Regression: Linear Regression Model Implementation

Welcome to the Regression repository! This project, developed by Maaz Waheed, provides a clear and concise implementation of a Linear Regression model from scratch (or using common libraries, specify which). It aims to demonstrate the fundamental concepts behind one of the most popular supervised learning algorithms.

📝 Overview

Linear Regression is a statistical method used for modeling the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. This implementation covers:

🛠️ Technologies Used

• Python: The primary programming language.
• NumPy: For numerical computations and array manipulations.
• Pandas: (If used) For data loading and manipulation.
• Matplotlib / Seaborn: (If used) For plotting and visualization.
• Scikit-learn: (If used for comparison, dataset splitting, or metrics)

📂 Project Structure

The project is organized as follows:

• MY_RLS.py: Custom implementation of Linear Regression from scratch.
• skl.py: Linear Regression using scikit-learn for comparison.
• data/: Directory to store datasets (e.g., House Price India.csv).
• requirements.txt: List of required Python packages.

🚀 Getting Started

Prerequisites

Ensure you have Python 3.x installed. You'll also need the following libraries:

pip install -r requirements.txt

📊 Dataset

The dataset used for demonstration and testing is the "House Prices India" dataset, available on Kaggle. It contains various features of houses in India, which can be used to predict their prices.

• Source: House Prices India on Kaggle
• Usage: Download the dataset (e.g., House Price India.csv) and place it in a data/ directory within the project, or update the data loading path in your scripts accordingly.

👨‍💻 Author

Maaz Waheed

    GitHub: @42Wor

    (Optional: Add LinkedIn, Email, etc.)

🙏 Acknowledgements

(Optional: Any resources, courses, or inspirations you'd like to thank)