Kolkata Zomato Dataset Analysis

The dataset contains 7388 rows and 7 columns. The details of the restaurant those provided are –

 'name' [Name of the restaurant]  'voteCount' [Number of votes received from the user]  'rating' [rating given to the restaurant by the user]  'address' [Address of the restaurant]  'cusine' [Category of cuisine offered]  'cost' [Average price of the restaurant]  'timing' [Opening and closing time of the restaurant]

We will be performing EDA(exploratory data analysis) on the given Dataset.

Libraries required –  Pandas – Data exploration  Numpy – Mathematical operation  Matplotlib – Data Visualization  Seaborn – Data Visualization

WorkFlow –

Our workflow will primarily consist of two segments. Data cleaning and data visualization. The data cleaning process will consume more time than the visualization.

Data Cleaning –

 Vote count , rating and cost , these three columns are type casted as object. We need to convert them in numerical category for our calculative functionality.  We need to find the missing values and then handle them with mean, median or mode operation based on it’s relevance.  We need to look for duplicate values in the dataset.  Let’s create different data frames based on given conditions –

• high_end_restos – 1896 , AvgRating – 3.17 , AvgVotecount - 288 • cheap_restos – 2193 , AvgRating – 2.11 , AvgVotecount - 32 • midnight_restos – 670, AvgRating – 2.67 , AvgVotecount - 128 • genral_timed_resto – 6718 , AvgRating – 2.49 , AvgVotecount - 107

 Let’s find out the most popular cuisines in the market –

• North Indian • Chinese • Fast Food • Café • Biriyani • Bengali

Data Visualization –