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.