ML Models

This repository is a collection of various machine learning models, personally developed and implemented. The project aims to provide a comprehensive set of examples for different machine learning techniques, ranging from basic algorithms to more advanced models, showcasing a wide array of applications and methodologies in the field of machine learning.

Getting Started

These instructions will guide you on how to get a copy of the project up and running on your local machine for development and experimentation.

Prerequisites

To work with the ML models in this repository, you will need:

• Python 3.x
• Jupyter Notebook or JupyterLab
• Relevant Python libraries as specified in requirements.txt

Installation

1. Clone the Repository

   Begin by cloning the ML_models repository to your local machine:

   git clone https://github.com/matusoff/ML_models.git
   cd ML_models
   
   

Exploring the Models

Each model is contained within its own Jupyter Notebook. To explore a model, navigate to its corresponding notebook and open it using JupyterLab or Jupyter Notebook:

jupyter notebook <notebook_name>.ipynb

Models Included

• Linear Regression
• Logistic Regression
• Decision Trees
• Random Forest
• XGBoost
• Neural Networks
• Image Analysis with Tensorflow

Acknowledgments

• Thanks to all the open-source projects and libraries that made this repository possible.
• Original data for RNA_seq_cancer model can be found here: https://archive.ics.uci.edu/datasets