Data mining refers to analyzing data sets to discover hidden patterns and trends. This information can help companies improve strategies for marketing, analyzing customers and markets, increasing revenue, and forecasting sales. Data mining has proven useful in business, computing, biotechnology, and analyzing stock markets. While a relatively new term, data mining has long been used by large corporations to analyze large data sets and draw conclusions. Microsoft has introduced the SQL Server Data Mining Add-ins for Office 2007 to make data mining accessible through a familiar Microsoft Office environment. It connects Excel to the powerful data mining algorithms in SQL Server Analysis Services. The add-in allows users to perform tasks like data preparation, modeling, and validating models with just a few clicks.

2. What is DATA MINING Data mining (or Knowledge Discovery) refers to the process of analyzing a give data set from different precepts and scenarios in order to discover patterns in the given data set. This information can help reveal the hidden trends about products, customer, market, employees which prove very important while designing new strategies for product marketing, market analysis, increasing revenue or cost cutting, forecasting sales figures or analyze those components that are critical to the success of the company. Data mining has proved its worth in many fields such as business, computers (finding patterns in data required for machine learning, AI), biotechnology (data mining DNA codes to find out how changes in its structure affect human health and immunity to diseases like cancer etc), share market forecasts etc, thus making data mining a rapidly growing field with numerous possibilities and uses. Data mining, though a relatively new term has long been used by large corporations to churn through large data sets to incur conclusions with the help of powerful computers. As computers became faster and more capable, new and more advanced data mining techniques/algorithms have been developed in order to return more precise conclusions.

12. Data Preparation- Explore Data Histogram as Numeric Here we select the Income column to be explored. Histogram as Discrete Here we have used the tool to explore the Income column of the data set. We can see that maximum of the customers have income between the range of 30000 to 50000 and very few people have income in the range 150000-170000, so that we may market our product accordingly. If required we can add this data as a column in our table

14. Data Preparation-Clean Data( outliers ) Here we select the income column to find outliers In the histogram we may chose Min as ‘27580’ and Max as ‘144500’

15. Data Preparation-Clean Data( outliers ) Instead of Min and Max we may also choose to set a minimum count for a particular value. Here we may choose any of the above actions to clean our data.

17. Data Preparation-Clean Data( re-label ) Here we may choose to change 1,2… to one, two etc. We can see how 1,2,3.. Have been re-labeled as one, two ..respectively..

26. Data Modeling - Estimate Here we study how various factors affect the monthly income of an individual/customer

29. Data Modeling - Associate This tools creates Association Rules based model that uses data from the excel table. This model analyzes the data to detect items that appear together in transaction and is most suitable for giving recommendations to buy other related products based on the products they have brought and is mostly used in online shopping and market basket analysis. It employs the Microsoft Association Algorithm and finds patterns (associations) between different items of the data set. The data provided to the Associate must have its Identifier attribute (ID) sorted and the associate must be informed which I the ID column and the columns containing he items for transaction How to use it : We have to select the column that identifies the transaction and also the column that identifies the items contained in the transaction. NOTE : The transaction data must be I a one-to-many type relations and the column identifying the transactions must be arranged in ascending order. What do we get : We will get a Association model of the selected columns.