This project contains an exploratory data analysis (EDA) and a machine learning model to predict whether students will take a specific type of exam (SM).

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├── data/                   # Directory to store datasets (datasets are not uploaded to GitHub)
├── notebooks/              # Jupyter notebooks for EDA and model training
│   ├── eda.ipynb           # Exploratory Data Analysis notebook
│   └── model_training.ipynb# Model training and evaluation notebook
├── README.md               # Project overview and instructions
└── requirements.txt        # Python dependencies

• Python 3.7 or higher
• Jupyter Notebook

1. Clone the repository:

   git clone https://github.com/LeviJesus/students_evasion_with_machine_learning.git
   cd students_evasion_with_machine_learning

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

1. Run the Jupyter Notebook server:

   jupyter notebook

2. Open and run the notebooks in the notebooks/ directory:

   • eda.ipynb: Perform exploratory data analysis.
   • model_training.ipynb: Train and evaluate the machine learning model.

Heavy datasets are not included in this repository.
Please download them from this Google Drive link.

The EDA notebook (eda.ipynb) includes:

• Loading and cleaning the datasets.
• Visualizing the distribution of access times.
• Analyzing the temporal patterns of student access.
• Examining the distribution of different types of exams.

The model training notebook (model_training.ipynb) includes:

• Defining the target variable.
• Creating features based on student access patterns.
• Normalizing the data.
• Training and evaluating multiple machine learning models.
• Selecting the best model based on performance metrics.

Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.