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.
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Updated
Aug 16, 2025 - Jupyter Notebook
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residual-diagnostics
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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
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Dec 17, 2025 - Jupyter Notebook
Project work for Time Series Analysis. Includes exploratory analysis, ARIMA modeling, diagnostics, forecasting, and evaluation using R. Covers trend/seasonality modeling, stationarity checks, ACF/PACF analysis, model selection, and forecast accuracy assessment.
r statistics time-series model-selection forecasting data-analysis acf arima garch time-series-analysis statistical-modeling stationarity sarima pacf residual-diagnostics
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Mar 16, 2026 - R
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