\n",
-       "      <th>origins</th>\n",
-       "      <th>origins_tags</th>\n",
-       "      <th>origins_en</th>\n",
-       "      <th>manufacturing_places</th>\n",
-       "      <th>manufacturing_places_tags</th>\n",
-       "      <th>labels</th>\n",
-       "      <th>labels_tags</th>\n",
-       "      <th>labels_en</th>\n",
-       "      <th>emb_codes</th>\n",
-       "      <th>emb_codes_tags</th>\n",
-       "      <th>first_packaging_code_geo</th>\n",
-       "      <th>cities</th>\n",
-       "      <th>cities_tags</th>\n",
-       "      <th>purchase_places</th>\n",
-       "      <th>stores</th>\n",
-       "      <th>countries</th>\n",
        "      <th>...</th>\n",
-       "      <th>potassium_100g</th>\n",
-       "      <th>chloride_100g</th>\n",
-       "      <th>calcium_100g</th>\n",
-       "      <th>phosphorus_100g</th>\n",
-       "      <th>iron_100g</th>\n",
-       "      <th>magnesium_100g</th>\n",
-       "      <th>zinc_100g</th>\n",
-       "      <th>copper_100g</th>\n",
-       "      <th>manganese_100g</th>\n",
-       "      <th>fluoride_100g</th>\n",
-       "      <th>selenium_100g</th>\n",
-       "      <th>chromium_100g</th>\n",
-       "      <th>molybdenum_100g</th>\n",
-       "      <th>iodine_100g</th>\n",
-       "      <th>caffeine_100g</th>\n",
-       "      <th>taurine_100g</th>\n",
-       "      <th>methylsulfonylmethane_100g</th>\n",
-       "      <th>ph_100g</th>\n",
-       "      <th>fruits-vegetables-nuts_100g</th>\n",
-       "      <th>fruits-vegetables-nuts-dried_100g</th>\n",
-       "      <th>fruits-vegetables-nuts-estimate_100g</th>\n",
-       "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
-       "      <th>collagen-meat-protein-ratio_100g</th>\n",
-       "      <th>cocoa_100g</th>\n",
-       "      <th>chlorophyl_100g</th>\n",
-       "      <th>carbon-footprint_100g</th>\n",
-       "      <th>carbon-footprint-from-meat-or-fish_100g</th>\n",
-       "      <th>nutrition-score-fr_100g</th>\n",
-       "      <th>nutrition-score-uk_100g</th>\n",
-       "      <th>glycemic-index_100g</th>\n",
        "      <th>water-hardness_100g</th>\n",
        "      <th>choline_100g</th>\n",
        "      <th>phylloquinone_100g</th>\n",
@@ -2361,36 +2299,6 @@
        "      <td>NaN</td>\n",
        "      <td>1740205422</td>\n",
        "      <td>2025-02-22T06:23:42Z</td>\n",
-       "      <td>Limonade artisanale a la rose</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>en:fr</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2402,36 +2310,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
@@ -2445,36 +2323,6 @@
        "      <td>bodysupport</td>\n",
        "      <td>1750061386</td>\n",
        "      <td>2025-06-16T08:09:46Z</td>\n",
-       "      <td>M&amp;amp;M white</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>80 gram</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Fitpiggy</td>\n",
-       "      <td>xx:fitpiggy</td>\n",
-       "      <td>fitpiggy</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>en:fr</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2486,36 +2334,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
@@ -2529,36 +2347,6 @@
        "      <td>teolemon</td>\n",
        "      <td>1751035658</td>\n",
        "      <td>2025-06-27T14:47:38Z</td>\n",
-       "      <td>Chocolate n3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>80 g</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Jeff de Bruges</td>\n",
-       "      <td>xx:jeff-de-bruges</td>\n",
-       "      <td>jeff-de-bruges</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Green Dot,Made in France</td>\n",
-       "      <td>en:green-dot,en:made-in-france</td>\n",
-       "      <td>Green Dot,Made in France</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>France</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2570,36 +2358,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
@@ -2613,36 +2371,6 @@
        "      <td>NaN</td>\n",
        "      <td>1743653496</td>\n",
        "      <td>2025-04-03T04:11:36Z</td>\n",
-       "      <td>Paleta gran reserva - Sierra nevada-</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>750ml</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>AdvoCare</td>\n",
-       "      <td>xx:advocare</td>\n",
-       "      <td>advocare</td>\n",
-       "      <td>Bebidas y preparaciones de bebidas, Bebidas</td>\n",
-       "      <td>en:beverages-and-beverages-preparations,en:bev...</td>\n",
-       "      <td>Beverages and beverages preparations,Beverages</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Spanien, Germany</td>\n",
        "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2654,36 +2382,6 @@
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.011335</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
@@ -2697,67 +2395,7 @@
        "      <td>altroconsumo</td>\n",
        "      <td>1749171851</td>\n",
        "      <td>2025-06-06T01:04:11Z</td>\n",
-       "      <td>Filets de poulet blanc x2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>240-400 g</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>SoLo, selbstgemacht 2 Liter</td>\n",
