This project predicts the price of used cars in India based on various factors such as brand, model, year, fuel type, transmission, mileage, and more. The aim is to help buyers and sellers estimate fair market prices using machine learning techniques.
- Data cleaning and preprocessing
- Exploratory data analysis (EDA) to understand pricing trends
- Feature selection and engineering
- Machine learning model training and evaluation
- Model deployment for real-world predictions
- Python
- Pandas, NumPy
- Scikit-learn
- Matplotlib, Seaborn
- Jupyter Notebook
The dataset consists of used car listings with attributes such as:
- Car Brand & Model
- Manufacturing Year
- Fuel Type (Petrol, Diesel, CNG, Electric, etc.)
- Transmission Type (Manual, Automatic)
- Mileage & Engine Capacity
- Owner Type (First, Second, Third Owner)
- Selling Price (Target variable)
- Linear Regression
- Decision Tree Regressor
- Random Forest Regressor
- XGBoost Regressor
The models are evaluated based on RMSE, MAE, and R-squared values. Random Forest and XGBoost provide the best predictions for used car prices.
📂 Indian-Used-Car-Price-Prediction
🌍-- 📂 data (Dataset & processed data)
🌍-- 📂 notebooks (Jupyter Notebooks)
🌍-- 📂 models (Trained models)
🌍-- 📂 images (Code and Results Screenshots)
🌍-- 📄 README.md (Project documentation)
Include images of code and results in the images
folder. Example:
- Clone the repository:
git clone https://github.com/rohitinu6/Indian-Used-Car-Price-Prediction.git
- Navigate to the project folder:
cd Indian-Used-Car-Price-Prediction
- Install dependencies:
pip install -r requirements.txt
- Run the Jupyter Notebook or Python scripts to train and test models.
- GitHub Repository: Indian Used Car Price Prediction
- Portfolio: Rohit Dubey
- GitHub Profile: rohitinu6
- LinkedIn: Rohit Dubey
- Twitter/X: @rohitdubey003
Machine Learning
Car Price Prediction
Used Cars
Regression
Data Science
Python
EDA
This project is licensed under the MIT License.
💡 For any queries or collaboration opportunities, feel free to connect! 🚀