Intermediate Predictive Analytics in Python
Nele Verbiest
Senior Data Scientist @PythonPredictions
# Import the linear_model module from sklearn import linear_model
# Predictive variables variables = ["gender","age", "donations_last_year", "ratio_month_year"]
# Select predictors and target X = basetable[variables] y = basetable[["target"]]
# Construct the logistic regression model logreg = linear_model.LogisticRegression() logreg.fit(X, y)
# Import the linear_model module from sklearn import linear_model
# Predictive variables variables = ["gender","age", "donations_last_year", "ratio_month_year"]
# Select predictors and target X = basetable[variables] y = basetable[["target"]]
# Construct the logistic regression model logreg = linear_model.LogisticRegression() logreg.fit(X, y)
# Make predictions
predictions = logreg.predict_proba(X)[:,1]
# Import roc_auc_score module from sklearn.metrics from sklearn.metrics import roc_auc_score
# Calculate the AUC auc= roc_auc_score(y, predictions) print(round(auc,2))
0.56
# Discretize the variable in 5 bins and add to the basetable basetable["ratio_month_year_disc"] = pd.qcut(basetable["ratio_month_year"], 5)
# Construct the predictor insight graph table pig_table = create_pig_table(basetable, "target","ratio_month_year_disc") ```{python} # Plot the predictor insight graph plot_pig(pig_table, "ratio_month_year_disc")
Intermediate Predictive Analytics in Python