-       "      <td>xx:solo,xx:selbstgemacht-2-liter</td>\n",
-       "      <td>solo,selbstgemacht-2-liter</td>\n",
-       "      <td>Protein powders</td>\n",
-       "      <td>en:dietary-supplements,en:bodybuilding-supplem...</td>\n",
-       "      <td>Dietary supplements,Bodybuilding supplements,P...</td>\n",
-       "      <td>île d’Orléans,Québec,Canada</td>\n",
-       "      <td>en:canada,en:quebec,fr:ile-d-orleans</td>\n",
-       "      <td>Canada,Québec,fr:ile-d-orleans</td>\n",
-       "      <td>Ancenis</td>\n",
-       "      <td>ancenis</td>\n",
-       "      <td>Organic, EU Organic, French meat, Bee Friendly...</td>\n",
-       "      <td>en:organic,en:eu-organic,en:french-meat,en:bee...</td>\n",
-       "      <td>Organic,EU Organic,French meat,Bee Friendly,Fr...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Brasilien, Germany</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.0001</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0.052506</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
@@ -2775,17 +2413,52 @@
        "</div>"
       ],
       "text/plain": [
-       "   code  ... carbohydrates-total_100g\n",
-       "0    54  ...                      NaN\n",
-       "1    63  ...                      NaN\n",
-       "2   114  ...                      NaN\n",
-       "3   105  ...                      NaN\n",
-       "4     2  ...                      NaN\n",
+       "   code                                                url  creator  \\\n",
+       "0    54  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "1    63  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "2   114  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "3   105  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "4     2  http://world-en.openfoodfacts.org/product/0000...  kiliweb   \n",
+       "\n",
+       "    created_t      created_datetime  last_modified_t last_modified_datetime  \\\n",
+       "0  1582569031  2020-02-24T18:30:31Z       1733085204   2024-12-01T20:33:24Z   \n",
+       "1  1673620307  2023-01-13T14:31:47Z       1750061386   2025-06-16T08:09:46Z   \n",
+       "2  1580066482  2020-01-26T19:21:22Z       1751035658   2025-06-27T14:47:38Z   \n",
+       "3  1572117743  2019-10-26T19:22:23Z       1738073570   2025-01-28T14:12:50Z   \n",
+       "4  1722606455  2024-08-02T13:47:35Z       1749171851   2025-06-06T01:04:11Z   \n",
+       "\n",
+       "  last_modified_by  last_updated_t last_updated_datetime  ...  \\\n",
+       "0              NaN      1740205422  2025-02-22T06:23:42Z  ...   \n",
+       "1      bodysupport      1750061386  2025-06-16T08:09:46Z  ...   \n",
+       "2         teolemon      1751035658  2025-06-27T14:47:38Z  ...   \n",
+       "3              NaN      1743653496  2025-04-03T04:11:36Z  ...   \n",
+       "4     altroconsumo      1749171851  2025-06-06T01:04:11Z  ...   \n",
+       "\n",
+       "  water-hardness_100g choline_100g phylloquinone_100g beta-glucan_100g  \\\n",
+       "0                 NaN          NaN                NaN              NaN   \n",
+       "1                 NaN          NaN                NaN              NaN   \n",
+       "2                 NaN          NaN                NaN              NaN   \n",
+       "3                 NaN          NaN                NaN              NaN   \n",
+       "4                 NaN          NaN                NaN              NaN   \n",
+       "\n",
+       "  inositol_100g carnitine_100g sulphate_100g nitrate_100g acidity_100g  \\\n",
+       "0           NaN            NaN           NaN          NaN          NaN   \n",
+       "1           NaN            NaN           NaN          NaN          NaN   \n",
+       "2           NaN            NaN           NaN          NaN          NaN   \n",
+       "3           NaN            NaN           NaN          NaN          NaN   \n",
+       "4           NaN            NaN           NaN          NaN          NaN   \n",
+       "\n",
+       "  carbohydrates-total_100g  \n",
+       "0                      NaN  \n",
+       "1                      NaN  \n",
+       "2                      NaN  \n",
+       "3                      NaN  \n",
+       "4                      NaN  \n",
        "\n",
        "[5 rows x 214 columns]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2810,7 +2483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -2835,7 +2508,7 @@
        "True"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2844,7 +2517,7 @@
     "### taking out lines without nutriscore because it's non informative (both fot the train and the test df)\n",
     "filtered_df, filtered_df_test = dfj.filter_nutriscore_data(df_train, df_test)\n",
     "\n",
-    "### Cleaning the variables \n",
+    "### Cleaning the variables : get rid of vars with too many missing values etc\n",
     "cat_df, cat_df_test = dfj.categorical_filter(filtered_df, filtered_df_test, cat_keep= True)\n",
     "num_df, num_df_test = dfj.numerical_filter(filtered_df,filtered_df_test, num_drop= True)\n",
     "final_df, final_df_test = dfj.final_df(cat_df, num_df, cat_df_test, num_df_test)\n",
@@ -2856,6 +2529,31 @@
