A machine learning project that predicts used car prices based on various features such as make, model, year, engine capacity, and more.

---

Overview

Predicting the price of used cars is a valuable exercise for both car dealerships and individual car owners. By analyzing historical data, we can use machine learning techniques to estimate the market value of a used car based on features like make, model, mileage, engine capacity, fuel type, and more.

This project showcases:

• End-to-end data handling (cleaning, feature engineering)
• Multiple regression algorithms (e.g., Linear Regression, Random Forest, XGBoost)
• Model tuning and evaluation

Goal: Create a predictive model that accurately estimates a car’s price given its attributes.

---

Dataset

Source: Car Price Prediction Dataset by Hellbuoy on Kaggle (https://www.kaggle.com/datasets/hellbuoy/car-price-prediction)

This dataset contains various features like:

• Car name (brand/model)
• Year of manufacture
• Selling price
• Present price (original price)
• Kilometers driven
• Fuel type, Seller type, Transmission
• And more…

Note: Please check the dataset’s licensing and usage permissions before commercial use.

---

• data/: Contains raw or preprocessed data (or a README with a link to the dataset).
• notebooks/: Jupyter notebooks for EDA, model training, and experiments.
• src/: Python scripts for data preprocessing, modeling, etc.
• models/: Serialized model files for quick loading.

---

Getting Started

1. Clone the Repository

   git clone https://github.com/Jackhammer9/Car-Price-Predictor.git
   cd Car-Price-Predictor
   

2. Create a Virtual Environment (Optional but Recommended)

   Using conda

   conda create -n car-price-predictor python=3.8
   conda activate car-price-predictor
   

   or using venv

   python -m venv env
   source env/bin/activate
   

3. Install Dependencies

   pip install -r requirements.txt
   

4. Download the Dataset If not included, download the dataset from Kaggle: https://www.kaggle.com/datasets/hellbuoy/car-price-prediction and place it in the data/ folder (e.g., Car-Data.csv).

---

EDA (Exploratory Data Analysis)

During EDA, we examine:

• Missing values and possible imputation strategies
• Distribution of numeric variables (mileage, price, etc.)
• Categorical variable analysis (fuel type, seller type, etc.)
• Correlation between features and target (selling price)

---

Modeling Approach

We tried multiple algorithms to find the best performer:

1. Linear Regression

   • Pros: Interpretable, fast to train
   • Cons: May not capture nonlinear relationships well

2. Random Forest

   • Pros: Handles nonlinearities, robust to outliers, can measure feature importance
   • Cons: Can be slower, may overfit if not tuned properly

3. XGBoost

   • Pros: Often achieves high accuracy on tabular data, can handle missing data well
   • Cons: Tuning can be more involved

Hyperparameter Tuning: We used GridSearchCV or RandomizedSearchCV for each model to find optimal parameters (e.g., max depth, n_estimators, learning rate).

---

Future Improvements

1. Advanced Feature Engineering:

   • Derived features like car age, brand-specific average prices, etc.

2. Ensemble Methods:

   • Combine multiple models (e.g., stacking) for improved performance.

3. Deep Learning:

   • Experiment with neural networks on tabular data (though benefits may vary).

4. Deployment:

   • Containerize with Docker or deploy to AWS/Azure/GCP.

---

Contributing

Contributions, issues, and feature requests are welcome! Feel free to fork this repo and submit a pull request, or open an issue.

---

License

Distributed under the MIT License. See LICENSE for more information.