Data Handling and Business Intelligence-2

Data handling and Business Intelligence -2

TABLE OF CONTENTS
PART 1............................................................................................................................................3
Determine the decline in the sales/Profit.....................................................................................3
PART 2............................................................................................................................................7
2.1 Presenting the screenshot of clustering i.e. K-means............................................................8
2.2 Common data mining methods............................................................................................12
2.3 Pros and cons of using SPSS over Excel.............................................................................13
REFERENCES..............................................................................................................................15

PART 1
Determine the decline in the sales/Profit
Evaluating the use of Excel for pre-processing the data
Data pre-processing is the data mining technique that is used to involves transforming
raw data into an understandable format. Also, it is all about cleaning, instance selection,
normalization, transformation, feature extraction and selection etc. However, in the real-world,
data is often incomplete, inconsistent and also likely to contain many errors. There are many
tools and techniques used for pre-processing the data which include Weka software, Excel etc.
Nariswari and Nugraha (2020) stated that for non-programmers, Microsoft Excel is the great tool
used for pre-processing and handling structure data. It is so because it has many functions and
techniques which makes it easier to clean structure data. Thus, it can be reflected that with the
help range of functions within Excel, can determine the impact or interrelationship between sales
and profit.
In addition to this, Excel is one of the top tools for pre-processing of data due to the use
of variety of functions which includes Pivot table, If-function, Count, Trim etc. In the context of
present dataset, it has been examined that it is a huge data, and for pre-processing, Excel is used
that helps to filter the data in order to properly respond the questions. This in turn assist to
answer the questions and with the help of effective visualization, presentation of the report will
be improved and helps to interpret the data in more effective manner (HINDASAH and
NURYAKIN, 2020). On the critical note, it has been examined that it is difficult to manage
advanced pricing rules and due to lack of control and security, Excel cannot be used by the data
analysis because it might leak the important data which affected the results as well. Besides this,
it is also clearly reflected that in pricing term, the excel cannot be managed the terms easily
because it is difficult to manage advanced pricing rules, this might affect the results as well.
Overall, it has been clearly reflected that with the help of excel, superstore market can be
easily determining the fluctuation within sales due to years. Also, through this, it can be stated
that it helps to analyse the performance of a company over years and which steps need to be
chosen in order to raise the sales (Kohli, Godwin and Urolagin, 2021). So that, it can be stated
that Excel is a strong and effective pre-processing tool that helps in improving the decision and

also derive best results accordingly. In the context of present study or chosen dataset, it can be
explained that by using different options within Excel, the superstore can determine the
importance of sales and the trend which can affected the overall performance of the company.
As per the provided data set of the superstore, it has been examined that charts and
graphs has been used as an excel function. Further, the tool is best suitable because it assists to
determine the sales figure over the years mentioned in order to determine whether the sales is
increases by year or not. This in turn also helps in determine the relationship of sales with profit.
However, many secondary research indicates that there is a positive relationship between the
variables. The steps are enumerated below:
 In the first step, there is a need to filter all data, which in turn assist to extract the
essential data.
 By using filter option, select a range in which sales can be identified. Under this, data
related to sales can be identified easily from the huge data.
 By selecting the range, for a specific year, apply the formula of sum in order to determine
total sales for four consecutive years i.e. 2009, 2010, 2011 and 2012.
 The same steps apply in the profit side, where total profit of the respective years can be
identified. This in turn helps to examine total sum of the profit and sales in order to
analyze the relationship with both variables.
 Further, to drawn the graphs and charts, select the data range and click on insert in order
to present the graphs or charts that helps to make a report more presentable and identify
the relationship directly.
 Also, with the help of generating graphs from the selected data, it has been identified that
both have shared a positive relationship with each other and this in turn reflected that
company have to focused upon the products so that sales can be increases that contribute
towards a profit.
Overall, it can be stated that by following the above mentioned steps, it will be easy for the
scholar to determine the answer of the questions and understand any relationship between the
variables as well.
Evaluation of the data-set