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Mohamedmxz/MIT-ELO2-

Domain:

climate

Record type:

project
Creator:
Moh
Host:
Machine learning project predicting temperature and rainfall trends in Sudan to support local farmers and communities. # Sudan Climate Analysis — Temperature & Rainfall (ELO2) Short project tagline: Data-driven analysis and forecasting of temperature and rainfall across five regions of Sudan using NASA POWER data. Table of Contents - Sudan Climate Analysis — Temperature \& Rainfall (ELO2) - Research Question and Overview - Motivation \& Goals - Repository structure - Modeling the Climate Domain - Non-technical explanation of findings - Limitations of the Project - Future Research Directions - Communicating the Results - Contact --- ## Research Question and Overview >**How have temperature and rainfall patterns in Sudan changed from 1990 to 2024, and how accurately can machine learning models predict future trends?** This project examines historical temperature and rainfall data for five regions of Sudan (North, Central, East, West, South) sourced from NASA POWER. The objective is to prepare clean regional time-series data, perform EDA to identify trends and seasonality, and develop predictive models for temperature and precipitation to support agricultural planning, research, and local decision-making. ## Motivation & Goals - Provide accessible, cleaned, aggregated climate data for Sudan by region. - Identify long-term trends and seasonal patterns in temperature and rainfall. - Build and evaluate machine learning models to forecast annual/seasonal climate variables. - Communicate findings via visualizations, simple dashboard and notebook narrative. ## Repository structure - `0_domain_study/` — Background, problem context, and domain motivation (contains a README describing goals and motivation). - `1_datasets/` — Raw datasets and final prepared dataset(s). Subfolders: - `Raw_datasets/` (per-region raw CSVs downloaded from NASA POWER) - `Final_dataset/` (cleaned & merged datasets used for analysis) - `2_data_preparation/` — Notebooks and scripts used to clean, standardize, merge, and validate the data. - `3_data_exploration/` — EDA notebooks, plots, and summary statistics. …

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