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README.md

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 short_description: Predict the weather of Saigon
 license: unknown
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-# 🌦️ Saigon Temperature Forecasting Application
-**An Interactive Dual-Model Forecasting Web App**
-
-An interactive web application built with Streamlit to forecast the weather in Ho Chi Minh City (Saigon). This app provides both **5-day (daily) forecasts** using a Champion Stacking model and **24-hour (hourly) forecasts** using 24 specialized LightGBM models.
-
-![ảnh](https*://i.imgur.com/your-screenshot-url.png)
-*(Gợi ý: Thay thế URL này bằng ảnh chụp màn hình ứng dụng của bạn)*
-
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-## 📋 Table of Contents
-- [Project Goal](#-project-goal)
-- [Features](#-features)
-- [Tech Stack](#-tech-stack)
-- [Project Structure](#-project-structure)
-
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-## 🎯 Project Goal
-The primary objective of this project is to build an end-to-end machine learning application that forecasts the temperature in Ho Chi Minh City. The project covers the complete ML lifecycle, from data collection and feature engineering to model training, evaluation, and deployment as an interactive web app with two distinct forecasting systems (daily and hourly).
-
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-## ✨ Features
-- **Dual Forecasting Modes**: Provides both 5-day (Daily) and 24-hour (Hourly) predictions.
-- **Interactive Visualizations**:
-    - **Historical Context**: Displays actual temperatures for the 14 days (Daily) or 24 hours (Hourly) leading up to the forecast.
-    - **Smart Forecast vs. Actual**: Compares the forecast against real temperatures, automatically hiding "Actual" data if it's not yet available (for future dates).
-    - **Training Set Overview**: A full plot of the training data with an interactive range slider and fixed Y-axis for easy exploration.
-- **Model Explainability (XAI)**:
-    - **Forecast Insights (Why?)**: Dynamically generated insights based on key input features (e.g., "Yesterday was very hot...").
-    - **Feature Inspector**: An expandable section detailing the exact feature values the model used for its prediction (e.g., `temp_lag_1`, `humidity`, `day_of_year`).
-- **Performance Dashboard**:
-    - **Performance Degradation**: Visualizes how model error (RMSE) and accuracy (R²) degrade over the 5-day or 24-hour horizon.
-    - **Interactive Scatter Plot**: A slider allows users to dynamically inspect the "Forecast vs. Actual" performance for any specific horizon (Day 1-5 or Hour 1-24).
-    - **Model Diagnostics**: An expandable "Deep Dive" section with residual plots to prove model stability.
-
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-## 🛠️ Tech Stack
-- **Backend & Modeling**:
-    - **Python**: Core programming language.
-    - **Pandas**: Data manipulation and analysis.
-    - **Scikit-learn**: For Stacking Regressor and model evaluation.
-    - **LightGBM**: The gradient boosting model used for hourly forecasting.
-    - **Joblib**: For model persistence (loading `.pkl` files).
-- **Frontend & Visualization**:
-    - **Streamlit**: For building the interactive web application.
-    - **Plotly**: For creating interactive charts and visualizations.
-
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-## 📂 Project Structure
-| |-- data/ | |-- final_dataset_tree.csv # Daily features/targets | |-- final_hourly_feature_dataset.csv # Hourly features/targets | |-- final_5_day_results_df.csv # Daily model performance (RMSE/R2) | |-- hourly_120h_evaluation_results.csv # Hourly model performance (RMSE) | |-- results_df_all_tuned.csv # Model selection leaderboard | |-- models/ | |-- champion_stacking_day1.pkl # ... (5 daily models) | |-- lgbm_model_target_temp_next_1h.pkl # ... (24 hourly models) | |-- src/ | |-- benchmark_utils.py # Utility for loading the leaderboard | |-- diagnostic_plots.py # Utility for plotting performance graphs | |-- app.py # The main Streamlit application script |-- requirements.txt # A list of all required Python packages |-- README.md # This file
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