We’ll use the Boston data set from the sklearn.datasets for predicting the median house value (mdev), in Boston Suburbs, using different predictor variables. Steps:

• Step 1: Create a new python project and import/ load the dataset.
• Step 2: Run different statistical inspection and preprocess on the dataset; plot Correlation Heatmap
• Step 3: Split the data into training and testing datasets
• Step 4: Create the regression tree, extract important features
• Step 5: Evaluate model; find the best “complexity parameter” value that minimize the prediction error RMSE (root mean squared error).
• Step 6: Use the model to predict a value

Using tools: python, Sklearn, matplotlib, graphviz, seaborn, numpy