Question: APPENDIX 1 - DATA DICTIONARY Note: The meaning of each value for the internal codes of the organization is unknown. Besides blanks, 'Unkn' and '???'

APPENDIX 1 - DATA DICTIONARY Note: The meaning of each value for the internal codes of the organization is unknown. Besides blanks, 'Unkn' and '???' are expressions in the dataset that denote missing values. With this consideration, read in the dataset as a Pandas dataframe, and state the variables that contain missing values. (5 marks) Question 2 As part of data preparation, treat the missing data, and explain your rationale of the treatments. (15 marks) Question 3 Explain and implement three (3) other data preparation tasks required for further analysis of the data. Any appropriate Python related libraries, functions, methods (e.g. pandas.to_datetime) can be used. (15 marks) Question 4 Analyse the data and describe three (3) insights into the corporate claims processing of the insurance company, with at least one (1) supporting visualization created to illustrate each insight. (30 marks) Question 5 Perform linear regression modelling to predict the delay in days (between the Planned and Actual date) in processing the claims, explaining the approach taken, including any further data pre-processing needed for modelling. (25 marks) Question 6 Discuss the results obtained from the modelling and state the linear regression equation. APPENDIX 1 - DATA DICTIONARY Note: The meaning of each value for the internal codes of the organization is unknown. Besides blanks, 'Unkn' and '???' are expressions in the dataset that denote missing values. With this consideration, read in the dataset as a Pandas dataframe, and state the variables that contain missing values. (5 marks) Question 2 As part of data preparation, treat the missing data, and explain your rationale of the treatments. (15 marks) Question 3 Explain and implement three (3) other data preparation tasks required for further analysis of the data. Any appropriate Python related libraries, functions, methods (e.g. pandas.to_datetime) can be used. (15 marks) Question 4 Analyse the data and describe three (3) insights into the corporate claims processing of the insurance company, with at least one (1) supporting visualization created to illustrate each insight. (30 marks) Question 5 Perform linear regression modelling to predict the delay in days (between the Planned and Actual date) in processing the claims, explaining the approach taken, including any further data pre-processing needed for modelling. (25 marks) Question 6 Discuss the results obtained from the modelling and state the linear regression equation

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