🚗 Car Price Prediction - Advanced Machine Learning

A robust car price prediction system built with advanced Machine learning techniques, achieving 92.7% R² accuracy.

This project demonstrates a complete end-to-end machine learning pipeline — from exploratory data analysis and sophisticated feature engineering to ensemble modeling, model interpretability with SHAP, and a production-ready web application.

✨ Features

• Comprehensive EDA with insightful visualizations
• Advanced Data Cleaning & Feature Engineering (including derived features like vehicle age, condition, and usage)
• Dimensionality Reduction — PCA & t-SNE
• Clustering Analysis — K-Means
• Feature Selection — Recursive Feature Elimination (RFE)
• Multiple Models:
  • Random Forest
  • Gradient Boosting
  • Stacking Ensemble
• Model Interpretability using SHAP values
• Interactive Web App built with Streamlit for instant price predictions

📊 Project Highlights

• Best Performance: 92.7% R² Score
• Technologies: Python, scikit-learn, pandas, numpy, matplotlib/seaborn, SHAP, Streamlit
• Deployment Ready: Streamlit app included (car_app.py)

🛠️ Tech Stack

Component	Technologies
Language	Python 3
Data Analysis	pandas, numpy
Visualization	matplotlib, seaborn, plotly
ML Models	scikit-learn (RF, GB, Stacking)
Interpretability	SHAP
Web App	Streamlit
Others	Joblib (model persistence)

📁 Repository Structure

├── car price prediction.ipynb          # Main ML pipeline & analysis
├── Advanced MLC analysis.ipynb         # Advanced techniques (PCA, t-SNE, Clustering, RFE)
├── car_app.py                          # Streamlit web application
├── requirements.txt                    # Project dependencies
├── assets/                             # Images & banners
├── docs/                               # Reports & documentation
│   ├── Analysis Report .pdf
│   └── Car price prediction report.pdf
└── README.md

The goal was to build a data driven solution that can assist buyers, sellers, and businesses in making informed pricing decisions.