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Machine Learning Analysis fundamentals in Retail

IntermediateGuided Project

This lab is dedicated to learning the basic Machine Learning methods for analysis of Retail based on Global Food Prices data from the World Food Programme covering foods such as maize(corn), rice, beans, fish, and sugar for 76 countries and 1,500 markets.

4.3 (23 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Retail

Enrollment Count

  • 236

Skills You Will Learn

  • Machine Learning, Artificial Intelligence, Data Science

Offered By

  • IBM

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • May 11, 2025
About this Guided Project

This lab uses the basic methods of machine learning to predict prices in markets around the world.
Three different types of forecasting prices for purchases are considered:
  1. Establishment of functional relationships between groups of goods in the markets of a particular country, the dependencies found, the construction of the forecast, and sensitivity analysis between price fluctuations for different groups of goods
  2. Establishing relationships between prices in the different markets of a country
  3. Analyzing the impact of the price of goods in exporting countries and the domestic market price of the importing country
The main difficulty of analyzing real data is that it is prepared and presented in a form inconvenient for machine learning methods. There are no clear algorithms or rules for choosing machine learning methods for analysis.

This lab shows the use of a set of machine learning methods to solve these problems.


What you will learn

After completing this lab, you will be able to:
  • Download a DataSet from *.csv files
  • Create new and recalculate the values of existing columns
  • Transform a table
  • Join DataSets
  • Visualize data with pandas and seaborn
  • Make correlation analysis
  • Apply basic methods of machine learning such as Linear regression and simple Neural Networks
  • Calculate the accuracy of models
  • Make forecasting 
  • Calculate the sensitivity of models

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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Olha Vdovichena

Content Creator

Olha Vdovichena – associate professor Department of Management, Marketing and International logistics Chernivtsi Institute of Trade and Economic of State University of Trade and Economic. She is an author of over 93 scientific works, 17 monographs (sections of the monographs), and books, a member of the Editorial Board of scientific journal "Journal Chernivtsi Institute of Trade and Economic of State University of Trade and Economic. Economic sciences". She defended her doctoral dissertation on the topic "Exhibition and trade fair activity as a factor of socio-economic development of the region" and became an Associate Professor of the Department of Commodity Science and Marketing. Olha’s scientific interests focus on research of development of an exhibition activity as a factor of socio-economic growth of the region; retail, marketing, logistics; improving management processes of international investments, regional economy and tourism sphere; formation of mechanisms regulating macroeconomic imbalances in both national and global economies under the conditions of inclusive development and bio-economic orientation.

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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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Contributors

Rav Ahuja

Global Program Director, IBM Skills Network

Rav Ahuja is a Global Program Director at IBM. He leads growth strategy, curriculum creation, and partner programs for the IBM Skills Network. Rav co-founded Cognitive Class, an IBM led initiative to democratize skills for in demand technologies. He is based out of the IBM Canada Lab in Toronto and specializes in instructional solutions for AI, Data, Software Engineering and Cloud. Rav presents at events worldwide and has authored numerous papers, articles, books and courses on subjects in managing and analyzing data. Rav holds B. Eng. from McGill University and MBA from University of Western Ontario.

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