• Purpose

• Problem Statement

• Mission

• Measurement Criteria

• A few more details

• Solution

• Project Setup

• RESTful URLs

The purpose of this problem is to evaluate your abilities in several dimensions at once.

1. Do you understand the principles of ML/AI/data science/
2. Can you build something that works
3. Do you have a grasp of the tool chain from code on your local to code in production
4. Can you explain your design and thinking process
5. Are you excited by learning and challenges

We process documents related to mortgages, aka everything that happens to originate a mortgage that you don't see as a borrower. Often times the only access to a document we have is a scan of a fax of a print out of the document. Our system is able to read and comprehend that document, turning a PDF into structured business content that our customers can act on.

This dataset represents the output of the OCR stage of our data pipeline. Since these documents are sensitive financial documents we have not provided you with the raw text that was extracted. Instead we have had to obscure the data. Each word in the source is mapped to one unique value in the output. If the word appears in multiple documents then that value will appear multiple times. The word order for the dataset comes directly from our OCR layer, so it should be roughly in order.

Here is a sample line:

CANCELLATION NOTICE,641356219cbc f95d0bea231b ... [lots more words] ... 52102c70348d b32153b8b30c

The first field is the document label. Everything after the comma is a space delimited set of word values.

The dataset is included as part of this repo.

Train a document classification model. Deploy your model to a public cloud platform (AWS/Google/Azure/Heroku) as a webservice, send us an email with the URL to you github repo, the URL of your publicly deployed service so we can submit test cases and a recorded screen cast demo of your solution's UI, its code and deployment steps. Also, we use AWS so we are partial to you using that ... just saying.

We will measure your solution on the following criteria:

1. Does your webservice work?
2. Is your hosted model as accurate as ours? Better? (think confusion matrix)
3. Your code, is it understandable, readable and/or deployable?
4. Do you use industry best practices in training/testing/deploying?
5. Do you use modern packages/tools in your code and deployment pipeline like this?
6. The effectiveness of your demo, did you frame the problem and your approach to a solution, did you explain your thinking and any remaining gaps, etc?
7. Are we able to run your testcases against your webservice? Can we run them against our webservice?

Webservice spec:

• RESTful API
• Respect content-type header (application/json and text/html minimum other bonus)
• Discoverable from root path
• URL encoded GET parameter "words" returns predicted document type (confidence is a bonus) in field "prediction" and "confidence"
• HTML pages should be readable by a human and allow for action, aka input field and submit buttons etc.
• Even a broken clock is right twice a day. A working webservice is a good first goal. It could return the highest likelihood doc class.

Technologies Used

• Python Programming Language
• Numpy, Pandas, scikit-learn libraries for data processing and creating LSA model
• json for saving LSA model
• Flask for creating RESTful APIs

Note: Final Folder in Solution Holds the finished and tested files

1. To download Python: click here
   
   Install the software as mentioned in it, and add its path to the system environment variable

2. To download Pip: click here
   
   Save the file ctrl+s (file should save in .py format)
   Open command prompt in the download location: python get-pip.py

3. To download Postman: click here
   what is postman?

4. To install Libraries that used in the project
   Type in the command prompt: pip install -r requirements.txt in root dir of the project

5. To check if everything installed properly
   In command prompt: python
   In Python console: import flask, numpy, pandas, json, sklearn
   If you get no error, Project Setup is Done

6. To run the project:
   Extract shuffled-full-set-hashed.csv file in Problem folder in the same dir.
   Open Command Prompt and navigate to Solution/Final where app.py file is located and type: python app.py
   In the web browser type localhost:5000/ to start using the web interface of the project

• '/' : GET
  To get started and to check if project is running or not.

• '/DataRead' : GET | POST
  GET- Read the data from default location
  POST- Read the data from the path given in post request
  Format = { "path" : ("<data_path>")stringType }

• '/DataSplit' : GET | POST
  GET- Take default data splitting Value, Ratio of 7:3 (Training : Testing)
  POST- assign new splitting ratio
  Format = { "ratio" : (<splitting_ratio>)floatType }

• '/DataPrcessing' : GET
  Process the training data and create mapper dictionary
  

• '/TFIDF' : GET
  This will start TFIDF process on the training data
  To Know more on TFIDF click here

• '/TopWords' : GET
  This will create rareWords dictionary from processed training data with the help of TFIDF API

• '/SaveLSA' : GET
  It will save LSA model in the local repository, which can be used later

• '/LoadLSA' : GET | POST
  GET- It will load LSA model from Default dir
  POST- It will load LSA model from the path given in post request
  Format = { "path" : ("<data_path>")stringType }
  I highly recommend to save and load LSA model and then run new inputs for predictions on them, it will save data and TFIDF processing time.

• '/PredictOne' : POST
  It will predict the output for one string statement and return { "prediction" : <("predicted")StringType>, "confidence" : <("confidence")FloatType> }
  Format = { "loaded" : ("< True:False (use loaded LSA?:use new LSA?) >")boolType, "data" : ""(stringType) }

• '/PredictMany' : POST It will predict the output for multiple string statements and return { "prediction": [ {"prediction" : <("predicted")StringType>, "confidence" : <("confidence")FloatType>} ] }
  Format = { "loaded" : ("< True:False (use loaded LSA?:use new LSA?) >")boolType, "data" : [""(stringType)] }

• '/Testing' : GET It will setup testing environment and take care of variables which are needed for getting further results.

• '/ConfusionMatrix' : GET return the dictionary type confusion matrix of the LSA model
  Format = {<("categoryKey")stringType> : <ConfusionMatrixforEachKey( [ [00,01], [10,11] ] )>}

• '/Score' : POST return the dictionary type of LSA model Score
  Format = {<("categoryKey")stringType> : <ScoreforEachKey( {"Accuracy": float, "Precision": float, "Recall": float, "F1_Score": float, "Support": int} )> }

• '/ScoreView' : POST return a string type of score of LSA model, which will give a better visual of score
  

It will map all the unique encoded words from string input to a unique ID

Top 100 most important words for each category of output, like BILL, RETURNED CHECK, POLICY CHANGE, etc.