Machine Learning Algorithms from Scratch

This repository contains an implementation of various machine learning algorithms from scratch using Python. The algorithms are designed to provide a clear understanding of their underlying principles and functionalities.

Algorithms Implemented

1. Binary Bayes Classifiers
   Implemented learning algorithms based on Maximum Likelihood estimation for binary classification tasks.

2. Multiclass Bayes Classifiers
   Developed algorithms to handle multiple classes using Maximum Likelihood estimation.

3. Overfitting and Underfitting Analysis in Regression
   A comprehensive analysis to demonstrate the concepts of overfitting and underfitting in regression models.

4. Bayesian Regression
   Implementation of Bayesian methods for regression tasks, providing a probabilistic framework for inference.

5. Logistic Regression
   Developed from scratch, this model is used for binary classification problems.

6. Decision Tree
   A simple yet powerful algorithm for classification and regression tasks, built from fundamental principles.

7. Random Forest
   An ensemble learning method that combines multiple decision trees to improve predictive accuracy.

8. AdaBoost
   An implementation of the Adaptive Boosting algorithm, which combines weak classifiers to create a strong classifier.

9. Lasso Regression
   Developed an implementation of Lasso regression, which includes L1 regularization to reduce overfitting.

Usage

You can use these implementations as a reference or as a foundation for your own machine learning projects. Each algorithm is modular and can be easily integrated into larger systems.

Requirements

Make sure you have the following libraries installed:

• NumPy
• Pandas
• Matplotlib (for visualization)

You can install the required libraries using pip:

pip install numpy pandas matplotlib