This is a template repository for the module 2 group assignment for PSTAT197A. The assignment objective is to develop a predictive model for webpage classification based on scraping text content.

• data contains several .RData files:

  • claims-raw.RData contains raw HTML and class labels for 2,165 webpages

  • claims-test.RData contains raw HTML for 929 unlabeled webpages

  • claims-clean.RData contains cleaned data for use in training; file generated by scripts/nlp-model-development.R and should be replaced as preprocessing pipeline is updated

• scripts contains some starter R scripts

  • preprocessing.R contains functions for quickly ingesting raw HTML and outputting analysis-ready data; this file is intended to be sourced

  • nlp-model-development.R shows example codes for preprocessing data and training a neural network and exporting the model

  • prediction.R imports a trained model and generates predictions

• results contains example technical outputs: a trained model and a set of predictions

• writeups contains templates for written summaries

Analytical tasks for this assignment fall under two categories:

• The preliminary tasks extend the in-class analysis in various ways; these are more exploratory in nature.

• The primary task is to develop a predictive model. This is open-ended but should be informed to some degree by the in-class analysis and the preliminary tasks.

We have provided a collection of unlabeled pages for you to classify. Once projects are complete we'll evaluate the accuracy of your model using the actual labels.

1. Augment the HTML scraping strategy so that header information is captured in addition to paragraph content. Are binary class predictions improved using logistic principal component regression?

2. Perform a secondary tokenization of the data to obtain bigrams. Fit a logistic principal component regression model to the word-tokenized data, and then input the predicted log-odds-ratios together with some number of principal components of the bigram-tokenized data to a second logistic regression model. Based on the results, does it seem like the bigrams capture additional information about the claims status of a page?

3. Build and train a neural network model. Experiment with architectures and training configurations until you find a model that performs at least as well as principal component logistic regression from task 1. Quantify the predictive accuracy.

1. Develop the best predictive model you can for (a) binary classification and also for (b) the multi-class setting. You can use any preprocessing and any modeling technique you like, including ones not discussed in class (consider exploring RNN or SVM). Export a deployable copy of each model and a set of predictions from each model on the test data claims-test.RData. Predictions should be formatted as a single data frame named pred_df with columns

   • .id containing the URL ID

   • bclass.pred containing the predicted label for the binary class setting

   • mclass.pred containing the predicted label for the multiclass setting

If this were a technical project, you might be expected to provide a brief executive summary of your work and a deployable version of your predictive model that could be easily used by another team or individual. In addition, you might use your model to classify new pages. Deliverables are designed to emulate these project outputs.

• Deliverable 1: write a 1-page summary of findings for tasks 1-3; store the rendered document in the writeups directory.

• Deliverable 2: write a 1-page summary of methods and findings for task 4. This should describe text preprocessing, your predictive models, and estimated prediction accuracy for each model. Store the rendered document in the writeups directory.

• Deliverable 3: a set of multiclass and binary predictions on the test data, stored as results/preds-group[N].RData .

• Deliverable 4: deployable copies of your fitted predictive models, stored in the results directory, with a short script illustrating their use to generate predictions on the test data, stored in the scripts directory.

Your written summaries should be very high-level with a minimum of technical detail. The audience for your writeups is a project mentor or supervisor: assume the reader is familiar with the project and has a working knowledge of the methods you've used. The aim of each summary is to convey the task(s) and result(s) as simply as possible in a linear fashion: task 1, result 1; task 2, result 2; and so on. Note that your results for each task are simply a predictive accuracy (one or a few numbers) and a comment on the accuracy (generally, comparison with another result).