Data Science For Marketing The Metrics And Models
Before we talk about models, we need to agree on what we're measuring. Marketers care about a handful of key numbers. Each one tells a different part of the story.
Time Series
Cross Validation Strategies K Fold Stratified Grou
Before we can understand why specialized splitters exist, we need to understand what standard K-Fold actually does and what it assumes about your data. The assumption has a fancy name—i.i.d.—but the idea is simple.
Classical Forecasting Libraries Compared Statsmode
Let’s make the failure concrete. We’ll generate three years of monthly sales data with a clear trend, a strong December holiday spike, and a mild summer dip.
Building A Sales Forecasting Model With Prophet St
Before writing a single line of code, we need to understand what we're working with. The UCI Online Retail dataset is a transactional log — each row is one line item from one invoice, not a daily sales total.
Machine Learning In Supply Chain Demand Planning A
Before we talk about fancy ML, we need to understand why the classic forecasting paradigm (minimize RMSE) fails for inventory. The intuition comes from a simple inventory model: the newsvendor problem.
Machine Learning in Retail and Ecommerce: Recommendations, Pricing, and Demand Forecasting
You open the ShopWave app on a lazy Sunday afternoon. First thing you see: a cozy hoodie you've been eyeing for weeks — on sale, no less. You tap, buy, and it shows up at your doorstep two days later.
The Five Step Algorithm Turning Nouns Verbs And St
Before you write a single line of code, list every noun in the problem description. These are your data objects or stateful objects. Do not default to 'class' yet. Start with the simplest container: a variable or a list.
Reference: Time Series Anatomy
A reference for time series anatomy: trend, seasonality, residual, autocorrelation, and stationarity, with Python decomposition examples and decision rules.
Why Your Forecast Fails on Christmas: Handling Multiple Seasonalities and Holiday Spikes
Learn to model overlapping seasonalities and holiday halo effects in Prophet with Fourier series and prior-scale tuning so forecasts survive the Christmas rush.
Time-Series Cross-Validation: Why K-Fold Breaks on Temporal Data
Learn why K-Fold causes temporal leakage on time-series data and how walk-forward validation delivers honest, production-ready R² metrics you can trust.
Prophet vs. Statistical Models: A Practical Forecasting Showdown
Compare Prophet and ARIMA head-to-head on messy real-world time series with structural breaks, holidays, and multiple seasonalities to choose the right model.
Gradient Boosting for Time Series: Using LightGBM to Forecast
Master LightGBM for time series forecasting: engineer lag and rolling features, apply time-aware validation, and prevent overfitting with regularization.
Feature Engineering for Time Series: Lags, Rolling Windows, and Seasonality
Learn how to engineer time series features like lags, rolling windows, and seasonal indicators to give your forecasting models the temporal context they need to predict accurately.
Evaluating Forecast Accuracy: MAPE, RMSE, and Why Averages Lie
Learn to evaluate forecast accuracy with MAE, RMSE, and MAPE, see why RMSE punishes big errors, and discover why a good average can hide a biased model.
Classical Forecasting vs. Machine Learning: When Does ARIMA Beat an LSTM?
Learn why ARIMA often beats LSTM on small time-series datasets, when to use classical vs. neural net forecasting, and how to avoid tuning bias in comparisons.
The 'Smart' Model that Failed
Learn why ARIMA and SARIMA outperform deep learning on small time-series datasets, with intuitive breakdowns of momentum, differencing, shocks, and seasonality.