Audit a real measurement table
Trace 150 UCI Iris observations, inspect repeat measurements and units, and build an honest exploratory report.
numpy · pandas · matplotlib · IPython
DATA-SCIENCE / 01–06
Data science & machine learning
6 independent, runnable lessons connecting questions, traceable inputs, transparent methods and interpretation. One notebook download starts each workflow.
Learning path 01 → 02 → 03 → 04 → 05 → 06; every lesson also runs independently.
Download this discipline in both languages6 recipes
Understand standardized PCA
Use all four Iris measurements to inspect scaling, singular values, component directions and reconstruction error.
numpy · pandas · matplotlib · IPython
Train and evaluate a classifier without leakage
Reserve a stratified test set, select k inside training-only folds, then report held-out confusion and macro-F1.
numpy · pandas · matplotlib · IPython
Inspect clusters in chemical measurements
Standardize 13 real Wine features, run reproducible k-means, and compare anonymous clusters with source cultivar codes.
numpy · pandas · matplotlib · IPython
Build an interpretable rental-count regression
Fit on 2011 and evaluate on 2012, distinguish conditional weather reconstruction from forecasting, and inspect error and drift.
numpy · pandas · matplotlib · IPython
Backtest one-day-ahead demand forecasts
Construct past-only lag features, refit expanding windows and compare ridge with two transparent persistence baselines.
numpy · pandas · matplotlib · IPython
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