Machine Learning Algorithms Repository

This repository contains implementations of various machine learning algorithms written from scratch. Each algorithm has its own folder, which includes both a Jupyter notebook (.ipynb) and a Python script (.py). Additionally, each folder contains a project demonstrating the practical application of the respective algorithm.

Algorithms Included

1. Decision Tree

   • Added: May 26, 2024
   • Implementation of a decision tree algorithm from scratch. A project demonstrating the classification of data using decision trees is also included.

2. K-Means Clustering

   • Added: May 26, 2024
   • A full implementation of the K-Means clustering algorithm, along with a project to visualize and cluster datasets.

3. Linear Regression

   • Added: May 21, 2024
   • Implementation of linear regression for predictive modeling. This includes a project that applies linear regression to real-world datasets.

4. Logistic Regression

   • Added: May 26, 2024
   • A complete implementation of logistic regression for binary classification problems, demonstrated with a practical use case in the project.

5. Naive Bayes

   • Added: May 21, 2024
   • Implementation of the Naive Bayes classifier, along with a project that demonstrates its application in text classification.

6. Principal Component Analysis (PCA)

   • Added: May 26, 2024
   • An implementation of PCA for dimensionality reduction, along with a project showcasing its use in high-dimensional data visualization.

7. Perceptron

   • Added: May 26, 2024
   • Implementation of the perceptron algorithm, demonstrated in a project involving simple binary classification.

8. Random Forest

   • Added: May 26, 2024
   • Implementation of the Random Forest ensemble method, with a project demonstrating its performance on a classification problem.

9. Support Vector Machine (SVM)

   • Added: May 26, 2024
   • Implementation of SVM for both classification and regression, along with a project to illustrate its application in separating non-linear data.

Repository Structure

├── DecisionTree
│   ├── DecisionTree.ipynb
│   ├── decision_tree.py
│   └── decision_tree_project.ipynb
├── KMeans
│   ├── KMeans.ipynb
│   ├── kmeans.py
│   └── kmeans_project.ipynb
├── LinearRegression
│   ├── LinearRegression.ipynb
│   ├── linear_regression.py
│   └── linear_regression_project.ipynb
├── LogisticRegression
│   ├── LogisticRegression.ipynb
│   ├── logistic_regression.py
│   └── logistic_regression_project.ipynb
├── NaiveBayes
│   ├── NaiveBayes.ipynb
│   ├── naive_bayes.py
│   └── naive_bayes_project.ipynb
├── PCA
│   ├── PCA.ipynb
│   ├── pca.py
│   └── pca_project.ipynb
├── Perceptron
│   ├── Perceptron.ipynb
│   ├── perceptron.py
│   └── perceptron_project.ipynb
├── RandomForest
│   ├── RandomForest.ipynb
│   ├── random_forest.py
│   └── random_forest_project.ipynb
├── SVM
│   ├── SVM.ipynb
│   ├── svm.py
│   └── svm_project.ipynb
└── README.md