This repository contains an Artificial Neural Network (ANN) model trained to predict second-hand car prices. Leveraging supervised learning techniques and advanced data preprocessing, this model offers accurate price predictions based on a dataset sourced from Kaggle. The project is implemented using Python with TensorFlow 2.0 library and incorporates Keras, Seaborn, NumPy, and Pandas for data processing, visualization, and evaluation.
Predicting second-hand car prices is crucial for both buyers and sellers in the automotive market. This project offers a solution by utilizing an Artificial Neural Network (ANN) model trained on a dataset of second-hand car listings. The model provides accurate price predictions based on various features such as brand, model, year, mileage, fuel type, and more.
The dataset used for training and evaluation is sourced from Kaggle, consisting of a comprehensive collection of second-hand car listings. It contains information about various attributes of the cars, including their brands, models, manufacturing years, mileage, fuel types, transmission types, and prices.
Link to the dataset: Kaggle Second-Hand Car Prices Dataset
To run the project locally, follow these steps:
- Clone this repository:
git clone https://github.com/your-username/second-hand-car-price-prediction.git
- Install the required dependencies:
pip install -r requirements.txt
- Preprocess the dataset:
python preprocess.py
- Train the ANN model:
python train.py
- Evaluate the model:
python evaluate.py
- Make predictions:
python predict.py
Contributions are welcome! Feel free to open issues or pull requests for any improvements, bug fixes, or new features.
Free and Open Source.