Learn how to conduct customer segmentation analysis and predict consumer behaviour using machine learning
Sub Category
- Business Analytics & Intelligence
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Objectives
- Learn how to conduct customer segmentation analysis using k means clustering
- Learn how to build customer spending prediction model using decision tree regressor
- Learn how to build customer churn prediction model using support vector machine
- Learn the basic fundamentals of customer segmentation analytics, technical challenges and limitations in customer analytics, and its use cases in marketing
- Learn about predictive customer analytics workflow. This section covers data collection, feature selection, model selection, model training, and prediction
- Learn how to segment customer by age and gender
- Learn how to segment customer by education level
- Learn how to calculate average customer spending by country
- Learn how to find correlation between purchase frequency and customer spending
- Learn how to find correlation between customer income and customer spending
- Learn how to conduct feature importance analysis using random forest
- Learn how to evaluate model accuracy and performance using k fold cross validation method
- Learn how to deploy machine learning model and create user interface using Gradio
- Learn how to handle class imbalance with synthetic minority oversampling technique
- Learn about factors that influence consumer behaviour, such as psychological, economic, social, technology, personal, and culture
- Learn how to clean dataset by removing missing values and duplicates
- Learn how to find and download customer spending data from Kaggle
Pre Requisites
- No previous experience in customer analytics is required
- Basic knowledge in python and statistics
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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