CSCI-3002: Data-Science-Semester-Project

Cook County Housing Data Analysis

This project is part of a class assignment where I explore housing data from Cook County and build a machine learning model to predict housing prices.

Project Overview

The project is divided into two main parts:

1. Part 1: Data Exploration
   In this part, I conducted an exploratory data analysis (EDA) on the housing dataset, identifying key features, understanding data trends, and performing the necessary data cleaning and preprocessing steps.

2. Part 2: Predicting Housing Prices
   In the second part, I utilized Pandas, Seaborn, and Scikit-learn to build and evaluate multiple regression models for predicting housing prices. Additionally, I addressed potential biases and ensured fair outcomes in the predictive analysis, using Matplotlib and Plotly for visualization and interpretation.

What I Learned

• Data Exploration: Handling and preprocessing real-world datasets, and deriving useful insights through exploratory analysis.
• Machine Learning: Building and evaluating regression models, tuning hyperparameters, and interpreting model results.
• Bias Mitigation: Understanding and addressing potential biases to ensure fair predictions in machine learning models.

Technologies Used

• Python (Jupyter Notebooks)
• Pandas, NumPy for data manipulation
• Seaborn, Matplotlib, and Plotly for data visualization
• Scikit-learn for machine learning