This repository contains a collection of foundational machine learning (ML) algorithms implemented using scikit-learn and other Python libraries. The purpose is to provide clear, well-documented examples of how these algorithms can be applied to real-world datasets and tasks.
-
Linear Regression
Predicts continuous outcomes by modeling the relationship between dependent and independent variables using a linear equation. Commonly used for price prediction, forecasting, and trend analysis. -
Logistic Regression
A supervised learning algorithm used for binary and multi-class classification, applying the logistic (sigmoid) function to predict class probabilities. Ideal for tasks such as spam detection and disease prediction. -
Perceptron
A simple linear binary classifier inspired by the structure of a biological neuron. Often used as a building block for more complex neural networks. -
Backpropagation (Neural Networks)
A fundamental technique for training neural networks by minimizing prediction errors using gradient descent and error propagation through layers. -
AdaBoost (Adaptive Boosting)
An ensemble method that combines multiple weak classifiers into a strong one by focusing more on difficult-to-classify instances. Effective for improving model performance on imbalanced datasets. -
Clustering (K-Means, Hierarchical)
Unsupervised learning algorithms that group data into clusters based on feature similarity. Commonly used for customer segmentation, image compression, and anomaly detection. -
Bayesian Classifiers (Naive Bayes)
Probabilistic models applying Bayes' theorem for classification tasks. Suitable for text classification, spam filtering, and recommendation systems. -
K-Nearest Neighbors (KNN)
A non-parametric algorithm that classifies a data point based on the majority class of its nearest neighbors in the feature space. Simple yet powerful for classification and regression.
- Clone the repository:
git clone https://github.com/yourusername/ml-algorithms.git cd ml-algorithms