Second-Hand Car Price Prediction ML Model

This repository contains a machine learning model for predicting the prices of second-hand cars. The model is trained on a dataset that includes various features such as the age of the car, mileage, condition, ratings, economy, top speed, horsepower, and torque.

Project Overview

In this project, we aimed to develop a predictive model that can estimate the prices of second-hand cars based on their characteristics. We utilized TensorFlow, a popular deep learning framework, to build a neural network model for regression. The model was trained using a labeled dataset, where each data point includes the car's features and the corresponding price.

Features

The key features used in the model for predicting car prices are:

• Age: The age of the car in years.
• Mileage: The total distance the car has traveled in kilometers.
• Condition: A measure of the car's overall condition on a scale from 1 to 10.
• Ratings: Ratings assigned to the car by users or experts.
• Economy: Fuel economy of the car in miles per gallon (mpg).
• Top Speed: The maximum speed that the car can achieve.
• Horsepower: The power output of the car's engine in horsepower (hp).
• Torque: The twisting force produced by the engine in Newton-meters (Nm).

Model Architecture

The model architecture consists of a neural network with several layers. It includes input and normalization layers to preprocess the data, followed by multiple dense layers to capture complex relationships in the data. The final output layer predicts the price of the car.

Evaluation

The model's performance was evaluated using Mean Absolute Error (MAE), a common metric for regression tasks. Additionally, Root Mean Squared Error (RMSE) was used to assess the model's prediction accuracy. The lower the MAE and RMSE values, the better the model's performance in predicting car prices.

Usage

To use the model, follow these steps:

1. Install the required dependencies, including TensorFlow and scikit-learn.
2. Load the trained model weights from the provided file.
3. Preprocess your input data by scaling and transforming it to match the training data.
4. Feed the preprocessed data into the model for predictions.

Future Improvements

While the current model achieves reasonable accuracy in predicting car prices, there are several areas for potential improvement. Some possible enhancements include:

• Incorporating additional features that might influence car prices, such as brand, model, or location.
• Experimenting with different model architectures or hyperparameters to optimize performance.
• Collecting a larger and more diverse dataset to improve the model's generalization capabilities.

Conclusion

The Second-Hand Car Price Prediction ML Model offers a solution for estimating the prices of second-hand cars based on their features. By leveraging machine learning techniques, this model can provide valuable insights to buyers, sellers, and industry professionals in the used car market.

Feel free to explore the code and experiment with the model. If you have any suggestions or questions, please don't hesitate to reach out.

Note: Please refer to the documentation and license files for more detailed information about the project and its usage.

Enjoy predicting second-hand car prices with the power of machine learning!