This notebook investigated and predicted the median value of housing prices in Boston based on features such as using Linear Regression Model
It also explored other Machine Learning Models such as Decision Tree, Support Vector Regressor and Multiple Layer Perceptron.
The Models were evaluated using the adjusted R-squared metric and it was concluded that the MLP regressor model performed better that is, predicted the median value on the test housing prices data with R^2 of 87%. Therefore, increasing savings of $40,000 -$50,000 for home buyers.