Critically analyse and explain the concept of Artificial Neural Network and Decision Tree

ssignment Brief

Module Title	Business analytics and Intelligence			Assignment Number and Weighting
Module Code:	M121SSL			Assignment Title	Individual report

Assessment Information

This assignment is an individual work and it is designed to assess learning outcomes 1, 2, 3, 4 and 5:

Intended learning outcomes

1. Define and evaluate key concepts of business analytics.

2. Critically apply business analytics skills for decision making.

3. Critically analyse and interpret the outputs of data mining models and forecasting results for end-users.

4. Solve managerial problems and make systematic decisions by applying business data analysis techniques.

5. Have ability to apply business analytics to various international business contexts by selecting appropriate techniques.

Task One

1. Critically analyse and explain the concept of Artificial Neural Network and Decision Tree [15 marks]

Task Two- Artificial Neural Network Analysis

You have been provided with a data file on the frequent use of social media in the UK- called ‘social_media_data_CW2.xls’

1. Perform a descriptive analytics to illustrate the summary statistic that quantitatively describes or summarises key features of the data collected [15 marks]
2. Perform correlation analysis for the independent and dependent variables and determine the relationship between the variables [15 marks]
3. Predict the frequency of social media usage on society by using Artificial Neural Network technique in SPSS [NB: use ‘Impact of Social Media’ as the dependent variable] [15 marks]
4. Interpret the data output with supporting academic literature [15 marks].

Task Three: Exponential smoothing forecasting method

Table 2 below presents the combined gross (in millions of pounds) of Porcelain Cement sales from 1999 to 2018 in the UK. Use the Exponential Smoothing forecasting method with an alpha value of 0.4 to forecast the sale of cement for 2019.

Table 2: Movie releases from 1999 to 2018

Years	Demand (actual)
1999	234
2000	243
2001	244
2002	230
2003	235
2004	225
2005	240
2006	237
2007	243
2008	226
2009	232
2010	239
2011	236
2012	232
2013	224
2014	237
2015	228
2016	234
2017	245
2018	246

a. Compute the forecasted sale of cement using an alpha value of 0.4 [5 marks]

b. Plot the data for the actual sales and the forecasted figures on the same chart. Describe the main features of the series. [10 marks]

b. Calculate the Error, Mean Absolute Deviation (MAD) error, Mean Square Error (MSE) and Mean Absolute Percentage error (MAPE). Interpret the error values. [10 marks]

Word Count

Word count= 2250 excludes the graphs and calculations

There will be a penalty of a deduction of 10% of the mark (after internal moderation) for a report exceeding 10% or more.

The word limit includes quotations, but excludes the final reference list and appendices.