At a Glance

Name: Machine Learning Analysis fundamentals in Retail
Price: Free CAD
Rating: 4.3 (23 reviews)
Author: rav_ahuja, olha_vdovichena, yaroslav_vyklyuk_2, kateryna_hazdiuk

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.

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:

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
Establishing relationships between prices in the different markets of a country
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

Offered By: IBM

Machine Learning Analysis fundamentals in Retail

At a Glance

What you will learn