Predicting Student Academic Performance

This project uses data science and machine learning techniques to predict student academic performance based on behavioral, socio-economic, and demographic factors.

Tools & Technologies

• Python (Pandas, Scikit-learn, Matplotlib)
• Jupyter Notebook
• Tableau
• CSV dataset (131 records, 20 features)

Models Used

• Decision Tree Classifier
• Random Forest Classifier
• Support Vector Machine (SVM)

Results

• The Random Forest model showed the highest accuracy.
• Key insights were visualized using Tableau dashboards.

Project Structure

EDA.ipynb: Exploratory Data Analysis

Models.ipynb: ML model training and evaluation

dataset/: Contains the cleaned CSV file

images/: Contains screenshots of dashboards and visualizations