     "final_df.columns.equals(final_df_test.columns) #checking if we have the same variables in each df "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[ 15.   4.   6. -11.   3.  20.   9.   7.  -1.  -4.  45.  17.   0.  13.\n",
+      "  18.  -5.  22.  11.  14.   8.  -3.  27.  12.   2.   5.  31.  -2.  -7.\n",
+      "  28.  21.  19.  -6.  23.  38.  30.   1.  24.  25. -16.  35.  33.  34.\n",
+      "  40. -12.  29.  26.  16.  50.  10.  -8. -10.  -9.  44.  37.  32.  39.\n",
+      "  43.  36.  41.  49.]\n"
+     ]
+    }
+   ],
+   "source": [
+    "final_df['nutriscore_score'].dropna(inplace=True)\n",
+    "final_df_test['nutriscore_score'].dropna(inplace=True)\n",
+    "print(final_df['nutriscore_score'].unique())\n",
+    "\n",
+    "#doing this again because there was a bugg"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2865,41 +2563,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "              pnns_groups_1      PNNS_pro\n",
-      "6                   unknown            NA\n",
-      "9                   unknown            NA\n",
-      "11    Fruits and vegetables   Plant_based\n",
-      "12                  unknown            NA\n",
-      "14          Composite foods     Processed\n",
-      "...                     ...           ...\n",
-      "4936   Cereals and potatoes   Plant_based\n",
-      "4945         Fish Meat Eggs  Animal_based\n",
-      "4946                unknown            NA\n",
-      "4953        Composite foods     Processed\n",
-      "4997          Sugary snacks        Snacks\n",
+      "              pnns_groups_1     PNNS_pro\n",
+      "6                   unknown           NA\n",
+      "9                   unknown           NA\n",
+      "11    Fruits and vegetables  Plant_based\n",
+      "12                  unknown           NA\n",
+      "14          Composite foods    Processed\n",
+      "...                     ...          ...\n",
+      "9937          Sugary snacks       Snacks\n",
+      "9943        Composite foods    Processed\n",
+      "9957   Cereals and potatoes  Plant_based\n",
+      "9980          Sugary snacks       Snacks\n",
+      "9994        Composite foods    Processed\n",
       "\n",
-      "[439 rows x 2 columns]\n",
+      "[1208 rows x 2 columns]\n",
       "                pnns_groups_1      PNNS_pro\n",
-      "10       Cereals and potatoes   Plant_based\n",
-      "17             Fish Meat Eggs  Animal_based\n",
-      "52      Fruits and vegetables   Plant_based\n",
-      "55                    unknown            NA\n",
-      "57                    unknown            NA\n",
+      "3     Milk and dairy products  Animal_based\n",
+      "9               Sugary snacks        Snacks\n",
+      "18              Sugary snacks        Snacks\n",
+      "38             Fat and sauces     Processed\n",
+      "49            Composite foods     Processed\n",
       "...                       ...           ...\n",
-      "4938          Composite foods     Processed\n",
-      "4952     Cereals and potatoes   Plant_based\n",
-      "4975            Sugary snacks        Snacks\n",
-      "4989          Composite foods     Processed\n",
-      "4998  Milk and dairy products  Animal_based\n",
+      "9960            Sugary snacks        Snacks\n",
+      "9981           Fat and sauces     Processed\n",
+      "9990           Fish Meat Eggs  Animal_based\n",
+      "9996                  unknown            NA\n",
+      "9997    Fruits and vegetables   Plant_based\n",
       "\n",
-      "[770 rows x 2 columns]\n"
+      "[3565 rows x 2 columns]\n"
      ]
     }
    ],
@@ -2926,7 +2624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2956,7 +2654,7 @@
         {
          "name": "brands_tags",
          "rawType": "object",
-         "type": "unknown"
+         "type": "string"
         },
         {
          "name": "ingredients_analysis_tags",
@@ -2993,6 +2691,16 @@
          "rawType": "float64",
          "type": "float"
         },
+        {
+         "name": "trans-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cholesterol_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
         {
          "name": "carbohydrates_100g",
          "rawType": "float64",
@@ -3023,6 +2731,26 @@
          "rawType": "float64",
          "type": "float"
         },
+        {
+         "name": "vitamin-a_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-c_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "calcium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "iron_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
         {
          "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
          "rawType": "float64",
@@ -3064,151 +2792,181 @@
          "type": "float"
         }
        ],
-       "ref": "2451aee8-103a-49ce-8621-44a06881b9c4",
+       "ref": "23cf0cc5-1dd0-4394-ab64-d44c0a30981c",
        "rows": [
         [
-         "10",
-         "Aliments et boissons à base de végétaux, Aliments d'origine végétale, Céréales et pommes de terre, Pains, Baguettes",
-         "Cereals and potatoes",
-         "Bread",
-         "xx:la-campaniere",
-         "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown",
-         "584019351.0",
-         "0.0",
-         "4.0",
