The zomato exploratory data analysis is for the foodies to find the best restaurants, value for money restaurants in their locality. It also helps to find their required cuisines in their locality.
- Define the problem statement
-Define the datatypes and create data definitions
-Import the libraries
-Read the data
-Understand and Prepare the Data
-Understand the variables
-Check for Missing Values
-Study Correlation
-Detect Outliers
-Ask questions to obtain more insights
Here the major idea was to carry out indepth analysis of understanding how the hotels are distributed around India who are partnered with zomato.
Several important questions were answered such as :
- What is the distribution of hotels?
- Which are the most favorite cuisines?
- What are the top highlights offered by a restaurant?
- Which is the most expensive hotel in the Mumbai city?
Pandas = 1.0.3
Numpy = 1.18.4
Matplotlib = 3.2.1
Geopandas = 0.7.0
Seaborn = 0.10.1
data_analysis_pipeline_notebook.ipynb -> contains the notebook for carrying out end-to-end data analysis
Here we have tried to carry out in depth exploratory data analysis of the zomato dataset and find out answers to the various questions asked to make it clear to the reader. We have always tried to understand the basic steps one needs to take in order to carry out exploratory data analysis.
-We we have understood that Momos, Tea and Salad were the favorite cuisine of India.
-The hotels have a great concentration in the northern, western and southern part whereas it is less in the eastern and central part of Inida.
-The top highlights offered by restuarants are Table Reservation Not Required followed by Delivery
- The costliest resaurant in Mumbai is Ocean - The Private Dining Room - Sahara Star
How to plot a heatmap using Seaborn?
https://medium.com/@darshangandhi1151/exploratory-data-analysis-for-beginners-a29bf2d0c494