Chrun Rate yet again

machine_learning/decision_tree_churn/churn_cal.py

@@ -0,0 +1,52 @@
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from sklearn import model_selection, tree
+
+
+def calculate_churn_rate(filename: str = "churn_modelling.csv") -> np.ndarray:
+ """
+ Calculate the churn rate of customers using a decision tree classifier.
+ >>> calculate_churn_rate()
+ array([0, 0, 0, 0, 1])
+ """
+ churn_md = pd.read_csv(Path(__file__).with_name(filename).absolute())
+
+ # Sorting the dependent and independent values
+ x = churn_md[
+ [
+ "CreditScore",
+ "Age",
+ "Tenure",
+ "Balance",
+ "NumOfProducts",
+ "HasCrCard",
+ "IsActiveMember",
+ "EstimatedSalary",
+ ]
+ ].to_numpy()
+ y = churn_md["Exited"]
+
+ # Splitting the dataset into training and testing data
+ x_test, x_train, y_test, y_train = model_selection.train_test_split(
+ x, y, test_size=0.3, random_state=3
+ )
+
+ # Creating the decision tree classifier
+ decision_tree = tree.DecisionTreeClassifier(criterion="entropy", max_depth=4)
+ decision_tree.fit(x_train, y_train)
+
+ # Predicting the values
+ pred_tree = decision_tree.predict(x_test)
+
+ # Returning the predicted values
+
+ return pred_tree[0:5]
+
+
+if __name__ == "__main__":
+ from doctest import testmod
+
+ testmod()
+ print(f"{calculate_churn_rate('churn_modelling.csv')=}")