-         "1125.0",
+         "3",
+         "Efterrätter, Fryst mat, Frysta efterrätter, Glass och sorbet, Glass, Glassbyttor, en:Sundae ice cream",
+         "Milk and dairy products",
+         "Ice cream",
+         "xx:mcdonald-s",
+         "en:may-contain-palm-oil,en:non-vegan,en:maybe-vegetarian",
+         "12444.0",
          "3.0",
-         "0.3",
-         "47.9",
-         "3.8",
-         "5.5",
-         "9.4",
-         "1.3",
-         "0.52",
-         "0.0",
-         "Plant_based",
+         "13.0",
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
+         null,
          "0.0",
+         "Animal_based",
          "0.0",
          "0.0",
          "0.0",
          "0.0",
-         "1.0"
+         "1.0",
+         "0.0"
         ],
         [
-         "17",
-         "Produits de la mer, Poissons et dérivés, Poissons, Poissons gras, Saumons, Poissons fumés, Saumons fumés, Saumons fumés à la ficelle",
-         "Fish Meat Eggs",
-         "Fish and seafood",
+         "9",
+         "Breakfasts, Spreads, Sweet spreads, Bee products, Farming products, Sweeteners, Honeys",
+         "Sugary snacks",
+         "Sweets",
+         "moon-shine-trading-co",
+         "en:palm-oil-content-unknown,en:vegan-status-unknown,en:vegetarian-status-unknown",
+         "1245309274.0",
+         "0.0",
+         "18.0",
+         "1276.0",
+         "0.0",
          null,
          null,
-         "5869.0",
          null,
-         "17.0",
-         "1059.0",
-         "17.0",
-         "2.6",
-         "0.5",
+         "80.95",
+         "76.19",
+         null,
+         "0.0",
+         "0.0",
          "0.0",
          null,
-         "23.0",
-         "2.5",
-         "1.0",
          null,
-         "Animal_based",
+         null,
+         null,
          "0.0",
+         "Snacks",
          "0.0",
          "0.0",
          "0.0",
+         "1.0",
          "0.0",
-         "1.0"
+         "0.0"
         ],
         [
-         "52",
-         "Fresh papayas",
-         "Fruits and vegetables",
-         "Fruits",
-         "xx:curate",
-         "en:palm-oil-free,en:vegan,en:vegetarian",
-         "599990534.0",
-         "0.0",
-         "-3.0",
-         null,
-         null,
-         null,
+         "18",
+         "Botanas, Snacks dulces, Cacao y sus productos, Chocolates, Chocolates negros, Chocolates con almendras, en:Dark chocolate bar with dried fruits",
+         "Sugary snacks",
+         "Chocolate products",
+         "xx:hacendado",
+         "en:palm-oil-free,en:vegan-status-unknown,en:vegetarian-status-unknown",
+         "12480.0",
+         "1.0",
+         "18.0",
+         "2450.0",
+         "47.0",
+         "21.0",
          null,
          null,
+         "27.0",
+         "22.0",
+         "10.0",
+         "11.0",
+         "0.0",
+         "0.0",
          null,
          null,
          null,
          null,
-         "100.0",
-         "Plant_based",
+         "25.0",
+         "Snacks",
+         "1.0",
          "0.0",
          "0.0",
-         "1.0",
          "0.0",
          "0.0",
          "0.0"
         ],
         [
-         "55",
-         "Snacks, Snacks sucrés",
-         "unknown",
-         "unknown",
+         "38",
+         "Condiments, Sauces, Curry pastes, Groceries",
+         "Fat and sauces",
+         "Dressings and sauces",
+         "golden-curry",
+         "en:palm-oil,en:vegan-status-unknown,en:vegetarian-status-unknown",
+         "12579.0",
+         "5.0",
+         "37.0",
+         "2000.0",
+         "29.0",
+         "17.0",
          null,
          null,
-         "600002458.0",
+         "44.0",
+         "8.4",
+         null,
+         "6.5",
+         "11.1",
+         "4.44",
          null,
-         "25.0",
-         "2464.0",
-         "47.0",
-         "29.0",
-         "34.0",
-         "31.0",
          null,
-         "5.9",
-         "0.0",
-         "0.0",
          null,
-         "NA",
+         null,
+         "10.1073745265152",
+         "Processed",
          "0.0",
          "0.0",
-         "1.0",
          "0.0",
          "0.0",
+         "1.0",
          "0.0"
         ],
         [
-         "57",
-         "Drink mix",
-         "unknown",
-         "unknown",
-         "tclinics-usa",
-         "en:may-contain-palm-oil,en:non-vegan,en:vegetarian-status-unknown",
-         "600020002.0",
-         "6.0",
-         "25.0",
-         "33472.0",
-         "0.0",
-         "0.0",
-         "300.0",
+         "49",
+         "Plats préparés, Pizzas tartes salées et quiches, Pizzas",
+         "Composite foods",
+         "Pizza pies and quiches",
+         "xx:entremont",
+         null,
+         "1266016819.0",
+         null,
+         "13.0",
+         "1054.0",
+         "10.0",
+         "5.1",
+         null,
+         null,
+         "27.0",
+         "2.5",
+         null,
+         "11.0",
+         "1.2",
+         "0.48",
+         null,
+         null,
+         null,
+         null,
+         null,
+         "Processed",
          "0.0",
-         "200.0",
-         "1500.0",
-         "18.75000125",
-         "7.5000005",
-         "0.429687499999986",
-         "NA",
          "0.0",
