ML-For-Climate-Project

Overview

This project is a machine learning solution built in Python. It includes data preprocessing, model training, evaluation, and predictions. The goal is to solve ____

Table of Contents

• Overview
• Features
• Technologies Used
• Getting Started
• Installation
  • 1. Cloning the Repository
  • 2. Setting Up the Virtual Environment
  • 3. Installing Dependencies
  • 4. Setting Up Pre-Commit Hooks
• Usage
• Project Structure

Features

• Data loading and preprocessing
• Exploratory Data Analysis (EDA)
• Model training and evaluation
• Prediction and visualization

Technologies Used

• Python 3.x
• [pandas, numpy, sklearn, pytorch]

Getting Started

To get a local copy up and running, follow these steps.

Installation

1. Cloning the Repository

git clone https://github.com/yourusername/skincare_ml.git
cd skincare_ml

2. Setting Up the Virtual Environment

It’s recommended to use a virtual environment to manage dependencies.

For macOS and Linux

python3 -m venv venv
source venv/bin/activate

For Windows

python -m venv venv
venv\Scripts\activate

3. Installing Dependencies

Once the virtual environment is activated, install the dependencies. For production dependencies only:

pip install -r requirements.txt

For development dependencies (including linters and test tools):

pip install -r requirements-dev.txt

Usage

1. Activate the virtual environment before running the code:

   • macOS/Linux: source venv/bin/activate
   • Windows: venv\Scripts\activate

2. Run the main script for data processing, model training, or predictions:

   python main.py

3. Deactivate the virtual environment when done:

   deactivate

4. Setting Up Pre-Commit Hooks

This project uses pre-commit hooks to automatically check code formatting and linting before each commit.

1. Install pre-commit:

   pip install pre-commit

2. Install the Pre-Commit Hook: After installing pre-commit, run:

   pre-commit install

3. Verify the Hook Setup: To test the hooks on all files, run:

   pre-commit run --all-files

Project Structure

ML-For-Climate-Project/
├── data/                   # Dataset and raw data files
├── notebooks/              # Jupyter notebooks for EDA and experiments
├── src/                    # Source code for the project
│   ├── __init__.py
│   ├── data_preprocessing.py
│   ├── model_training.py
│   ├── evaluation.py
├── main.py                 # Main script for running the project
├── scripts/                # scripts to extract data
├── requirements.txt        # Project dependencies
├── requirements-dev.txt    # Project development dependencies
└── README.md               # Project README