This project aims to predict students' math scores using machine learning, based on a variety of features including demographic, social, and school-related factors. It seeks to uncover the key determinants of students' performance in math, providing valuable insights for educational strategies.
- Gender: Sex of students → (Male/Female) 👨👩👧👦
- Race/Ethnicity: Ethnicity of students → (Group A, B, C, D, E) 👲🏽👳🏾♂️👩🏼🦱🧑🏿👱🏻♀️
- Parental Level of Education: Parents' final education → (Bachelor's degree, Some college, Master's degree, Associate's degree, High school) 👨🎓👩🎓
- Lunch: Having lunch before test → (Standard or Free/reduced) 🍱
- Test Preparation Course: Complete or not complete before test ✍️
- Math Score ➕🖩
- Reading Score 📖
- Writing Score ✍️
The project applies various machine learning models to predict students' performance and analyze the impact of different features on their academic outcomes. It involves data preprocessing, exploratory data analysis (EDA), model selection, training, and evaluation to understand the underlying patterns and make predictions.
To run this project, follow these steps:
- Clone the Repository: First, clone the repository to your local machine using the following command:
git clone https://github.com/singh-manavv/Students-Performance.git
- Set Up Your Environment: Ensure you have Python installed on your machine. It's recommended to create a virtual environment for this project to manage dependencies efficiently. You can create a virtual environment using:
python -m venv venv
Activate the virtual environment:
- On Windows:
venv\Scripts\activate - On Unix or MacOS:
source venv/bin/activate
- Install Dependencies: Install all the required dependencies by running:
pip install -r requirements.txt
- Run the Project: Navigate to the project directory and run the main script
app.pyusing following command:
python app.py
Contributions to this project are welcome! Whether it's improving the machine learning models, adding new features, or fixing bugs, your help can make a big difference. Please feel free to fork the repository, make your changes, and submit a pull request.
This project is open-sourced under the MIT License. See the LICENSE file for more details.
For more information and updates, please visit the project's GitHub repository.