Welcome to the Restaurant Revenue Prediction Application! In the competitive world of the restaurant industry, making informed decisions is crucial for success. Our application leverages the power of machine learning to predict monthly revenue for restaurants, helping owners and managers strategize more effectively.
The report for this project can be found inside Ece Teker Mi-POWER Final Case.pdf. Or you can view it on google docs.
The presentation for this project be found inside Restaurant Revenue Prediction Ece Teker.pdf. Or you can view it on google docs. You can also watch me present it here :)
This is a sample Flask application developed in Python for web as part of the Mi-Power Women Empowerment Program by Group 7 (All the improvements made after the group project are by Ece Teker). The primary purpose of this application is to provide restaurant owners with a tool to forecast their monthly revenue based on various input factors. By inputting key details about their restaurant's operations, such as the number of customers, menu prices, marketing spend, cuisine type, average customer spending, promotions, and reviews, users can receive a data-driven prediction of their expected revenue.
- User-Friendly Interface: Our application features a clean, intuitive interface where users can easily input relevant data about their restaurant.
- Machine Learning Models: The application utilizes advanced machine learning models, including Random Forest Regressor, Gradient Boosting Regressor, and XGBoost, to ensure accurate and reliable predictions.
- Data Insights: Gain insights into how different factors impact your restaurant's revenue, helping you make strategic decisions to optimize profitability.
- Real-Time Predictions: Get instant predictions by submitting your data through the application's web interface.
- Input Data: Users provide information such as the number of customers, menu prices, marketing spend, cuisine type, average customer spending, promotions, and reviews.
- Model Processing: The application processes the input data through pre-trained machine learning models.
- Revenue Prediction: Based on the processed data, the application predicts the expected monthly revenue for the restaurant.
- Results Display: The predicted revenue is displayed on the screen, providing users with valuable insights.
- Clone the repository:
https://github.com/ecetkr/mikrogrup-final-ece-teker.git - Open the project in Visual Studio Code.
- Open the terminal and install requirements.
pip install -r requirements.txt - Run app.py
- Open the webpage on localhost
http://127.0.0.1:5000
Contributions are welcome! If you'd like to contribute to this project, please follow these steps:
- Fork the repository.
- Create a new branch (
git checkout -b feature/yourfeature). - Make your changes.
- Commit your changes (
git commit -am 'Add new feature'). - Push to the branch (
git push origin feature/yourfeature). - Create a new pull request.
This project was originally created by Ece Teker, Fatmanur Beyza Tolan, Şeydanur Kuvvetli You can go the original repository here.
This project was improved by Ece Teker