Shivani Soni presented on data mining. Data mining involves using computational methods to discover patterns in large datasets, combining techniques from machine learning, statistics, artificial intelligence, and database systems. It is used to extract useful information from data and transform it into an understandable structure. Data mining has various applications, including in sales/marketing, banking/finance, healthcare/insurance, transportation, medicine, education, manufacturing, and research analysis. It enables businesses to understand customer purchasing patterns and maximize profits. Examples of its use include fraud detection, credit risk analysis, stock trading, customer loyalty analysis, distribution scheduling, claims analysis, risk profiling, detecting medical therapy patterns, education decision making, and aiding manufacturing process design and research.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
This presentation includes major application areas of data mining and its techniques in real world.This ppt includes various field where data mining is playing a crucial role in the development of every sector by its techniques.i hope it would be helpful to everyone.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
Introduction to Web Mining and Spatial Data MiningAarshDhokai
Data Ware Housing And Mining subject offer in Gujarat Technological University in Branch of Information and Technology.
This Topic is from chapter 8 named Advance Topics.
This presentation includes major application areas of data mining and its techniques in real world.This ppt includes various field where data mining is playing a crucial role in the development of every sector by its techniques.i hope it would be helpful to everyone.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
Know different types of tips about Importance of dataware housing, Data Cleansing and Extracting etc . For more details visit: http://www.skylinecollege.com/business-analytics-course
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation briefly explains the following topics:
Why is Data Analytics important?
What is Data Analytics?
Top Data Analytics Tools
How to Become a Data Analyst?
introduction, data mining, why data mining, application of data mining, steps of data mining, threat of data mining, solution of data mining, role of data mining, data warehouse, oltp & olap, data warehouse, data mining tools, latest research
Financial Data Mining and Algo Trading presented at the SAS Data Mining Confe...Robert Golan
Algorithmic Trading has changed the world the way the Traders trade and Trade Support supports. There is a Brave New World happening with the "hands on" Trading evolving into "hands off" Algo Trading. Not all trades need to be made in ultra low latency timing. Future trading will rely on a broader set of data which will be mined for relevance. For example, an important series of XBRL Financial Reporting events are happening throughout the world and especially in the USA. A critical mass of financial data will be ready for mining which will be a boon for transparent "low touch" fundamental style algorithmic trading.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
Why Data Science is Getting Popular in 2023?kavyagaur3
Data science employs mathematics, statistics, advanced programming techniques, analytics and artificial intelligence (AI) to uncover insights that drive business value for their organisation. Then, this information can be used for strategic planning and decision-making.
Data has flooded in massive amounts as a result of digitization. Businesses are making their utmost efforts to take advantage of every opportunity to increase their businesses. This makes the best opportunity for individuals who want to pursue Data Science. The first step is to get the best data science training.
INTRODUCTION TO DATA MINING
This word document contain the notes of data mining. It tells the basics of data mining like what is Data mining, it's types, issues, advantages, disadvantages, applications, social implications, basis tasks and KDD process etc. While making this notes, I had taken help from different websites of google.
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
Data science is the practice of extracting, analyzing, and interpreting large amounts of data to identify trends, correlations, and patterns. It combines machine learning, statistics, programming, and data engineering tools to uncover insights that can inform business decisions. Data scientists collect, organize, and analyze large amounts of data to find valuable insights and make predictions. Data science can be used in various industries, from finance and health care to retail and advertising. By leveraging data-driven decision-making, companies are able to gain a better understanding of their customers, identify new growth opportunities, and optimize their operations.
In our increasingly Data-driven world, it's more important than ever to have accessible ways to view and understand data.
After all, employees' demand for data skills steadily increases each year.
Employees and Business owners at every level need to understand data and its impact.
That's where Data Visualization comes in handy.
To make Data more accessible and understandable, Data Visualization in Dashboards is the go-to tool for many businesses to Analyze and share Information.
Running Head CONSUMER BEHAVIOR ANALYSISCONSUMER BEHAVIOR ANALMalikPinckney86
Running Head: CONSUMER BEHAVIOR ANALYSIS
CONSUMER BEHAVIOR ANALYSIS 10
CONSUMER BEHAVIOR ANALYSIS
Student’s Name: HEJIE ZHENG
Course: CIS4321
Date:04/20/19
Contents
PROPOSAL 2
CONSUMER BEHAVIOUR ANALYSIS 2
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS. 3
CONSUMER BEHAVIOUR DATA SET 3
IMPLEMENTATION OF CUSTOMER BEHAVIOUR DATA SET 5
CUSTOMER BEHAVIOR DATA MINING TECHNIQUES 7
Association Mining 7
Transaction study unit 7
CONCLUSION 7
REFERENCES 8
PROPOSAL
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective.CONSUMER BEHAVIOUR ANALYSIS
Our project is consumer behavior analysis. Research has been conducted and presented on the behavior of consumers and how the data obtained is important in solving real-world problems. In analyzing consumer behavior in this paper, we will embrace data mining techniques. Each data mining technique has its pros and cons. For this reason, we will choose the best technique to mine our database. The main objective is identifying psychological conditions that affect customer’s behavior at the time of purchase and the key data mining tool that is convenient for each method of purchase. Furthermore, there is an association rule that is employed in customer mining from the sales data in the retail industry.
