house-price-regression-in-python

This notebook includes coding and notes for predicting California house prices using multiple and polynomial regression in python.

Highlights

• Successfully trained, tested and deployed California house price machine learning model in python using multiple and polynomial regression algorithms.

• Acheived an r2 score of 0.5912 using both multiple and polynomial regression on the highest performing features.

• While the model is not as accurate as expected it compares favorably with an r2 score of 6.110 for the entire training set using the same features.

• Visualized feature performance with correlation heatmaps and pairplots.

• Created custom function to visualize selected dataframes, features, and plot layout.

• Visualized results for all models.

• Deployed the model on fictional housing data.