---
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
```bash
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.