🌦️ Real-Time Weather Prediction using Machine Learning

A personal project that predicts real-time temperature (°C) using historical weather data and live API integration.
The model is trained on 3 years of weather data and deployed to deliver instant predictions with evaluated accuracy metrics.

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🚀 Features

• 📈 Predicts temperature (°C) in real-time using weather API inputs
• 🧠 Machine Learning model trained using regression algorithms
• 📊 Displays RMSE and R² Score for performance evaluation
• 🌐 Live visualization of actual vs. predicted temperature trends
• 🖥️ Interactive and lightweight design for quick experimentation

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🧰 Tech Stack

• Language: Python
• Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, Requests
• Tools: Jupyter Notebook / VS Code
• Data Source: Kaggle Dataset(3 years of historical) + Weather API(live data)

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🧩 Application Pages

🏠 1. Home Page

Displays the project introduction, purpose, and navigation to other pages.

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📅 2. Current Weather

Shows real-time weather data from API (Temperature, Humidity, Precipitation, Cloud Cover).

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🤖 3. Model Prediction along with Performance Comparison

Displays evaluation metrics (RMSE, R²) for Random Forest models compared to actual API predictions across different weather parameters.

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⚙️ How It Works

1. Fetches weather data via API and preprocesses it for model prediction.
2. Used trained MultiForestRegression model on 3 years of data for temperature prediction.
3. Evaluates the model using RMSE and R² metrics.
4. Plots API vs. predicted temperatures for visual insight.

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📊 Model Performance

Metric	Value
TEMPC — RMSE	1.37
TEMPC — R²	0.804
HUMIDITY — RMSE	15.22
HUMIDITY — R²	0.519

*Values are based on the final trained model testing and may vary with API data.

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💻 Installation & Usage

# 1. Clone the repository
git clone https://github.com/Aaryan10000/Weather-Prediction-ML.git

# 2. Navigate to the project directory
cd Weather-Prediction-ML

# 3. Install dependencies
pip install -r requirements.txt

# 4. Get personal API key
goto https://openweathermap.org/api and create free account.
Get basic api key and replace it in the pages/*.py files.

# 5. Run the Streamlit app
streamlit run Landing_Page.py

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📚 Learning Highlights

• Implemented real-time data integration using APIs

• Understood regression evaluation metrics (RMSE & R²)

• Enhanced data visualization and model interpretability

• Gained experience in deploying ML models through Streamlit