This Python script creates a password-protected ZIP file using the pyzipper library. It allows you to specify the files to include in the ZIP and set a password for encryption. The resulting ZIP file requires the provided password to access its contents, providing an additional layer of security.
Building a Decision Tree to Predict Customer Churn
Imagine you are a data analyst at a telecom company. The marketing department has noticed an increase in customer churn and needs your help to identify which customers are most likely to leave next month.
Your Tasks
In this exercise, you will build a decision tree model to predict customer churn for a telecom company. Customer churn refers to when a customer stops doing business with a company. Predicting churn is crucial for businesses to retain customers by addressing their issues proactively.
Click Launch Cloud Gateway in the bottom of the navigation menu
From the AWS console, search for SageMaker, in the left side menu click Notebooks, then click Notebook instances
Click Create notebook instance
When the instance is ready, click Open Jupyter
When in the notebook, click New and select conda_python3
Copy the code below on this page and paste it into a notebook cell, then run the cell
Delete the notebook instance after reviewing the solution on the next page.
Dataset Description
We will use a synthetic dataset for this exercise. The dataset contains the following columns:
CustomerID: A unique identifier for each customer.
Age: The age of the customer.
MonthlyCharge: The monthly bill amount for the customer.
CustomerServiceCalls: The number of times the customer contacted customer service.
Churn: This is our target variable, indicating whether the customer churned (Yes) or not (No).
Step-by-Step Instructions
Setup the Environment:
Import necessary libraries: Pandas for data manipulation, Scikit-learn for machine learning, and Matplotlib for visualization.
Create the Dataset:
Use Python to create a synthetic dataset. We'll make a small dataset for simplicity.
Data Preparation:
Split the data into features (X) and the target variable (y).
Further split the dataset into training and testing sets.
Build the Decision Tree Model:
Use Scikit-learn to create a DecisionTreeClassifier.
Train the model on the training data.
Evaluate the Model:
Make predictions on the test set.
Calculate the accuracy of the model.
Visualize the Decision Tree:
Use Matplotlib to visualize how the decision tree makes decisions.
Discuss the Results:
Interpret the decision tree.
Discuss how it can be used by the company to reduce customer churn.
Python Code Implementation
Import necessary libraries
import pandas as pd
import matplotlib.pyplot as plt
import warnings
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn import tree
warnings.filterwarnings('ignore')
Creating a synthetic dataset
This dataset simulates customer data for a telecom company
data = {
'CustomerID': range(1, 101), # Unique ID for each customer
'Age': [20, 25, 30, 35, 40, 45, 50, 55, 60, 65]10, # Age of customers
'MonthlyCharge': [50, 60, 70, 80, 90, 100, 110, 120, 130, 140]10, # Monthly bill amount
'CustomerServiceCalls': [1, 2, 3, 4, 0, 1, 2, 3, 4, 0]10, # Number of customer service calls
'Churn': ['No', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No', 'Yes']10 # Churn status
}
df = pd.DataFrame(data)
Splitting the dataset into features and target variable
Features include age, monthly charge, and customer service calls
The target variable is churn (Yes or No)
X = df[['Age', 'MonthlyCharge', 'CustomerServiceCalls']]
y = df['Churn']
Splitting the dataset into training and testing sets
70% of the data is used for training and 30% for testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
This visualization helps in understanding how the model makes decisions
plt.figure(figsize=(12,8))
tree.plot_tree(clf, filled=True, feature_names=['Age', 'MonthlyCharge', 'CustomerServiceCalls'], class_names=['No Churn', 'Churn'])
plt.title('Decision Tree for Predicting Customer Churn')
plt.show()
Solution: Decision Tree
When you visualize a decision tree, especially one created using Scikit-learn's DecisionTreeClassifier, you'll notice several terms on each node of the tree. Understanding these terms is crucial for interpreting the tree's decision-making process. Let's break down each of these terms:
Gini
The Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset.
The Gini impurity ranges from 0 to 0.5, where 0 indicates that all elements in the subset belong to the same class (perfect purity), and 0.5 means the data is randomly distributed across various classes.
