🧠 Machine Learning Projects

This repository contains a comprehensive collection of Machine Learning projects and notebooks, organized into clear sections for Supervised and Unsupervised learning techniques, along with fundamental Python and ML Algorithms concepts.

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📁 Folder Structure

MachineLearning/
├── Datasets/                       # Dataset 
├── DataVisualization/              # visualization python codes
├── Materials-Docs/                 # Reference materials and documentation
├── Standadization(Normalization)   # Mean-Varience-SD & Scaling
├── ML_Algorithms/                  # Implementation of core ML algorithms
├── PythonBasics/                   # Foundational Python concepts
├── Supervised/                     # Supervised Learning notebooks and models
├── Unsupervised/                   # Unsupervised Learning notebooks
├── .gitignore                      # Git ignored files
└── README.md                       # Project overview

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Supervised Learning

Notebooks inside the Supervised/ folder include:

• SimpleLinearRegression.ipynb
• Multi_Linear_regression.ipynb
• DecisionTree.ipynb, RandomForest.ipynb
• Logistic_Regression.ipynb
• SVM.ipynb
• MultiClass_Classification.ipynb
• One-Hot Encoding.ipynb
• GradiantDecent.ipynb

Model files like .pkl for reuse:

• multi_reg_model.pkl
• simple_linear.pkl

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Unsupervised Learning

• K_means_Clustering.ipynb - Partitions data into k distinct non-overlaping subgroups(Clusters).
• PCA.ipynb — Principal Component Analysis for dimensionality reduction.

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🧰 ML Algorithms

• Handcrafted implementations and explanations of core ML algorithms.

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🔍 Data Visualization

• Contains tools and notebooks for understanding and visualizing dataset patterns.

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🐍 Python Basics

• Fundamental Python concepts relevant to ML workflows and experimentation.

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📁 Datasets

• Datasets used in projects.

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🚀 Getting Started

1. Clone the repository:
  
   git clone https://github.com/your-username/MachineLearning.git
   cd MachineLearning

2. pip install -r requirements.txt

3. jupyter notebook

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