• Car Price Prediction is a machine learning project designed to predict the prices of cars based on a set of attributes and features. This project employs predictive modeling techniques to assist users in estimating the market value of a car.
• The idea is to create a model that can predict car prices accurately by analyzing historical data.

Download the dataset for custom training data.

The dataset used for this project contains information about various cars and their corresponding prices. The dataset typically includes the following attributes:

• Make: The manufacturer or brand of the car (e.g., Toyota, Honda).
• Model: The specific model of the car (e.g., Camry, Civic).
• Year: The year in which the car was manufactured.
• Mileage: The number of miles a vehicle can travel per unit of fuel.
• Fuel Type: The type of fuel the car uses (e.g., Gasoline, Diesel).
• Transmission: The type of transmission (e.g., Automatic, Manual).
• Location: The geographical location where the car is being sold.
• Price: The actual price of the car (the target variable we want to predict).

The project is organized into the following directories and files:

• Data: The data folder contains both raw and processed data used in this project.
• Notebooks: This folder contains Jupyter notebooks with code covering data exploration and model building.
• Models: This folder consists a collection of trained machine learning models.
• Requirements: A list of Python packages required for running the code.
• README: The current README file providing an overview and instructions for the project.

To get started with the project, follow these steps:

1. Clone this repository to your local machine:

git clone https://github.com/usmanbvp/Car-Price-Prediction.git

2. Install the project dependencies by running the following command:

pip install -r requirements.txt

3. Now, run each cell in jupyter notebook to build the machine learning model for predicting car price.

Once you have successfully built the machine learning model, you can use it to predict car prices by inputting new data. Follow these steps:

1. Open the Jupyter notebook named CAR-PRICE-PREDICTION-WITH-MACHINE-LEARNING.ipynb in the notebooks directory.

2. In the notebook, locate the section where new data can be input for prediction.

3. Input the relevant features of the car for which you want to predict the price. Ensure that the input data matches the format and structure used during training.

4. Run the cell to execute the prediction. The model will use the input data to provide an estimated price for the given car.

5. Explore the predictions and analyze the results. You can also modify the input data to observe how changes in features affect the predicted prices.

This project is licensed under the MIT License - see the LICENSE file for details.

The MIT License is a permissive open source license that allows you to use, modify, and distribute this project for both commercial and non-commercial purposes.

If you have any feedback, suggestions, or questions regarding the project, please create an issue in the repository or contact me at [email protected].

Your star is a great way to let us know you appreciate our work and find value in this project. Thank you! ⭐

Happy analyzing and predicting❤️!