Causal and Predictive Analytics – Homework 3
Individual Assignment
This dataset gives the characteristics of applicants to a major credit card. The key dependent variable is card, which indicates whether a consumer was approved for a credit card. The remaining variables contain other relevant information about each consumer. The data on realworld setting that appeared in Greene, 2003.
However, I have modified the initial dataset, while keeping the relationships between variables intact. Download the training dataset that corresponds to the last digit of your student number. That is, if your student number ends in 4, you should download “Homework 3 Training Data – Number 4.csv”.
Assignment Materials for Download: 因果与预测分析代写
- An Rmarkdown template
- A dataset corresponding to the last digit of your student number.
Submission Checklist:
To help us grade the assignments efficiently and correctly, we ask that you submit your assignments in a specific format. A complete submission will submit the following to blackboard:
o A .rmd Rmarkdown file, based on the template for this assignment with all the code used to estimate your models.
o An R workspace containing your two chosen models. I have provided code in the template to save the models for you.
o You don’t need submit an html file, so long as you provide an Rdata file and an rmd file
o Place all files in a single zip before submission
Data Guide: 因果与预测分析代写
card: Boolean. Was the application for a credit card accepted?
reports: Number of major derogatory reports.
age: Age in years plus twelfths of a year. income Yearly income (in USD 10,000).
share: Ratio of monthly credit card expenditure to yearly income.
expenditure: Average monthly credit card expenditure.
owner: Boolean. Does the individual own their home?
selfemp: Boolean. Is the individual self-employed?
dependents: Number of dependents.
months: Months living at current address.
majorcards: Number of major credit cards held.
active: Number of active credit accounts.
Predictive Analysis (16 Marks) 因果与预测分析代写
Now, you will estimate a predictive model to predict whether a consumer is approved for a credit card, using the dataset that corresponds to the last digit of your student number. This might be useful to a firm that is selecting which consumers to target, choosing how much to pay for the contact information of a consumer, or a firm that is simply trying to forecast demand. Firms with better predictive models will be able to more efficiently target consumers, or make better purchasing decisions. Similarly, the quality of your predictions will form part of your grade here.
You will submit two predictive models:
a) The first predictive model, stored as model1A can use all the data except reports
b) The second predictive model, stored as model1B, can use all the provided independent variables, including reports
Save your models to an R Workspace with the code provided in the template.
To keep the computational burden low for this assignment, you may only use linear regressions or MARS models in this section. You can complete this section using the runif, subset, lm, earth, predict, and mean functions.
Your final submission will include a Rdata file with your two models. We will also look at your RMD file to see how you trained your model.
The two models will be graded out of 8 marks. The marks will be assigned as follows:
- Correctly submitting: 2 marks
- Run at least 10 different model specifications: 1 mark.
- Use k-fold cross validation following the steps in the notes. Do not use the built-in crossvalidation in the earth function as you will not get consistent results. 1 mark.
- Tune your model by trying different model specifications. This includes different types, formulas, and tuning parameters. Vary all three of these. 2 marks.
- I have held back a sizable portion of each dataset to evaluate your predictions. The graders will use this to evaluate the quality of your predictions, in terms of average out of sample mean-squared error. They will look at the distribution of predictions for your data set, and give marks based on the relative quality of your predictions. This will be used to assign 2 marks.
To improve your predictions, be thoughtful about the models you are running. Look to your previous model estimates and the data exploration process to see what variables worked in your context. The best assignment estimated only a fraction of the models that others did, but they learned with each model they estimated. Other groups estimated thousands of models, but didn’t think through their approach, and had worse predictions.
Bibliography 因果与预测分析代写
Greene, W. (. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.
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