          "0.0",
          "1.0",
          "0.0",
-         "0.0",
          "0.0"
         ]
        ],
        "shape": {
-        "columns": 25,
+        "columns": 31,
         "rows": 5
        }
       },
@@ -3241,13 +2999,9 @@
        "      <th>nutriscore_score</th>\n",
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
-       "      <th>saturated-fat_100g</th>\n",
-       "      <th>carbohydrates_100g</th>\n",
-       "      <th>sugars_100g</th>\n",
-       "      <th>fiber_100g</th>\n",
-       "      <th>proteins_100g</th>\n",
-       "      <th>salt_100g</th>\n",
-       "      <th>sodium_100g</th>\n",
+       "      <th>...</th>\n",
+       "      <th>calcium_100g</th>\n",
+       "      <th>iron_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
        "      <th>PNNS_pro</th>\n",
        "      <th>PNNS_pro_Animal_based</th>\n",
@@ -3260,161 +3014,184 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Aliments et boissons à base de végétaux, Alime...</td>\n",
-       "      <td>Cereals and potatoes</td>\n",
-       "      <td>Bread</td>\n",
-       "      <td>xx:la-campaniere</td>\n",
-       "      <td>en:palm-oil-free,en:vegan-status-unknown,en:ve...</td>\n",
-       "      <td>584019351.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1125.0</td>\n",
+       "      <th>3</th>\n",
+       "      <td>Efterrätter, Fryst mat, Frysta efterrätter, Gl...</td>\n",
+       "      <td>Milk and dairy products</td>\n",
+       "      <td>Ice cream</td>\n",
+       "      <td>xx:mcdonald-s</td>\n",
+       "      <td>en:may-contain-palm-oil,en:non-vegan,en:maybe-...</td>\n",
+       "      <td>1.244400e+04</td>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>47.9</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>9.4</td>\n",
-       "      <td>1.300000</td>\n",
-       "      <td>0.52</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0.000000</td>\n",
-       "      <td>Plant_based</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>Animal_based</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>Produits de la mer, Poissons et dérivés, Poiss...</td>\n",
-       "      <td>Fish Meat Eggs</td>\n",
-       "      <td>Fish and seafood</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5869.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1059.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>2.6</td>\n",
-       "      <td>0.5</td>\n",
+       "      <th>9</th>\n",
+       "      <td>Breakfasts, Spreads, Sweet spreads, Bee produc...</td>\n",
+       "      <td>Sugary snacks</td>\n",
+       "      <td>Sweets</td>\n",
+       "      <td>moon-shine-trading-co</td>\n",
+       "      <td>en:palm-oil-content-unknown,en:vegan-status-un...</td>\n",
+       "      <td>1.245309e+09</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>1276.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>2.500000</td>\n",
-       "      <td>1.00</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>Animal_based</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>Snacks</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>52</th>\n",
-       "      <td>Fresh papayas</td>\n",
-       "      <td>Fruits and vegetables</td>\n",
-       "      <td>Fruits</td>\n",
-       "      <td>xx:curate</td>\n",
-       "      <td>en:palm-oil-free,en:vegan,en:vegetarian</td>\n",
-       "      <td>599990534.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
+       "      <th>18</th>\n",
+       "      <td>Botanas, Snacks dulces, Cacao y sus productos,...</td>\n",
+       "      <td>Sugary snacks</td>\n",
+       "      <td>Chocolate products</td>\n",
+       "      <td>xx:hacendado</td>\n",
+       "      <td>en:palm-oil-free,en:vegan-status-unknown,en:ve...</td>\n",
+       "      <td>1.248000e+04</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>2450.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>Plant_based</td>\n",
+       "      <td>25.000000</td>\n",
+       "      <td>Snacks</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>Snacks, Snacks sucrés</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>600002458.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>2464.0</td>\n",
-       "      <td>47.0</td>\n",
+       "      <th>38</th>\n",
+       "      <td>Condiments, Sauces, Curry pastes, Groceries</td>\n",
+       "      <td>Fat and sauces</td>\n",
+       "      <td>Dressings and sauces</td>\n",
+       "      <td>golden-curry</td>\n",
+       "      <td>en:palm-oil,en:vegan-status-unknown,en:vegetar...</td>\n",
+       "      <td>1.257900e+04</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>2000.0</td>\n",
        "      <td>29.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>31.0</td>\n",
+       "      <td>...</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>5.9</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.00</td>\n",
        "      <td>NaN</td>\n",
-       "      <td>NA</td>\n",
+       "      <td>10.107375</td>\n",
+       "      <td>Processed</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>57</th>\n",
-       "      <td>Drink mix</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>unknown</td>\n",
-       "      <td>tclinics-usa</td>\n",
