Update README.md

README.md

@@ -1,8 +1,8 @@
 ---
 title: TimeSeriesForecasting
-emoji: 🚀
-colorFrom: red
-colorTo: red
+emoji: 📈
+colorFrom: blue
+colorTo: green
 sdk: docker
 app_port: 8501
 tags:
@@ -12,9 +12,33 @@ short_description: LightGBM-based time series forecasting app that predicts...
 license: mit
 ---
 
-# Welcome to Streamlit!
+# 📈 Sales Forecast (LightGBM)
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+This Streamlit app predicts **`num_sold`** using a trained **LightGBM** model with time-based features and lag features.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## What it does
+- Takes calendar features (year/month/week/dayofweek/dayofyear, weekend)
+- Uses lag features (lag_364, lag_365, lag_371)
+- Uses categorical inputs (country/store/product) via saved encoders
+- Outputs a `num_sold` prediction
+
+## Files required (put in the repo root)
+- `app.py`
+- `lgbm_model.pkl`
+- `feature_names.pkl` (list of feature names in correct order)
+- `encoders.pkl` (dict of LabelEncoders for `country`, `store`, `product`)
+- `fill_map.pkl` (optional: medians for numeric feature filling)
+
+## How to save artifacts in your notebook (training side)
+
+```python
+import joblib
+
+joblib.dump(model_lgb, 'lgbm_model.pkl')
+joblib.dump(FEATURES, 'feature_names.pkl')
+joblib.dump(encoders, 'encoders.pkl')
+
+# optional numeric medians for filling missing
+num_cols = [c for c in FEATURES if c not in ['country', 'store', 'product']]
+fill_map = train_fe[num_cols].median().to_dict()
+joblib.dump(fill_map, 'fill_map.pkl')