Gebruik van evolutievariabelen

Gevorderde voorspellende analyse in Python

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Senior Data Scientist @PythonPredictions

Voorspelmodellen bouwen

# 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)

Voorspellingen maken

# 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]

Voorspelmodellen evalueren met AUC

# 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")

De predictor insight graph

# 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")

Interpretatie van de predictor insight graph

Laten we oefenen!

Gevorderde voorspellende analyse in Python