This repository contains my full machine learning project, built as part of Assignment #2 in the Introduction to Data Science course at Reichman University.
The goal of the project was to analyze a large cars dataset, clean and enrich the data, visualize insights, engineer features, and ultimately train two predictive models:
Regression model — Predicting car prices
Classification model — Categorizing cars into Low / Mid / High price ranges
Both final models are exported as .pkl files and available here in the repository.
📊 1. Dataset Overview
The dataset includes almost 19,000 cars, with features such as:
Manufacturer & Model
Production Year
Mileage
Engine Volume
Gearbox Type
Fuel Type
Drive Wheels
Color
Airbags
and more.
Before modeling, I performed cleaning, handled outliers, and created new engineered features.
🔍 2. Exploratory Data Analysis (EDA)
Key steps:
Visualizing the raw price distribution
Applying log-transform to stabilize variance
Identifying outliers using quantiles
Removing the top/bottom 1% noisy records
Plotting cleaned price distribution
This resulted in a cleaner dataset with 18,864 rows, much more suitable for modeling.
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⚙️ 3. Feature Engineering
I engineered several new features and cleaned others:
Car_age — calculated from production year
Engine_volume_clean — cleaned numeric version of engine volume
Doors_clean — fixed inconsistencies
Removed unnecessary columns (ID, Levy, Doors, raw Engine Volume)
One-Hot Encoding for all categorical variables
Scaling numeric features
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🌀 Clustering Feature
I also added a brand-new feature using KMeans clustering (Cluster).
This helped group vehicles into 3 natural segments and improved model performance.
📈 4. Regression Models
I trained 3 regression models:
Linear Regression (baseline + improved)
Decision Tree
Random Forest (winner)
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🏆 Winning Model
The best model was Random Forest Regressor, achieving strong results (lowest MAE/RMSE and highest R²).
The final model is exported here as:
car_price_random_forest.pkl
🔄 5. Classification
I converted the price variable into 3 balanced classes using qcut:
0 = Low
1 = Mid
2 = High
Then I trained:
Logistic Regression (🔥 Winner — ~76% accuracy)
KNN
Random Forest Classifier
The Logistic Regression model gave the best results and is exported as:
car_classification_logistic.pkl
🤖 6. Files Included in This Repository
File
Description
car_price_random_forest.pkl
Final regression model
car_classification_logistic.pkl
Final classification model
Assignment_notebook.ipynb
Full Google Colab notebook
README.md
This documentation
🎬 7. Video Presentation
A video walkthrough of the entire project, including:
Dataset overview
EDA
Feature engineering
Clustering
Model training
HuggingFace upload
Will be attached here.
✨ 8. Summary
This project gave me practical experience with:
Data cleaning
EDA
Feature engineering
Clustering
Regression & classification models
Exporting models
Managing a HuggingFace repository
All work is included in this repository and submitted as required.
