Regression-with-feature-engineering

• House price prediction using the California Housing prices dataset
• Tried Logistic Regression, Decision Trees, KNN and Random Forest Model
• Feature engineered two extra features
• Current best model is Random Forest. With hyperparameter tuning, achieved a R-squared(R2) of 0.822, and Root mean squared error of 48959.675

To-do:

• Try support vector regressor
• Try removing outliers to see if that improves the performance
• Better documentation