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Classification Fundamentals for Marketing

IntermediateGuided Project

This lab is dedicated to the study of machine learning classification methods. The goal is to determine the impact of marketing campaigns and predict whether customers will purchase the product.

4.5 (30 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Marketing

Enrollment Count

  • 178

Skills You Will Learn

  • Machine Learning, Artificial Intelligence, Data Science

Offered By

  • IBM

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • May 5, 2025
About this Guided Project
The key problems to be solved in this lab involve classifying customers and analysing various marketing campaigns that are directed at them.

The crucial prerequisite for classification analysis is adequate data set preparation. Additionally, there are numerous alternative classification techniques that are currently available. Each of them has unique traits and analytical possibilities. This lab shows several classifiers in action and combines them into an ensemble. It also demonstrates ways to combine a pipeline during all phases of training preparation and analysis.

What you will learn

In this lab, we will learn how to download and pre-prepare data, classify and combine classifiers into an ensemble. This lab consists of the following steps:

  • Download data - download and display data from a file
  • Preliminary data preparation - preliminary analysis of data structure, change of data structure and tables
  • Pipeline classification - classification and analysis by grouping stages
    • Logistic regression - classification and analysis of accuracy and errors using logistic regression
    • Over-sampling problem - solve the problem of uneven distribution of data
    • Ensemble of classifiers - study various classifiers and methods of combining them into an ensemble
    • Decision tree - shows how to visualize the decision tree and determine the importance of factors

Prerequisites

Instructors

Yaroslav Vyklyuk

Full Professor, Doctor of Computer Science, PhD

Dr. Yaroslav Vyklyuk is a full professor at the Lviv Polytechnic National University, Department of Artificial Intelligence Systems. He is an author of over 210 scientific works, 10 monographs, and books, a member of the Editorial Board of 6 international scientific journals, member of the Academic Councils on protection Ph.D. and DrSc thesis in "Mathematical modeling and computational methods". Research Interests: Data Science, Applied System Analysis, Mathematical Modeling, and Decision Making of Complex Dynamic Systems (socio-economic, geographical, tourist, and crisis systems) using Artificial Intelligence Technology, DataMining, Big Data, Parallel Calculations, Statistics, Econometrics, Econophysics and other Advanced Mathematical Methods with implementation into information, WEB, and geographic information systems.

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Kateryna Hazdiuk

PhD of Software Engineering

I am an assistant professor at the Yuriy Fedcovych Chernivtsi National University, Software of Computer Systems Department; an author of over 40 scientific works and 10 training manuals. Research Interests: Mathematical Modeling of Complex Dynamic Systems (bio-like systems, socio-economic, geographical systems), Data Science, Decision Making using Artificial Intelligence Technology, DataMining, Big Data, Parallel Calculations, Statistics, and other methods.

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