New restaurants are opening every day. However it has become difficult for them to compete with already established restaurants. The key issues that continue to pose a challenge to them include high real estate costs, rising food costs, shortage of quality manpower, fragmented supply chain and over-licensing. This Zomato data aims at analysing demography of the location. Most importantly it will help new restaurants in deciding their theme, menus, cuisine, cost etc for a particular location. It also aims at finding similarity between neighborhoods of Bengaluru on the basis of food. The dataset also contains reviews for each of the restaurant which will help in finding overall rating for the place.
It contains one jupyter notebook (of .ipynb format and name of file should be the complete name of the participant) and it is going to contain the entire project from cleaning to visualizations.
Understand the shape of dataset , drop duplicates if any ,drop unnecessary columns if any , understand the structure of dataset using various functions like info() , describe().
Clean each column values using below functions or other
isnull().sum
fillna()
unique()
rename()
value_counts()
Treat both numeric and categorical columns .
For numeric columns convert them into integers or floating point values .
For categorical columns understand the count of various categories and work on them .
Using plots of seaborn and matplotlib answer below questions through visualizations and find more insights from data.
Q1) Which locations are having maximum restaurants? So which will be a suitable location to open a restaurant?
Q2) Visualize online order facility location wise.
Q3) Visualize book_table vs Rate
Q4) Visualize online_order vs Rate
Q5) Visualizing Book Table facility Location Wise
Q6) Visualizing Types of Restaurants vs Rate
Q7) Types of Restaurants location wise
Q8) Visualizing Top Cuisines wrt votes