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Machine Learning for Telecom Customers Churn Prediction
Ryan Ahmed
Ryan Ahmed
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.
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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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/u4lk3l/machine
What's inside
Syllabus
Telecom Customers Churn Prediction using several Machine Learning Classification problems
Welcome to “Machine Learning Classification: Telecom Customers Churn Prediction”. This is a project-based course which should take approximately 2 hours to finish. Before diving into the project, please take a look at the course objectives and structure.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Designed for learners based in the North America region
Develops a strong foundation in telecom customer churn prediction
Emphasizes hands-on learning through practical projects
Taught by experienced instructors with industry knowledge
Utilizes a variety of classification algorithms, providing a comprehensive understanding of the subject
Helps learners analyze customer churn rate based on multiple factors, improving their decision-making skills
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Reviews summary
Highly rated telecom churn prediction course
Learners say this course on machine learning is super useful and a great path to learn ML algorithms. Reviewers agree that this course has good and clear content.
Activities
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We're preparing activities for
Machine Learning for Telecom Customers Churn Prediction.
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Machine Learning for Telecom Customers Churn Prediction will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use data to solve business problems. They analyze data to find trends and patterns, and they use this information to make recommendations. This course may be useful for Data Analysts who want to learn more about Machine Learning and how it can be used to predict customer churn.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products or services. They work with customers to identify and resolve problems. This course may be useful for Customer Success Managers who want to learn more about Machine Learning and how it can be used to predict customer churn.
Fraud Analyst
Fraud Analyst
Fraud Analysts are responsible for investigating and preventing fraud. They use data to identify fraudulent transactions and develop prevention strategies. This course may be useful for Fraud Analysts who want to learn more about Machine Learning and how it can be used to predict customer churn.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying Machine Learning models. They work closely with Data Scientists to translate business problems into technical solutions. This course may be helpful in building a foundation in Machine Learning for aspiring Machine Learning Engineers, who need to have a strong understanding of various classification algorithms.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. They use data to understand customer behavior and target marketing efforts. This course may be useful for Marketing Managers who want to learn more about Machine Learning and how it can be used to predict customer churn.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment recommendations. They analyze financial data to identify trends and patterns. This course may be useful for Financial Analysts who want to learn more about Machine Learning and how it can be used to predict customer churn.
Risk Manager
Risk Manager
Risk Managers are responsible for identifying and managing risks. They use data to assess risks and develop mitigation strategies. This course may be useful for Risk Managers who want to learn more about Machine Learning and how it can be used to predict customer churn.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. They work with engineers and designers to create products that meet customer needs. This course may be useful for Product Managers who want to learn more about Machine Learning and how it can be used to predict customer churn.
Insurance Analyst
Insurance Analyst
Insurance Analysts use data to assess risks and develop insurance policies. They work with insurance companies to determine the appropriate coverage and pricing for insurance policies. This course may be useful for Insurance Analysts who want to learn more about Machine Learning and how it can be used to predict customer churn.
Business Analyst
Business Analyst
Business Analysts use data to understand business problems and opportunities. They work with stakeholders to define requirements and develop solutions. This course may be useful for Business Analysts who want to learn more about Machine Learning and how it can be used to predict customer churn.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. They work with sales representatives to develop and execute sales strategies. This course may be useful for Sales Managers who want to learn more about Machine Learning and how it can be used to predict customer churn.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use data to improve healthcare outcomes. They work with healthcare providers to identify trends and patterns in patient data. This course may be useful for Healthcare Analysts who want to learn more about Machine Learning and how it can be used to predict customer churn.
Data Scientist
Data Scientist
Data Scientists use advanced Machine Learning algorithms to uncover actionable insights in data. They apply their expertise in statistics and programming to solve complex business problems and predict future trends. This course may be useful in building a solid foundation in Machine Learning for Data Scientists, who often use these algorithms to understand customer behavior and predict churn rate.
Education Analyst
Education Analyst
Education Analysts use data to improve educational outcomes. They work with schools and educators to identify trends and patterns in student data. This course may be useful for Education Analysts who want to learn more about Machine Learning and how it can be used to predict student churn.
Operations Manager
Operations Manager
Operations Managers are responsible for planning and executing operations. They work with employees to ensure that operations are efficient and effective. This course may be useful for Operations Managers who want to learn more about Machine Learning and how it can be used to predict customer churn.
For more career information including salaries, visit:
OpenCourser.com/course/u4lk3l/machine
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Machine Learning for Telecom Customers Churn Prediction.
Comprehensive guide to machine learning for finance. It covers the underlying theory and algorithms, as well as practical applications in a variety of domains.
Provides a practical introduction to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Save
Comprehensive guide to deep learning. It covers the underlying theory and algorithms, as well as practical applications in a variety of domains.
Save
Comprehensive guide to computer vision. It covers the underlying theory and algorithms, as well as practical applications in a variety of domains.
Comprehensive guide to speech and language processing. It covers the underlying theory and algorithms, as well as practical applications in a variety of domains.
Comprehensive guide to reinforcement learning. It covers the underlying theory and algorithms, as well as practical applications in a variety of domains.
Save
Comprehensive guide to generative adversarial networks. It covers the underlying theory and algorithms, as well as practical applications in a variety of domains.
Comprehensive guide to natural language processing with deep learning. It covers the underlying theory and algorithms, as well as practical applications in a variety of domains.
Save
Comprehensive guide to data mining. It covers a wide range of topics, including data preprocessing, feature engineering, and model evaluation.
Introduces readers to a wide range of machine learning algorithms, including supervised and unsupervised learning. It also covers evaluation metrics and model selection techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/u4lk3l/machine
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Designed for learners based in the North America region
Develops a strong foundation in telecom customer churn prediction
Emphasizes hands-on learning through practical projects
Taught by experienced instructors with industry knowledge
Utilizes a variety of classification algorithms, providing a comprehensive understanding of the subject
Helps learners analyze customer churn rate based on multiple factors, improving their decision-making skills
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