https://github.com/csengupta1101/zomato-kolkata-eda

The Zomato dataset (Kolkata) contains 7388 rows and 7 columns. The repository is an EDA(exploratory data analysis) on the given Dataset.
https://github.com/csengupta1101/zomato-kolkata-eda

eda jupyter-notebook kolkata python3 restaurant vscode zomato

Last synced: 7 days ago
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The Zomato dataset (Kolkata) contains 7388 rows and 7 columns. The repository is an EDA(exploratory data analysis) on the given Dataset.

• Host: GitHub
• URL: https://github.com/csengupta1101/zomato-kolkata-eda
• Owner: Csengupta1101
• Created: 2021-11-14T07:42:57.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2021-11-14T07:49:24.000Z (about 3 years ago)
• Last Synced: 2024-11-07T18:13:30.062Z (about 2 months ago)
• Topics: eda, jupyter-notebook, kolkata, python3, restaurant, vscode, zomato
• Language: Jupyter Notebook
• Homepage:
• Size: 890 KB
• Stars: 1
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
![zomato](https://github.com/Csengupta1101/Zomato-Kolkata-EDA/blob/main/ZomatoKol.png)

# 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 –