Sea Ice Prediction Project

This project focuses on predicting Arctic sea ice extent using a combination of linear regression, Long Short-Term Memory (LSTM) networks, and Gradient Boosting Regressor (GBR) models. The goal is to improve prediction accuracy by leveraging ensemble learning techniques and advanced neural network architectures.

The project involves:

1. Loading and preprocessing historical sea ice data.
2. Training a Linear Regression model to predict sea ice extent.
3. Incorporating polynomial features and adding lag to targets.
4. Training and validating an LSTM network with attention mechanisms.
5. Using a Gradient Boosting Regressor to enhance predictions.
6. Combining LSTM and GBR predictions through ensemble learning.
7. Evaluating the model performance and visualizing results.

Prerequisites:

• Python 3.6+
• Required Python packages (listed in requirements.txt)

You can install the required packages using pip:

pip install -r requirements.txt