Reference no: EM133775440

Business Intelligence

Design, implementation and evaluation of a business intelligence solution (Group)

Part A - Report (4000 Words) Part B -

Presentation

Introduction

In the context of this group evaluation, you will:

Analyze the methods applicable for predictive and prescriptive analytics using provided datasets.
Create and put into action a business intelligence solution.
Construct components of the proposed solution.
As a team, you will be responsible for delivering a written report and delivering a presentation.

The assignment relies on the dataset found in the file Assignment3_Question_Dataset.xlsx, which can be downloaded from LMS.

Case Study: Loan Prediction Dataset:

Data Description:
Loan_ID: This is a unique identifier or reference number for each loan application. It is used to distinguish one loan application from another.

Gender: This column likely records the gender of the loan applicant, indicating whether they are male or female. Married: This column may indicate the marital status of the applicant, specifying whether the applicant is married or not.
Dependents: This column typically records the number of dependents or family members financially reliant on the applicant. Education: This column indicates the educational background of the applicant, specifying whether they are educated or not. Self Employed: This column may show whether the applicant is self-employed or works for someone else.
Monthly Applicant Income ($): This column likely records the monthly income of the primary applicant in dollars.

Monthly Coapplicant Income ($): This column probably records the monthly income of any coapplicants, like a spouse or partner, in dollars. Loan Amount ($): This column typically indicates the amount of the loan applied for, usually in dollars.
Loan Amount Term: This column is likely used to specify the term or duration of the loan, such as the number of months for repayment. Credit History: This column may contain information about the credit history of the applicant, often indicating whether it is good or bad. Property Area: This column likely represents the geographical area or location of the property for which the loan is sought.
Loan Status: This column usually indicates the status or outcome of the loan application, such as whether it was approved or denied.

Task:

Tasks - PART A

The major assessment task is a comprehensive project involving predictive and prescriptive analytics on loan prediction datasets, which will ultimately result in the design and implementation of a business intelligence solution.

Let's break down the task and elaborate on each component:

Examination of Techniques for Predictive and Prescriptive Analytics:
In this phase, your group will explore and analyze various data analytics techniques and methods used for loan prediction. This typically involves studying statistical, machine learning, and data mining techniques that can be applied to historical loan data to make predictions about future loans. Predictive analytics aims to

forecast future events, while prescriptive analytics goes a step further to provide recommendations on what actions to take based on the predictions. Your group will need to research and understand these techniques, including the data preprocessing steps, model selection, and evaluation metrics.

Design and Implementation of a Business Intelligence Solution:
After gaining a deep understanding of the techniques, your group will be tasked with designing a business intelligence (BI) solution. A BI solution involves creating a system or platform that integrates and analyzes data to provide valuable insights for decision-making. In this context, it means creating a system that can handle loan data and provide insights into whether a loan applicant is likely to be approved or denied. The design phase involves planning how the system will be structured, what data sources will be used, and how the analytics will be applied.
The implementation phase is about actually building the BI solution. This may involve developing software applications, setting up databases, and integrating various tools and technologies. You'll also need to implement the predictive and prescriptive analytics models that were examined in the first phase. This might include using programming languages like Python or R, and machine learning libraries such as Scikit-Learn or TensorFlow.

Development of Elements of the Proposed Solution:
This component refers to the practical work of creating different components of the BI solution. This could include data collection and cleaning, model training and testing, integration with visualization tools, and the creation of a user interface if necessary. It's the hands-on work that transforms your design into a functional system.

Report and Presentation:
Once the design and implementation phases are complete, your group will need to compile a report that documents the entire process. The report should detail the techniques examined, the design of the BI solution, the steps taken in the development phase, and the results obtained. It should also include insights gained from the analytics, any challenges faced, and recommendations for improving the solution or addressing potential issues.

The presentation component involves summarizing the report's key findings and presenting them to an audience, such as your peers or instructors. This is an opportunity to showcase your work, explain your methodology, and share the insights your solution has generated. Effective communication and visualization of your results are crucial during this phase.

In summary, this assessment task encompasses a full cycle of data analytics and business intelligence development, from research and analysis to the practical implementation and reporting. It's a comprehensive project that allows your group to apply theoretical knowledge to a real-world problem, demonstrating your ability to harness data for decision-making in the context of loan prediction.

Tasks - PART B

Each member of the group will deliver a concise 5-minute oral presentation on the submitted business report and the accompanied visual dashboard.
Your submission should be divided into two distinct files:

Submit a Microsoft Word document containing your comprehensive business report, detailing the insights obtained from the completion of Part A.
Provide a separate Microsoft PowerPoint presentation containing the slides used for your presentation.