Machine_Learning

Project Overview

This repository contains a collection of machine learning models implemented in Python. It is intended as a resource for learning and understanding various machine learning algorithms, with examples and practical implementations. The projects are organized by algorithm type, such as KNN, Decision Trees, Neural Networks, and more.

Contents

This repository is structured into folders, each focusing on a specific algorithm or project:

1. Basics

• Description: Introduction to Python and NumPy basics, providing a foundation for data manipulation and mathematical operations.

2. KNN (K-Nearest Neighbors)

• Description: Implementation of the K-Nearest Neighbors algorithm. Projects include Handwritten Digits Recognition, Dating Model demonstrating how KNN can be applied to classification problems.

ADABOOST

• Description: Implementation of the AdaBoost algorithm, an ensemble learning method that combines multiple weak classifiers to create a strong classifier. Includes a project using a dataset for Horse Colic.

Decision Trees

• Description: Implementation of decision tree models for classification tasks. Includes a project using a dataset for lenses, and checking is it a fish?.

Heart Disease

• Description: Predictive model for heart disease using various machine learning techniques. This project aims to identify the likelihood of heart disease based on patient data.
• Key Topics: Data Preprocessing, Model Training, Evaluation Metrics.
• Dataset: (Heart Disease Dataset).

KMeans

• Description: Implementation of K-means clustering, including a detailed explanation of the algorithm. It clusters data points into K groups based on their features.Includes a project of clustering Significant Earthquake details whose data was being fetched using API (Earthquake details).
• Key Topics: Clustering, Centroid Calculation, Iterative Optimization.

LogisticRegression

• Description: Application of Logistic Regression for binary classification problems, explaining concepts like sigmoid function and decision boundary.Improved Horse colic project using this concept.

NaiveBayes.py

• Description: Implementation of the Naive Bayes classifier, a simple yet effective method for classification problems.Include project to clissify whther mail recieved is spam or not.

Neural_Network

• Description: Basic neural network implementation using Python. This project covers the essential concepts of neural networks, including forward and backward propagation.
• Includes three Projects:
  1. Classifying Newswires.
  2. Movie reviews.
  3. Predicting House Prices.

SVM (Support Vector Machines)

• Description: Implementation of Support Vector Machine (SVM) for classification problems, with an emphasis on understanding hyperplanes and support vectors.

Usage

To get started with the repository, follow these steps:

1. Clone the Repository:

   git clone https://github.com/chitwan6804/Machine_Learning.git
   cd Machine_Learning

2. Set Up the Environment:

   • Ensure that you have Python 3.x installed.
   • Install the required libraries using the following command:

     pip install -r requirements.txt

3. Run the Projects:

   • Navigate to the specific folder of interest (e.g., cd Heart Disease) and run the script using:

     python Heart_disease.py

4. Jupyter Notebooks:

   • If a project is provided as a Jupyter Notebook, you can run it using:

     jupyter notebook

   • This will open Jupyter in your web browser, allowing you to explore the code interactively.

Contact Information

For any questions or feedback, feel free to reach out via the Issues tab of this repository or contact me at [6804chitwan.gujrani@gmail.com]. I am happy to discuss improvements or help with any issues you encounter while using the repository.

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