This assignment builds on Lectures 10 to 12 and on Tutorials 8 to 11. You might want to consider using parts of the Python code discussed in those tutorials to answer some of the questions below.

Important: It is important that you do not change the type (markdown vs. code) of any cell, nor copy/paste/duplicate any cell! If the cell type is markdown, you are supposed to write text, not code, and vice versa. Provide your answer to each question in the allocated cell. Do not create additional cells. Answers provided in any other cell will not be marked. Do not rename the assignment files. All files in the assignment directory should be left as is.

Setting

Equifax Australia has provided us with synthetic loan application data from Australian proprietary companies. This data was generated to match the characteristics of actual lending proposals approved between February 2017 and March 2018. The Equifax data consists of two parts, which, to make it easier for you, we have merged together into one data set : Company Business Trading History Data: This first part of the data set contains historical business trading data from 25,000 Australian proprietary companies who were granted a loan between February 2017 and March 2018. Director Data: This second part of the data set contains information on up to four directors of each company. In case a company has more than one director, the corresponding data has been averaged across directors at the company level.
Since this is proprietary data that belongs to Equifax, we are not allowed to give you direct access to it. However, thanks to Jupyter Hub, you are able to access it remotely. In particular, using your knowledge from Tutorial 9, you are able to analyse it at an aggregate level and to use it for the estimation of credit scoring models.

The file called Equifax_Data_Dictionary.xlsx provides you with the dictionary for both company and director level data.

Helpful commands

The merged Equifax dataset will be referred to by the name assignment5. Please see Tutorial 9 for the details of how to run functions on remote data. We have implemented additional functions to help you with this assignment. Details of these functions are below : send_grouped_mean_request(): This function takes inputs in the form of a dictionary containing names of two columns, and returns a Pandas dataframe that contains grouped means of a column with respect to another column. For example, using this function with input {"data": "tutorial9.1","var": "age", "y": "SeriousDlqin2yrs"} will return a dataframe where each entry has a value for age and the respective mean of SeriousDlqin2yrs for applicants' of that age (see Tutorial 10).
send_glm_request(): This function takes inputs in the form of a reference to the remote data ("data") and the dependent variable ("y"), i.e., {"data": data, "y": y}, and outputs the detailed results of a full-fledged logistic regression model without feature selection.
send_logit_request(): This function is similar to the send_nn_request() method, but it performs the remote estimation of a customised logistic regression. It takes inputs in the form of a dictionary {"data": data, "test": 0.2, "x": features,"y": y, "scale":"True"} where : "data": Reference to the remote data (see below) "test": Fraction of the data used for testing "x": List of features (independent variables) used by the model "y": Target variable (dependent variable) of the model "scale": Indicator ("True"/"False") for scaling

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