https://github.com/pavankumarshridhar31/food-delivery-analysis-and-profit-prediction

https://github.com/pavankumarshridhar31/food-delivery-analysis-and-profit-prediction

Last synced: about 1 month ago
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• Host: GitHub
• URL: https://github.com/pavankumarshridhar31/food-delivery-analysis-and-profit-prediction
• Owner: Pavankumarshridhar31
• Created: 2024-10-11T07:32:21.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-11-21T04:19:46.000Z (about 1 month ago)
• Last Synced: 2024-11-21T05:20:52.637Z (about 1 month ago)
• Language: Jupyter Notebook
• Size: 2.84 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
This project analyzes and predicts the profitability of food delivery services using operational data from platforms like Zomato and Swiggy. The Random Forest Regressor model achieved a high R-squared (R²) value of 0.99, indicating excellent predictive accuracy. Key steps included data preprocessing, feature engineering, and performance evaluation through cross-validation. The analysis provided insights into cost factors impacting profit margins, supporting strategic decision-making. Technologies used included Python, Pandas, Scikit-learn, Matplotlib, and Seaborn. This project demonstrates effective application of data science to enhance business efficiency and profitability

![image](https://github.com/user-attachments/assets/de1caf38-338b-4321-96dc-f04213c7fc9d)

![image](https://github.com/user-attachments/assets/9190b1ed-22fb-45e0-9463-a1f1299e5fef)

![image](https://github.com/user-attachments/assets/f7f1cb24-0d7a-48b6-b735-11bd72318f42)

![image](https://github.com/user-attachments/assets/725c711d-4d2b-4b80-9d96-34e4c1947881)