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Saudi Electronic University Business Analytics Analytical Techniques Tasks Paper

Saudi Electronic University Business Analytics Analytical Techniques Tasks Paper


Instructions for Tasks

The full details of the tasks are in the attached Word Document. There are total of three tasks; you are expected to answer all the questions of the tasks. Task 1 is application of descriptive data. The task 2 will practically be an application and understanding of Regression and Classification Technique. The task 3 is a Data Mining Report. The maximum permitted word count for all the tasks is 2000 words. A guided word count for each task is provided.

General Instructions:

  1. Word Limit: Ensure you adhere to the total word limit of 2000 words for all tasks combined. Any content beyond this limit will not be considered for grading.
  2. Referencing: Ensure any external sources, data, or references are appropriately cited. Plagiarism is strictly prohibited. No use of GPT

Task 1: Application of Descriptive Data (Approx. 100 words):

  1. Dataset Analysis: Begin by understanding and exploring the dataset provided. Identify key variables and their types.
  2. Descriptive Statistics: Using tools such as Excel, R, or Python, calculate the basic descriptive statistics like mean, median, mode, standard deviation, and variance.
  3. Visualization: Develop relevant visualizations like bar charts, histograms, or scatter plots to represent the data distribution and patterns.
  4. Interpretation: Provide insights on the data distribution, trends, and any patterns you observe.
  5. Conclusion: Summarize the findings from your descriptive analysis.

Task 2: Regression and Classification Technique (Approx. 300 words):

  1. Selection of Technique: Decide whether regression or classification is more appropriate based on the nature of your dependent variable.
  2. Data Preparation: Ensure the data is cleaned, missing values are handled, and is split into training and testing sets.
  3. Model Development:
    1. For Regression: Determine the dependent and independent variables. Use a relevant regression technique (linear, logistic, etc.) to develop your model.
    2. For Classification: Choose an appropriate algorithm (e.g., Decision Trees, k-NN, SVM) based on the nature of your data.
  4. Model Evaluation: Use appropriate metrics (like R-squared for regression, accuracy/precision/recall for classification) to evaluate your model’s performance on the test data.
  5. Interpretation: Discuss the model’s results, the significance of variables, and any patterns observed.
  6. Conclusion: Sum up the findings and potential implications of your model.

Task 3: Data Mining Report (Approx. 1600 words):

  1. Introduction: Provide a brief overview of the context and objectives of your data mining task.
  2. Data Understanding: Describe the dataset, including the source, variables, and any initial observations.
  3. Data Preparation: Document any preprocessing steps you undertook, like normalization, handling missing values, or feature engineering.
  4. Data Mining Techniques: Discuss the techniques you employed (e.g., clustering, association rule mining, neural networks) and the rationale behind choosing them.
  5. Results: Present the findings from your data mining process. Use visualizations where necessary to showcase patterns or insights.
  6. Challenges: Highlight any challenges faced during the process, whether related to data quality, model performance, or interpretation.
  7. Conclusion and Recommendations: Conclude your report by summarizing key findings. Additionally, provide recommendations or potential applications of your insights.

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