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HR / People Analytics · how-to
Predict Employee Churn from Organizational Network — in Python
A network-driver analysis. Every step below is the same analysis rendered for Python — grounded in the source, honest where Python can't do a step cleanly.
Same analysis, your tool
DERIVE
convert the network into quantitative signals per person
df["derived"] = exprJOIN · mechanical
link network position to who stayed or left
df.merge(other, on="key", how="left")CORRELATE
identify which network signals predict staying
df["driver_j"].corr(df["outcome"])FIT-CLASSIFIER
model resignation odds from connectedness and context
LogisticRegression().fit(X, y)PREDICT
flag well-connected people worth retaining
model.predict(df)Grounded in
Predictive HR Analytics with Excel — §17.8/17.9 ONA graph metrics & churn
Doing HR / People Analytics work?
The full HR / People Analytics guide covers this and the whole workflow around it — reconciled from the field’s best books.
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