Classical Machine Learning Algorithms

This repository contains well-structured Jupyter notebooks that implement and demonstrate a variety of classical machine learning algorithms using Python and Scikit-learn. It is designed for learners and practitioners who want to understand how traditional models work and how to apply them effectively on real-world datasets.

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📁 Repository Structure

Notebook	Description
01_Perceptron_and_Adaline.ipynb	Implementation of Perceptron and Adaptive Linear Neuron (Adaline)
02_classification_algorithms.ipynb	Classification models: Logistic Regression, SVM, Decision Trees, KNN
03_data_preprocessing.ipynb	Data cleaning, normalization, encoding, and splitting
04_data_compression.ipynb	Dimensionality reduction using PCA and LDA
05_model_evaluation.ipynb	Model validation and performance metrics
06_ensemble_methods.ipynb	Ensemble methods: Random Forest, Bagging, Boosting, XGBoost
07_regression_analysis.ipynb	Regression using Linear Regression and RANSAC
08_clustering_analysis.ipynb	Unsupervised learning: K-Means, DBSCAN, Hierarchical clustering

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🔍 Features Covered

• Dimensionality Reduction

  • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)

• Classification Algorithms

  • Logistic Regression
  • Support Vector Machine (SVM)
  • Decision Trees
  • K-Nearest Neighbors (KNN)

• Ensemble Methods

  • Random Forest
  • Bagging
  • AdaBoost & Gradient Boosting
  • XGBoost

• Regression Analysis

  • Linear Regression
  • RANSAC Regressor

• Clustering Algorithms

  • K-Means
  • DBSCAN
  • Hierarchical Clustering

• Model Evaluation Techniques

  • K-Fold Cross-Validation
  • Confusion Matrix
  • ROC Curve and AUC
  • Precision, Recall, F1 Score

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🧰 Technologies Used

• Python
• NumPy, Pandas
• Matplotlib, Seaborn
• Scikit-learn
• XGBoost

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