Classification, Regression, and Clustering

edu Kanea > Lessons > Classification, Regression, and Clustering

Objective: Explore key ML techniques—classification, regression, and clustering—and demonstrate their application through a simple telecom-focused demo.

• Classification: Predicting a categorical outcome (e.g., yes/no, churn/no-churn).
  • How It Works: Algorithms (e.g., logistic regression, decision trees) learn patterns from labeled data to classify new data.
  • Telecom Example: Predicting whether a customer will churn based on features like call frequency, data usage, and complaints.
  • MTN-Specific Example: MTN Ghana uses classification to identify Mobile Money users likely to stop using the service, enabling targeted offers (e.g., bonus airtime).
• Regression: Predicting a continuous numerical outcome.
  • How It Works: Algorithms (e.g., linear regression) model relationships between inputs and numerical outputs.
  • Telecom Example: Predicting call drop rates based on network traffic and tower performance.
  • MTN-Specific Example: MTN Ghana could predict data usage (in GB) for 5G subscribers to optimize bandwidth allocation.
• Clustering: Grouping similar data points without predefined labels.
  • How It Works: Algorithms (e.g., k-means clustering) identify natural groupings in data.
  • Telecom Example: Grouping customers by usage patterns for targeted marketing (e.g., high-data users for premium bundles).
  • MTN-Specific Example: MTN Ghana clusters customers into segments (e.g., students, businesses) to offer tailored Mobile Money or data plans.
• Demo: Simple Classification in Excel/Python:
  • Scenario: Predict customer churn using a small dataset (e.g., call frequency, data usage, churn label).
  • Excel Demo: Use logistic regression in Excel to classify customers as “churn” or “no-churn” based on sample data.
  • Python Demo: Use scikit-learn in Google Colab to train a logistic regression model, showing accuracy metrics.
  • MTN Context: The demo uses a mock MTN dataset (e.g., 100 customers with usage and churn data) to predict churn, illustrating practical application.