Used Cars Price Prediction & Classification
Regression • Classification • Feature Engineering • Clustering
This project builds a complete Machine Learning workflow for predicting and classifying used car prices using a large real-world dataset from Craigslist.
📌 Presentation Video
📌 Dataset Overview
Source: Kaggle — Craigslist Cars & Trucks DataRows: 426,880 → 339,346 after cleaningFeatures: numeric, categorical, geolocationTargets:
Regression: priceClassification: 3 price tiers (Low / Mid / High)
📌 Exploratory Data Analysis (EDA)
Key Questions & Insights
🔹 What is the distribution of prices?
Price Histogram Prices are heavily right-skewed, with most cars priced under $20,000.
🔹 Do newer cars cost more?
Year vs Price Clear positive trend: newer cars command higher prices.
🔹 How does mileage affect price?
Odometer vs Price Strong negative correlation: more mileage → lower value.
🔹 How does manufacturer affect price?
Manufacturer Price Boxplot Significant variation across manufacturers (e.g., BMW > Toyota > Chevrolet).
📌 Feature Engineering
To improve model performance, multiple new features were created:
car_age = 2023 - yearmileage_per_yearmanufacturer_popularity (frequency-based)Engineered flags: is_electric, is_hybrid
Season extracted from posting date
One-hot encoding for all categorical variables
Clustering
Applied K-Means (k=5) on numeric features
Added cluster_kmeans to capture structural patterns
Visualized using PCA (2D)
This contributed strongly to non-linear model performance.
📌 Regression Modeling
Baseline Model: Linear Regression
Performance (no feature engineering):
MAE: 5775RMSE: 8811R²: 0.645
Improved Models (After Feature Engineering)
Model R² RMSE Winner Linear Regression + FE ~0.70 ↓ Gradient Boosting ~0.78 ↓ Random Forest Regressor 0.783 6818 🏆 Yes
Winner: Random Forest Regressor
📥 Model file: random_forest_used_cars_model.pkl
📌 Classification (Regression → Classes)
Price converted into three balanced classes using quantile binning:
0 = Low (bottom 33%)
1 = Mid (middle 33%)
2 = High (top 33%)
Why recall > precision?
Missing high-value cars (false negatives) is more harmful than mistakenly predicting a car as high-value.
📌 Classification Models
Model Macro F1 Accuracy Logistic Regression 0.62 0.63 Gradient Boosting 0.78 0.79 Random Forest Classifier 0.82 0.82
Winner: Random Forest Classifier
📥 Model file: used_cars_price_classifier_rf.pkl
📌 Repository Structure
yoav-omer-used-cars-price-random-forest
│
├── random_forest_used_cars_model.pkl # Regression model
├── used_cars_price_classifier_rf.pkl # Classification model
│
├── price_hist.png
├── year_price.png
├── odometer_price.png
├── manufacturer_price.png
│
└── README.md📌 Summary
This project demonstrates a full machine learning pipeline on a complex real-world dataset, including:
Data cleaning and robust EDA
Strong feature engineering & clustering
Regression modeling
Classification modeling
Model export & deployment
Random Forest delivered the best results in both regression and classification tasks.