SIGNIFICANCE OF ANALYSING CONSUMER BEHAVIOURS.
Analyzing consumer behavior is important as the data obtained is converted to a format that is statistical and a technical technique is used to analyses the data (Stoll, 2018). Business enterprises also use the knowledge of consumer behavior in the following ways:
I. Determining the psychology of consumers in terms of their feeling, reasoning, and thinking and how best they can choose between the alternatives.
II. Businesses also determine how the business environment affects consumers’ mindset.
III. Businesses can determine the behavior of customers at the time of purchasing their goods and services.
IV. Companies also find out how customer motivation affects customers' choice of goods of utmost importance.
V. Finally, Business finds ways of improving their marketing strategies based on the available data that they will gather.CONSUMER BEHAVIOUR DATA SET
The modern consumer behavior perspective is just the same as the traditional consumer behavior perspective. The patterns used by consumers in the day to day lives are also applicable in the online context. Koufaris (2002) in his article argues that online consumer behaviors are similar to traditional behaviors. However, online consumers have additional advantages as besides being customers, they easily access the information about the goods and services they want. The contents of our datasets pertaining the consumer behaviors can be found in Montgomery, Li, Srinivasan, and Liechty (2004.)
In the present world, a normal consumer is regarded as a constant generator whom his or her data is treated in diverse contexts as unstructured, contemporary ...
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<a href="https://www.excelr.com/business-analytics-training-in-bangalore">ExcelR data analytics courses</a>
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<a href="https://www.excelr.com/business-analytics-training-in-bangalore">ExcelR data analytics courses</a>
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Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
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The French Revolution Class 9 Study Material pdf free download
Application of data mining
1. Presenting by: Shivani Soni
Course: B-Tech(CSE 7th)
Roll no.: 13/NR/UT/CS005
Presenting to: Mr. Vishal Patyal
2. O Data Mining
OWhy Data Mining ?
O Where we use data mining ?
O Applications of Data Mining…….
3. • Data Mining is an interdisciplinary subfield of Computer
Science.
• Data Mining is the Computational Process of discovering
Pattern in Large Data Sets involving Method at the intersection
of artificial intelligence , machine learning statistics and
Database Systems.
• The Overall goal of the Data Mining process is to
extract information from a data set and transform it
into an understandable structure for further use.
4. Data Mining is a process that analyse
a large amount of data to find new and
hidden information that improves business .
Example:-
7. 1. Data Mining Applications in Sales/Marketing
2. Data Mining Applications in Banking / Finance
3. Data Mining Applications in Health Care and Insurance
4. Data Mining Applications in Transportation
5. Data Mining Application in Medicine
6. Data Mining Applications in Education
7. Data Mining Applications in Manufacturing Engineering
8. Research analysis
8. Data mining enables businesses to understand the hidden
patterns inside historical purchasing transaction data.
Data mining is used for Market Basket Analysis to provide information
on what product combinations were purchased together when they were
bought and in what sequence. This information helps businesses promote
their most profitable products and maximize the profit. In addition, it
encourages customers to purchase related products that they may have
been missed or overlooked.
9. O Retail companies use data mining to identify customer’s behaviour
buying patterns.
10. OFraud Detection
OCredit card spending by customer groups can be identified
by using data mining.
OThe hidden correlation’s between different financial
indicators can be discovered by using data mining.
OFrom historical market data, data mining enables to
identify stock trading rules.
O Data mining is used to identify customers loyalty by analysing the
data of customer’s purchasing activities .
11. OData mining helps determine the distribution
schedules among warehouses and outlets and
analyses loading patterns.
12. Data mining is applied in insurance industry lately but brought tremendous
competitive advantages to the companies who have implemented it
successfully. The data mining applications in insurance industry are listed
below:
O Data mining is applied in claims analysis such as identifying
which medical procedures are claimed together.
O Data mining enables to forecasts which customers will potentially
purchase new policies.
O Data mining allows insurance companies to detect risky
customers’ behaviour patterns.
O Data mining helps detect fraudulent behaviour.
13.
14. O Data mining enables to characterize patient activities to see incoming
office visits.
O Data mining helps identify the patterns of successful medical therapies
for different illnesses.
Example:- Smart Health Prediction in Data Mining
15.
16. O There is a new emerging field, called Educational Data Mining,
concerns with developing methods that discover knowledge from data
originating from educational Environments. The goals of EDM are
identified as predicting students’ future learning behaviour, studying
the effects of educational support, and advancing scientific knowledge
about learning. Data mining can be used by an institution to take
accurate decisions and also to predict the results of the student. With
the results the institution can focus on what to teach and how to teach.
Learning pattern of the students can be captured and used to develop
techniques to teach them.
17. O Knowledge is the best asset a manufacturing enterprise would possess.
Data mining tools can be very useful to discover patterns in complex
manufacturing process. Data mining can be used in system-level
designing to extract the relationships between product architecture,
product portfolio, and customer needs data. It can also be used to
predict the product development span time, cost, and dependencies
among other tasks.
18. History shows that we have witnessed revolutionary changes in research.
Data mining is helpful in data cleaning, data pre-processing and
integration of databases. The researchers can find any similar data from
the database that might bring any change in the research. Identification of
any co-occurring sequences and the correlation between any activities can
be known. Data visualisation and visual data mining provide us with a
clear view of the data.