ANL252 Python for Data Analytics:Plagiarism and Collusion

Plagiarism and Collusion 

Plagiarism and collusion are forms of cheating and are not acceptable in any form in a student’s work, including this ECA. Plagiarism and collusion are taking work done by others or work done together with others respectively and passing it off as your own. You can avoid plagiarism by giving appropriate references when you use other people’s ideas, words or pictures (including diagrams). Refer to the APA Manual if you need reminding about quoting and referencing. You can avoid collusion by ensuring that your submission is based on your own individual effort.

The electronic submission of your ECA will be screened by plagiarism detection software. For more information about plagiarism and collusion, you should refer to the Student Handbook (Section 5.2.1.3). You are reminded that SUSS takes a tough stance against plagiarism or collusion. Serious cases will normally result in the student being referred to SUSS’s Student Disciplinary Group. For other cases, significant mark penalties or expulsion from the course will be imposed. 

The text file “ship.csv” contains the data of reported ship damage incidents. They are taken from P. McCullagh and J.A. Nelder and can be found in their book Generalized Linear Models (New York: Chapman & Hall, 1983). The study’s main objective is to investigate how the number of ship damage incidents (variable: Y) is related to the following independent variables: • the aggregate months of service (MS), • ship type (T: 1−5), • year of construction (A: 1 for 1960-64, 2 for 1965-1969, 3 for 1970-1974, and 4 for 1975-1979), • period of operation (P: 1 for 1960-1974, and 2 for 1975-1979). Use JupyterLab to solve the following questions and attach screenshots to demonstrate the output of your Python program to each task

Question 1

Prepare the ship data stored in “ship.csv” for future analytics tasks using functions and methods of NumPy and pandas, respectively.

(a) Design your own Python program to carry out the following tasks:

(i) Read in “ship.csv” as pandas DataFrame called “ship”. Since there are 6 observations where MS and Y are “.” to indicate that they are missing values, declare this character as missing values in your program accordingly.

(ii) Since the variable names of this dataset are rather short and do not really describe the nature of the variables, rename the ship types to “types”, construction years to “c_years”, operation periods to “o_periods”, the aggregated months of service to “s_months”, and the number of incidents to “incidents”.

(iii) For better understanding of the data, compute the average service months and the average number of incidents for the cross-products of every category in types and operation periods. The averages should be rounded to the nearest integers. Store the resulting table to an object named “shipgroup”.

(iv) Replace the missing values in the variable “s_months” and “incidents” by the respective means of the other ships that share the same type AND the same operation period. Add comments to elaborate your Python program as well.

(v) Construct a Python program to save the target variable “incidents” in a pandas DataFrame named “Y”. 

(b) Except for the months of service and number of incidents, all the other variables, including “types”, “c_years”, and “o_periods” are actually nominal and not interval/ratio.

(i) Perform an appropriate data type conversion for these variables so that they can be recognised as categorical variables.

Question 2

In their book Generalized Linear Models (New York: Chapman & Hall, 1983), the authors P. McCullagh and J.A. Nelder used the Poisson regression to study the ship dataset. Poisson regression is a special case of the generalised linear models in which the target variable, or dependent variable, is Poisson distributed. Since one of the main application areas of Poisson regression is to fit linear models on count data, we can therefore use Poisson regression to predict the number of incidents (which are also counts) given some input variables. Mathematically, Poisson regression is a linear model in which the expected value of the target variable Y is calculated by where β0 is the intercept, β1, β2, … , βk are the coefficients of the independent variables X1, X2, …, Xk. E(Y) is the predicted, or expected value of Y, which will be transformed by the natural logarithm function.

(a) Find the corresponding scikit-learn module in the official website of scikit-learn and discuss the corresponding module, estimator, fit and predict functions, as well as their parameters in your own words. 

(b) Analyse the data by fitting a Poisson regression based on the DataFrames X and Y generated in Question 1. Follow the instruction in the official website and report the parameters of the estimated model. Create a Python program to fit a Poisson regression and generate a table or a DataFrame to present the coefficients with the corresponding labels.

The deviance of Y and its expected value E(Y), estimated by the model constructed in c), measures the goodness of fit of the model. The lower the deviance, the better is the model. Below is the equation of how it should be calculated. If Y = 0, the expression log[Y/exp(E(Y))] will be taken as zero. Employ your own Python program to compute D without using the score() function of the scikit-learn package.

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