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| language: [en]
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| license: mit
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| tags: [tabular-classification, loan-approval, loan-prediction, machine-learning, regression, classification, scikit-learn, streamlit]
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| ---
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| ---
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| # ⛈️ Thunderstorm Forecasting with MLflow Tracking
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| An end-to-end **machine learning system for thunderstorm forecasting**, combining predictive modeling with **MLflow experiment tracking** to build, evaluate, and manage machine learning experiments.
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| The project covers the complete ML workflow from **data preparation and feature engineering to model training, hyperparameter tuning, experiment tracking, and deployment**.
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| ## 🚀 Key Features
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| * ⛈️ Thunderstorm occurrence prediction
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| * 🌦️ Weather data preprocessing
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| * 📊 Exploratory data analysis
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| * 🔧 Feature engineering
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| * 🤖 Machine learning models
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| * ⚙️ Hyperparameter tuning
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| * 📈 Model evaluation
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| * 🧪 MLflow experiment tracking
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| * 📦 Model versioning
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| * 🌐 Deployment
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| ## 🖼️ Project Preview
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| <p align="center">
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| <img src="https://camo.githubusercontent.com/5d0066b22fc7be560fb201f27182d47afae97cf972eb29ab85f6b3cda40edac2/68747470733a2f2f6361726565722d706c6174666f726d2d6d61792d323032362e73332e61702d736f7574682d312e616d617a6f6e6177732e636f6d2f6b726973686e61696b2e696e2f6d656469612f70726f6a6563745f62616e6e6572732f2d7468756e64657273746f726d2d666f726563617374696e672d65303661653735303830633936326139386130303264306339363961316464652e6a7067" alt="Thunderstorm Forecasting Project" width="800">
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| </p>
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| ## 🏗️ System Architecture
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| <p align="center">
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| <img src="https://camo.githubusercontent.com/3181e91609f2daaad3bfd1d6965ccee4ceb4bdb439bf29c869bddd8a5f730e85/68747470733a2f2f6361726565722d706c6174666f726d2d6d61792d323032362e73332e61702d736f7574682d312e616d617a6f6e6177732e636f6d2f6b726973686e61696b2e696e2f6d656469612f70726f6a6563745f6172636869746563747572655f6469616772616d732f457863616c69647261775f5768697465626f6172645f2d5f476f6f676c655f4368726f6d655f315f355f323032365f395f31315f35345f504d2e706e67" alt="Thunderstorm Forecasting Architecture" width="850">
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| </p>
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| ## 🧠 ML Pipeline
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| ```text
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| Weather Data
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| ↓
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| Data Validation
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| Data Preprocessing
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| Feature Engineering
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| Train / Test Split
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| Model Training
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| Hyperparameter Tuning
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| ↓
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| MLflow Experiment Tracking
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| Model Evaluation
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| ↓
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| Model Registry
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| ↓
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| Deployment
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| ↓
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| Thunderstorm Prediction
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| ```
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| ## 📋 Model Details
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| | Property | Details |
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| | ------------------- | --------------------------------- |
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| | Task | Weather / Thunderstorm Prediction |
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| | Domain | Weather Forecasting |
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| | Data Type | Weather / Tabular Data |
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| | Framework | Scikit-learn |
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| | Experiment Tracking | MLflow |
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| | Optimization | Hyperparameter Tuning |
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| | Deployment | Web Application / API |
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| ## 🧪 MLflow Tracking
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| MLflow is used to track the machine learning lifecycle, including:
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| * Model parameters
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| * Evaluation metrics
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| * Training experiments
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| * Model artifacts
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| * Experiment runs
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| * Model versions
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| This makes experiments **reproducible, comparable, and easier to manage**.
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| ## 📊 Workflow
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| 1. Load weather data.
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| 2. Validate and preprocess the dataset.
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| 3. Perform exploratory data analysis.
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| 4. Engineer relevant weather features.
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| 5. Train machine learning models.
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| 6. Tune model hyperparameters.
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| 7. Track experiments using MLflow.
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| 8. Compare model performance.
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| 9. Save the best-performing model.
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| 10. Deploy the prediction pipeline.
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| ## 📤 Output
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| The system predicts the likelihood of thunderstorm occurrence:
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| ```text
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| Thunderstorm Prediction: Yes / No
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| Probability: <VALUE>
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| ```
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| The prediction should be treated as a **machine learning forecast**, not an official meteorological warning.
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| ## 💻 Run Locally
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| ```bash
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| git clone https://github.com/mdzaheerjk/Thunderstorm-Forecasting-with-MLFlow-Tracking.git
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| cd Thunderstorm-Forecasting-with-MLFlow-Tracking
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| pip install -r requirements.txt
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| mlflow ui
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| streamlit run app.py
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| ```
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| ## 🛠️ Tech Stack
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| **Python • Pandas • NumPy • Scikit-learn • MLflow • Matplotlib • Seaborn • Streamlit**
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| ## ⚠️ Limitations
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| Forecasting performance depends on the quality, coverage, and geographical diversity of the weather data.
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| Real-world weather systems are highly dynamic, and predictions may be affected by unseen atmospheric conditions, data drift, and regional differences.
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| This project should not replace official meteorological forecasting or emergency warning systems.
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| ## 🔮 Future Improvements
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| * Real-time weather data integration
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| * Advanced time-series models
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| * LSTM / GRU forecasting
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| * Transformer-based weather models
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| * Automated model retraining
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| * MLflow Model Registry integration
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| * Model monitoring and drift detection
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| * Real-time weather alerts
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| * Geospatial thunderstorm prediction
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| ## 👨💻 Author
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| **Md Zaheer JK**
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| AI/ML • Deep Learning • Generative AI • Computer Vision • NLP • MLOps
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| GitHub: https://github.com/mdzaheerjk
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| Hugging Face: https://huggingface.co/zaheerjk
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| ## 📜 License
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| MIT License
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| ---
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| ### ⛈️ From Weather Data to Actionable Thunderstorm Predictions
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