Business Intelligence: Text Mining, AI for Business & Data Mining

This assignment provides a comprehensive overview of business intelligence, focusing on text mining, artificial intelligence (AI), and data analysis. It begins by defining text mining and differentiating it from data mining, outlining the technologies and tools required for text mining, and providing applications such as risk management and cybercrime prevention. The assignment then explores the importance of AI in business, including its role in automating processes, increasing productivity, and improving customer experience, while also addressing the ethical considerations and limitations of AI. Furthermore, the assignment includes a practical data mining analysis using the J48 algorithm in WEKA, applied to bank data, with detailed results and interpretations for various attributes. Finally, it briefly describes a sales cycle dashboard and its significance in optimizing business processes. Desklib offers this solved assignment and many more resources for students.

Business Intelligence

Contents
1. Answer for Q1..........................................................................................................................2
2. Answer for Q2..........................................................................................................................3
3. Answer for Q3..........................................................................................................................6
4. Answer of Q4.........................................................................................................................19
References......................................................................................................................................21
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1. Answer for Q1
Text Mining
Text Analytics, otherwise called text mining, is the way toward looking at huge
accumulations of composed assets to create new data, and to change the unstructured text into
organized information for use in encourage investigation. Text mining distinguishes realities,
connections and declarations that would some way or another stay covered in the mass of printed
huge information. These certainties are removed and transformed into organized information, for
investigation, representation (e.g. through html tables, mind maps, graphs), joining with
organized information in databases or distribution centres, and further refinement utilizing
machine learning (ML) frameworks.
Difference between Text mining and Data Mining
Data mining is centredon data subordinate exercises, for example, bookkeeping, buying,
production network, CRM, and so on. The required data is anything but difficult to get to and
homogeneous. When calculations are characterized, the arrangement can be immediately
conveyed. The multifaceted nature of the data prepared make text mining ventures longer to
convey. Text mining checks a few go-between semantic phases of examination before it can
improve text (dialect speculating, tokenization, division, morpho-syntactic investigation,
disambiguation, cross-references, and so forth) (Mitsa, 2010). Next, significant terms extraction
and metadata affiliation steps handle organizing the unstructured substance to support area
particular applications. Additionally, tasks may include some heterogeneous dialects,
organizations or spaces. At long last, few organizations have their own scientific categorization.
Nonetheless, this is compulsory for beginning a text mining venture and it can take a couple of
months to be created
Technologies and tools required for text mining
1. Sentiment analysis tool
2. Topic modelling technique
3. Named entity recognition and event extraction technique
Applications
1 – Risk administration
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Regardless of the business, insufficient hazard investigation is regularly a main source of
disappointment. This is particularly valid in the budgetary business where reception of Risk
Management Software in view of text mining innovation can drastically expand the capacity to
moderate hazard, empowering complete administration of thousands of sources and petabytes of
text reports, and giving the capacity to interface together data and have the capacity to get to the
correct data at the opportune time.
2 – Knowledge administration
Not having the capacity to discover imperative data rapidly is dependably a test while
overseeing vast volumes of text reports—simply ask anybody in the human services industry.
Here, associations are tested with a gigantic measure of data—many years of research in
genomics and sub-atomic methods, for instance, and in addition volumes of clinical patient
information—that could possibly be helpful for their biggest benefit focus: new item
improvement. Here, learning administration programming in light of text mining offer an
unmistakable and solid answer for the "data excess" issue.
3 – Cybercrime counteractive action
The unknown idea of the web and the numerous correspondence highlights worked
through it add to the expanded danger of web based wrongdoings. Today, text mining knowledge
and against wrongdoing applications are making web wrongdoing counteractive action less
demanding for any venture and law implementation or insight organizations.
4 – Customer mind benefit
Text mining, and characteristic dialect handling are visit applications for client mind.
Today, text investigation programming is much of the time received to enhance client encounter
