README.md

metadata

title: TimeSeriesForecasting
emoji: 📈
colorFrom: blue
colorTo: green
sdk: docker
app_port: 8501
tags:
  - streamlit
pinned: false
short_description: LightGBM-based time series forecasting app that predicts...
license: mit

📈 Sales Forecast (LightGBM)

This Streamlit app predicts num_sold using a trained LightGBM model with time-based features and lag features.

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)

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')