Comprehensive Report on Data Handling, Excel, and Smile Clinic SPSS

This report delves into data handling techniques, emphasizing the importance of data pre-processing using Excel for sales and profit analysis. It explores the smile_clinic.csv dataset using SPSS, providing insights into gender and age analysis, customer preferences, and cluster analysis. The report also discusses common data mining methods applicable to businesses and evaluates the advantages and disadvantages of using SPSS for data analysis. The findings highlight the significance of data quality, the role of technology in increasing sales and customer retention, and the strategic use of data-driven decision-making in business operations. Desklib offers this report as a valuable resource for students studying data analysis and provides access to a wide range of solved assignments and study materials.

Data Handling 2

TABLE OF CONTENTS
INTRODUCTION...........................................................................................................................3
MAIN BODY..................................................................................................................................3
PART 1............................................................................................................................................3
Sales/profits over the years and the use of Excel for pre-processing the data.............................3
PART 2............................................................................................................................................7
The smile_clinic.csv provided in conjunction with SPSS...........................................................7
The most common data mining methods...................................................................................14
The advantages/disadvantages of SPSS.....................................................................................14
CONCLUSION..............................................................................................................................15
REFERENCES................................................................................................................................1

INTRODUCTION
Data handling is the process for ensuring that research about the data that could stored,
disposed and achieve off while for keeping safe and secure for future aspects. While in such kind
of things this might be during and conclusion for the research and includes many of the
developments about the procedures and policies while for just managing many data and handled
the electronically as well as non-electronically means. This report will explain about the sales
and profits over the years and the use of excel in pre-processing data along with visualising data.
Moreover, make the proper use of charts and graph plus about the smile_clinic.csv provided in
conjunction with SPSS. Furthermore, about the data mining which could be used for the business
and discuss the advantage and disadvantage of the SPSS.
MAIN BODY
PART 1
Sales/profits over the years and the use of Excel for pre-processing the data
The unrefined data contains missing thusly, loud and botches, so this can't among the use
that setup in AI models. The better data pre-handling has extended accuracy of the model. So the
data pre-handling is by and large critical in AI and significant learning models. The all essential
groups are imported for data with high worth and make about their all the more simple way for
computation. The used for group action performed on the datasets (Ariannezhad and et.al.,
2021). This could likewise significant for thinking about the Excel in which they can without
much of a stretch ensure for having some appropriate information assortment and hotspots for
consummation about the given their legitimate information assortment.

from the above report been come to think about how the sources have been taken with having the
high deals and benefits. While having such things this could likewise significant for thinking
about how the association is functioning for their client and making their superstore for working
with all the more high. This could likewise fundamental for taking the deals and benefits
information for the association while in which they can undoubtedly come to think about they
can work and deal with their work with having many terms and condition (Karlstetter and et.al.,
2021). This may be significant for taking their appropriate deals about their items and
administrations for which they can undoubtedly ponder numerous procedure so that could assist
them for expanding their business volume with 76% in year and ensure for having their high
assessment about different things. The dataset has expanding all the more high volumes in string
plan. The simulated intelligence model just allowed arithmetical characteristics. The string
changed over into the numerical characteristics regard techniques. While such sort of things this
could likewise significant for thinking about their eachyears deals volumes in which they can
work and ensure about their business volume to be development. 34.56 deals have been taken
their high dynamic places and keeping many significant viewpoints about their sources in which
those terms and condition can work. In such sort of things this could likewise being so useful for
them for about the benefits can expanded while by saving worker for working with numerous
exercises in association. 45% to 89% superstore can undoubtedly their items and keeping all the
more high innovation measure for which having many terms without much of a stretch come to
think about their market technique.

From the graph, this could seems that organization need to keep their profit with high
values and easily make sure about their more working things. This might be also important for
taking the employee for working with many things and make sure about their those effective
places for which they can work. This might be also essential for keeping the working process and
procures in which they can easily have their various activities. This might be playing their role
for completion goals and make more successfully towards those places which could having more
termination and sanction about their profits. In keeping more attraction towards their profits
superstore needs to work with more technology and make sure for their sustainability products
and services (List, Sims and Forkel, 2021). Along with this also come to know about their those
things which could keeping them for working with many terms and policies and also keeping
them with more effective terms. Technology must be important for knowing those things and
that could also important for the organization about their customer. As increasing the sales this
could also increased with having many benefits and keeping their customer with long term. As
74% to 86% superstore need to increase their products and services in which the customer can
easily gets attraction and also come to know about their high impact over through which they
can work. This might be also important among the market position for taking many terms and
make sure for their valuable things and that could also be effective. 34.01 this could be profits
which could be increasing and keeping their customer with working more process and make sure
about their more policies . As the organization can also work with employee while by having
many strategy for employee and also working with many new things in organization.

