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Consumer Confidence in the United Kingdom decreased to -19 points in July from -18 points in June of 2025. This dataset provides the latest reported value for - United Kingdom Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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Finland Consumer Confidence Indicator: Own Economy Now data was reported at 8.300 % in Oct 2018. This records an increase from the previous number of 6.400 % for Sep 2018. Finland Consumer Confidence Indicator: Own Economy Now data is updated monthly, averaging 4.400 % from Oct 1995 (Median) to Oct 2018, with 277 observations. The data reached an all-time high of 9.800 % in Aug 2006 and a record low of -4.000 % in Oct 1995. Finland Consumer Confidence Indicator: Own Economy Now data remains active status in CEIC and is reported by Statistics Finland. The data is categorized under Global Database’s Finland – Table FI.H008: Consumer Confidence Indicator.

The Consumer Confidence Index (CCI) of the United Kingdom was 99.3 in June 2025, compared with 99 in the previous month. Consumer confidence in the UK fell sharply from summer 2021 onwards but recovered somewhat in late 2022.

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Consumer Confidence In the Euro Area increased to -14.70 points in July from -15.30 points in June of 2025. This dataset provides the latest reported value for - Euro Area Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Consumer trends time series dataset up to Quarter 1 (January to March) 2025.

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One of the key changes in societal trends and lifestyles witnessed over the past few years has been the move on-line of many consumers and the way they have become increasingly sophisticated in their media consumption habits. Have these recent changes to consumer and commercial practices developed in such a way that consumers are (in)voluntarily signing away their fundamental right to privacy? This CONSENT project seeks to examine how consumer behaviour, and commercial practices are changing the role of consent in the processing of personal data. While consumer consent is a fundamental value on which the European market economy is based, the way consumer consent is obtained is questionable in popular user-generative/user-generated (UGC) online services (including sites like MySpace, YouTube and Facebook), whose commercial success depends to a large extent on the disclosure by their users of substantial amounts of personal data. (https://www.consent.law.muni.cz/) WP7: Quantitative measurement of end-user attitudes towards privacy WP8: Qualitative study of UGC users and UGC non-users attitudes towards privacy

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Market Basket Analysis

Market basket analysis with Apriori algorithm

The retailer wants to target customers with suggestions on itemset that a customer is most likely to purchase .I was given dataset contains data of a retailer; the transaction data provides data around all the transactions that have happened over a period of time. Retailer will use result to grove in his industry and provide for customer suggestions on itemset, we be able increase customer engagement and improve customer experience and identify customer behavior. I will solve this problem with use Association Rules type of unsupervised learning technique that checks for the dependency of one data item on another data item.

Introduction

Association Rule is most used when you are planning to build association in different objects in a set. It works when you are planning to find frequent patterns in a transaction database. It can tell you what items do customers frequently buy together and it allows retailer to identify relationships between the items.

An Example of Association Rules

Assume there are 100 customers, 10 of them bought Computer Mouth, 9 bought Mat for Mouse and 8 bought both of them. - bought Computer Mouth => bought Mat for Mouse - support = P(Mouth & Mat) = 8/100 = 0.08 - confidence = support/P(Mat for Mouse) = 0.08/0.09 = 0.89 - lift = confidence/P(Computer Mouth) = 0.89/0.10 = 8.9 This just simple example. In practice, a rule needs the support of several hundred transactions, before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.

Strategy

• Data Import
• Data Understanding and Exploration
• Transformation of the data – so that is ready to be consumed by the association rules algorithm
• Running association rules
• Exploring the rules generated
• Filtering the generated rules
• Visualization of Rule

Dataset Description

• File name: Assignment-1_Data
• List name: retaildata
• File format: . xlsx
• Number of Row: 522065

• Number of Attributes: 7

  • BillNo: 6-digit number assigned to each transaction. Nominal.
  • Itemname: Product name. Nominal.
  • Quantity: The quantities of each product per transaction. Numeric.
  • Date: The day and time when each transaction was generated. Numeric.
  • Price: Product price. Numeric.
  • CustomerID: 5-digit number assigned to each customer. Nominal.
  • Country: Name of the country where each customer resides. Nominal.

