Introduced Random Forest Regressor with Regularization for Improved Accuracy

[ChitteshKumar]

Description

This PR introduces significant improvements to the machine learning model for house price prediction. A new Random Forest Regressor has been implemented in the model, replacing the previous linear regression approach, and regularization techniques were applied to improve model generalization. The changes led to a notable enhancement in both training and testing accuracy.

Issue Resolved: #30

Changes Made

• Model Update in ImprovedModel.py:

  • Introduced a Random Forest Regressor with tuned hyperparameters:
    • n_estimators=200
    • max_depth=8
    • min_samples_split=12
  • Implemented regularization techniques to prevent overfitting and improve model generalization.
  • Achieved 79% training accuracy and 71% testing accuracy, showing an improvement from the previous model.

• Modifications in predict.py:

  • Updated the prediction process to use the newly trained Random Forest model from ImprovedModel.py.
  • Ensured the input data transformation and prediction methods are compatible with the updated model architecture.

Screenshots or Videos

Checklist

• ✅ My code follows the current project structure
• ✅ I have thoroughly reviewed and updated the requirements.txt file to include any new packages
• ✅ The predict.py file includes a properly implemented model_details() function, or the notebook contains this function to print a detailed model report. The model will not be accepted without this function, as it is essential for generating the necessary model details.
• ✅ I have added comments in the code where needed.
• ✅ This PR is submitted under Hacktoberfest.
• ✅ This PR is submitted under GirlScript Summer of Code (GSSoC-Extd).

[ChitteshKumar]

Hi @yashasvini121 , @sanjay-kv. I wanted to request to kindly have the review of the this merge and accept it.