Update README.md

README.md

@@ -1,8 +1,8 @@
 ---
 title: RainfallPredictionClassification
-emoji: 🚀
-colorFrom: red
-colorTo: red
+emoji: 🌧️
+colorFrom: blue
+colorTo: indigo
 sdk: docker
 app_port: 8501
 tags:
@@ -12,9 +12,29 @@ short_description: App that predicts rainfall probability (0–1)
 license: mit
 ---
 
-# Welcome to Streamlit!
+# 🌧️ Rainfall Probability Predictor (LogReg)
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+This Streamlit app predicts the **probability of rainfall (0–1)** from daily weather features using a trained **Logistic Regression** model.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## 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.