ICT706 Data Analytics: Data Analysis and Sales Prediction for Cameras

This project report focuses on leveraging data analytics and Python to enhance the usability of a camera sales data model for effective decision-making. A dummy dataset is created to facilitate better analysis, with Python code generating training and testing graphs, along with regression models to predict sales. The analysis aims to assist a camera company's board of directors in understanding monthly sales data, identifying product priorities, and making informed decisions to increase profit. Key findings include geographic region targeting, product prioritization, and the impact of free shipping on sales, providing actionable recommendations for the company's implementation.

ICT706 Data Analytics

EXECUTIVE SUMMARY
The main aim of this project report is to use the basics of the Data Analytics and some tools
in order to increase the usability of the data model and to help in making an effective solution
that can help in making a decision in order to find out the products priority with the help of
the raw data sets. A dummy dataset is developed that is going to help in making the better
decision analysis with the help of Python Code. The Python Code is going to helpful in
generating the Graphs these graphs these graphs include Test Graphs over the dataset and the
Training Graph over the Dataset. There are other graphs too that are going to be helpful in
depicting the Regression Model Graphs. These graphs will give an insight into the type of
data and the Sales could be predicted using that.
For analysing the dataset, the company is going to need help for understanding the raw data
and make some predictions over it using the Some relations. Board of Directors of the
company need a Data Scientist that could be able to depict the monthly sales data and help in
making a better prediction for their sales and help them in gaining profit using that prediction
model. The Dataset that is going to be used is Camera Dataset.
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Table of Contents
EXECUTIVE SUMMARY........................................................................................................2
ASSUMPTIONS MADE...........................................................................................................5
BACKGROUND........................................................................................................................6
INTRODUCTION......................................................................................................................7
RESEARCH METHODOLOGY...............................................................................................8
QUALITATIVE METHODOLOGY:....................................................................................8
QUANTITATIVE METHODOLOGY:.................................................................................9
ANALYTICAL FINDINGS....................................................................................................10
DATASET:...........................................................................................................................10
CLUSTERING AND DATA MINING:..............................................................................11
DATA TRAINING AND TESTING...................................................................................14
RECOMMENDATION BASED ON ANALYSIS..................................................................17
GEOGRAPHIC REGION THAT SHOULD BE TARGETED TO INCREASE SALES
AND GENERATE PROFIT.................................................................................................17
PRODUCT THAT SHOULD BE PRIORITIZED FOR SALES.........................................18
IMPACT ON SALE AFTER FREE SHIPPING..................................................................18
RECOMMENDATIONS FOR THE COMPANY...................................................................19
IMPLEMENTATION PLAN FOR THE RECOMMENDATIONS.......................................20
IMPLEMENTATION OF THE PYTHON CODE..................................................................21
CONCLUSION........................................................................................................................23
REFERENCES.........................................................................................................................24
APPENDIX..............................................................................................................................25
List of Figures
Figure 1: Camera Dataset in the gadgets.csv file.....................................................................10
Figure 2: Data Analysis............................................................................................................12
Figure 3: Graph Showing the launch year and the Customer who have bought the Camera of
that year....................................................................................................................................12
Figure 4: No of customer who bought the Product versus the Data........................................13
Figure 5: Product Prize versus Monthly Sales.........................................................................13
Figure 6: Training Graph for the Monthly Sales and the Camera Prize..................................14
Figure 7: Test Set implementation of the Dataset....................................................................14
Figure 8: Linear Regression.....................................................................................................15
Figure 9: Final Regression for Prediction................................................................................15
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Figure 10: Recommendations..................................................................................................17
Figure 11: Recommendation....................................................................................................18
Figure 12: main.py...................................................................................................................21
Figure 13: Test and Training plotting Over Dataset................................................................22
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ASSUMPTIONS MADE
There are various assumptions that have been made in order to achieve the clarity over the
data:
 Dataset is developed for the Camera Segment
 Developed dataset is built from Scratch
 Some dummy Geographic location of the places where the Camera had delivered is
added
 A delivery person usage is not calculated
 It consists of various number of Cameras from the previous years that are build till
now
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BACKGROUND
Shopping is the most popular activity on the web. Anyone can display their pages, specific
good, and services. In 1991 internet was opened for commercial use and that time e-
commerce became possible. Since that year millions of business opened their pages for their
specific good and services. At first, e-commerce was only meant process of moneymaking
businesses with the help of innovative methods such as interchange of electronic information
and automated cash transfer (Orderhiven, 2018). Basically, the internet took 4 years to grown
up and in 2000 there are many companies in the US and Europe who represented their
services in the world.
