Diamonds

About Dataset

Context

This classic dataset contains the prices and other attributes of almost 54,000 diamonds. It's a great dataset for beginners learning to work with data analysis and visualization.

Content

price price in US dollars (\$326--\$18,823)

carat weight of the diamond (0.2--5.01)

cut quality of the cut (Fair, Good, Very Good, Premium, Ideal)

color diamond colour, from J (worst) to D (best)

clarity a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))

x length in mm (0--10.74)

y width in mm (0--58.9)

z depth in mm (0--31.8)

depth total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)

table width of top of diamond relative to widest point (43--95)

@kaggledatasets https://www.kaggle.com/datasets/shivam2503/diamonds

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Features Carat :

• Carat weight of the Diamond.
• Cut : Describe cut quality of the diamond.
  • Quality in increasing order Fair, Good, Very Good, Premium, Ideal .
• Color : Color of the Diamond. . With D being the best and J the worst.
• Clarity : Diamond Clarity refers to the absence of the Inclusions and Blemishes. 0 (In order from Best to Worst, FL = flawless, 13= level 3 inclusions) FL, IF, VVS1, VVS2, VS1, VS2, SI1, SI2, 11, 12, 13
• Depth : The Height of a Diamond, measured from the Culet to the table, divided by its average Girdle Diameter.
• Table: The Width of the Diamond's Table expressed as a Percentage of its Average Diameter.
• Price : the Price of the Diamond.
• X : Length of the Diamond in mm.
• Y : Width of the Diamond in mm.
• Z : Height of the Diamond in mm.

Qualitative Features (Categorical) : Cut, Color, Clarity. Quantitative Features (Numerical) : Carat, Depth, Table, Price , X, Y, Z. Price is the Target Variable.

Folder Structure

├── README.md
├── data                  # <-- Directory with raw and intermediate data
│   ├── data.xml          # <-- Initial XML StackOverflow dataset (raw data)
│   ├── data.xml.dvc      # <-- .dvc file - a placeholder/pointer to raw data
│   ├── features          # <-- Extracted feature matrices
│   │   ├── test.pkl
│   │   └── train.pkl
│   └── prepared          # <-- Processed dataset (split and TSV formatted)
│       ├── test.tsv
│       └── train.tsv
├── evaluation
│   ├── importance.png    # <-- Feature importance plot
│   └── plots             # <-- Data points for ROC, PRC, confusion matrix
│       ├── confusion_matrix.json
│       ├── precision_recall.json
│       └── roc.json
├── dvc.lock
├── dvc.yaml              # <-- DVC pipeline file
├── model.pkl             # <-- Trained model file
├── params.yaml           # <-- Parameters file
├── evaluation.json       # <-- Binary classifier final metrics (e.g. AUC)
└── src                   # <-- Source code to run the pipeline stages
    ├── evaluate.py
    ├── featurization.py
    ├── prepare.py
    ├── requirements.txt  # <-- Python dependencies needed in the project
    └── train.py

Notes:

data["z"].agg(["skew", "kurtosis"])

References:

• 10 Things to do when conducting your Exploratory Data Analysis (EDA) by Alifia C Harmadi Data Folks Indonesia Medium
• Exploratory Data Analysis(EDA) Python by Kaushik Katari Towards Data Science
• EDA Exploratory Data Analysis With Python What is EDA
• A Beginner’s Guide to Exploratory Data Analysis with Python
• Detailed exploratory data analysis with python Kaggle
• Exploratory Data Analysis EDA For Categorical Data by Kurtis Pykes Heartbeat
• Comprehensive Confidence Intervals for Python Developers Pythonic Excursions
• Diamonds In-Depth Analysis Kaggle
• Diamond Price Prediction Kaggle
• Regression on Diamonds Dataset (95% score) Kaggle