Classic Kaggle.com dataset run Python using Llama-2 13B Chat

Welcome to the Global Temperatures Project repository! This project is designed to analyze and visualize global temperature trends to enhance our understanding of climate change. Through meticulous data analysis and machine learning models, we aim to provide insights into how global temperatures have changed over time and predict future trends.

This project utilizes historical temperature data to uncover patterns and trends in global temperatures. Our analysis includes data preprocessing, exploratory data analysis (EDA), trend visualization, and predictive modeling using machine learning algorithms.

• Data Preprocessing: Cleaning and preparing the temperature data for analysis.
• Exploratory Data Analysis (EDA): Analyzing the dataset to summarize its main characteristics, often with visual methods.
• Trend Visualization: Creating visual representations of global temperature trends over time.
• Predictive Modeling: Employing machine learning algorithms to predict future temperature trends based on historical data.

To run this project, you will need Python installed on your system, along with the following Python libraries:

• pandas
• numpy
• matplotlib
• seaborn
• scikit-learn

You can install these packages using pip:

pip install pandas numpy matplotlib seaborn scikit-learn

1. Clone this repository to your local machine.
2. Ensure you have the required Python packages installed.
3. Run the Jupyter notebooks in the following order for a complete analysis:
   • data_preprocessing.ipynb: For cleaning and preparing the data.
   • exploratory_data_analysis.ipynb: For initial data analysis.
   • trend_visualization.ipynb: For visualizing the data trends.
   • predictive_modeling.ipynb: For building and evaluating the predictive models.

The temperature data used in this project is sourced from Berkeley Earth, which provides comprehensive datasets of global temperature changes.

We welcome contributions to the Global Temperatures Project! If you have suggestions for improvements or want to contribute analysis, please open an issue or submit a pull request.

This project is licensed under the MIT License - see the LICENSE file for details.

• Thanks to Berkeley Earth for providing the global temperature dataset.
• Thanks to all contributors who have helped shape this project.

This README provides all the necessary information for anyone interested in the Global Temperatures Project. Whether you're looking to contribute, learn from our analysis, or utilize our findings, we hope this repository serves as a valuable resource.