Machine Learning

A personal repository documenting my journey through learning and implementing machine learning algorithms. Each folder contains code, explanations, and experiments for different ML topics, including linear regression, classification, and more.

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Table of Contents

• About
• Project Structure
• Getting Started
• Library Requirements
• Usage
• Implemented Algorithms
• Results
• Contributing
• License
• Acknowledgements

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About

This repository is a collection of Jupyter notebooks and scripts as I explore machine learning concepts. The goal is to understand the theory and practical implementation of various algorithms, and to build a reference for myself and others interested in ML.

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Project Structure

Machine-Learning/
│
├── SupervisedLearning/
│   ├── LinearRegression
│   ├── LogisticRegression
│   ├── DecisionTrees
│   ├── SupportVectorMachine
│   └── NaiveBayes
│   └── KNearestNeighbors
│   └── RandomForest
├── UnsupervisedLearning/
│   └── KMeansClustering
├── requirements.txt
└── README.md

• SupervisedLearning/: Notebooks and scripts for regression and classification algorithms.
• UnsupervisedLearning/: Notebooks for clustering and dimensionality reduction.
• requirements.txt: List of dependencies.
• README.md: This file.

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Getting Started

To get a local copy up and running:

1. Clone the repository:

   git clone https://github.com/VaibhavUPratap/Machine-Learning.git
   cd Machine-Learning

2. Install dependencies:

   pip install -r requirements.txt

3. Open notebooks:

   • Use Jupyter Notebook or VSCode to explore and run .ipynb files.

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Library Requirements

• Python 3.x
• numpy
• pandas
• scikit-learn
• matplotlib
• seaborn
• jupyter

Install all dependencies with:

pip install -r requirements.txt

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Usage

• Navigate to the relevant folder (e.g., SupervisedLearning/).
• Open the notebook you want to explore.
• Run cells step by step to see code, outputs, and explanations.
• Some notebooks may require datasets; ensure they are in the data/ folder or update the path as needed.

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Implemented Algorithms

• Linear Regression
• Logistic Regression
• K-Nearest Neighbors
• Decision Trees
• K-Means Clustering
• Principal Component Analysis (PCA)
• (More coming soon...)

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Results

Each notebook contains visualizations, evaluation metrics, and discussion of results. Screenshots or sample outputs may be added for a quick overview.

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Contributing

Contributions are welcome!

• Fork the repository
• Create a new branch for your feature or bugfix
• Commit your changes
• Open a pull request

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License

Distributed under the MIT License. See LICENSE for more information.

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Acknowledgements

• Inspired by open-source ML learning resources and courses.
• Special thanks to the authors of referenced books, courses, and datasets.

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Happy Learning!

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