Supervised Learning - Regression 📈

This repository contains an implementation of the Linear Regression algorithm from scratch in Python using Numpy and Pandas for the task of regression.

Overview ℹ️

In this project, we implement Linear Regression, a fundamental supervised learning algorithm used for regression tasks. Linear Regression aims to establish a linear relationship between input features and a continuous target variable. This implementation provides a clear understanding of how Linear Regression works and demonstrates its implementation from scratch using Python.

Implementation Details 🛠️

Libraries Used 📚

• Numpy: For numerical computing.
• Pandas: For data manipulation and analysis.
• Matplotlib: For data visualization.

Linear Regression 🔍

• Linear Regression is implemented to predict continuous target variable values based on input features.
• The algorithm calculates the coefficients using the normal equation.
• The predictions are made using the learned coefficients.

Usage 🚀

1. Clone the repository:

   git clone https://github.com/your_username/supervised-learning-regression.git

2. Install the required libraries:

   pip install numpy pandas matplotlib

3. Run the Python script:

   python linear_regression.py

Files 📁

• linear_regression.py: Contains the main script for implementing Linear Regression.
• Medical Price Dataset (1).csv: Dataset containing medical price information.

Results 📊

The coefficients obtained from the Linear Regression model are:

• Coefficients: [256.86, 339.19, 475.50, -131.31, 23848.53, -352.96, -1035.02, -960.05]
• Intercept: -11938.54

Contributing 🤝

Contributions are welcome! If you find any issues or have suggestions for improvements, feel free to open an issue or create a pull request.

License 📝

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