Reference no: EM133511288 , Length: word count:2000

Business Intelligence

LO1: Analyse and apply strategies processes and underlying technologies for effective management of data to make evidence based decisions;
LO2: Critically analyse organisational and societal problems using descriptive and predictive analysis and internal and external data sources to generate insight, create value and support evidence based decision making;
LO3: Communicate effectively in a clear and concise manner in written report style for both senior and middle management with correct and appropriate acknowledgment of the main ideas presented and discussed.

Task 1 Exploratory data analysis and data preparation

Discuss the purpose and main objectives of exploratory data analysis (EDA) and explain how it helps in understanding a dataset and identifying important patterns, trends, and anomalies. (700 words)

Discuss the impact of missing data and outliers on predictive analytics results and suggest suitable strategies for handling missing data and detecting outliers (800 words)

Task 2 Exploratory Data Analysis and Linear Regression Analysis Carefully study Ecommerce_Customers.csv data set (See Table 1) and accompanying description of each variable. Each record in the Ecommerce_Customers.csv data set contains seven variables that determine Yearly Amount Spent (eighth variable)

Table 1 Ecommerce Customer - Data Dictionary

Variable	Description	Type
Email	Customer's email address- independent variable	Text
Address	Customer's physical address - independent variable	Text
Avatar	Customer's avatar color - independent variable	Text
Avg. Session Length	Average session duration in minutes - independent variable	Numeric
Time on App	Time spent on the company's app in minutes - independent variable	Numeric
Time on Website	Time spent on the company's website in minutes - independent variable	Numeric
Length of Membership	Length of customer's membership in years - independent variable	Numeric
Yearly Amount Spent	Amount spent by the customer yearly in dollars - Outcome dependent	Numeric

Task 2.1 Conduct and report on exploratory data analysis (EDA) of the Ecommerce_Customers.csv data set using RapidMiner Studio data mining tool. Note this will require use of a number of RapidMiner operators.

Provide following for Task 2.1: (300 words)
(i) a screen capture of your final EDA process, briefly describe your EDA process.
(ii) summarise key results of your exploratory data analysis in Table 2.1 Results of Exploratory Data Analysis for Ecommerce_Customers.csv. Table 2.1 should include key characteristics of each variable in Ecommerce_Customers.csv data set such as maximum, minimum values, average, standard deviation, most frequent values (mode), missing values and invalid values etc.
(iii) Discuss key results of exploratory data analysis presented in Table 2.1 and provide a rationale for selecting top 5 variables for predicting the yearly amount spent by customers (Yearly Amount Spent), in particular focusing on the relationships of independent variables with each other and with dependent variable Yearly Amount Spent drawing on results of EDA analysis and relevant literature on determinates of Yearly Amount Spent.

Hint: Statistics Tab and Chart Tab in RapidMiner Studio provide a lot of descriptive statistical information and the ability to create useful charts like Barcharts, Scatterplots, Boxplot charts etc. for EDA analysis. You might also like to look at running correlations and/or chi square tests as appropriate to determine which variables contribute most to predicting the yearly amount spent by customers (Yearly Amount Spent).

Task 2.2 Build and report on your Linear Regression model for predicting the yearly amount spent by customers (Yearly Amount Spent) using RapidMiner data mining process and appropriate set of data mining operators and a reduced set of variables from Ecommerce_Customers.csv data set as determined by your exploratory data analysis in Task 2.1.

Provide the following for Task 2.2: (200 words)
(i) A screen capture of Final Linear Regression Model process and briefly describe your Final Linear Regression Model process
(ii) Table 2.2 named Results of Final Linear Regression Model for Task 2.2 for Ecommerce_Customers.csv data set.
(iii) Discuss the results of Final Linear Regression Model for Ecommerce_Customers.csv data set drawing on key outputs (coefficients, standardised coefficients, t-statistics values, p-values and significance levels etc.) for predicting the yearly amount spent by customers (Yearly Amount Spent) and relevant supporting literature on interpretation of a Linear Regression Model.

Include all appropriate outputs such as RapidMiner Processes, Graphs and Tables that support key aspects of exploratory data analysis and linear regression model analysis of the Ecommerce_Customers.csv data set in your Report 2.

Note: export Processes and Graphs from RapidMiner using File/Print/Export Image option, include in Task 2 section or in Appendix 2 of Report 2.

Attachment:- Business Intelligence.rar