Simple Machine Learning Project

This is a simple machine learning project that demonstrates how to implement both linear (with Multi-Feature equation and Gradient Descent) and polynomial regression models using Jupyter Notebook.

Prerequisites

• Python 3.1.1
• Jupyter Notebook
• NumPy
• Pandas
• Scikit-learn
• matplotlib

Installation

1. Clone this repository.
2. Install the required packages using pip install -r requirements.txt

Usage

1. Open the MLProject.ipynb file in Jupyter Notebook.
2. Run the cells in the notebook to see how the models are implemented.
3. Modify the code to suit your needs.

Linear Regression Model

The linear regression model is implemented using scikit-learn's LinearRegression class. The model is trained on a dataset of car prices and mileage.

Polynomial Regression Model

The polynomial regression model is implemented using scikit-learn's PolynomialFeatures class and LinearRegression class. The model is trained on a dataset of car prices and mileage.

Conclusion

This project demonstrates how to implement both linear and polynomial regression models using Jupyter Notebook. You can use this project as a starting point for your own machine learning projects.