Supervised-Learning-Classification-Algorithms

This repository provides implementations and comparisons of various supervised learning classification algorithms. The algorithms are categorized into Linear and Non-Linear models. Each section contains an overview of the algorithm, common use cases, as well as implementation and analysis insights.

Table of Contents

• Algorithms Overview
  • Linear Algorithms
  • Non-Linear Algorithms
• Comparisons
  • Comparing Linear Models
  • Comparing Non-Linear Models
  • Linear Regression vs. Multi Linear Regression
  • Linear Regression vs. Logistic Regression
  • Comparing Naive Bayes Models
  • Decision Trees vs. Random Forest
  • Perceptron vs. MLP

Algorithms Overview

Linear Algorithms

1. Logistic Regression

   • Overview: Logistic Regression is a linear model used for binary classification tasks. It predicts the probability of a binary outcome using a logistic function.
   • Use Cases: Commonly used in binary classification problems like spam detection, disease diagnosis, and credit scoring.
   • Implementation & Analysis: Logistic Regression Implementation. This section includes code examples, model fitting, evaluation, and insights into the model's performance.

2. Linear Discriminant Analysis (LDA)

   • Overview: LDA is a classification technique that finds a linear combination of features that separates classes. It is particularly useful when the classes have similar covariance matrices.
   • Use Cases: Widely used in face recognition, marketing, and medical diagnosis.
   • Implementation & Analysis: LDA Implementation. This section provides code examples, model interpretation, and an analysis of how LDA works with different datasets.

3. Perceptron

   • Overview: The Perceptron is a simple linear classifier used for binary classification. It is the foundation of neural networks.
   • Use Cases: Suitable for linearly separable datasets, used in early forms of neural networks.
   • Implementation & Analysis: Perceptron Implementation. This section includes basic implementation, learning curves, and analysis of convergence properties.

4. Support Vector Machines (SVM)

   • Overview: SVM is a powerful linear model that finds the hyperplane that maximizes the margin between different classes.
   • Use Cases: Effective in text categorization, image classification, and bioinformatics.
   • Implementation & Analysis: SVM Implementation. This section covers implementation, kernel trick usage, and performance evaluation on complex datasets.

Non-Linear Algorithms

1. K-Nearest Neighbors (KNN)

   • Overview: KNN is a simple, non-parametric algorithm that classifies a sample based on the majority class among its k-nearest neighbors.
   • Use Cases: Used in recommendation systems, anomaly detection, and pattern recognition.
   • Implementation & Analysis: KNN Implementation. This section provides examples of distance metrics, k-value tuning, and analysis on high-dimensional data.

2. Decision Trees

   • Overview: Decision Trees are non-linear models that split data into branches based on feature values to make predictions.
   • Use Cases: Common in decision analysis, customer segmentation, and credit scoring.
   • Implementation & Analysis: Decision Tree Implementation. This section includes tree construction, pruning techniques, and a discussion on overfitting.

3. Random Forest

   • Overview: Random Forest is an ensemble method that builds multiple decision trees and merges them to improve accuracy and control overfitting.
   • Use Cases: Effective in large datasets with many features, commonly used in finance, healthcare, and marketing analytics.
   • Implementation & Analysis: Random Forest Implementation. This section covers feature importance analysis, model tuning, and comparison with single decision trees.

4. Naive Bayes

   • Overview: Naive Bayes is a probabilistic classifier based on Bayes' theorem, assuming feature independence.
   • Use Cases: Suitable for text classification, sentiment analysis, and spam filtering.
   • Implementation & Analysis: Naive Bayes Implementation. This section includes various Naive Bayes models (Gaussian, Multinomial), implementation details, and performance evaluation on text data.

Comparisons

Comparing Linear Models

Explore the strengths and limitations of various linear models such as Logistic Regression, LDA, Perceptron, and SVM.

Comparing Non-Linear Models

Understand how non-linear models like KNN, Decision Trees, Random Forest, and Naive Bayes differ in terms of complexity, accuracy, and application.

Linear Regression vs. Multi Linear Regression

A comparison focusing on the application of simple linear regression versus multiple linear regression, with practical examples and analysis.

Linear Regression vs. Logistic Regression

Compare linear regression and logistic regression, with a focus on their different applications in regression and classification tasks.

Comparing Naive Bayes Models

An in-depth comparison of different Naive Bayes models, including Gaussian, Multinomial, and Bernoulli, and their performance in various scenarios.

Decision Trees vs. Random Forest

A detailed comparison of Decision Trees and Random Forest, examining how ensemble methods can improve performance and reduce overfitting.

Perceptron vs. MLP

Understand the differences between the Perceptron and Multi-Layer Perceptron (MLP), focusing on their capabilities in handling linear and non-linear data.

Getting Started

To run the code examples provided in this repository, clone the repository and install the required dependencies listed in requirements.txt.

git clone https://github.com/babupallam/Supervised-Learning-Classification-Algorithms.git
cd Supervised-Learning-Classification-Algorithms
pip install -r requirements.txt