Implement and evaluate multiple regression models with performance co…

[kaleb-kebede]

Summary of Changes

This PR completes the regression analysis assignment. I have implemented, trained, and evaluated five different models to predict the target variable.

Key Implementations

1. Model Training: Implemented the following algorithms:

   • K-Nearest Neighbors (KNN) - Best Performer (K=9)
   • Multiple Linear Regression
   • Decision Tree (Max Depth 4)
   • Polynomial Regression (Degree 3)
   • Simple Linear Regression

2. Evaluation:

   • Calculated R2 Score and RMSE for all models.
   • Created a dataframe to rank models by accuracy.

3. Visualization & Conclusion:

   • Added a bar chart visualizing the R2 score comparison.
   • Wrote a final conclusion analyzing why KNN and Multiple Linear Regression outperformed the simpler models (Area-only).

Results

The KNN model (K=9) achieved the highest accuracy with an R2 Score of ~0.65, closely followed by Multiple Linear Regression.