In this lab, you'll practice fitting a multiple linear regression model on the Ames Housing dataset!

You will be able to:

• Determine if it is necessary to perform normalization/standardization for a specific model or set of data
• Use standardization/normalization on features of a dataset
• Identify if it is necessary to perform log transformations on a set of features
• Perform log transformations on different features of a dataset
• Use statsmodels to fit a multiple linear regression model
• Evaluate a linear regression model by using statistical performance metrics pertaining to overall model and specific parameters

Using the specified continuous and categorical features, preprocess your data to prepare for modeling:

• Split off and one hot encode the categorical features of interest
• Log and scale the selected continuous features

import pandas as pd
import numpy as np

ames = pd.read_csv('ames.csv')

continuous = ['LotArea', '1stFlrSF', 'GrLivArea', 'SalePrice']
categoricals = ['BldgType', 'KitchenQual', 'SaleType', 'MSZoning', 'Street', 'Neighborhood']

# Log transform and normalize

# One hot encode categoricals

# combine features into a single dataframe called preprocessed

# Your code here

# Your code here - Check that the coefficients and intercept are the same as those from Statsmodels

Make sure to transform your variables as needed!

• LotArea: 14977
• 1stFlrSF: 1976
• GrLivArea: 1976
• BldgType: 1Fam
• KitchenQual: Gd
• SaleType: New
• MSZoning: RL
• Street: Pave
• Neighborhood: NridgHt

Congratulations! You pre-processed the Ames Housing data using scaling and standardization. You also fitted your first multiple linear regression model on the Ames Housing data using statsmodels and scikit-learn!