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Libraries in R

First, we need to load required libraries. Shortly I describe all libraries.

• arules - Provides the infrastructure for representing, manipulating and analyzing transaction data and patterns (frequent itemsets and association rules).
• arulesViz - Extends package 'arules' with various visualization. techniques for association rules and item-sets. The package also includes several interactive visualizations for rule exploration.
• tidyverse - The tidyverse is an opinionated collection of R packages designed for data science.
• readxl - Read Excel Files in R.
• plyr - Tools for Splitting, Applying and Combining Data.
• ggplot2 - A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
• knitr - Dynamic Report generation in R.
• magrittr- Provides a mechanism for chaining commands with a new forward-pipe operator, %>%. This operator will forward a value, or the result of an expression, into the next function call/expression. There is flexible support for the type of right-hand side expressions.
• dplyr - A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
• tidyverse - This package is designed to make it easy to install and load multiple 'tidyverse' packages in a single step.

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Data Pre-processing

Next, we need to upload Assignment-1_Data. xlsx to R to read the dataset.Now we can see our data in R.

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After we will clear our data frame, will remove missing values.

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To apply Association Rule mining, we need to convert dataframe into transaction data to make all items that are bought together in one invoice will be in ...

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Finland Consumer Confidence Indicator: Own Threat of Unemployment Now data was reported at 4.800 % in Oct 2018. This records a decrease from the previous number of 8.100 % for Sep 2018. Finland Consumer Confidence Indicator: Own Threat of Unemployment Now data is updated monthly, averaging 1.250 % from Mar 2004 (Median) to Oct 2018, with 176 observations. The data reached an all-time high of 9.800 % in May 2018 and a record low of -18.800 % in Mar 2009. Finland Consumer Confidence Indicator: Own Threat of Unemployment Now data remains active status in CEIC and is reported by Statistics Finland. The data is categorized under Global Database’s Finland – Table FI.H008: Consumer Confidence Indicator.

Consumer Confidence in South Korea increased to 110.80 points in July from 108.70 points in June of 2025. This dataset provides the latest reported value for - South Korea Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Finland Consumer Confidence Indicator: Household Financial Situation Now data was reported at 37.300 % in Nov 2018. This records an increase from the previous number of 36.000 % for Oct 2018. Finland Consumer Confidence Indicator: Household Financial Situation Now data is updated monthly, averaging 30.750 % from Oct 1995 (Median) to Nov 2018, with 278 observations. The data reached an all-time high of 37.300 % in Nov 2018 and a record low of 14.700 % in Nov 1995. Finland Consumer Confidence Indicator: Household Financial Situation Now data remains active status in CEIC and is reported by Statistics Finland. The data is categorized under Global Database’s Finland – Table FI.H008: Consumer Confidence Indicator.

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To assist the upgrading and transformation of the cosmetics industry, the Bureau has implemented the "Voluntary Cosmetics Good Manufacturing Practices (GMP) Verification System" since 2008, guiding the industry to establish cosmetic GMP manufacturing systems and conducting official audits to ensure the quality, safety, and hygiene of cosmetics, and enhance consumer confidence. In order to facilitate the public and relevant authorities' understanding of companies and product categories that have passed the verification of good manufacturing practices for cosmetics, the Industrial Development Bureau of the Ministry of Economic Affairs has specially established the "List of Companies and Product Categories that have Passed the Verification of Good Manufacturing Practices for Cosmetics" dataset to provide information on cosmetic GMP products for public inquiry. In line with the government's promotion of open data measures, the Industrial Development Bureau of the Ministry of Economic Affairs is now opening the list of companies and product categories that have passed verification of good manufacturing practices for cosmetics, providing the names of verified companies, verification items, product names, and dosage forms for download, and welcoming the public to make good use of the information.

Consumer Confidence in Ireland decreased to 59.10 points in July from 62.50 points in June of 2025. This dataset provides the latest reported value for - Ireland Consumer Confidence - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.