https://github.com/pravincoder/mlproject
https://github.com/pravincoder/mlproject
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/pravincoder/mlproject
- Owner: pravincoder
- Created: 2023-11-01T14:23:53.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-12-23T08:19:01.000Z (over 2 years ago)
- Last Synced: 2025-02-15T17:40:23.194Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 1.25 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
## Student Performance Prediction Model
This small end-to-end machine learning project predicts student performance based on various features such as gender, race_ethnicity, parental education level, lunch, test prep course, reading score, and writing score. The project utilizes popular Python libraries such as scikit-learn (sklearn), Flask, XGBoost, CatBoost, dill, Seaborn, NumPy, and Pandas.
### Project Overview
The goal of this project is to build a machine learning model that predicts student performance based on demographic and academic-related features. The model is trained on a dataset containing information about students, including their gender, race_ethnicity, parental education level, lunch type, test prep course completion, and scores in reading and writing.
### Getting Started
1. Clone the repository:
` git clone https://github.com/your-username/MLProject.git `
2. Navigate to the project directory:
` cd MLProject `
3. Install the required packages:
` pip install -r requirements.txt `
4. Explore the data and develop the machine learning model using the provided Jupyter notebook(s) in the `notebook` directory.
5. Once the model is trained, run the Flask web application:
`` cd app
python app.py ``
6. Open your browser and go to http://localhost:5000 to use the web interface for predicting student performance.
### License
This project is licensed under the MIT License - see the LICENSE file for details.