Optimal-Location-Selection-for-a-New-Restaurant-in-Chennai

The goal of this project is to identify optimal locations for opening popular restaurant types in Chennai, including Indian, Fast Food, Vegetarian, Chinese, Asian, and Italian restaurants. The analysis is based on factors such as venue density distribution in different neighborhoods. The project utilizes web scraping, data preprocessing, visualization, and clustering techniques to provide insights and recommendations for stakeholders.

Steps Performed

1. Data Collection

   • Scraped the neighborhood information of Chennai city from the Wikipedia website.
   • Used Python's geocoder library to obtain the coordinates of each neighborhood.
   • Fetched venue data within each neighborhood using the Foursquare API.

2. Data Preprocessing

   • Cleaned and preprocessed the neighborhood and venue data.
   • Filtered the venues to focus on restaurants only.

3. Data Visualization

   • Visualized the neighborhoods of Chennai using Folium to gain a geographical understanding.
   • Created a heatmap to visualize the density distribution of restaurants in Chennai.

4. Data Analysis and Clustering

   • One-hot encoded the venue categories to prepare the data for clustering.
   • Calculated the mean frequency of occurrence for each category in each neighborhood.
   • Clustered the neighborhoods based on the venue frequency data.

5. Analysis

   • Interpreted the clusters and provided insights on the characteristics of each cluster.
   • Discussed the findings and implications for stakeholders in terms of optimal restaurant locations.
   • Highlighted additional factors to consider, such as competition, future growth, health regulations, and safety/crime rates.

6. Results

   • Cluster 0: High market density with fewer medium popular restaurant types. Approximately 60% of the area in this cluster has a promising opportunity for opening a new restaurant. This cluster has no Asian and Italian restaurants.
   • Cluster 1: High restaurant density, similar to Cluster 2. Approximately 26% of the area in this cluster has an opportunity to open a new restaurant.
   • Cluster 2: Full of restaurants and other food shops. Only 23% of the areas in this cluster have an opportunity to open a new restaurant.
   • Cluster 3: Negligible restaurant density. Out of the 3 areas in this cluster, 2 areas do not have a single restaurant.

Chennai Map with Clusters

The above image shows the map of Chennai with clusters marked using different colors. Each cluster represents a group of neighborhoods with similar characteristics in terms of restaurant density. The clusters provide valuable insights into the distribution of restaurant opportunities in Chennai.

Please refer to the notebook for more detailed explanations and code implementation.

Structure

• Data Source: Wikipedia (https://en.wikipedia.org/wiki/Areas_of_Chennai)
• notebooks: Directory containing Jupyter notebooks for each step of the analysis.
• results: Repository containing intermediate and final results obtained from the analysis.
• gitignore: File listing the dependencies and libraries required to run the code.

Please refer to the individual notebooks for more detailed explanations and code implementation.

Conclusion

This project provides valuable insights into the optimal locations for opening popular restaurant types in Chennai. Stakeholders can make informed decisions based on the analysis results, considering various factors such as venue density, competition, future growth, health regulations, and safety/crime rates. The code and analysis can be further customized and expanded upon to suit specific requirements and preferences.

For more information, please refer to the detailed documentation and code implementation in the repository.