Student Outcome Classification

Overview

This project builds and evaluates multiple supervised machine learning models to predict student success outcomes in higher education.

The target variable represents whether a student drops out, remains enrolled, or graduates.

The project follows an end-to-end machine learning workflow including data preprocessing, exploratory data analysis (EDA), model training, model evaluation, and model comparison.

Dataset

The dataset used in this project comes from Kaggle:

Higher Education Predictors of Student Retention
https://www.kaggle.com/datasets/thedevastator/higher-education-predictors-of-student-retention

The raw dataset includes demographic, academic performance, and socioeconomic features related to student performance and retention of first-year college students.

Project Structure

student-outcome-classification/
├── data/
│   ├── raw/
│   │   └── student_data_raw.csv                    # Original dataset from Kaggle
│   └── processed/
│       └── student_data_processed.csv              # Cleaned and preprocessed dataset
├── notebooks/
│   ├── 00_run_in_colab.ipynb                       # Interactive notebook for user execution. 
│   ├── 01_data_preprocessing_and_cleaning.ipynb    # Cleaned and preoprocessed the raw student dataset in preparation for modeling. 
│   ├── 02_exploratory_data_analysis.ipynb          # Performed EDA using a multiple linear regression model.
│   ├── 03_logistic_regression_model.ipynb          # Implements a multinomial logistic regression model. 
│   ├── 04_gradient_boosting_model.ipynb            # Implements a gradient boosting classifier model. 
│   └── 05_model_comparison_and_conclusions.ipynb   # Compares both models and summarizes findings. 
└── README.md

Models Implemented

• Multiple Linear Regression (EDA only)
• Multinomial Logistic Regression
• Gradient Boosting Classifier

Models are evaluated using accuracy, classification reports, and confusion matrices.

Technologies & Tools Used

Programming & Environment

• Python
• Jupyter Notebook
• Google Colab

Data Analysis & Machine Learning

• Pandas
• NumPy
• Scikit-learn
• SciPy
• Matplotlib
• Seaborn

Models & Techniques

• Gradient Boosting Classifier
• Multinomial Logistic Regression
• Exploratory Data Analysis (EDA)
• Multiple Linear Regression
• Feature Scaling & Encoding
• Cross-Validation & Hyperparameter Tuning

Interface

• ipywidgets (interactive UI)

How to Run

1. Open 00_run_in_colab.ipynb
2. Follow the setup instructions
3. Run notebooks in numerical order