Car Price Prediction using RandomForestRegressor

Description: Welcome to the "Car Price Prediction with Random Forest Regressor" repository! This project focuses on predicting the prices of cars using the power of machine learning and the Random Forest Regressor algorithm. If you're interested in the automotive industry, machine learning, or predictive modeling, this repository is a perfect starting point.

Table of Contents:

• Introduction
• Dataset
• Installation
• Usage
• Results
• Contributing
• License

Introduction: In the field of automotive sales, predicting car prices accurately can be a challenging task due to the multitude of factors that influence a car's value. This project aims to simplify this process using the Random Forest Regressor algorithm, a powerful ensemble learning technique that can handle complex relationships between features and target variables.

Dataset: The dataset used for this project contains a comprehensive collection of car listings, each with a variety of features such as make, model, year, mileage, fuel type, and more. This dataset serves as the foundation for training and evaluating the Random Forest Regressor model.

Installation: To get started with this project on your local machine, follow these steps:

1. Clone the repository: git clone https://github.com/ashishyadav2/ML-Mini-Project.git
2. Navigate to the project directory: cd ML-Mini-Project
3. Set up a virtual environment (optional but recommended): python -m venv venv
4. Activate the virtual environment:
   • On Windows: venv\Scripts\activate
   • On macOS and Linux: source venv/bin/activate
5. Install the required dependencies: pip install -r requirements.txt

Usage:

1. Ensure you have the dataset (car_data.csv) in the project directory.
2. Open the Jupyter Notebook (car_price_prediction.ipynb) to see the entire workflow.
3. Follow the step-by-step instructions in the notebook to load, preprocess, train, and evaluate the Random Forest Regressor model.
4. Experiment with hyperparameters, feature engineering, and other techniques to improve predictions.

Results: The results of this project include insights into the predictive performance of the Random Forest Regressor model for car price estimation. The Jupyter Notebook provides visualizations, metrics, and explanations of the model's behavior. You can observe how different features contribute to the price prediction and gain a deeper understanding of the factors influencing car prices.

Contributing: Contributions to this project are welcome and encouraged. If you find any issues or have ideas for improvements, feel free to create issues or pull requests. Please ensure that your contributions adhere to the established coding standards and documentation.

License: This project is licensed under the MIT License, which means you're free to use, modify, and distribute the code as long as you include the original license and attribute the authors.

Start exploring the fascinating world of car price prediction using the Random Forest Regressor today! Your contributions and insights can make a significant impact on improving the accuracy of car price estimations.