baqius/Abeokuta-Weather-forecast

This project builds a deep learning model to **forecast the next 7 days of daily temperature** in **Abeokuta, Ogun State, Nigeria** — using 9 years of historical weather data (2017–2026). # 🌤️ Abeokuta 7-Day Weather Forecast ### Multi-Step Temperature Forecasting with GRU (PyTorch) + Streamlit Dashboard --- ## Overview This project builds and deploys a deep learning model that forecasts the **next 7 days of daily temperature** for **Abeokuta, Ogun State, Nigeria**, using 9 years of historical weather data (2017–2026). Abeokuta has a tropical climate with a distinct **wet season (April–October)** and **dry season (November–March)**. This seasonal structure makes temperature patterns learnable from historical data — which this project exploits using a Gated Recurrent Unit (GRU) neural network. The project has two components: - **`Forecasting_Abeokuta_Weather_Portfolio.ipynb`** — the full research notebook covering data collection, EDA, model training, and evaluation. - **`app.py`** — a Streamlit web app that loads the trained model and serves a live 7-day forecast dashboard. --- ## Project Structure ``` ├── Forecasting_Abeokuta_Weather_Portfolio.ipynb # Training & research notebook ├── app.py # Streamlit dashboard app ├── GRU_model.pth # Trained model weights (generated by notebook) ├── weather_data/ # Folder of downloaded CSV files │ ├── weather_2017_2017.csv │ ├── weather_2018_2018.csv │ └── ... └── README.md ``` --- ## How It Works ### 1. Data Collection Historical daily weather data is fetched from the Visual Crossing Weather API, one year at a time, to stay within free-tier limits. Each yearly file is cached locally so downloads are never repeated. The data includes temperature (°F), humidity, precipitation, wind speed, and weather conditions. ### 2. Exploratory Data Analysis (EDA) The notebook visualises 9 years of temperature data, including daily values, 30-day rolling means, and seasonal decomposition — confirming the clear wet/dry seasonal rhythm expected in Abeokuta. ### 3. Preprocessing - Columns used: `datetime` …