/Contrails_Kaggle

Machine learning model to identify contrails in the atmosphere

Primary LanguagePython

Identify Contrails - Reduce Global Warming Project

Contrail Image

Welcome to the Identify Contrails - Reduce Global Warming project! This repository, hosted by katiarp, contains all the necessary information, code, and resources for our project aimed at identifying contrails from satellite images to help reduce their impact on global warming.

Project Overview

Project Description: In this project, we tackle the important issue of identifying contrails, the visible line-shaped clouds produced by aircraft engine exhaust. Contrails play a significant role in climate change, and our goal is to develop algorithms and models to detect and analyze them in satellite images.

Getting Started

To get started with this project, you'll find all the essential resources in this repository. Here's a brief guide:

  1. Project Goals: The project objective is to develop a machine learning model that can effectivly identrify contrails in images, contribute to the broader goal of reducing global worming through better cotrails detection..

  2. Dataset: You can find information about the dataset used in this project and how to obtain it.

  3. Code and Notebooks: Explore the code and Jupyter notebooks in the notebooks/ directory to see our data analysis and model development processes.

Repository Contents

This repository is organized to help you understand and contribute to our project:

  • notebooks/: This directory contains Jupyter notebooks that document our data exploration, preprocessing, and model development processes.

  • data/: If the project involves data files, you can place them in this directory for easy access within the notebooks.

  • src/: Custom code and scripts related to the project can be found in this directory.

  • results/: Any project results, visualizations, or reports can be stored here.

  • README.md: You are currently reading the README file, which provides an overview of the project and repository structure.

Dependencies

If you wish to run or contribute to the project code, you'll need a Python environment with the required libraries. These dependencies may include:

  • NumPy
  • Pandas
  • Matplotlib
  • Seaborn
  • Scikit-Learn
  • TensorFlow (or any other deep learning framework, if applicable)

You can install these dependencies using pip or conda as needed:

pip install numpy pandas matplotlib seaborn scikit-learn tensorflow