Machine Learning for Telecom Customers Churn Prediction

Coursera Project Network
학습자는 이 안내 프로젝트에서 다음을 수행하게 됩니다.

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
Cloud다운로드 필요 없음
Video분할 화면 동영상
Comment Dots영어
Laptop데스크톱 전용

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.

개발할 기술

Artificial Intelligence (AI)Machine LearningPython ProgrammingclassificationComputer Programming

단계별 학습

작업 영역이 있는 분할 화면으로 재생되는 동영상에서 강사는 다음을 단계별로 안내합니다.

  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

안내형 프로젝트 진행 방식

작업 영역은 브라우저에 바로 로드되는 클라우드 데스크톱으로, 다운로드할 필요가 없습니다.

분할 화면 동영상에서 강사가 프로젝트를 단계별로 안내해 줍니다.

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자주 묻는 질문

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