Skip to content

This repository is dedicated to practicing the implementation of various machine learning models across different datasets to predict outcomes. It serves as a learning resource for building, training, and evaluating models, while exploring diverse techniques such as regression, classification, and more.

Notifications You must be signed in to change notification settings

adil-ahmed/learn-and-predict

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

4 Commits
 
 
 
 
 
 

Repository files navigation

Learn and Predict

Welcome to the Learn and Predict repository! This repository contains a collection of projects aimed at practicing various machine learning models on different datasets to predict outcomes. Each project focuses on a specific dataset and demonstrates the application of different machine learning techniques.

Projects

1. Rock vs Mine Classification

This project explores the use of Logistic Regression to classify objects detected by sonar as either rocks or mines. A dataset containing sonar readings of various objects was used to train and evaluate the model.

  • Dataset: Sonar Object Dataset
  • Model: Logistic Regression

This project serves as a basic introduction to machine learning algorithms, focusing on data preparation, model training, and evaluation.

2. Diabetic Prediction

This project implements a Support Vector Machine (SVM) to predict whether an individual is likely to have diabetes based on a set of health-related features.

This project demonstrates the application of a classification model to a medical dataset, highlighting the potential of machine learning in healthcare.


How to Use

  1. Clone the repository:
    git clone https://github.com/yourusername/learn-and-predict.git
  2. Explore each project folder to find the dataset, code, and explanations for each model.

Future Additions

This repository will continue to grow as more machine learning models are implemented on different datasets. Stay tuned for more projects!

About

This repository is dedicated to practicing the implementation of various machine learning models across different datasets to predict outcomes. It serves as a learning resource for building, training, and evaluating models, while exploring diverse techniques such as regression, classification, and more.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published