This project demonstrates how to build an end-to-end machine learning project using a modular programming approach. The project is divided into several modules, each of which performs a specific task. This modular approach makes the project easier to understand, maintain, and extend.
- Data loading and preprocessing: This module loads the data from a file and performs basic preprocessing tasks, such as cleaning and formatting the data.
- Feature engineering: This module creates new features from the existing data.
- Model training: This module trains a machine-learning model on the data.
- Model evaluation: This module evaluates the performance of the trained model.
- Model deployment: This module deploys the trained model to a web application.
- NumPy: For numerical computing
- Pandas: For data manipulation and analysis
- Scikit-learn: For machine learning
- Flask: For web development
To run the project, you will need to install the following dependencies:
pip install numpy pandas scikit-learn flask
Once you have installed the dependencies, you can run the project by following these steps:
Clone the GitHub repository. Create a virtual environment and activate it. Install the project dependencies. Run the app.py file. The project will be deployed to a local web server. You can access the web application at http://localhost:5000.
The benefits of using a modular programming approach for machine learning projects include:
- Increased code readability and maintainability
- Easier to extend the project with new features
- Improved debugging and testing
- Increased portability of the project
This project is open source and contributions are welcome. If you find any bugs or have suggestions for improvement, please open an issue or submit a pull request.
Thank you for your interest in this project