Currently, there are 5 largest internet retailers. Those are:
1. Dell
2. Staples
3. Amazon
4. Hewlett Packard
5. Office Depot
According to the research of 2008, amazon.com have about 615 million users per year and
they like the best thing is their review system. e-commerce history is evolving like customer
advantages’ updating.
The company only sells cameras in limited areas and those areas that very repudiated image
of this company as its main department is camera and unlike other e-commerce company, it
does not indulge with the sellers for the process of delivery. Also, the Customer service of
this company is very supportive.
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INTRODUCTION
E-commerce is known as electronic commerce. Electronic commerce is the process of
purchasing and selling products on online websites. E-commerce is a most famous method of
making money on the internet and a great opportunity. Many companies and start-ups are
fighting with each other to be on top and the technology became advanced so it is tougher for
them to reach their goals(Embitel, 2018). Electronic commerce is not used for only sales but
covers also: consulting of users, online payment, preparations of estimates online, real-time
management of product and item possibility, after sale services and delivery tracking.
This report is useful for an electronic commerce company or organization those want to
improve their profit by data analytics. This report tells about last month sales and how can we
increase in sell in an upcoming month. It takes care of what product should be prioritized,
what product can give with free shipping and by using python programming it is done. It
takes care of what be to prioritize, what product can give with free shipping and by using
python programming it is done.
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RESEARCH METHODOLOGY
The goal of this research is to identify answers to question from the action of scientific
methods. the main purpose of research is to search the fact which is invisible. there are many
types of the research methodology such as story completion test, sentence completion test,
and other techniques. Research methodology is a procedure or method used to select,
identify, process and evaluate information about a subject.
Research methodology is a method to store data and information with the intention of
building a business. The research methodology may involve interviews, publication research,
surveys and different research methods, and could involve both historical and present
information. It is a symmetrical idea to resolve a problem. Research methodology examines
and generates study designs, research process, and dimension instruments especially for
human-associated research. Research methodology basically divided into 2 parts. Such as
1. Qualitative research
2. Quantitative research
QUALITATIVE METHODOLOGY:
Qualitative research methodology is related to qualitative phenomenon; this phenomenon
involves quality or property. Motivation research is a type of qualitative research
methodology. The goal of Motivation research is to identify the underlying desires and
motives, using in deep interviews for the reason. This research methodology is very crucial in
the behavioural science which is used to identify human behaviour (Krishnakumar 2018).
Qualitative research is used to handle those events that are impossible or difficult to quantify
mathematically. Like meanings, symbols, attributes, and beliefs.
Benefits of Qualitative research methodology:
 Good for hypothesis generation and explorative research
 The contributor is capable to provide information in their particular words
 Fewer assumption and restriction are located on the information to be composed
Limitations of Qualitative research methodology:
 Time consuming
 It is very difficult to identify the reliability and validity of lingual data or information
 Data or information overload
There are 5 kinds of qualitative methodology:
 Case study
 Ethnography
 Grounded theory
 Narrative
 Phenomenological
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QUANTITATIVE METHODOLOGY:
This research methodology depends upon the amount or measurement of quality. It can be
present in the form of quantity. It describes resolves problems and infers using numbers. The
purpose of quantitative research methodology is to generate mathematical models and
theories. This research is made with the help of scientific techniques, which can involve:
 Collection of experimental information and data
 The development of theories, hypotheses and models
 Modelling and evaluation of data
 Analysis of results
 The generation of methods and techniques for measurement
 Handling of variables
Benefits of Quantitative research methodology
 This methodology permits research to analyse and measure information or data
 Quantitative research methodology is used to check hypotheses in application
Limitations of Quantitative research methodology
 In Quantitative research methodology the reference of the experiment and study is
ignored
 A large number of population must be learned for more actual results
Quantitative methodology is divided into 4 types:
 Correlational
 Descriptive
 Experimental Research
 Quasi-Experimental
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ANALYTICAL FINDINGS
DATASET:
This dataset is in Comma Separated Format and it has the following Metadata:
 ProductID
 ProductName
 ProductPrice
 ShippingType
 MonthlySales
 GeographicRegion
 NoOfCustomersWhoBoughtTheProduct
 Customer Type
 YearLaunched
Figure 1: Camera Dataset in the gadgets.csv file
Figure 1 shows the snippet of the Dataset that is being used in this Data Analysis report
project. The First Line represents the Metadata that is stored in it while the Commas act as a
Separator in that is going to help in distinguishing the data and keep them in the same data
columns.
