Understanding The Dataset : Attributes Name Explanation Type

The header section contains meta-information lines, each one starts with the characters “##”, and a header line starting with the character “#” representing the first 8 fixed column names present in the data section. The "FORMAT" onwards are optional and are included to encapsulate per-sample/genome genotype data.

* Collect and store data which may be converted into information at some later point in time

I have put over way 100 hours linking, tying and correcting the data that is in Mineral Soft. That’s not including the time it took me to create the spreadsheets.

The structure of the tool so formed was applied to the new incoming data and modifications were made to accommodate all the themes from the new data ensuring that the final structure accounted for all coded data.

I certify that I am the author of this paper and that any assistance I received in its preparation is fully acknowledge and disclosed in the paper. I have also cited any sources from which I used data, ideas of words, whether quoted directly or paraphrased. I also certify that this paper was prepared by me specifically for this course.

The clustering hierarchy for text clustering or documents clustering we used cluster dendrogram presentation by R implementation. Some people find cluster dendrograms to be fairly clear to read. Others simply find them perplexing. Here, we can see many more groups that are identifiable in the dendrogram. Here, I have arbitrarily chosen to look at five clusters, as indicated by the red boxes in figure

Following up on the proposal to build a Data Warehouse for Suite Spot, the decision support team feels, the next logical step would be to invest some time on setting up an infrastructure to enable Suite Spot to do data mining on the centralized data warehouse.

Note: Several of the attributes in the database could be used as a "class" attribute. This data set having 205 instances and 26 attributes. Attributes are of type categorical, Integer and Real. This is multivariate data set.

While the data set is being assembled, I will begin designing the initial architecture for the

When talking about interpreting the results, first we have to check whether the formed classes are solid one. To do this we have to check whether the class resemblance score which should be higher than the domain resemblance score. In this case both Positive and Negative class has score higher than the domain resemblance score. They are only slightly higher because of the nature of the values of the attribute. The scores are shown below.

Attribute Name Description Data type Format Range Label Table: Employee EmpNum Unique Employee ID Varchar2(4) L999 E001-E999 PK, Emp_pk EmpFName First name Varchar2(15) LL..LL EmpLName Last name Varchar2(15) LL..LL CommissionRate Commission Rate Number(2,2) 99.99 0-40 YrlySalary Annual salary Number(8,2) 999999.99 0-200,000 DepID Employee’s Department Varchar2(4) L999 D01-D99 FK,

Information tеchnology is now еssеntial in еach part of our livеs which hеlp businеss and еntеrprisе to makе usе of applications likе dеcision support systеm, rеporting and quеry onlinе analytical procеssing, and prеdictivе еxamination and businеss routinе managеmеnt. A data warеhousе is a rеpository of rеlational databasеs dеsignеd for quеry which is analyzеd by data mining tеchniquе allowing еnormous data sеts to bе еxplorеd so as to yiеld hiddеn and unidеntifiеd еxpеctations that can bе usеd in futurе for еffеctivе dеcision making.

Python’s dictionary (dict) data type is a structure composed of “‘key:value” pairs where a value is associated with a key used to look-up that value. In other languages, similar data structures are often known as associative arrays.

⦁ As we can see the difference in plots of the attributes against the class variable for the highest ranked attributes(for example, pixel_14_15) and the lowest ranked ones(pixel_2_11), in the former plot we can see that if we select this attribute and split it using some value range then we will get more meaningful sub-datasets and we can use those sub-datasets to classify the instances however in the second case if select that attribute and try to split it, we will get impure data and we won't be able to classify data as almost all instances have similar value for pixel_2_11 attribute. Here, the first attribute has high information gain however the second attribute has 0 information gain.

Here I got an opportunity to work on my interest area of Robotic Process Automation, Data Analytics and Machine learning, an area where data play a key role. Here I acquainted with how a superior enterprise application should have structured and exhaustive data collection capabilities to support the enterprise in various ways, e.g. finding process improvement areas, arriving/ predicting future sales goals, optimization of resource usage and many more. I can tell my keenness to grasp this part at this time.