Deep learning system for hyper-local weather forecasting across all 47 Kenya counties. Trains and compares LSTM, GRU & ConvLSTM models on 10 years of historical meteorological data. Interactive 7-day forecast map built with Streamlit & Folium.
# 🌦️ Kenya Local Weather Forecasting System
A deep learning-based weather forecasting system that predicts local weather conditions across all **47 counties in Kenya** using historical meteorological data. Built with LSTM, GRU, and ConvLSTM neural networks, deployed as an interactive Streamlit web application with user authentication and personalised forecasts powered by Supabase.
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## 🚀 Live Demo
> **Launch App →**
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## 📌 Overview
Kenya's diverse geography — from coastal plains to central highlands and arid semi-arid lands (ASALs) — creates complex microclimates that are poorly served by broad national forecasts. This system addresses that gap by training deep learning models on 10 years of historical weather data to generate **county-level, 7-day forecasts** for temperature, rainfall, humidity, wind speed, and atmospheric pressure.
Users can create an account, set their home county and preferred weather variables, and receive a personalised forecast dashboard on every login.
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## 📸 App Pages
| Page | Access | Description |
|------|--------|-------------|
| 🔐 **Login / Sign Up** | Public | Create an account or sign in to access the app |
| 🏠 **My Dashboard** | Logged in | Auto-forecast for your home county on login with personalised variable charts |
| 🗺️ **Kenya Map** | Logged in | Interactive map with all 47 county markers — click any for a 7-day forecast popup |
| 📍 **County Forecast** | Logged in | Select any county to view detailed daily predictions with trend charts |
| 📊 **Model Comparison** | Logged in | Side-by-side evaluation of LSTM, GRU, and ConvLSTM using MAE, RMSE, and R² |
| 📈 **Predicted vs Actual** | Logged in | Scatter and time-series plots validating model accuracy against real observations |
| 📋 **Forecast History** | Logged in | All your past forecast lookups saved automatically with dates and summaries |
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## 👤 User Features
- **Sign up** with name, email, home county, occupation, and preferred weather variables
- **Perso …