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CLUSTERING AND DATA MINING:
Data Mining is a process which is used to extract the data from the large size of data. Data
mining is a process of minimizing information from data. Data mining is used in many
applications such as-
 Production Control
 Science Investigation
 Market Analysis
 Consumer Retainer
 Fraud Detection
Applications of Data Mining
 Market Evaluation and Management
 Fraud Detection’
 Risk Analysis and Management
Market Evaluation and Management:
 Consumer profiling- it helps to identify what type of people purchase what type of
item.
 Identify consumer buying pattern- it helps in determining consumers buying pattern.
 Determining consumer requirements- it helps in determining the best item for
different consumers.
 Target marketing- it helps to search cluster model of consumer who uses the similar
characteristics like income, interest.
Risk analysis and management:
 Competition- it includes market directions and monitoring competitors.
 Resource planning- it includes comparing and summarizing the resources.
Cluster analysis is task of collecting a set of items in such a style that item in the same style is
more relevant to one another those in another style. Clustering is also known as an
unsupervised learning process (Tutorialspoint 2018). Clustering contains 2 types of
algorithms:
1. Hierarchical- hierarchical clustering is a process of cluster evaluation which asks to
make a hierarchy of clusters. Hierarchical clustering use two types of strategies:
 Divisive- divisive is a top-down method. In divisive examination starts in one
cluster and separation are achieved recursively.
 Agglomerative- agglomerative is a down bottom-up method. Every
examination starts in its personal cluster and group of clusters are combined.
2. Non-hierarchical- non-hierarchical clusters search a collection of things which
minimizes evaluation criterion.
In non-hierarchical clustering algorithm connection within clusters is undeterminable.
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MONTHLY SALES PREDICTION
Naïve Bayes is a simple machine learning algorithm which is used for clustering. Naïve
Bayes clustering classifier is used for multi-class and binary class classification problems. It
is known as naïve Bayes because the computation of possibility for every hypothesis is
clarified to make their computation tractable (Towards Data Science, 2018). Naïve bays
clustering classifier is a group of division algorithms depends upon Bayes theorem. Naïve
Bayes is depended upon probability models that unified strong freedom assumptions.
DATA ANALYSIS
Figure 2: Data Analysis
Figure 2 shows some Histograms that represents this data in order to create predictions over
this data.
Figure 3: Graph Showing the launch year and the Customer who have bought the Camera of that year
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Figure 3 shows that the customer bought those cameras that are launched very recently. This
shows that the company should prioritize the newly launched cameras that could help in
increasing the sales and could generate profit.
Figure 4: No of customer who bought the Product versus the Data
Figure 5: Product Prize versus Monthly Sales
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DATA TRAINING AND TESTING
The Whole Data is divided into the Test Set and the Training set with the ration of 1/3.0. This
implies that the test size is going to be the one-third of the training size and the dataset could
be better predicted and could help in making a better analysis report.
Figure 6: Training Graph for the Monthly Sales and the Camera Prize
Figure 6 shows the analysis of the training set of the Data. The Training Set is created in
order to achieve the best detection over this data and it could help in making a better
predictive model.
Figure 7: Test Set implementation of the Dataset
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Figure 7 shows the test Set figure of the Camera Price with the monthly sales of particular
camera. This helps in fitting the data in a better way and the analysis over the data could be
depicted for the betterment of the prediction. This shows how much the data is better and
what type of data is helpful.
Figure 8: Linear Regression
Figure 8 shows the Linear Regression Graph that is implemented over the Created Dataset
this dataset is going to help in deciding the future prediction over that data. The Linear
Regression have an 85% accuracy after the training and the Test Data result.
Figure 9: Final Regression for Prediction
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Figure 9 shows the Final Regression that is going to help in making the Final Prediction over
the Created dataset. This dataset is very predictive as it uses the Test Case for the Making of
the Linear Regression model. Also, it is going to be helpful in making a prediction for the
upcoming month. Those products that are near or on the regression line they are going to
prioritized for the Sale and should be able to create an effective model. This model is 91%
effective for this data and it shows that if the company follows this path the Profit could be
generated and the system could be much more effective using that.