-       "      <td>en:may-contain-palm-oil,en:non-vegan,en:vegeta...</td>\n",
-       "      <td>600020002.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>33472.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>300.0</td>\n",
+       "      <th>49</th>\n",
+       "      <td>Plats préparés, Pizzas tartes salées et quiche...</td>\n",
+       "      <td>Composite foods</td>\n",
+       "      <td>Pizza pies and quiches</td>\n",
+       "      <td>xx:entremont</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1.266017e+09</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>1054.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Processed</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>200.0</td>\n",
-       "      <td>1500.0</td>\n",
-       "      <td>18.750001</td>\n",
-       "      <td>7.50</td>\n",
-       "      <td>0.429687</td>\n",
-       "      <td>NA</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                           categories  ... PNNS_pro_Snacks\n",
-       "10  Aliments et boissons à base de végétaux, Alime...  ...             1.0\n",
-       "17  Produits de la mer, Poissons et dérivés, Poiss...  ...             1.0\n",
-       "52                                      Fresh papayas  ...             0.0\n",
-       "55                              Snacks, Snacks sucrés  ...             0.0\n",
-       "57                                          Drink mix  ...             0.0\n",
+       "                                           categories  \\\n",
+       "3   Efterrätter, Fryst mat, Frysta efterrätter, Gl...   \n",
+       "9   Breakfasts, Spreads, Sweet spreads, Bee produc...   \n",
+       "18  Botanas, Snacks dulces, Cacao y sus productos,...   \n",
+       "38        Condiments, Sauces, Curry pastes, Groceries   \n",
+       "49  Plats préparés, Pizzas tartes salées et quiche...   \n",
        "\n",
-       "[5 rows x 25 columns]"
+       "              pnns_groups_1           pnns_groups_2            brands_tags  \\\n",
+       "3   Milk and dairy products               Ice cream          xx:mcdonald-s   \n",
+       "9             Sugary snacks                  Sweets  moon-shine-trading-co   \n",
+       "18            Sugary snacks      Chocolate products           xx:hacendado   \n",
+       "38           Fat and sauces    Dressings and sauces           golden-curry   \n",
+       "49          Composite foods  Pizza pies and quiches           xx:entremont   \n",
+       "\n",
+       "                            ingredients_analysis_tags          code  \\\n",
+       "3   en:may-contain-palm-oil,en:non-vegan,en:maybe-...  1.244400e+04   \n",
+       "9   en:palm-oil-content-unknown,en:vegan-status-un...  1.245309e+09   \n",
+       "18  en:palm-oil-free,en:vegan-status-unknown,en:ve...  1.248000e+04   \n",
+       "38  en:palm-oil,en:vegan-status-unknown,en:vegetar...  1.257900e+04   \n",
+       "49                                                NaN  1.266017e+09   \n",
+       "\n",
+       "    additives_n  nutriscore_score  energy_100g  fat_100g  ...  calcium_100g  \\\n",
+       "3           3.0              13.0          NaN       NaN  ...           NaN   \n",
+       "9           0.0              18.0       1276.0       0.0  ...           NaN   \n",
+       "18          1.0              18.0       2450.0      47.0  ...           NaN   \n",
+       "38          5.0              37.0       2000.0      29.0  ...           NaN   \n",
+       "49          NaN              13.0       1054.0      10.0  ...           NaN   \n",
+       "\n",
+       "    iron_100g  fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "3         NaN                                           0.000000       \n",
+       "9         NaN                                           0.000000       \n",
+       "18        NaN                                          25.000000       \n",
+       "38        NaN                                          10.107375       \n",
+       "49        NaN                                                NaN       \n",
+       "\n",
+       "        PNNS_pro  PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  \\\n",
+       "3   Animal_based                    0.0              0.0          0.0   \n",
+       "9         Snacks                    0.0              0.0          0.0   \n",
+       "18        Snacks                    1.0              0.0          0.0   \n",
+       "38     Processed                    0.0              0.0          0.0   \n",
+       "49     Processed                    0.0              0.0          0.0   \n",
+       "\n",
+       "    PNNS_pro_Plant_based  PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "3                    0.0                 1.0              0.0  \n",
+       "9                    1.0                 0.0              0.0  \n",
+       "18                   0.0                 0.0              0.0  \n",
+       "38                   0.0                 1.0              0.0  \n",
+       "49                   1.0                 0.0              0.0  \n",
+       "\n",
+       "[5 rows x 31 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3430,7 +3207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3440,7 +3217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -3482,6 +3259,16 @@
