Predictive Analysis in Business Decision-Making
Budget: ₹1,000 – ₹1,500 INR
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This report focuses on Predictive and/or Prescriptive Data Analysis using machine learning techniques. The objective is to demonstrate the ability to use analytical tools to build predictive models based on a chosen dataset within a specific industry or enterprise context. The analysis should identify opportunities for growth, potential threats, and hidden data patterns that can support leadership decision-making.
Purpose
The purpose of this report is to evaluate the ability to:
Apply analytical tools effectively
Build and evaluate predictive or prescriptive models using machine learning
Interpret results within a real industry or business context
Identify opportunities, risks, and hidden patterns
Support leadership decision-making through data-driven insights
The analysis should be based on the dataset previously explored and should include scenario simulations, forecasting, prediction, and optimisation techniques.
Use of Digital Tools
AI text-generation tools (including ChatGPT): Not permitted
Online translation or paraphrasing tools: Limited use for individual words or short phrases only
Grammar, spelling, and punctuation tools (e.g., Grammarly): Permitted
Task Overview
Using the opportunities, threats, and relationships identified earlier, the dataset should be used to conduct multiple scenario analyses through predictive models and machine learning techniques. The report should demonstrate how predictions, forecasts, and optimisation methods can support effective business decision-making and leadership strategy.
The work should include simulation of scenarios to assist leadership in evaluating future outcomes and strategic directions.
Report Structure
The report should follow this structure:
Cover Page
Include the report title, student name, student ID, and word count.
Overview of Predictive and Prescriptive Models
A brief explanation of relevant predictive and prescriptive analytics models and machine learning techniques.
Software or Tool Description
Description of the analytical software or tools used for the analysis.
Analytical Findings
Discussion of the predictive and/or prescriptive insights derived from the data.
Business Justification
Explanation of how the analysis supports industry or enterprise decision-making, growth opportunities, and strategic direction.
Relevant Research or Case Study
Discussion of supporting academic research or real-world case studies.
Bibliography
Minimum of five peer-reviewed academic references using Harvard (Anglia) referencing style.
Formatting and Submission Guidelines
Word limit: 1,000 words (±10%) excluding references
Font: Arial 10pt or Times New Roman 12pt
Spacing: Single-spaced
Referencing style: Harvard (Anglia)
Submission format: Word document only (.doc or .docx)
Have to use attached software
This report focuses on Predictive and/or Prescriptive Data Analysis using machine learning techniques. The objective is to demonstrate the ability to use analytical tools to build predictive models based on a chosen dataset within a specific industry or enterprise context. The analysis should identify opportunities for growth, potential threats, and hidden data patterns that can support leadership decision-making.
Purpose
The purpose of this report is to evaluate the ability to:
Apply analytical tools effectively
Build and evaluate predictive or prescriptive models using machine learning
Interpret results within a real industry or business context
Identify opportunities, risks, and hidden patterns
Support leadership decision-making through data-driven insights
The analysis should be based on the dataset previously explored and should include scenario simulations, forecasting, prediction, and optimisation techniques.
Use of Digital Tools
AI text-generation tools (including ChatGPT): Not permitted
Online translation or paraphrasing tools: Limited use for individual words or short phrases only
Grammar, spelling, and punctuation tools (e.g., Grammarly): Permitted
Task Overview
Using the opportunities, threats, and relationships identified earlier, the dataset should be used to conduct multiple scenario analyses through predictive models and machine learning techniques. The report should demonstrate how predictions, forecasts, and optimisation methods can support effective business decision-making and leadership strategy.
The work should include simulation of scenarios to assist leadership in evaluating future outcomes and strategic directions.
Report Structure
The report should follow this structure:
Cover Page
Include the report title, student name, student ID, and word count.
Overview of Predictive and Prescriptive Models
A brief explanation of relevant predictive and prescriptive analytics models and machine learning techniques.
Software or Tool Description
Description of the analytical software or tools used for the analysis.
Analytical Findings
Discussion of the predictive and/or prescriptive insights derived from the data.
Business Justification
Explanation of how the analysis supports industry or enterprise decision-making, growth opportunities, and strategic direction.
Relevant Research or Case Study
Discussion of supporting academic research or real-world case studies.
Bibliography
Minimum of five peer-reviewed academic references using Harvard (Anglia) referencing style.
Formatting and Submission Guidelines
Word limit: 1,000 words (±10%) excluding references
Font: Arial 10pt or Times New Roman 12pt
Spacing: Single-spaced
Referencing style: Harvard (Anglia)
Submission format: Word document only (.doc or .docx)
Have to use attached software