Getting Started

This guide will help you get the ml-grid project up and running.

Installation

This project includes convenient installation scripts for Unix/Linux/macOS and Windows. These scripts will create a virtual environment, install all necessary dependencies, and register a Jupyter kernel for you.

Quick Install using Scripts

1. Clone the repository:

   git clone https://github.com/SamoraHunter/ml_binary_classification_gridsearch_hyperOpt.git
   cd ml_binary_classification_gridsearch_hyperOpt
   

2. Run the installation script:

   • For a standard installation:

     • On Unix/Linux/macOS:

       chmod +x install.sh
       ./install.sh
       

     • On Windows:

       install.bat
       

     This will create a virtual environment named ml_grid_env.

   • For a time-series installation (includes all standard dependencies):

     • On Unix/Linux/macOS:

       chmod +x install.sh
       ./install.sh ts
       

     • On Windows:

       install.bat ts
       

     This will create a virtual environment named ml_grid_ts_env.

Usage

After installation, activate the virtual environment to run your code or notebooks.

• To activate the standard environment:

  • On Unix/Linux/macOS: source ml_grid_env/bin/activate

  • On Windows: .\ml_grid_env\Scripts\activate

• To activate the time-series environment:

  • On Unix/Linux/macOS: source ml_grid_ts_env/bin/activate

  • On Windows: .\ml_grid_ts_env\Scripts\activate

If you are using Jupyter, you can also select the kernel created during installation (e.g., Python (ml_grid_env)) directly from the Jupyter interface.