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.
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.
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Clone the repository:
git clone https://github.com/your-username/MLProject.git -
Navigate to the project directory:
cd MLProject -
Install the required packages:
pip install -r requirements.txt -
Explore the data and develop the machine learning model using the provided Jupyter notebook(s) in the
notebookdirectory. -
Once the model is trained, run the Flask web application:
cd app python app.py -
Open your browser and go to http://localhost:5000 to use the web interface for predicting student performance.
This project is licensed under the MIT License - see the LICENSE file for details.