At a Glance

Name: Customer behavior prediction using machine learning
Price: Free CAD
Rating: 5.0 (70 reviews)
Author: kateryna_hazdiuk, yaroslav_vyklyuk_2

The aim of this laboratory work is to develop various types of classifiers based on artificial intelligence and their ensembles for classifying clients in banking.

After completing this laboratory work, you will be able to achieve the following results:

Compare different types of classifiers: You will have the opportunity to explore and compare various types of classifiers, such as the naive Bayes classifier, decision trees, support vector machines (SVM), clustering algorithms, and others. This will allow you to assess the effectiveness of each type of classifier in the context of customer classification in banking.
Create an ensemble of models: You will have the opportunity to develop an ensemble of models that use different classifiers and their combinations. An ensemble of models can improve the accuracy and robustness of classification by combining the predictions of several models.
Create a neural network-based classifier ensemble: You will have the opportunity to develop a classifier ensemble using neural networks and their combinations. This will enable you to leverage the power of neural networks to achieve better classification results.
Perform customer classification based on the developed models: You will be able to apply the developed models and classifier ensembles to real customer data in banking. This will allow you to classify customers based on the learned models and obtain valuable results that can be used in the banking sector.

The overall outcome of this laboratory work will be your ability to develop and use various types of classifiers, create model ensembles, and conduct customer classification in banking using artificial intelligence.

Offered By: IBM

Customer behavior prediction using machine learning

At a Glance