Data Analysis for Business Applications using Python | Desklib

Title:Data Analytics
Task: Data analysis using Python fro business application
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Executive summary
Objective of paper
The purpose of our study is to employ different data mining and and data analysis
techniques in order to solve a number of business problems facing Infinity e-
commerce company.
What the paper covers
Our study explores classification techniques, descriptive statistics, and predictive
modelling for the data provided by the company in aim of determining the previous
sales patterns recorded by the sales and finance department, the factors that affect the
sales and how the products sold by the company are correlated. Further, we will
recommend better business practices that if employed will ensure an edge over our
competitors.
Assumptions
We assume that the products and the factors affecting the sales are uniformly
distributed across all the trade regions. We also assume that application of the
proposed methods will affect all the regions normally, i.e. all the variables whether
introduced or existent are normally distributed. In addition we assume that the cost of
advertisement reflects the extent of advertisement campaigns
Key words
Forecasting, Regression modelling, classification, SEM( Structural Equation
modelling)

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Introduction
In the recent past there have been growing interest for the E-commerce sector of
business operations partly due to its non-geographical restrictions and also for its
security and ability to reach a wide range of customers despite their location.
Consequently, the interest has brewed competition between old players and new
entries into the business. Infinity company is an e-commerce company that
specialized in supply of Electronic products as well as other major products. The
company has a range of other products with reputable brands in clothes, household
commodities, toys, and gadgets. The electronic line of goods dealt with are:
 Samsung electronics
 Microsoft products (laptops and accessories)
 LG appliances
 Hp computer products and accessories
 Sony Home entertainment products
 AUCMA electronics
 Apple mobile and computer appliances
 Techno mobile phones and accessories
The recent concern of declining of sales expressed by the executive has bee attributed
to new competition as well as shift in consumer preference across the business
regions. The new development therefore prompts for new methods of business
approaches, even better, the innovation and adoption of suitable business methods
For our analysis we need to:
i. Examine sales patterns of the previous business years

ii. Examine the relationship between the sales and current business patterns
iii. Propose new methods of business sales promotion
To enable us explore all the aforementioned problems, we will use data classification
to explore relationships and regression to predict sales, given new business practices.
Research Methodology
Data
The data for this study was obtained from the sales department and the financial
records department for the past three years. The entries available from the data are as
in:
Variable description Size Denotation/ measure
Product 1386  Samsung electronics
 Microsoft products
(laptops and
accessories)
 LG appliances
 Hp computer products
and accessories
 Sony Home
entertainment
products
 AUCMA electronics
 Apple mobile and
computer appliances
 Techno mobile phones

and accessories
Shipping method 1386 Paid- P
Free- F
Sales recorded 1386 AUS dollars
Geographical region 1386 Asia
America
Europe
Australia
Other parts
Number of customers 1386 unspecified
Price of product 1386 AUS dollars
Customer type 1386 New- N
Existing- E
Advertisement 1386 AUS dollars
For our classification method we employed logistic regression and explored the
relationship between data variables. We use logistic regression in examining how
sales are influenced by shipping methods, also how sales are spread across the
marketing regions for the company. Moreover, we will determine the interrelationship
between the independent variables and the response variable. According to an article
on logistic regression by NCSS (2016), “Logistic regression analysis
studies the association between a categorical dependent variable and a set of
independent (explanatory) variables.” We used linear regression for predictive