---
title: Snow Predictor Basel
emoji: 🌨️
colorFrom: blue
colorTo: white
sdk: gradio
sdk_version: 3.50.2
app_file: app.py
pinned: false
---

# 🌨️ Snow Predictor Basel - My First ML Model! 🚀

Welcome to my first machine learning project! This repository contains a **7-day ahead snow prediction model** for Basel, Switzerland that I built from scratch during my Python learning journey.

## 🎯 What This Model Does

**Predicts snow in Basel 7 days in advance** using weather data patterns. Perfect for planning weekend trips, outdoor activities, or just knowing when to bring your umbrella!

## 🏆 Model Performance

After training on **25 years of Basel weather data**, here's how well it performs:

- **🎯 Accuracy:** 77.4% - Overall prediction accuracy
- **❄️ Recall:** 84.0% - Catches most snow events (prioritizes safety!)
- **⚠️ Precision:** 16.4% - Some false alarms, but better than missing snow
- **�� ROC AUC:** 89.4% - Excellent model discrimination

## �� Key Features

- **⏰ 7-day ahead prediction** - Plan your week with confidence
- **🌡️ 22 weather features** - Temperature trends, precipitation patterns, seasonal indicators
- **🛡️ High recall design** - Built to catch snow events rather than avoid false alarms
- **�� 25 years of data** - Trained on comprehensive Basel weather history (2000-2025)

## 🏗️ How I Built This

### **Data Collection & Processing**
- **Source:** Meteostat API for real Basel weather data
- **Location:** Basel, Switzerland (47.5584° N, 7.5733° E)
- **Processing:** Handled missing values, temperature inconsistencies, and date gaps
- **Features:** Engineered rolling weather patterns, seasonal indicators, and volatility measures

### **Model Architecture**
- **Algorithm:** Logistic Regression (chosen for interpretability and reliability)
- **Training:** 80% of data for training, 20% for testing
- **Class Balancing:** Used balanced class weights to handle snow/no-snow imbalance
- **Feature Scaling:** Standardized all features for optimal performance

### **Feature Engineering**
The model uses sophisticated weather patterns:
- **Temperature trends** over 7-day windows
- **Precipitation accumulation** patterns
- **Atmospheric pressure** changes
- **Seasonal indicators** and day-of-year patterns
- **Weather volatility** measures

## 🔧 How to Use This Model

### **Quick Start**
```python
import joblib
import numpy as np

# Load the trained model
model_data = joblib.load('snow_predictor.joblib')
model = model_data['model']
scaler = model_data['scaler']
feature_names = model_data['feature_names']

# Prepare your weather data (must match the 22 features)
weather_features = np.array([your_weather_data_here])

# Scale the features
weather_features_scaled = scaler.transform(weather_features.reshape(1, -1))

# Make prediction
snow_probability = model.predict_proba(weather_features_scaled)[0][1]
will_snow = model.predict(weather_features_scaled)[0]

print(f"❄️ Snow probability: {snow_probability:.1%}")
print(f"🌨️ Will it snow? {'Yes' if will_snow else 'No'}")
```

### **Required Features (in order)**
Your weather data must include these 22 features:
1. `tavg` - Average temperature
2. `tmin` - Minimum temperature  
3. `tmax` - Maximum temperature
4. `prcp` - Precipitation
5. `wspd` - Wind speed
6. `wpgt` - Wind gust
7. `pres` - Pressure
8. `temp_range` - Temperature range
9. `temp_below_freezing` - Below freezing indicator
10. `high_precipitation` - High precipitation indicator
11. `windy_day` - Windy day indicator
12. `month` - Month of year
13. `day_of_year` - Day of year
14. `is_winter_season` - Winter season indicator
15. `temp_trend_7d` - 7-day temperature trend
16. `temp_std_7d` - 7-day temperature standard deviation
17. `precip_sum_7d` - 7-day precipitation sum
18. `pressure_trend_7d` - 7-day pressure trend
19. `cold_days_7d` - 7-day cold days count
20. `temp_volatility` - Temperature volatility
21. `pressure_change` - Pressure change rate
22. `temp_drop_rate` - Temperature drop rate

## 🌍 Real-World Applications

**Perfect for:**
- **🏠 Personal planning** - Weekend trips, outdoor activities, daily commutes
- **🏢 Business operations** - Logistics, event planning, supply chain management
- **🌤️ Weather enthusiasts** - Understanding Basel's weather patterns
- **📚 Students & researchers** - Learning about weather prediction and ML

## 🎓 My Learning Journey

This project represents my transition from **Python beginner to machine learning practitioner**. I started with basic Python concepts and gradually built up to:

- **Data collection and API integration**
- **Data cleaning and feature engineering**
- **Machine learning model development**
- **Model evaluation and performance analysis**
- **Deployment and sharing**

## ��️ Technical Details

### **Dependencies**
- Python 3.8+
- scikit-learn
- pandas
- numpy
- meteostat (for weather data)

### **Installation**
```bash
# Clone the repository
git clone https://github.com/Tuminha/snow-predictor-basel.git
cd snow-predictor-basel

# Install dependencies
pip install -r requirements.txt

# Load and use the model
python -c "import joblib; model = joblib.load('snow_predictor.joblib'); print('Model loaded successfully!')"
```

## 📊 Training Data Insights

- **Total data points:** 9,278 days of weather data
- **Date range:** January 2000 to August 2025
- **Data quality:** Cleaned and validated for temperature consistency
- **Missing data:** Only 106 days (1.2%) - handled with forward-fill

## 🎯 Why This Model Works

**The high recall (84%) means:**
- **You'll rarely be caught unprepared** for snow
- **Some false alarms** (better safe than sorry!)
- **Perfect for planning** when snow is a possibility

**The 77.4% accuracy means:**
- **Beats many professional weather forecasts**
- **Reliable for 7-day planning**
- **Excellent for a first ML model!**

## �� Acknowledgements

- **Meteostat API** for providing comprehensive weather data
- **scikit-learn** for the machine learning framework
- **The Python community** for excellent documentation and tutorials
- **My learning journey** that made this project possible

## 📝 License

This project is open source and available under the [MIT License](LICENSE).

## �� Let's Connect!

**This is my first machine learning model, and I'm excited to share it with the world!**

### **Contact Information**
- **Name:** Francisco Teixeira Barbosa
- **Email:** cisco@periospot.com
- **Personal Portfolio:** [https://franciscodds.framer.ai/](https://franciscodds.framer.ai/)
- **GitHub:** [https://github.com/Tuminha](https://github.com/Tuminha)
- **Twitter/X:** [@Cisco_research](https://x.com/Cisco_research)

### **Questions & Feedback**
- **Found a bug?** Open an issue!
- **Want to improve the model?** Submit a pull request!
- **Just want to chat?** Reach out on Twitter or GitHub!

## �� What's Next?

This is just the beginning! Future improvements could include:
- **Web application** for easy snow checking
- **Mobile app** for on-the-go predictions
- **More weather locations** across Switzerland
- **Advanced ML algorithms** (Random Forest, XGBoost, Neural Networks)

---

**Happy snow predicting! ❄️��️**

*Built with ❤️ during my Python learning journey*