🧠 Overview

This project implements and compares regression models — including Linear Regression and Random Forest — to predict academic scores using structured numerical features. It supports educational planning through interpretable modeling and performance metrics.

📂 Project Repository

⚙️ Tools & Technologies

📊 Model Performance

Best Model: Linear Regression

R² Score: 0.88  |  RMSE: 2.73

Linear Regression achieved the highest stability and accuracy across validation folds, outperforming Random Forest on interpretability and generalization.

📈 Key Visualizations (Samples)

1. Feature Distributions

Histograms for each input variable

Feature distributions chart

2. Correlation Heatmap

Visual overview of feature correlations

Correlation heatmap

3. Actual vs Predicted

Prediction comparison for both models

Scatter plots of predictions

4. Model Evaluation Table

MAE, RMSE, and R² scores

Evaluation metrics table

5. Feature Importance (RF)

Lollipop chart for Random Forest importance

Feature importance Random Forest

6. Coefficients (Linear)

Bar chart of Linear Regression coefficients

Feature coefficients Linear Regression