ruturaj0626/Environmental-Data-Analysis

The project has collected data from approximately 497 unique locations across various regions in Rwanda, including farmlands, cities, and power plants. The data spans the years 2019 to 2021, which are included in the training dataset. The primary task of this project is to develop models # Environmental-Data-Analysis ## Project Overview The ability to accurately monitor carbon emissions is a critical step in the global fight against climate change. Precise carbon readings provide invaluable insights for researchers and governments, helping them understand the sources and patterns of carbon mass output. While Europe and North America have well-established systems for monitoring carbon emissions, the same infrastructure is often lacking in many parts of Africa. This project addresses this gap by focusing on Rwanda, a country in East Africa. Rwanda is characterized by diverse landscapes, including farmlands, cities, and power plants. Approximately 497 unique locations have been carefully selected from various regions in Rwanda to collect carbon emissions data. ## Project Objectives 1. **Data Collection and Curation**: Gather comprehensive carbon emissions data from the selected 497 locations across Rwanda. Ensure data quality, consistency, and accuracy. 2. **Data Analysis and Modeling**: Develop sophisticated machine learning models and data analysis techniques to make accurate predictions of carbon emissions. The data spans the years 2019 to 2021, which will serve as the training dataset. The primary task is to predict CO2 emissions data for the year 2022 through November. 3. **Visualization and Reporting**: Create informative visualizations and reports to communicate the trends and patterns of carbon emissions in Rwanda. These visuals will help policymakers and researchers make informed decisions. 4. **Open-Source Collaboration**: Encourage collaboration and contributions from the open-source community, including data scientists, environmentalists, and developers. Open-source tools and methodologies will be at the core of this project's development. ## Repository Contents - **Data**: Contains datasets used for training and testing machine learning models. - **Notebooks**: Jupyter notebooks with data analysis, modeling, and visualization co …