Time-Series Forecasting with XGBoost in Python: Predict Daily Restaurant Customers (Part 1) скачать в хорошем качестве

Time-Series Forecasting with XGBoost in Python: Predict Daily Restaurant Customers (Part 1)

Part 1: Data Prep & Feature Engineering in Python (JupyterLab Online) Overview Hands-on build in JupyterLab (online) using self-service restaurant data (2019–2024). We clean and structure the data; engineer temporal, statistical, and external features (lags, rolling windows, exponential smoothing, autocorrelation, holidays before/after, weather); and visualize to validate signals. This is the foundation for accurate forecasts. Watch the series Paper Review (context first):   • Review Research Paper: Forecast Restaurant...   This video — Part 1 (Prep & Features):    • Time-Series Forecasting with XGBoost in Py...   Part 2 — Train XGBoost & Evaluate:    • How We Beat the World's Best Forecasters U...   Part 3 — LSTM, Linear Regression, Random Forest :    • Time-Series Forecasting in Python: Predict...   What you’ll learn Robust time-series prep in pandas Feature engineering: baseline, calendar (dow, dom, month, season), lags, rolling means, exponential smoothing Autocorrelation features that add signal Holiday effects (before/after) and external weather enrichment Visual checks to prevent garbage-in, garbage-out Chapters 00:00 Intro & dataset overview 03:15 Data loading and cleaning 08:24 Feature creation (lags, rolling, exponential smoothing) 12:05 Holiday and weather features 15:35 Visual diagnostics 17:33 Export modeling dataset Follow YouTube:    / @robmulla   Discord: /discord · Twitch: /medallionstallion_ · Twitter: /rob_mulla Tags #python #pandas #timeseries #featureengineering #xgboost #forecasting #restaurant #demandforecasting #matplotlib #jupyterlab