Solution: Customer Churn

# Task 1: Read the dataset 'ad_clicks.csv'
df_adclicks = pd.read_csv('ad_clicks.csv')
# Task 2: Select the following features for your model
#         Age, Gender, Income, Internet Usage, Time Spent
X = df_adclicks[['Age', 'Gender', 'Income', 'Internet Usage', 'Time Spent']]
# Task 3: Select 'Is Clicked' as the target label
y = df_adclicks['Is Clicked']
# Task 4: Split training and testing datasets (features and labels)
#         Use 30% of the dataset for testing
#         Use random_state=123 to have the same result as the solution
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=123)
# Task 5: Instantiate the model with max_iter=1000
#         And train your model with the training dataset
logreg = LogisticRegression(max_iter=1000)
logreg.fit(X_train, y_train)
# Task 6: Predict model performance with testing dataset
y_pred = logreg.predict(X_test)
# Task 7: Calculate the accuracy, precision, and recall of your model
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
print('Accuracy:',  round(accuracy, 1))
print('Precision:', round(precision, 1))
print('Recall:',    round(recall, 1))