Machine Learning Methods

A concise educational project showcasing the core concepts of supervised learning, with a clear distinction between regression and classification models, their pipelines, and evaluation metrics.

Conceptual Overview

Supervised Learning (X → y) Regression Continuous output Classification Discrete classes Linear Regression Logistic Regression K-Nearest Neighbors (KNN) Random Forest

Implemented Models

Linear Regression

Predicts continuous numerical values using a linear model.

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Logistic Regression

Binary classification using probabilities and decision thresholds.

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K-Nearest Neighbors

Distance-based classification without an explicit training phase.

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Random Forest

Ensemble of decision trees providing robust predictions.

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