/Multiple-Linear-Regression

This repository provides a comprehensive implementation of Multiple Linear Regression (MLR) for predictive modeling. It includes detailed steps for preprocessing data, handling categorical variables, and visualizing relationships between features.

Primary LanguageJupyter NotebookMIT LicenseMIT

scikit-learn version 1.5.1 statsmodels version 0.14.2

Multiple Linear Regression 🔥

📋 General Information

This repository provides an in-depth understanding and practical implementation of Simple Linear Regression (SLR), a foundational machine learning algorithm. SLR is used to model the relationship between two variables: a dependent variable (target) and an independent variable (predictor). It assumes a linear relationship between them and fits a straight line (y = mx + c) to the data.

Key features of this repository include:

Explanation of SLR concepts with mathematical derivations.
Python implementation using numpy, scikit-learn, and statsmodels.
Interactive visualizations for better understanding.
Datasets for testing and experimentation.
Performance evaluation metrics like RMSE and R².
This repo is ideal for beginners and enthusiasts aiming to master linear regression.

🛠️ Technologies Used

🚀 Getting Started (In Anaconda PowerShell Prompt)

  1. Clone the repository:

    git clone https://github.com/coder5omkar/Multiple-Linear-Regression.git

  2. Navigate to the project directory:

    cd Multiple-Linear-Regression

  3. Open the notebook:

    jupyter notebook MLR.ipynb

🤝 Acknowledgements

  • This project was inspired by IIT-B AI-ML program at Upgrad

Developed as part of the ML-1 Module assignment required for Post Graduate Diploma in Machine Learning and AI - IIIT,Bangalore.

This project is open source and available under the MIT License.

Contact

Created by @in/omkaramale - feel free to contact me!