Customer churn refers to the situation where customers stop using the services of a company. In the telecommunications industry, predicting customer churn is important because retaining existing customers is often more cost-effective than acquiring new ones. Machine learning techniques can analyze historical customer data and identify patterns that indicate whether a customer is likely to leave the service.

Decision Tree and Random Forest are supervised machine learning algorithms commonly used for classification problems such as churn prediction. These algorithms learn patterns from historical customer data and make predictions for new customers based on their features.

Target Variable

In this churn prediction problem, the target variable is the outcome we want to predict:

• Target Variable: Churn
• Output: It answers Yes (if the customer is likely to leave) or No (if the customer is likely to stay).

The model uses independent variables (features) like tenure, monthly charges, and contract type to predict this binary outcome.

Decision Tree

A Decision Tree is a tree used for classification and regression tasks. It splits the dataset into smaller subsets based on feature values, forming a structure consisting of nodes and branches.

The main components of a decision tree include:

• Root Node – The top node representing the entire dataset.
• Decision Nodes – Nodes where the dataset is split based on feature conditions.
• Leaf Nodes – The final nodes that represent the predicted class (e.g., churn or no churn).

Decision Trees use impurity measures to determine the best feature for splitting the data.

Common impurity measures include:

• Gini Impurity – Measures how often a randomly chosen element would be incorrectly classified.
• Entropy – Measures the uncertainty or impurity of class labels in a node.

Hyperparameters such as max_depth, min_samples_split, and min_samples_leaf control the structure of the tree and help prevent overfitting. Pruning techniques are also used to reduce unnecessary branches and improve model generalization.

Random Forest

Random Forest is an ensemble learning method that combines multiple Decision Trees to improve prediction accuracy and reduce overfitting. Instead of relying on a single tree, Random Forest builds many trees and aggregates their predictions.

Key concepts of Random Forest include:

• Multiple Decision Trees (Estimators) – A large number of trees are trained using different subsets of data.
• Bootstrap Sampling – Each tree is trained on a bootstrap sample (sampling with replacement) from the original dataset.
• Feature Randomness – At each split, only a random subset of features is considered.
• Majority Voting – The final prediction is determined by the majority vote of all trees.

Important hyperparameters in Random Forest include:

• n_estimators – Number of trees in the forest.
• max_depth – Maximum depth of each tree.
• max_features – Number of features considered when splitting a node.
• min_samples_split and min_samples_leaf – Control the minimum number of samples required to split or form a leaf node.

Decision Tree vs Random Forest

Decision Trees are simple and easy to interpret, making them useful for understanding decision rules in the data. However, they are prone to overfitting when the tree becomes too deep.

Random Forest overcomes this limitation by combining multiple trees, which improves prediction accuracy and robustness. While Random Forest models are generally more accurate, they are less interpretable compared to a single Decision Tree.

By training and comparing both models on telecom customer data, it becomes possible to evaluate which approach performs better for predicting customer churn and to understand how model parameters influence prediction performance.

Evaluation Measures

To evaluate the effectiveness of the models, the following measures are commonly used:

• Accuracy – The percentage of total predictions that were correct.
• Confusion Matrix – A table that summarizes the performance of the classification algorithm by showing the count of:
  • True Positives (TP): Correctly predicted "Yes".
  • True Negatives (TN): Correctly predicted "No".
  • False Positives (FP): Incorrectly predicted "Yes".
  • False Negatives (FN): Incorrectly predicted "No".
• Precision, Recall, and F1-Score – Metrics used to measure the quality of predictions, especially when dealing with imbalanced churn data.