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

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