Predicting Housing Prices in Cook County

Introduction

In this, you will specify and fit a linear model to a few features of the housing data to predict housing prices. Next, we will analyze the error of the model and brainstorm ways to improve the model’s performance. Finally, we’ll delve deeper into the implications of predictive modeling within the Cook County Assessor’s Office (CCAO) case study, especially because statistical modeling is how the CCAO valuates properties. Given the history of racial discrimination in housing policy and property taxation in Cook County, consider the impacts of your modeling results as you work through this assignment - and think about what fairness might mean to property owners in Cook County. After this homework, you should be comfortable with: - Implementing a data processing pipeline using pandas - Using scikit-learn to build and fit linear models

Import All Related Libraries

import numpy as np 

import pandas as pd 
from pandas.api.types import CategoricalDtype 

%matplotlib inline 
import matplotlib.pyplot as plt 
import seaborn as sns 

import warnings 
warnings.filterwarnings("ignore") 

import zipfile 
import os 

from ds100_utils import run_linear_regression_test 

# Plot settings 
plt.rcParams['figure.figsize'] = (12, 9) 
plt.rcParams['font.size'] = 12

Read Data

# Reload the data 
full_data = pd.read_csv("cook_county_train.csv") 

Split dataset

# Process the data using the pipeline for the first model
np.random.seed(1337)
train_m1, test_m1 = train_test_split(full_data)

# A custom function that applies log transformation
def log_transform(data, col):
  data['Log ' + col] = np.log(data[col])
return data

m1_pipelines = [
(remove_outliers, None, {
'variable': 'Sale Price',
'lower': 499,
}),

(log_transform, None, {'col': 'Sale Price'}),
(add_total_bedrooms, None, None),
(select_columns, ['Log Sale Price', 'Bedrooms'], None)
]

X_train_m1, y_train_m1 = process_data_gm(train_m1, m1_pipelines, 'Log Sale Price')
X_test_m1, y_test_m1 = process_data_gm(test_m1, m1_pipelines, 'Log Sale Price')

Linear Model

#creating linear model
from sklearn.linear_model import LinearRegression
reg = LinearRegression()

Fit Into Model

reg.fit(x_train_m1, y_train_m1)

Find the score

reg.score(x_test_m1, y_test_m1)

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