          "rawType": "float64",
          "type": "float"
         },
+        {
+         "name": "trans-fat_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "cholesterol_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
         {
          "name": "carbohydrates_100g",
          "rawType": "float64",
@@ -3512,6 +3299,26 @@
          "rawType": "float64",
          "type": "float"
         },
+        {
+         "name": "vitamin-a_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "vitamin-c_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "calcium_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
+        {
+         "name": "iron_100g",
+         "rawType": "float64",
+         "type": "float"
+        },
         {
          "name": "fruits-vegetables-nuts-estimate-from-ingredients_100g",
          "rawType": "float64",
@@ -3548,7 +3355,7 @@
          "type": "float"
         }
        ],
-       "ref": "1cf816e2-a31b-432c-875a-284f5e9597b7",
+       "ref": "03a4c1a2-5a98-4351-9065-dcf837faa087",
        "rows": [
         [
          "6",
@@ -3558,12 +3365,18 @@
          "2401.0",
          "12.0",
          "10.5",
+         "0.0",
+         "0.0",
          "13.0",
          "9.0",
          "36.0",
          "23.0",
          "0.3",
          "0.12",
+         null,
+         null,
+         null,
+         null,
          "0.0",
          "0.0",
          "0.0",
@@ -3580,6 +3393,8 @@
          "1520.0",
          "11.0",
          "2.0",
+         "0.0",
+         "0.01",
          "25.0",
          "0.98",
          "9.0",
@@ -3587,6 +3402,10 @@
          "0.95",
          "0.38",
          null,
+         null,
+         null,
+         null,
+         null,
          "0.0",
          "0.0",
          "0.0",
@@ -3602,12 +3421,18 @@
          "4.0",
          "1.0",
          "1.0",
+         null,
+         null,
          "1.0",
          "1.0",
          "1.0",
          "1.0",
          "1.0",
          "0.4",
+         null,
+         null,
+         null,
+         null,
          "0.0",
          "0.0",
          "0.0",
@@ -3624,12 +3449,18 @@
          "1510.0",
          "2.0",
          "0.5",
+         null,
+         null,
          "6.7",
          "1.7",
          "10.714286",
          "76.0",
          "1.5",
          "0.6",
+         "0.00023214286",
+         "0.07142857",
+         "0.17857143",
+         "0.008928571",
          "0.0",
          "0.0",
          "0.0",
@@ -3646,6 +3477,8 @@
          "293.0",
          "0.5",
          "0.06",
+         null,
+         null,
          "2.0",
          "0.24",
          "88.0",
@@ -3653,6 +3486,10 @@
          "0.275",
          "0.11",
          null,
+         "0.09",
+         null,
+         null,
+         null,
          "0.0",
          "0.0",
          "1.0",
@@ -3662,7 +3499,7 @@
         ]
        ],
        "shape": {
-        "columns": 19,
+        "columns": 25,
         "rows": 5
        }
       },
@@ -3691,12 +3528,14 @@
        "      <th>energy_100g</th>\n",
        "      <th>fat_100g</th>\n",
        "      <th>saturated-fat_100g</th>\n",
+       "      <th>trans-fat_100g</th>\n",
+       "      <th>cholesterol_100g</th>\n",
        "      <th>carbohydrates_100g</th>\n",
        "      <th>sugars_100g</th>\n",
-       "      <th>fiber_100g</th>\n",
-       "      <th>proteins_100g</th>\n",
-       "      <th>salt_100g</th>\n",
-       "      <th>sodium_100g</th>\n",
+       "      <th>...</th>\n",
+       "      <th>vitamin-c_100g</th>\n",
+       "      <th>calcium_100g</th>\n",
+       "      <th>iron_100g</th>\n",
        "      <th>fruits-vegetables-nuts-estimate-from-ingredients_100g</th>\n",
        "      <th>PNNS_pro_Animal_based</th>\n",
        "      <th>PNNS_pro_Drinks</th>\n",
@@ -3715,12 +3554,14 @@
        "      <td>2401.0</td>\n",
        "      <td>12.0</td>\n",
        "      <td>10.50</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
        "      <td>13.0</td>\n",
        "      <td>9.00</td>\n",
-       "      <td>36.000000</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>0.300</td>\n",
-       "      <td>0.12</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3737,12 +3578,14 @@
        "      <td>1520.0</td>\n",
        "      <td>11.0</td>\n",
        "      <td>2.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
        "      <td>25.0</td>\n",
        "      <td>0.98</td>\n",
-       "      <td>9.000000</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.950</td>\n",
-       "      <td>0.38</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3759,12 +3602,14 @@
        "      <td>4.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.00</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.00</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.000</td>\n",
-       "      <td>0.40</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3781,12 +3626,14 @@
        "      <td>1510.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.50</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>6.7</td>\n",
        "      <td>1.70</td>\n",
