Zomato-Restaurant-Clustering-And-Sentiment-Analysis

This is clustering of the restaurants available on Zomato and includes the sentiment analysis of customers satisfaction level.

Problem Statement Zomato is an Indian restaurant aggregator and food delivery start-up founded by Deepinder Goyal and Pankaj Chaddah in 2008. Zomato provides information, menus and user-reviews of restaurants, and also has food delivery options from partner restaurants in select cities.

India is quite famous for its diverse multi cuisine available in a large number of restaurants and hotel resorts, which is reminiscent of unity in diversity. Restaurant business in India is always evolving. More Indians are warming up to the idea of eating restaurant food whether by dining outside or getting food delivered. The growing number of restaurants in every state of India has been a motivation to inspect the data to get some insights, interesting facts and figures about the Indian food industry in each city. So, this project focuses on analysing the Zomato restaurant data for each city in India.

The Project focuses on Customers and Company, you have to analyze the sentiments of the reviews given by the customer in the data and made some useful conclusion in the form of Visualizations. Also, cluster the zomato restaurants into different segments. The data is vizualized as it becomes easy to analyse data at instant. The Analysis also solves some of the business cases that can directly help the customers finding the Best restaurant in their locality and for the company to grow up and work on the fields they are currently lagging in.

This could help in clustering the restaurants into segments. Also the data has valuable information around cuisine and costing which can be used in cost vs. benefit analysis

Data could be used for sentiment analysis. Also the metadata of reviewers can be used for identifying the critics in the industry.

METHODOLOGY

1. Problem Statement : "Stated Above"

2. Loading dataset

3. Understanding Dataset

4. Data Wrangling

5. Exploratory Data Analysis

6. Hypthesis Testing

7. Data Preprocessing a) Handling Null values b) Handling outliers c) Feature selection d) Feature Manipulation

8. Text data Preprocessing ( Using NLP)

9. Modelling

a) CLUSTERING * KMeans clustering * Agglomerative Hierarchichal clustering * DBSCAN

b) Sentiment Analysis * Logistic Regression * Decision Tree * Random forest * XGBoost classifier * K Nearest Neighbour

10. Evaluation

11. Conclusion