Built and validated a linear regression model to predict annual customer spend using session and membership features; achieved R² = 0.981 and RMSE ≈ $10 on hold-out test data.
-
Updated
Aug 16, 2025 - Jupyter Notebook
#
residual-diagnostics
Here are 2 public repositories matching this topic...
STL decomposition notebook with parameter analysis, diagnostics, seasonal strength metrics, forecasting, and alternative methods. Complete time series decomposition workflow with visualizations and statistical tests.
data-visualization time-series-analysis parameter-tuning stl-decomposition trend-analysis time-series-forecasting robust-statistics loess decomposition-algorithm time-series-modelling seasonal-analysis statmodels seasonal-trend-decomposition residual-diagnostics
-
Updated
Dec 17, 2025 - Jupyter Notebook
Improve this page
Add a description, image, and links to the residual-diagnostics topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the residual-diagnostics topic, visit your repo's landing page and select "manage topics."