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Analyze the Sentiments of the Reviews given by the Customer of Zomato Indian Restaurant | Realcode4you

Project Description

Business Context

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 make some useful conclusions 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.


Dataset Description



Main Libraries used:

  • Pandas for data manipulation, aggregation

  • Matplotlib and Seaborn for visualization and behavior with respect to the target variable

  • NumPy for computationally efficient operations

  • Scikit learn for model building


Project Architecture:


Data Files evaluation criteria

  • Understanding the Dataset and problem statement.

  • Efficient EDA

  • Dealing with missing values and outliers

  • Exploring Exceptional Cases

  • Selecting the approach and algorithm to be used.

  • Modeling- Use at least 2 algorithms.

  • Brief strategy for clusters formed.

  • Conclusion.

  • How is your project useful to stakeholders



Project Checklist

  • Prob statement

  • Business context

  • Data understanding

  • Dataset loading and cleanup

  • EDA

  • Feature Engineering: Creating new features, removing redundant ones.

  • Pre process: Feature scaling.

  • Finding ideal number of clusters: Elbow chart/ dendrogram.

  • Model Implementation and interpretation.

  • Insights and conclusion.

  • Application of your project in the industry

  • Github Commits at regular intervals



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