This could be appears to be that high successful places and make effective framework for
their functioning framework (Felkel and et.al., 2021). While for keeping 45%of deals volume in
2009-2010 and for this association need to keep their development about the 78% for having the
high sales and for that they can without much of a stretch come to think about the business
volume that could be development. In such sort of exercises for running superstore the
association are needed to high benefits in given the year. While according to having the high
interaction for the client where they can without much of a stretch come to think about their
those successful spots for which client can keep their legitimate fascination. In such sort of
exercises the association are needed to work with having more benefits in informational index.
Organizing reports, particularly execution the executives plan, will uncover when to explore all
the more high data. Overall, data and interpretation happens previous conveying a report record
or part fundamental noticing or judgment work out (Gronau, 2021). Regardless, experts
recommend that orchestrate these cycles component of typical assessment attempts will
additionally foster task learning and help with versatile administration, therefore further
introductory endeavor execution all. It very well may be likewise proposed that interface with
key accomplices unraveling results to guarantee data use and decisions that are reasonable close
by setting. It is reliably significant endorse or guineas pigs and ideas make since assessment and
comprehension. This endorsement ought to be probable with many accomplices, checking the
endeavor individuals themselves.

The whole data isolated into part of identical size and subsequently various methods are
performed work. One can replace all in arrangement segment mean or breaking point regards can
be used to complete the task. Since data evacuation plan that used arrangement with gigantic
proportion of all together. While working with colossal volume of data, hypothesis test turned
out to be all the more perseveringly in such cases. It intends to extend the limit expertise and
diminish data storing and study costs. Since data is routinely taken from different sources which
are conventionally not entirely trustworthy and that different associations, the larger part our
time consumed in overseeing data worth issues when managing an AI issue. It is basically crazy
to expect that the data will be great (Singh and et.al, 2021). There may be issues in light of
human slip-up, restrictions of gauge devices, surrenders data assortment measure. A genuine data
all things considered contains uproars and maybe unusable plan which can't be effectively used
for different sorts of models. Data pre-handling is required endeavors for association the data and
making it sensible for which moreover grows the precision and efficiency. Dataset may
associations for different purposes, for instance, accepting need to make for business reason, by
having some of the proper dataset will be particular with dataset required for liver patient. in
such kind of things this might be also shown that employment rate should be also proper for
which the organization and their countries can work.
PART 2
The smile_clinic.csv provided in conjunction with SPSS
Gender and age analysis
Gender

Frequency Percent Valid
Percent
Cumulative
Percent
Valid
Male 50 50.0 50.0 50.0
Female 50 50.0 50.0 100.0
Total 100 100.0 100.0
Among the 100 participants 50% of people are male and remaining 50 are female. The
mean value of age group is 1.5 which shows that on the basis of gender population can be
divided equally into groups.
Statistics
Gender Age
N Valid 100 100
Missing 0 0
Mean 1.5000 20.3500
Median 1.5000 19.0000
Mode 1.00a 22.00
a. Multiple modes exist. The smallest
value is shown
The average age of customers is of 20 years. The median age group of chosen customers
is 19 years and their mode value is 22 years.
Gender
Frequency Percent Valid Percent Cumulative
Percent
Valid Male 50 50.0 50.0 50.0

Female 50 50.0 50.0 100.0
Total 100 100.0 100.0
Age
Frequency Percent Valid Percent Cumulative
Percent
Valid
13.00 5 5.0 5.0 5.0
15.00 5 5.0 5.0 10.0
17.00 8 8.0 8.0 18.0
18.00 13 13.0 13.0 31.0
19.00 20 20.0 20.0 51.0
20.00 5 5.0 5.0 56.0
22.00 21 21.0 21.0 77.0
23.00 1 1.0 1.0 78.0
25.00 12 12.0 12.0 90.0
26.00 10 10.0 10.0 100.0
Total 100 100.0 100.0
The average age of the customers is 22 years old and it comprises nearly 21% of the total
number of customers. The customers of age group 18 years and 25 years also make good
proportion of the population.

From the graph it can be clearly seen that majority of the customers are in age group 19
and 22 years and minimum number of customers lies in the age group of 23 years.
Number of customers eating rice
Statistics
Customerseatingriceornot
N Valid 100
Missing 0
Mean .6000
Median 1.0000
Mode 1.00
Customerseatingriceornot
Frequency Percent Valid Percent Cumulative
Percent
Valid
No 40 40.0 40.0 40.0
Yes 60 60.0 60.0 100.0
Total 100 100.0 100.0

Out of 100 participants 60% customers used to prefer rice and they have it in their daily
meal. Contrary to this 40 people does not include rich in their meal. It is also indicated by the
mean value of 0.60
For k means clustering at first classify option is selected in analysis menu. After this a dialogue
box is opened in which all variables are selected as classify and iterate.

On clicking ok output window is opened and results can be interpreted.