Data Analytics for Business Case using Python

1Running head: ICT 706 DATA ANALYTICS
Title:Data Analytics
Assignment: Data analysis for a business case using python
Name of the Student
Name of the University
Authors note

2Business Data Analysis
1. Executive Summary
Purpose of study
The purpose of our report is to explore different data analytics methods in solving the
problem of diminishing sales as well as suggest new methods with which to conduct
business, partly due to rising competition in the e-commerce business as well as shifting trend
and nature of business.
Scope
We will cover a range of data analysis tools such as forecasting for classification and
predictive analysis. Moreover, we will explore the descriptive statistics of the company’s data
so as to draw gainful insights about the performance of NILA e-commerce company.

3Business Data Analysis
Table of Contents
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4Business Data Analysis
2. Introduction
E-commerce is one of the latest developing sectors of business, with both new and old
players and As a result drawing competition among the groups. As a result, there is need for a
firm to as given the company’s revenue sources, the number of customers purchasing a given
commodity and whether they are return or one-time purchasers as well as the region they
come from. As such, it is prudent to predict the product sales and performance of the products
both locally and internationally. Forecasting will enable the executive to apply new strategies
for product promotion and be able to export more of the best playing products in aim of
maximizing sales.

5Running head: ICT 706 DATA ANALYTICS
3. Research Methodology
To conduct our analysis paper we employ linear regression in developing a sales
prediction model for our given business regions for NILA e-commerce company. Our
regression analysis will be developed to incorporate a number independent variables to
enable us develop a more predictive and suitable model. Through linear regression
(Forecasting), the Company will be able to as given and exploit any available business gaps
and also curb the risks that may occur. Linear regression method in our business Company
will prove important in business decision making. Analysis of the company’s Product
demand is key in the forecasting on the the possible quantity of products that a customer buy
or which will be exported to a given region by our Company. However, the demand of the
company products is not only the factor to be considered in prediction of the performance of
the business. By the application of the linear regression method in data analysis, the business
Company will venture past estimating of just the sales into other factors which will be able to
display effect on the amount of sales made by the company in order to ensure more the sales
and consequently the revenue generated by the firms.
Moreover, Forecast analysis will be useful for a firm to establish specific relationship
among the different variables by determining the distribution patterns in the different
variables of our data-set which were previously absent. For instance, analysis of total sales
and customer records are useful in indication of the market patterns. Such patterns could
incorporate growth in demand for given products or in a given business regions of the
country.
application of this methods helps the company to reduce the tremendous amount
unstructured unprocessed data in applicable and productive information use by the business.
As a result, it will be stated that forecasting analysis promotes near accurate as well as better

6Business Data Analysis
business decisions aimed at improving the firms performance in the now competitive market.
In the forecasting model for instance, it may be concluded that the amount of products
purchased are dependent on consumer purchases in the specified region and on the total sales
of the product in the chosen geographical region. Analyzed statistics on these factors should
be taken into consideration and hence evaluation of best fit for the sales and other variables.
In our report we explore different stages of forecasting as a means of classification and
predictive modelling.