This repository contains all materials related to our big data project aimed at predicting property values in Cook County, Illinois. The project applies data analytics techniques to assess properties fairly and accurately using historical data and R programming.
- Code (Code.html): Contains the complete code and detailed explanations for data preprocessing, analysis, and modeling to predict property values.
The project is based on a property assessment challenge for Cook County, Illinois. It involves using property data from the Cook County Assessor’s Office (CCAO) to develop a predictive model for property values. The CCAO is responsible for assessing the fair market value of properties within the county, which includes the City of Chicago and over 130 other municipalities.
- Data Preprocessing: Cleansing and preparing data for analysis.
- Exploratory Data Analysis: Analyzing the data to find patterns and insights.
- Model Building: Using regression and other statistical techniques to predict property values.
- Validation: Assessing the accuracy and reliability of the models.
The analysis is performed using the historic_property_data.csv for historical property sales and predict_property_data.csv for the properties to assess.
- R Programming: All analysis is conducted using R.
- Libraries: dplyr, tidyr, ggplot2, corrplot, and other R packages.
- Yu-Lin Kao
Thanks to the instructors and TA's of the data science course for their guidance and support throughout the project.
This project is licensed under the MIT License.