Overview

Supervised machine learning is a branch of artificial intelligence that involves training models on labeled data to make predictions or decisions. This repository focuses on the key algorithms within supervised learning, including regression, classification, and ensemble methods.

Algorithms Included

Linear Regression: A basic yet powerful algorithm for predicting continuous values based on linear relationships between features and target variables. Logistic Regression: Used for binary classification tasks, logistic regression estimates the probability of a data point belonging to a particular class. Decision Trees: These versatile algorithms split data into branches based on feature values, enabling both classification and regression tasks. Random Forest: An ensemble method that combines multiple decision trees to improve predictive accuracy and handle complex datasets. Gradient Boosting Machines (GBM): Another ensemble technique that builds models sequentially, each correcting errors made by the previous ones, leading to highly accurate predictions. Usage Each algorithm is implemented in Python, accompanied by detailed comments and explanations to facilitate understanding. Users can explore the code, modify parameters, and experiment with different datasets to observe how these algorithms behave in various scenarios.

To get started:

Clone the repository to your local machine. Navigate to the algorithm of interest (e.g., Linear Regression, Decision Trees). Open the Python file and follow the instructions/comments to run the algorithm on sample datasets or your own data. Contributing Contributions to this repository are welcome! Whether you want to add new algorithms, improve existing code, or enhance documentation, your contributions can help enhance the learning experience for others interested in supervised machine learning.

To contribute:

Fork the repository. Make your changes or additions. Submit a pull request detailing your contributions. Resources For further learning and understanding of supervised machine learning, consider exploring the following resources:

Books: "Introduction to Statistical Learning" by Gareth James et al., "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron. Online Courses: Coursera's "Machine Learning" by Andrew Ng, Udacity's "Machine Learning Engineer Nanodegree." Documentation: Scikit-learn's official documentation for detailed explanations and examples of machine learning algorithms. Happy learning and exploring the fascinating world of supervised machine learning algorithms!