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What Drives the Price of a Used Car?

This project analyzes over 400,000 used car listings to identify what factors influence a car's price. I built this for a dealership that wants to fine-tune its inventory and pricing strategy based on real-world data. The project follows the CRISP-DM process and is structured for both technical and non-technical audiences.

🧠 Business Objective

  • Understand which vehicle attributes increase or decrease resale value
  • Guide dealership decisions for pricing and inventory
  • Present insights clearly for business use i.e. which brands are overpriced or undervalued

📁 Dataset Overview

  • Original source: Kaggle (3M+ listings)
  • Filtered version used: ~426,000 listings
  • After cleaning: ~93,000 dealership-relevant listings
  • Includes fields like:
    • Make and model
    • Year, mileage, fuel type, condition
    • Title status, transmission, drive type
    • Region and color

🛠️ Data Cleaning and Preparation

  • Removed listings with invalid VINs, duplicate entries, or missing critical data
  • Filtered out junk i.e. cars listed for under $1,000 or with salvage titles
  • Standardized inconsistent model names (e.g. “F 150” to “F-150”)
  • Created:
    • car_age (based on listing year)
    • log_price (to normalize skewed price distribution)
    • region_grouped (to reduce high-cardinality noise)
  • One-hot encoded all categorical variables for modeling

📊 Modeling Process

  • Split data into training and test sets (80/20)
  • Used pipelines with ColumnTransformer for scaling and encoding
  • Trained 3 models:
    • Linear Regression
    • Ridge Regression (best performer)
    • Lasso Regression (feature shrinkage)
  • Tuned with GridSearchCV and LassoCV
  • Evaluated using:
    • RMSE
    • R²
    • 5-fold cross-validated R²

📈 Performance Summary

Model R² Score CV R² (5-fold) RMSE
Ridge Regression 0.669 0.460 0.435
Linear Regression 0.669 0.460 0.435
Lasso Regression 0.669 0.458 0.435
  • Ridge was selected due to better generalization and handling of one-hot encoded features

🔍 Key Findings

  • Top positive price drivers:
    • Ferrari, Porsche, Tesla
    • Low mileage and newer year
    • Clean title and excellent condition
  • Top negative price drivers:
    • Models like PT Cruiser, Saturn, and Cobalt
    • Poor condition or excessive mileage
    • Older age or uncommon cylinder types
  • Depreciation insights:
    • Estimated ~36% price drop per year of age
    • Estimated ~17% depreciation per odometer log unit
  • Used grouped bar plots to visualize:
    • Top and bottom feature impacts by category
    • Manufacturer and model trends
    • Drive type, fuel type, and transmission effects

📂 Repo Contents

  • usedCarAnalysis.ipynb – Main notebook with step-by-step analysis
  • model_coefficients_comparison.csv – Feature weights for each model
  • model_results_summary.csv – Evaluation metrics comparison
  • vehicles.csv the dataset used for training the model
  • Visuals and grouped plots inline

🚙 Who This Helps

  • Dealerships pricing used cars for retail
  • Data-driven sales managers and inventory planners
  • Analysts trying to predict market pricing behavior

🧪 Price Predictor Tool

I also included a basic price prediction program inside the notebook. After training the Ridge model, the pipeline is saved and can be reused to estimate log-price for any new listing, which can then be converted back to dollar price.

You can test custom input data i.e. a 2018 Toyota Camry with 50,000 miles and clean condition, and the model will return a price estimate based on learned patterns.

This can help dealerships:

  • Run bulk price forecasts
  • Evaluate trade-ins quickly
  • Make smarter purchase decisions at auctions