422 Final Prject Used Car Price Prediction

Team Members

Introducing the talented individuals who contributed to this project:

1. Yate Zhang

2. Yuchen Bi

3. Chris Wang

4. Jiancong Zhu

Project Overview

This project aims to develop a machine learning model to predict the prices of used cars listed on Craigslist. The dataset is sourced from Kaggle and contains a variety of vehicle attributes, including brand, model, manufacturing year, mileage, fuel type, transmission, and more. By applying exploratory data analysis (EDA), data preprocessing, feature engineering, and machine learning models, this project provides an end-to-end pipeline for price prediction.

The project follows the CRISP-DM framework to ensure a structured approach to data processing, model training, and evaluation. The final goal is to produce an optimized predictive model that can assist buyers and sellers in accurately estimating used car values.

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Dataset

• Source: Kaggle - Craigslist Cars & Trucks Dataset
• Number of Records: 426,880
• Number of Features: 26
• Data Type: Mixed (Numerical & Categorical)

Key Columns

• price: The price of the used car (Target variable)
• year: The manufacturing year of the car
• manufacturer: The brand of the vehicle (e.g., Toyota, Ford, BMW)
• model: The specific model of the car
• odometer: The total miles driven
• fuel: The type of fuel used (e.g., Gasoline, Diesel, Electric)
• transmission: The transmission type (e.g., Automatic, Manual)
• drive: The drivetrain (e.g., FWD, RWD, 4WD)
• paint_color: The color of the car

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Project Structure

📂 Craigslist-Car-Price-Prediction
│── 📂 data                    # Dataset directory (Optional: Raw & Processed data)
│── 📂 notebooks                # Jupyter notebooks for EDA, preprocessing & modeling
│── 📂 models                   # Trained machine learning models
│── 📂 scripts                  # Python scripts for preprocessing, feature engineering, and training
│── ├── preprocess.py          # Data preprocessing & feature engineering script
│── ├── train_model.py         # Machine learning model training script
│── ├── evaluate_model.py      # Model evaluation & visualization script
│── ├── requirements.txt       # Required Python packages
│── README.md                   # Project documentation