BUSI 650
Instructor: Sana Ramzan
Name: ____________________________ Due Date: _______________________________
Assignment Guidelines:
Use the excel file named as “DatasetPM.csv” for this assignment.
Prepare a linear regression predictive model using the attached dataset. It should be a multiple variable prediction model.
Prepare a coding file for the prediction model on google colab. Attach a PDF file to the submission link. Your code should include the following items
⦁ Loading dataset code (1 mark)
⦁ Summarizing dataset (1 mark)
⦁ Visualization of dataset – Two graphs must be shown (2 marks)
⦁ Remove NA values from the dataset (1 mark)
⦁ Segregate the values in input Xs and output Y (1 mark)
⦁ Train the dataset (1 mark)
⦁ No training testing split is required
⦁ Provide prediction results of atleast 3 hypothetical values – make sure all hypothetical situations are seen in the code (1 mark)
⦁ Code for coefficient and intercept (1 mark)
⦁ Use the coefficient and intercept and check whether the model is accurate (1 mark)
In your model, you need to have many independent variables and one dependent variable. Please make sure that you have atleast 2 variables for the multiple regression analysis. You can select any column in the attached excel sheet as your variables.
Create a word document. Explain your findings in 500 – 1000 words. This word limit excludes tables, graphs and appendix
⦁ Explain the summarization dataset code (2 marks)
⦁ Explain the two visualizations from your code (4 marks)
⦁ What are the independent and dependent variables you used in your code and Why?
⦁ Provide explanation for the hypothetical values you created. (3 marks)
⦁ Explain your coefficient and intercept. (2 marks)
⦁ What are your conclusions from your prediction model? (2 marks)
organize your paper properly and make sure to follow the APA guidelines, if references are added. (5 marks)
Rubric
Key Points Grade allocation
Coding file in PDF format 10 points
Word file 15 points
Format and Organization of paper 5 points
Total 30 points
Naming convention:
Use the following format to name your files: Your name_Student ID_ PredictiveReport
Deadline:
November 27th, 11:59 PM
