Core Machine Learning Algorithms From Scratch

This repository contains implementations of core Machine Learning algorithms built completely from scratch using Python, without relying on pre-built ML model libraries such as Scikit-learn.

The goal of this project is to deeply understand how machine learning algorithms work internally by implementing the mathematics, training logic, and prediction workflows manually.

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Features

• Implemented algorithms from scratch
• Object-Oriented Programming based design
• Custom preprocessing workflows
• Training and prediction pipelines
• Educational and learning-focused implementations

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Implemented Algorithms

Classification Algorithms

• K-Nearest Neighbors (KNN)
• Logistic Regression
• Decision Tree Classifier
• Random Forest
• Support Vector Machine (SVM)
• Naive Bayes
• AdaBoost
• Multi-Layer Perceptron (MLP)

Probabilistic / Sequential Models

• Bayesian Networks
• Hidden Markov Models (HMM)

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Technologies Used

• Python
• NumPy
• Pandas
• Sklearn (Only for importing datasets in some programs)
• hmmlearn (For Hidden Markov Model implementation)
• Object-Oriented Programming (OOP)

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Repository Structure

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├── Adaboost.py
├── BayesianNetwork.py
├── DecisionTree.py
├── HMM.py
├── K-NearestNeighbour.py
├── LogisticRegression.py
├── MLPClassifier.py
├── NaiveBayes.py
├── RandomForest.py
├── SVM.py
├── Preprocess.py
├── dataset.csv