manansodha / supervised-machine-learning

supervised-machine-learning

Welcome to the Supervised Machine Learning Algorithms repository/mixtape! This repository contains a collection of popular supervised machine learning algorithms implemented in Python. These algorithms are essential tools for solving a wide range of predictive modeling and classification problems. Each algorithm is implemented from scratch, allowing you to gain a deeper understanding of their inner workings.

Table of Contents

• Introduction
• Algorithms
  • K-Nearest Neighbors (KNN)
  • Naive Bayes
  • Linear Regression
  • Multilinear Regression
  • Decision Tree
• Getting Started
• Usage
• Contributing
• License

Introduction

In the field of supervised machine learning, we often encounter tasks where we have input data and corresponding output labels, and our goal is to learn a mapping from inputs to outputs. This repository provides implementations of several fundamental supervised learning algorithms that can assist you in building predictive models and making informed decisions based on data.

Algorithms

K-Nearest Neighbors (KNN)

K-Nearest Neighbors is a simple yet powerful classification algorithm. Given a new data point, KNN assigns it a class label based on the majority class among its k-nearest neighbors in the training data.

Naive Bayes

Naive Bayes is a probabilistic algorithm based on Bayes' theorem. It's commonly used for classification tasks. Despite its "naive" assumption of feature independence, it often performs surprisingly well in practice.

Linear Regression

Linear Regression is a basic regression algorithm that models the relationship between a dependent variable and one or more independent variables. It finds the best-fitting linear equation to predict the target variable.

Multilinear Regression

Multilinear Regression is an extension of Linear Regression to multiple independent variables. It's suitable for scenarios where the target variable depends on more than one feature.

Decision Tree

Coming soon

Getting Started

To get started, follow these steps:

1. Clone this repository: git clone https://github.com/manansodha/supervised-machine-learning.git
2. Navigate to the repository: cd supervised-machine-learning
3. Install the required dependencies: pip install -r requirements.txt

Usage

Each algorithm is implemented in its own Python script. To use a specific algorithm, follow the instructions provided in the respective script. You can use your own dataset or explore the example datasets provided in the data directory.

python KNN.py
python NaiveBayes.py
python LinearRegression_Model1.py
python MultiLinearRegression_Model1.py
python decision_tree.py

Contributing

Contributions are welcome and encouraged! Whether you want to add more algorithms, improve existing code, or fix bugs, your help is appreciated.