In decision trees, a lower Gini impurity is generally preferred as it indicates greater purity of the node.
Samples
This value represents the number of samples (or records) that reach the node.
It gives an idea of how much of the training data is affected by the conditions leading to this node.
A high number of samples in a node means that the condition or rule associated with that node is relevant for a significant portion of the dataset.
Value
This shows the distribution of the samples in different classes at that particular node.
For a binary classification problem (like churn prediction with 'Yes' or 'No'), the value is presented as a list of two numbers. The first number indicates the count of samples in the first class, and the second number indicates the count of samples in the second class.
This distribution helps in understanding which class is predominant at a particular node.
Class
This indicates the class that would be predicted if the decision tree traversal ends at that node.
It is determined based on the majority class of the samples that reach the node. For instance, if most samples at a node belong to the 'No Churn' class, the node will predict 'No Churn'.
Feature Name (e.g., 'Monthly Charge')
This is not a standard part of the decision tree node description, but it may appear in the tree's branches.
It represents the feature (or attribute) used to split the data at that node.
For example, if you see "MonthlyCharge <= 80", it means that the tree is splitting the data at this node based on whether the monthly charge is less than or equal to 80.
Understanding these components is essential for interpreting how the decision tree makes its predictions and which features are influential in the decision-making process. This can provide valuable insights, especially in business contexts like customer churn prediction.
This exercise covered several key terms and concepts related to machine learning, particularly focusing on the process of training and testing a decision tree model.
Key Concepts
Splitting the Dataset - The dataset is divided into training and testing sets. Typically, 70% of the data is used for training the model, and the remaining 30% is reserved for testing.
Training Data vs. Testing Data - Training data is used to train the machine learning model. In contrast, testing data, which the model has not seen during training, is used to evaluate the model's performance and generalization ability.
Model Training Process - The process involves using a 'fit' method where the model is trained using features (X_train) and targets (Y_train). The testing data is not used in this stage.
Building a Decision Tree to Predict Customer Churn Imagine you are a data analyst at a telecom company. The marketing department has noticed an increase in customer churn and needs your help to identify which customers are most likely to leave next month.
Your Tasks In this exercise, you will build a decision tree model to predict customer churn for a telecom company. Customer churn refers to when a customer stops doing business with a company. Predicting churn is crucial for businesses to retain customers by addressing their issues proactively.
Click Launch Cloud Gateway in the bottom of the navigation menu From the AWS console, search for SageMaker, in the left side menu click Notebooks, then click Notebook instances Click Create notebook instance When the instance is ready, click Open Jupyter When in the notebook, click New and select conda_python3 Copy the code below on this page and paste it into a notebook cell, then run the cell Delete the notebook instance after reviewing the solution on the next page. Dataset Description We will use a synthetic dataset for this exercise. The dataset contains the following columns:
CustomerID: A unique identifier for each customer. Age: The age of the customer. MonthlyCharge: The monthly bill amount for the customer. CustomerServiceCalls: The number of times the customer contacted customer service. Churn: This is our target variable, indicating whether the customer churned (Yes) or not (No). Step-by-Step Instructions Setup the Environment: Import necessary libraries: Pandas for data manipulation, Scikit-learn for machine learning, and Matplotlib for visualization. Create the Dataset: Use Python to create a synthetic dataset. We'll make a small dataset for simplicity. Data Preparation: Split the data into features (X) and the target variable (y). Further split the dataset into training and testing sets. Build the Decision Tree Model: Use Scikit-learn to create a DecisionTreeClassifier. Train the model on the training data.
Evaluate the Model: Make predictions on the test set. Calculate the accuracy of the model.
Visualize the Decision Tree: Use Matplotlib to visualize how the decision tree makes decisions.
Discuss the Results: Interpret the decision tree. Discuss how it can be used by the company to reduce customer churn.
