Cook County Housing Cost Analysis

This project analyzes housing cost data from Cook County, Illinois with the goal of understanding the factors that influence residential property values and building a predictive model for housing prices.

Using publicly available assessment data, the project walks through:

• Exploratory data analysis (EDA) to understand distributions, relationships, and data quality
• Feature selection and preprocessing
• Linear regression modeling to predict housing values
• Model evaluation and interpretation

The analysis is organized into a sequence of Jupyter notebooks that document the full workflow from raw data to model results.

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Notebooks Overview

part1-eda.ipynb — Exploratory Data Analysis

This notebook focuses on understanding the dataset and preparing it for modeling. It includes:

• Loading the Cook County housing dataset
• Inspecting data types, missing values, and basic summary statistics
• Visualizing distributions of key variables
• Exploring relationships between features and housing value
• Identifying potential transformations or cleaning steps needed before modeling

The goal of this notebook is to build intuition about the data and inform modeling decisions.

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part2-model.ipynb — Housing Price Modeling

This notebook builds on the cleaned data from the EDA phase and focuses on prediction. It includes:

• Feature selection and preprocessing
• Train/test splitting
• Building a linear regression model to predict housing value
• Evaluating model performance using error metrics
• Interpreting model results and discussing limitations

This notebook demonstrates a complete, end-to-end modeling workflow using standard data science practices.

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Data Download Instructions

The raw dataset is not stored directly in this repository due to file size constraints.
Instead, the data and accompanying documentation are provided as a ZIP file hosted on Google Drive.

Download the Data

Download the ZIP file here:
cook_county_data.zip

The ZIP file contains:

• The CSV dataset used in the analysis
• A codebook describing the variables in the dataset

How to Use the Data

1. Download and unzip the file.
2. Place the CSV file into a local directory
3. Update the file path in the notebooks if necessary.
4. Run the notebooks from top to bottom.

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Requirements

To run the notebooks locally, you will need Python and the following libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn
• jupyter

You can install them with:

pip install pandas numpy matplotlib seaborn scikit-learn notebook