Climate change is one of the most pressing global challenges, and data science plays a crucial role in understanding and mitigating its impact. This project involves collecting historical and real-time climate data to analyze trends in temperature fluctuations, CO₂ levels, ice cap melting, and sea-level rise. The analysis may include time-series forecasting, anomaly detection, and predictive modeling to assess future climate risks. Various statistical models, machine learning algorithms, and geospatial analysis techniques will be used to gain insights into environmental changes and their effects on ecosystems and human societies.
Programming Languages: Python, R, SQL Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, TensorFlow/Keras (for deep learning models) Software/Tools: Jupyter Notebook, Google Colab, Tableau (for visualization), PostgreSQL/MySQL (for managing climate databases) Before Commencing the project the following links have to be examined.
https://www.kaggle.com/
https://science.nasa.gov/climate-change/
https://data.worldbank.org/
https://www.ipcc.ch/
