Machine Learning Algorithms

This repository contains implementations of various machine learning algorithms in Jupyter notebooks.

Algorithms

• ANN.ipynb: An implementation of a simple Artificial Neural Network.
• CLUSTERING-kmeans.ipynb: An implementation of the K-Means clustering algorithm.
• DECISION TREE.ipynb: An implementation of a Decision Tree classifier.
• LLINEAR REGRESSION.ipynb: An implementation of Linear Regression.
• MLE.ipynb: An implementation of Maximum Likelihood Estimation.
• NBC.ipynb: An implementation of a Naive Bayes Classifier for spam email detection.
• OR GATE.ipynb: A simple perceptron model to simulate the OR logic gate.
• XOR.ipynb: A simple perceptron model to simulate the XOR logic gate.

Data

• MLE data.xlsx: Data used for the Maximum Likelihood Estimation notebook.

How to Use

Each Jupyter notebook is self-contained and includes the necessary code and explanations for the implemented algorithm. You can open and run these notebooks in a Jupyter environment to see the algorithms in action.