This project is a comprehensive analysis of student performance data. The primary objective is to explore the relationships between various factors such as study habits, extracurricular activities, and academic performance. Additionally, several machine learning models were trained and evaluated to predict student success based on these factors.
All the custom libraries and scripts used in this project are located under <project root>/source
- <project root>/source/data_preprocessing.py: Contains all preprocessing functions, including feature scaling, encoding, and handling missing values.
- <project root>/source/model_training.py: Main script used to train and evaluate machine learning models.
- <project root>/source/predict_nn.py: Script for making predictions using the trained neural network model.
- Place any additional preprocessing functions/classes in source/data_preprocessing.py
- Place new model definitions or enhancements in source/model_definitions
- Add any new analysis or visualization scripts to source/data_analysis.py or source/data_visualization.py
- About
- Prerequisites
- Bootstrap Project
- Running the code using Jupyter
- Adding New Libraries
- TODO
- License
You need to have Python >= 3.9 installed on your machine. It's recommended to use a virtual environment for managing dependencies.
$ python3.9 -V
Python 3.9.7