Multiple Linear Regression in StatsModels - Lab

Introduction

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

Objectives

You will be able to:

Perform a multiple linear regression using StatsModels
Visualize individual predictors within a multiple linear regression
Interpret multiple linear regression coefficients from raw, un-transformed data

The Ames Housing Dataset

The Ames Housing dataset is a newer (2011) replacement for the classic Boston Housing dataset. Each record represents a residential property sale in Ames, Iowa. It contains many different potential predictors and the target variable is SalePrice.

import pandas as pd
ames = pd.read_csv("ames.csv", index_col=0)
ames

ames.describe()

We will focus specifically on a subset of the overall dataset. These features are:

LotArea: Lot size in square feet

1stFlrSF: First Floor square feet

GrLivArea: Above grade (ground) living area square feet

ames_subset = ames[['LotArea', '1stFlrSF', 'GrLivArea', 'SalePrice']].copy()
ames_subset

Step 1: Visualize Relationships Between Features and Target

For each feature in the subset, create a scatter plot that shows the feature on the x-axis and SalePrice on the y-axis.

# Your code here - import relevant library, create scatter plots

# Your written answer here - do these seem like good candidates for linear regression?

Step 2: Build a Simple Linear Regression Model

Set the dependent variable (y) to be the SalePrice, then choose one of the features shown in the subset above to be the baseline independent variable (X).

Build a linear regression using StatsModels, describe the overall model performance, and interpret its coefficients.

# Your code here - define y and baseline X

# Your code here - import StatsModels, fit baseline model, display results

# Your written answer here - interpret model results

Step 3: Build a Multiple Linear Regression Model

For this model, use all of the features in ames_subset.

# Your code here - define X

# Your code here - fit model and display results

# Your written answer here - interpret model results. Does this model seem better than the previous one?

Step 4: Create Partial Regression Plots for Features

Using your model from Step 3, visualize each of the features using partial regression plots.

# Your code here - create partial regression plots for each predictor

# Your written answer here - explain what you see, and how this relates
# to what you saw in Step 1. What do you notice?

Level Up (Optional)

Re-create this model in scikit-learn, and check if you get the same R-Squared and coefficients.

# Your code here - import linear regression from scikit-learn and create and fit model

# Your code here - compare R-Squared

# Your code here - compare intercept and coefficients

Summary

Congratulations! You fitted your first multiple linear regression model on the Ames Housing data using StatsModels.

foxshakeitoff/dsc-multiple-linear-regression-statsmodels-lab-v2-5