README.md

metadata

title: RainfallPredictionClassification
emoji: 🌧️
colorFrom: blue
colorTo: indigo
sdk: docker
app_port: 8501
tags:
  - streamlit
pinned: false
short_description: App that predicts rainfall probability (0–1)
license: mit

🌧️ Rainfall Probability Predictor (LogReg)

This Streamlit app predicts the probability of rainfall (0–1) from daily weather features using a trained Logistic Regression model.

What the app does

• Takes weather inputs (temperature, humidity, wind, pressure, cloud cover, sunshine, day of year)
• Creates a few engineered features (e.g., temperature range, humidity gap, seasonal sin/cos)
• Outputs a rainfall probability for ROC-AUC style prediction tasks

Files required

Place these files in the repo root (same folder as app.py):

• logistic_regression_model.pkl
• feature_names.pkl
• app.py
• requirements.txt

How to run locally

pip install -r requirements.txt
streamlit run app.py
Notes
The model expects the same feature order used in training.

feature_names.pkl is used to enforce the correct column order.

The app outputs probabilities, not hard class labels.