🎓 Student Dropout Prediction using Machine Learning

This project focuses on predicting student academic outcomes—specifically identifying potential dropouts—using various Machine Learning classification algorithms. The goal is to provide a data-driven approach to early intervention in educational environments.

📌 Project Overview

The pipeline covers the entire data science lifecycle, from rigorous data preprocessing and feature selection based on correlation to model optimization using Grid Search.

Key Features:

• Exploratory Data Analysis (EDA): Correlation matrix analysis to identify the most impactful features.
• Data Preprocessing: Handling missing values, categorical encoding (LabelEncoder), and feature scaling with MinMaxScaler.
• Feature Selection: Automated removal of low-correlation features to improve model efficiency.
• Model Implementation: Comparison between k-Nearest Neighbors (kNN), Random Forest (RF), and Support Vector Machines (SVM).
• Hyperparameter Tuning: Systematic optimization using GridSearchCV to maximize F1-Score.

🛠️ Tech Stack

• Language: Python 3.x
• Libraries: * scikit-learn: Model training and evaluation.
  • pandas: Data manipulation and analysis.
  • matplotlib: Visualization of results (Confusion Matrices).
  • joblib: Model persistence (saving/loading .pkl files).

📁 Project Structure

├── data/               # Original dataset files
├── models/             # Saved .pkl models and scalers
├── pre_processed/      # Cleaned and processed CSV files
└── scripts/
    ├── pre_processing.py      # Data cleaning and scaling
    ├── knn_classifier.py      # kNN implementation
    ├── rf_classifier.py       # Random Forest implementation
    ├── svm_classifier.py      # SVM implementation
    └── grid_search_opt.py     # Hyperparameter tuning scripts