Geopatial_Analysis

Geopatial Analysis Project Involving (Zomata Case Study) on Restuarants,Hotels in Bangalore.

Dataset:

download dataset here https://drive.google.com/drive/u/0/folders/11-efbrZ5FZRPtcHaytKbtqwA20DZiORn

Problem Statement => Data Cleaning, Find % of missing values, Deal with missing values

To find the % missing of values, use a list_conprehension iterate the columns to return all the features with null_na. and calculating the % missing of values we return the sum of null_na and divided it by the lenght of df and multiply by 100. {df[features].isnull().sum()/len(df)*100}

To deal with the missing values, a column with highest number of missing values(rate) was choosing and manipulations where done to remove and replace missing values other than null_na.

Problem Statement => Cal. Avg Rating of each Restuarant.

call a groupby function on the name and rate column to return the mean of all the restaurants.

Problem Statement => Get Distributions of Rating Column & try to find out what Distribution this Feature Support.

Used displot from searborn to visualized Restuarants with highest Avg. ratings.

Rating Distr.

Problem Statement => Top Restaurants Chains in Bangalore.

use value_counts() on the name column to return the number of restaurants and there outlet. you can visualize using barplot

Top Chains_rest.

Problem Statement => How many of the Restuarants Do Not Accep Online Orders .

Do a value_counts on the online_order column and visualize using pie chart.

Problem Statement => Ratio Between Restauarants that Provide Table and Restaurants that do not Provide Table.

Do a value_counts on book_table and visualize using graph_objs from plotly

NOTE:

Different plotly Extension:

1. import plotly.express as px ploting px.pie() the pie is lowercase.

2. import plotly.graph_objs as go ploting go.Pie() the Pie is uppercase. (graph_objs takes on uppercase)

Problem Statement => Indept Analysis on Types of Restaurants we have.

To discover the most populated species of Rest. Drop the null values in rest_type columns, do a value_count and visualize using Bar plot.

Types of Rest.

Problem Statement => Highest Voted Restaurants.

group the name and votes of rest. and Visulize using Bar

Most Voted_rest.

Problem Statement => Total Restaurants at different locations in Bangalore.

Create two list to store the group rest.locations and rest.names count. apply sort_values() function to return locations with highest number of rest.names_count.

Rest_count by locations.

Problem Statement => Total number of variety of Restaurants in Banglore

Do a Value_count on cuisines column and Visualize using Bar

Problem Statement => Appro Cost for 2 People Feature

we need to remove the ',' in the uique features before datatype can be converted from obj to int and also before seaborn can visualize it.

Problem Statement => Analyse 'Approx. Cost of 2 People' vs 'ratiing' Find out some Relationship.

use Scatterplot to find out there relationship and and apply hue over the online_order to come with a conslusion over the top_rated restaurants that accept online_order.

Problem Statement => Is there any difference b/w Votes of Restaurants Accepting and not Accepting Online_Order.

Do a box plot on Online Order vs votes to see the d/f.

Problem Statement => Is there any difference b/w Price of Restaurants Accepting and not Accepting Online_Order.

Do a box plot on Online Order vs Appro Cost (for two people) to see the d/f.

Problem Statement => Find Out the Most Luxurius Rest. in Banglore.

find the max price for two people and filter the df to return rest with max_price.

Problem Statement => Top 10 Most Expensive Rest. with Approx. Cost for two People.

Make a copy of the df and reset name as index find the nlargest and visualized similary for cheapest rest find the nsmallest and visualized.

Most Luxurius

Most Cheapest

Problem Statement => Find Out the Rest that are Below 500 as well as Affordable.

Filter the Cost of two people on the rest less than 500.

Problem Statement => Rest. with Rating > 4 and the Budget are Good/Affordable.

Filter the rate > 4 and approx_cost(for two people columns <=500 and return the len of the unique name.

Problem Statement => Total Afordabele Hotels in all the Locations in Bangalore.

Filter the rate > 4 and approx_cost(for two people columns <=500 and group the total number of hotels according to locations.

Problem Statement => Finding the Best Budget Restaurant in any Location.

Fileter the df on Cost of two peop., rate and return the location and restaurants list passed in the filter.

Problem Statement => Which Areas are the Foodie.

Do a value_count on the location and visualized the most foodie areas.

Geopatial Analysis.

In this project, get the locations name, get the location latitude and longitude, Merge with location Rest_count and convert the latitude and longitude to array and generate a baseMap based on the default_location and zoom_start from the Restaurants_locations, to generate a HeatMap apply the function on the lat, lon and count of Restaurants locations and use value_tollist to convert to array and add it back to the basemap in other to visualized the HeatMpap on tne Restaurants locations.

Basemap

HeatMap Basemap

Heat map of North Indian Restaurants

Filter the north_indian rest.count and merge it with the locations df and apply the HeatMap on the lat and lon from the basemap function to visualize.

Which are the Most Popular Casual Dining Restaurants Chains.

group the name of restaurants according to there rest_type and agg count, group again and sort according to url to get a total count, filter out the name, url and rest_type columns. To get Most Popular Casual Dining Restaurants Chains. Do a filter on the rest_type.