-       "      <td>10.714286</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>1.500</td>\n",
-       "      <td>0.60</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.071429</td>\n",
+       "      <td>0.178571</td>\n",
+       "      <td>0.008929</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3803,12 +3650,14 @@
        "      <td>293.0</td>\n",
        "      <td>0.5</td>\n",
        "      <td>0.06</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.24</td>\n",
-       "      <td>88.000000</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.275</td>\n",
-       "      <td>0.11</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.090000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3819,20 +3668,56 @@
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
+       "<p>5 rows × 25 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "    code  additives_n  ...  PNNS_pro_Processed  PNNS_pro_Snacks\n",
-       "6    4.0          0.0  ...                 0.0              1.0\n",
-       "9    6.0          NaN  ...                 1.0              0.0\n",
-       "11   7.0          0.0  ...                 0.0              0.0\n",
-       "12   8.0          1.0  ...                 0.0              0.0\n",
-       "14   9.0          NaN  ...                 0.0              0.0\n",
+       "    code  additives_n  nutriscore_score  energy_100g  fat_100g  \\\n",
+       "6    4.0          0.0              15.0       2401.0      12.0   \n",
+       "9    6.0          NaN               4.0       1520.0      11.0   \n",
+       "11   7.0          0.0               4.0          4.0       1.0   \n",
+       "12   8.0          1.0               6.0       1510.0       2.0   \n",
+       "14   9.0          NaN             -11.0        293.0       0.5   \n",
        "\n",
-       "[5 rows x 19 columns]"
+       "    saturated-fat_100g  trans-fat_100g  cholesterol_100g  carbohydrates_100g  \\\n",
+       "6                10.50             0.0              0.00                13.0   \n",
+       "9                 2.00             0.0              0.01                25.0   \n",
+       "11                1.00             NaN               NaN                 1.0   \n",
+       "12                0.50             NaN               NaN                 6.7   \n",
+       "14                0.06             NaN               NaN                 2.0   \n",
+       "\n",
+       "    sugars_100g  ...  vitamin-c_100g  calcium_100g  iron_100g  \\\n",
+       "6          9.00  ...             NaN           NaN        NaN   \n",
+       "9          0.98  ...             NaN           NaN        NaN   \n",
+       "11         1.00  ...             NaN           NaN        NaN   \n",
+       "12         1.70  ...        0.071429      0.178571   0.008929   \n",
+       "14         0.24  ...        0.090000           NaN        NaN   \n",
+       "\n",
+       "    fruits-vegetables-nuts-estimate-from-ingredients_100g  \\\n",
+       "6                                                 0.0       \n",
+       "9                                                 NaN       \n",
+       "11                                                0.0       \n",
+       "12                                                0.0       \n",
+       "14                                                NaN       \n",
+       "\n",
+       "    PNNS_pro_Animal_based  PNNS_pro_Drinks  PNNS_pro_NA  PNNS_pro_Plant_based  \\\n",
+       "6                     0.0              0.0          0.0                   0.0   \n",
+       "9                     0.0              0.0          0.0                   0.0   \n",
+       "11                    0.0              0.0          0.0                   1.0   \n",
+       "12                    0.0              0.0          1.0                   0.0   \n",
+       "14                    0.0              0.0          1.0                   0.0   \n",
+       "\n",
+       "    PNNS_pro_Processed  PNNS_pro_Snacks  \n",
+       "6                  0.0              1.0  \n",
+       "9                  1.0              0.0  \n",
+       "11                 0.0              0.0  \n",
+       "12                 0.0              0.0  \n",
+       "14                 0.0              0.0  \n",
+       "\n",
+       "[5 rows x 25 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3850,7 +3735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4271,7 +4156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4291,7 +4176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4722,7 +4607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4734,7 +4619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4766,7 +4651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4811,7 +4696,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4871,7 +4756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -4942,7 +4827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5415,7 +5300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -5430,7 +5315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5461,7 +5346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -5504,7 +5389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {