luizamfsantos / mauna-loa-co2-analysis

Examined 60 years of Mauna Loa CO2 data, utilizing Python, Jupyter, and essential libraries for preprocessing and advanced modeling, revealing key atmospheric trends.

Mauna Loa CO2 Analysis

This repository contains the end-to-end project focusing on the analysis of the Mauna Loa carbon dioxide (CO2) concentration data set. The data set, initiated by Charles David Keeling and currently managed by Ralph Keeling, has been instrumental in modern climate research, spanning over 60 years with consistent measurements. The goal of this project is to clean the data, perform time series analysis, and gain insights into the variations and trends in CO2 concentrations over time.

Data Description

The data set used for this project is provided in the CO2.csv file, including the monthly CO2 concentrations recorded at the Mauna Loa observatory starting from March 1958. The data set may contain missing values that will be handled appropriately during the analysis process.

Project Overview

The main focus of this project is to perform time series analysis, including data cleaning, visualization, and modeling techniques, to understand the trends and patterns in CO2 concentrations over the past six decades. Various statistical and machine learning methods will be employed to gain insights into the long-term behavior of atmospheric CO2 at the Mauna Loa site.

Contents

• Data preprocessing scripts
• Jupyter notebooks for data analysis and visualization
• Statistical modeling scripts
• Results and findings documentation

Dependencies

• Python 3.x
• Necessary Python libraries (pandas, matplotlib, seaborn, scikit-learn, etc.)
• Jupyter Notebook

Installation

Clone the repository to your local machine and ensure that the required dependencies are installed. Run the scripts and Jupyter notebooks to replicate the analysis and results.

Feel free to modify, contribute, or use the code and analysis for further research or educational purposes.