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RECOMMENDATION BASED ON ANALYSIS
GEOGRAPHIC REGION THAT SHOULD BE TARGETED TO INCREASE SALES
AND GENERATE PROFIT
There are several regions that have a minimum number of Clientele. To increase the sales the
company should increase the sales in those regions. There are some regions that have
minimum customers those are:
 coonamble
 beige
 Avalon
 Whyalla
 Melbourne
 Benolong
 Officer
 Cattle Creek
 Langsborough
 Mildura, etc
Among the above geographic area, the company should focus on the Benolong as it is the
region where there are very fewer Customers and the monthly sale is very less.
Figure 10: Recommendations
From the figure 10, it could be seen that the Region to be the focus is Benolong.
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PRODUCT THAT SHOULD BE PRIORITIZED FOR SALES
Figure 11: Recommendation
Figure 11 shows that the company should be focused on the Agfa ephoto CL18 that cost $179
and has the bestselling customer rate. This product is highly recommended and customer
found it reliable. So, this Product should be in Stock Every time the regions that have very
less Customer this product need to be prioritized. This will improve the reputation of the
company along with the sales that could help them in gaining profits. Although figure 11
shows that the company should give priority to the new camera but the analysis shows that
the company should give most of the priority of Agfa ephoto CL18.
IMPACT ON SALE AFTER FREE SHIPPING
This company usually give free shipping to the old customers or the members of the
company. This gives the company a better relationship with the old customers but the new
customers have to pay kind of Delivery Charges for getting their product at the doorstep.
So, due to this reason the company could not get any profit and new Customers very easily.
This could be minimised if the Company starts to give the free shipping to new customers
also that can help in making the company much more approached and the new customers are
going to be helpful in increasing the sales of the company and increasing the profit of the
company.
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RECOMMENDATIONS FOR THE COMPANY
 Improving average order price
 Final thoughts
 Control Devise and Merchandising Rules
 By building faith
 By proceeding to cross-sell
 Testing of products and items are necessary
 Product or item recommendations in the online websites
 Get Door to Door Delivery
 Improve the data storage capacity
 Time to Time Stock Check
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IMPLEMENTATION PLAN FOR THE RECOMMENDATIONS
This company should be focus on the following steps in order to get a better profit
 Consumer retention
 Planning for electronic commerce business
 Consumer benefit or acquisition
 Technology selection
 Consumer engagement
 Business evaluation and consumer insights
 Optimizing metrics
 The company should market those products that are recent and this could help in
generating more profit
 The warehouse should be stocked with the products that are launched recently
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IMPLEMENTATION OF THE PYTHON CODE
Figure 12: main.py
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Figure 13: Test and Training plotting Over Dataset
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CONCLUSION
This report is for gaining the profit and advantages for an e-commerce company. It has
figures from which we can prove the hypothesis. The main objective of this report is to
analyse dataset and find the way from which company gains more profit. A Python code is
made that uses Several Modules for the better analysis of the data.
For this report, data mining is done, and after that clustering, we can find the way from which
can adjust the products and gain more profit. Linear Regression is used to assume the next
month's sales and help the organization to focus from where should they can earn more profit
“Benolong” is the main region that needs to b focus and marketing if the company want profit
and be on top in e-commerce.
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REFERENCES
 Embitel. (2018). 7 Step Framework for Successful E-commerce Implementation.
[online] Available at https://www.embitel.com/blog/ecommerce-blog/framework-
successful-ecommerce-implementation [Accessed 2 Jun. 2018]
 Orderhive. (2018). E-commerce Product Recommendation to increase your sales.
[online] Available at https://www.orderhive.com/ecommerce-product-
recommendation-to-increase-your-sales [Accessed 2 Jun. 2018].
 Towards Data Science. (2018). Naive Bayes in Machine Learning – Towards Data
Science. [online] Available at https://towardsdatascience.com/naive-bayes-in-
machine-learning-f49cc8f831b4 [Accessed 2 Jun. 2018].
 Tutorialspoint. (2018). Data Mining Cluster Analysis. [online] Available at
http://www.tutorialspoint.com/data_mining/dm_cluster_analysis.htm [Accessed 2
Jun. 2018].
 Krishnakumar (2018). Types of Research. [online] Slideshare.net. Available at
https://www.slideshare.net/vaisalik/types-of-research [Accessed 3 Jun. 2018].

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APPENDIX
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