Machine Learning Repository

A comprehensive collection of machine learning implementations, tutorials, and learning materials covering various algorithms and techniques.

Repository Overview

This repository serves as a complete learning hub for machine learning concepts, from fundamental algorithms to advanced techniques. Each module is designed to provide both theoretical understanding and practical implementation.

Repository Structure

machine-learning/
├── README.md                      # Main repository overview
├── .gitignore                     # Git ignore configuration
└── Linear_Regression/             # Linear Regression module
    ├── README.md                  # Linear Regression specific documentation
    ├── linearregression_day1.ipynb    # Basic linear regression tutorial
    ├── LR_day2.ipynb                  # Ice cream sales prediction
    ├── LR_day2_sal.ipynb             # Salary prediction model
    ├── LR_day2_stuPro.ipynb          # Student performance analysis
    ├── Salary_dataset.csv            # Salary dataset
    ├── Student_Performance.csv       # Student performance dataset
    └── Theory materials/             # PDF resources and theoretical references

Learning Modules

Linear Regression - Complete

Location: /Linear_Regression/

Featured Notebook: linearregression_day1.ipynb

• Study hours vs exam scores relationship
• Complete ML pipeline from data to evaluation
• Perfect for beginners understanding linear regression fundamentals

Real-world Applications:

• Salary prediction based on experience
• Student performance analysis
• Ice cream sales prediction

Future Modules (Planned)

• Logistic Regression
• Decision Trees & Random Forest
• Support Vector Machines
• Neural Networks
• Clustering Algorithms

Technologies Used

• Python 3.x - Programming language
• pandas - Data manipulation
• numpy - Numerical computing
• matplotlib - Data visualization
• scikit-learn - Machine learning library
• jupyter - Interactive development

Quick Start

`Bash

1. Clone the repository

git clone https://github.com/Rahullll101/machine-learning.git cd machine-learning

2. Install dependencies

pip install pandas numpy matplotlib scikit-learn jupyter

3. Start learning

cd Linear_Regression jupyter notebook linearregression_day1.ipynb `

Learning Path

1. Start Here: Linear_Regression/linearregression_day1.ipynb
2. Apply Knowledge: Linear_Regression/LR_day2.ipynb
3. Real Data: Linear_Regression/LR_day2_sal.ipynb
4. Complex Analysis: Linear_Regression/LR_day2_stuPro.ipynb

Author

Rahullll101

• GitHub: @Rahullll101
• Email: [email protected]

License

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

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Happy Learning!

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