<title>Car Purchase Prediction using Regression</title>

Car Purchase Prediction using Regression

This project aims to predict the price of a car based on its features such as make, model, year, mileage, and other relevant factors using regression analysis.

Dataset

The dataset used in this project contains information about car sales from a dealership. It includes the following columns:

• Make: Make of the car (categorical)
• Model: Model of the car (categorical)
• Year: Year of the car (numerical)
• Mileage: Mileage of the car (numerical)
• Color: Color of the car (categorical)
• Price: Sale price of the car (numerical)

Requirements

• Python 3.6 or higher
• scikit-learn
• pandas
• matplotlib

Getting Started

1. Clone this repository:

<pre>
	git clone https://github.com/&lt;username&gt;/car-purchase-prediction.git
</pre>

<li>Install the required packages:</li>

<pre>
	pip install scikit-learn pandas matplotlib
</pre>

<li>Run the script to train and test the regression model:</li>

<pre>
	python car_purchase_prediction.py
</pre>

<p>The script will output the accuracy of the trained model and a scatter plot showing the actual prices vs. predicted prices.</p>

Customization

You can customize the model by modifying the parameters in the car_purchase_prediction.py script. The following parameters can be adjusted:

• TEST_SIZE: Proportion of data used for testing (default: 0.2)
• RANDOM_STATE: Random seed used for train-test split (default: 42)
• N_JOBS: Number of CPU cores used for training (default: -1)

You can also use different regression algorithms or tune hyperparameters to improve the accuracy of the model.

License

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