Machine Learning for Telecom Customers Churn Prediction

Coursera Project Network
在此指导 项目中,您将:

Understand the theory and intuition behind machine learning classifiers such as Logistic Regression, Support Vector Machines, and Random Forest.

Compare trained models by calculating AUC score and plot ROC curve

Train various classifier models using Scikit-Learn library

Clock2 hours
Comment Dots英语(English)

In this hands-on project, we will train several classification algorithms such as Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Random Forest Classifier to predict the churn rate of Telecommunication Customers. Machine learning help companies analyze customer churn rate based on several factors such as services subscribed by customers, tenure rate, and payment method. Predicting churn rate is crucial for these companies because the cost of retaining an existing customer is far less than acquiring a new one. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.


Artificial Intelligence (AI)Machine LearningPython ProgrammingclassificationComputer Programming



  1. Understand the problem statement and business case

  2. Import libraries/datasets and perform Exploratory Data Analysis

  3. Perform Data Visualization

  4. Prepare the data before model training

  5. Train and Evaluate a Logistic Regression model

  6. Train and Evaluate a Support Vector Machine Model

  7. Train and Evaluate a Random Forest Classifier model

  8. Train and Evaluate a K-Nearest Neighbor model

  9. Train and Evaluate a Naive Bayes Classifier model

  10. Compare the trained models by calculating AUC score and plot ROC curve






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