Python Code Implementation
Import necessary libraries
import pandas as pd import matplotlib.pyplot as plt import warnings from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score from sklearn import tree
warnings.filterwarnings('ignore')
Creating a synthetic dataset
This dataset simulates customer data for a telecom company
data = { 'CustomerID': range(1, 101), # Unique ID for each customer 'Age': [20, 25, 30, 35, 40, 45, 50, 55, 60, 65]10, # Age of customers 'MonthlyCharge': [50, 60, 70, 80, 90, 100, 110, 120, 130, 140]10, # Monthly bill amount 'CustomerServiceCalls': [1, 2, 3, 4, 0, 1, 2, 3, 4, 0]10, # Number of customer service calls 'Churn': ['No', 'No', 'Yes', 'No', 'Yes', 'No', 'Yes', 'Yes', 'No', 'Yes']10 # Churn status } df = pd.DataFrame(data)
Splitting the dataset into features and target variable
Features include age, monthly charge, and customer service calls
The target variable is churn (Yes or No)
X = df[['Age', 'MonthlyCharge', 'CustomerServiceCalls']] y = df['Churn']
Splitting the dataset into training and testing sets
70% of the data is used for training and 30% for testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
Training the Decision Tree model
clf = DecisionTreeClassifier() clf.fit(X_train, y_train)
Making predictions on the test set
y_pred = clf.predict(X_test)
Evaluating the model using accuracy
Accuracy is the proportion of correct predictions among the total number of cases processed
accuracy = accuracy_score(y_test, y_pred) print(f'Model Accuracy: {accuracy}')
Visualizing the decision tree
This visualization helps in understanding how the model makes decisions
plt.figure(figsize=(12,8)) tree.plot_tree(clf, filled=True, feature_names=['Age', 'MonthlyCharge', 'CustomerServiceCalls'], class_names=['No Churn', 'Churn']) plt.title('Decision Tree for Predicting Customer Churn') plt.show()
Solution: Decision Tree
When you visualize a decision tree, especially one created using Scikit-learn's DecisionTreeClassifier, you'll notice several terms on each node of the tree. Understanding these terms is crucial for interpreting the tree's decision-making process. Let's break down each of these terms:
Gini The Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset. The Gini impurity ranges from 0 to 0.5, where 0 indicates that all elements in the subset belong to the same class (perfect purity), and 0.5 means the data is randomly distributed across various classes. In decision trees, a lower Gini impurity is generally preferred as it indicates greater purity of the node. Samples This value represents the number of samples (or records) that reach the node. It gives an idea of how much of the training data is affected by the conditions leading to this node. A high number of samples in a node means that the condition or rule associated with that node is relevant for a significant portion of the dataset. Value This shows the distribution of the samples in different classes at that particular node. For a binary classification problem (like churn prediction with 'Yes' or 'No'), the value is presented as a list of two numbers. The first number indicates the count of samples in the first class, and the second number indicates the count of samples in the second class. This distribution helps in understanding which class is predominant at a particular node. Class This indicates the class that would be predicted if the decision tree traversal ends at that node. It is determined based on the majority class of the samples that reach the node. For instance, if most samples at a node belong to the 'No Churn' class, the node will predict 'No Churn'. Feature Name (e.g., 'Monthly Charge') This is not a standard part of the decision tree node description, but it may appear in the tree's branches. It represents the feature (or attribute) used to split the data at that node.
For example, if you see "MonthlyCharge <= 80", it means that the tree is splitting the data at this node based on whether the monthly charge is less than or equal to 80. Understanding these components is essential for interpreting how the decision tree makes its predictions and which features are influential in the decision-making process. This can provide valuable insights, especially in business contexts like customer churn prediction.
This exercise covered several key terms and concepts related to machine learning, particularly focusing on the process of training and testing a decision tree model.
Key Concepts Splitting the Dataset - The dataset is divided into training and testing sets. Typically, 70% of the data is used for training the model, and the remaining 30% is reserved for testing.
Training Data vs. Testing Data - Training data is used to train the machine learning model. In contrast, testing data, which the model has not seen during training, is used to evaluate the model's performance and generalization ability.
Model Training Process - The process involves using a 'fit' method where the model is trained using features (X_train) and targets (Y_train). The testing data is not used in this stage.