🚗 Car Price Prediction using Machine Learning

An end-to-end Machine Learning project that predicts the market price of a car based on its specifications. The project covers the complete ML workflow, from data preprocessing and feature engineering to model training, evaluation, and deployment through a Streamlit web application.

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🌐 Live Demo

🔗 Streamlit App: https://car-price-prediction-using-machine-learning-csvuu8ehykvsnwqbtq.streamlit.app/

📂 GitHub Repository: https://github.com/Shibaji157/Car-Price-Prediction-Using-Machine-Learning

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📌 Project Overview

The objective of this project is to build a machine learning model capable of accurately predicting car prices using various vehicle specifications.

The project includes:

• Data Cleaning
• Feature Engineering
• Exploratory Data Analysis (EDA)
• Model Training
• Model Comparison
• Cross Validation
• Model Evaluation
• Feature Importance Analysis
• Model Saving
• Streamlit Web Application Deployment

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📊 Dataset Information

Dataset: Car Price Prediction Dataset

• Total Records: 205
• Original Features: 26
• Engineered Features: 65
• Target Variable: Price

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🛠 Technologies Used

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• Joblib
• Streamlit
• Git
• GitHub

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🤖 Machine Learning Algorithms Compared

Model	R² Score
Linear Regression	0.9097
Decision Tree	0.9069
Random Forest	0.9595 ✅
Gradient Boosting	0.9246

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🏆 Best Performing Model

Random Forest Regressor

Performance Metrics

Metric	Value
R² Score	95.95%
MAE	1248.12
RMSE	1786.98

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📈 Project Workflow

Dataset
      │
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Data Cleaning
      │
      ▼
Feature Engineering
      │
      ▼
Exploratory Data Analysis
      │
      ▼
Model Training
      │
      ▼
Model Comparison
      │
      ▼
Model Evaluation
      │
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Model Saving
      │
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Streamlit Deployment

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📂 Project Structure

Car-Price-Prediction-Using-Machine-Learning
│
├── app.py
├── main.py
├── requirements.txt
├── README.md
│
├── data/
│   └── CarPrice_Assignment.csv
│
├── images/
│   ├── feature_importance.png
│   ├── prediction_plot.png
│   └── eda/
│
├── models/
│   └── best_model.pkl
│
├── outputs/
│   ├── model_metrics.csv
│   └── processed_dataset.csv
│
├── src/
│   ├── preprocessing.py
│   ├── feature_engineering.py
│   ├── eda.py
│   ├── model_training.py
│   ├── evaluation.py
│   ├── feature_importance.py
│   ├── model_saver.py
│   └── prediction.py
│
└── test_prediction.py

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🚀 Features

• Clean and modular project structure
• Automatic data preprocessing
• Feature engineering
• Exploratory data analysis
• Multiple regression models
• Model comparison
• Cross validation
• Feature importance visualization
• Model evaluation metrics
• Model persistence using Joblib
• Interactive Streamlit web application
• Live prediction support

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💻 Installation

Clone the repository

git clone https://github.com/Shibaji157/Car-Price-Prediction-Using-Machine-Learning.git

Move into the project directory

cd Car-Price-Prediction-Using-Machine-Learning

Install dependencies

pip install -r requirements.txt

Run the application

streamlit run app.py

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📷 Application Screenshots

Home Page

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Prediction Interface

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Prediction Result

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Feature Importance

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Prediction vs Actual

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Model Results

📈 Future Improvements

• Hyperparameter tuning using GridSearchCV
• XGBoost implementation
• LightGBM implementation
• User authentication
• Cloud database integration
• Docker deployment
• CI/CD pipeline
• REST API using FastAPI

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👨‍💻 Author

Shibaji Biswas

B.Tech in Artificial Intelligence & Machine Learning

Chandigarh University

GitHub: https://github.com/Shibaji157

LinkedIn: https://www.linkedin.com/in/shibaji-biswas

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⭐ Support

If you found this project useful, consider giving it a ⭐ on GitHub.

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📜 License

This project is developed for educational and portfolio purposes.