utilizing distinctive wellsprings of important data, for example, reviews, inconvenience tickets,
and client call notes to enhance the quality, viability and speed in settling issues. Text
examination is utilized to give a quick, computerized reaction to the client, drastically lessening
their dependence accessible if the need arises focus administrators to take care of issues (Witten,
Frank, Hall & Pal, 2017).
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2. Answer for Q2
Importance of Artificial Intelligence
Numerous organizations take up Artificial Intelligence (AI) innovation to attempt to
diminish operational costs, increment productivity, develop income and enhance client
encounter.
For most prominent advantages, organizations should take a gander at putting the full
scope of brilliant innovations - including machine learning, regular dialect preparing and then
some - into their procedures and items. In any case, even organizations that are new to AI can
receive significant benefits (Berlatsky, 2011).
AI affects business by conveying the correct AI innovation, your business may pick up
capacity to:
 spare time and cash via computerizing routine procedures and assignments
 increment profitability and operational efficiencies
 settle on quicker business choices in light of yields from psychological advancements
 stay away from mix-ups and 'human blunder', gave that keen frameworks are set up
appropriately
 utilize understanding to foresee client inclinations and offer them better, customized
involvement
 mine huge measure of information to create quality leads and develop your client
base
 accomplish cost funds, by upgrading your business, your workforce or your items
 increment income by recognizing and expanding deals openings
 develop aptitude by empowering examination and offering astute exhortation and
support
 As per an ongoing Infosys think about, the fundamental main thrust for utilizing AI in
business was contender advantage. From that point onward, the impetus originated
from:
 an official drove choice
 a specific business, operational or specialized issue
 an interior investigation
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 client request
 a surprising answer for issue
 a branch of another undertaking
Artificial intelligence helps to transform companies
 proposals and substance curation
 personalization of news sustains
 example and picture acknowledgment
 dialect acknowledgment - to process unstructured information from clients and deals
prospects
 promotion focusing on and streamlined, ongoing offering
 information investigation and client division
 social semantics and feeling investigation
 robotized website composition
 prescient client benefit
These are just a portion of the cases of AI utilizes as a part of business. With the pace of
advancement expanding, there will probably be considerably more to come sooner rather than
later.
Limitations of artificial Intelligence
With the quick improvement of AI, various moral issues have sprung up. These include:
 the capability of computerization innovation to offer ascent to work misfortunes
 the need to redeploy or retrain representatives to keep them in occupations
 reasonable dissemination of riches made by machines
 the impact of machine association on human conduct and consideration
 the need to dispose of inclination in AI that is made by people
 the security of AI frameworks (eg self-sufficient weapons) that can conceivably
cause harm
 the need to relieve against unintended outcomes, as savvy machines are thought to
learn and grow autonomously
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While these dangers can't be disregarded, it merits remembering that advances in AI can -
generally - make better business and better lives for everybody. On the off chance that actualized
capably, computerized reasoning has gigantic and helpful potential (Ennals, 2014).
3. Answer for Q3
Here, we will do the data mining analysis for bank data. The bank data contains the bank
data. The bank data analysis uses the J48 analysis in Weka data mining tool. Then, Do data
mining analysis by using the below steps and it shown below (Schémas commentés en santé
sécurité au travail , 2011).
First, Open Weka data mining tool. It is shown below
Click Explorer to Load the bank data set.
It is shown below.
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Once successfully load the data.
After, clicks classify tab and click choose to select the trees.
Then, click the J48 to do the J48 analysis.
The J48 analysis for each attributes is shown below.
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J48 Analysis for ID
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J48 Analysis for Income
Correctly Classified Instances 228 38 %
Incorrectly Classified Instances 372 62 %
Kappa statistic 0.0233
Mean absolute error 0.3326
Root mean squared error 0.4748
Relative absolute error 97.8135 %
Root relative squared error 115.1822 %
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Total Number of Instances 600
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure MCC ROC Area PRC
Area Class
0.617 0.610 0.451 0.617 0.521 0.007 0.495 0.447
INNER_CITY
0.243 0.244 0.288 0.243 0.263 -0.001 0.517 0.291 TOWN
0.188 0.075 0.321 0.188 0.237 0.141 0.576 0.246 RURAL
0.032 0.052 0.067 0.032 0.043 -0.028 0.522 0.108
SUBURBAN
Weighted Avg. 0.380 0.361 0.343 0.380 0.352 0.023 0.517 0.335
=== Confusion Matrix ===
a b c d <-- classified as
166 70 19 14 | a = INNER_CITY
110 42 13 8 | b = TOWN
53 19 18 6 | c = RURAL
39 15 6 2 | d = SUBURBAN
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