Challenge     I had the problem of developing a working , effective and efficient tool for predicting whether a   prospective customer is likely to default on a loan facility or not , using a Machine Learning algorithm .   Knowing very well that management had series of meetings , seminars and presentations every now   and then where figures and data were hurled at them , I had the duty to arouse their interests ,   sustain those interests and finally get their buy - in into my idea of predicting customer default   probability .     Selection     To do this I fell on story telling . The idea was to not just put across a convincing argument for my   proffered solution to the problem , but to connect the data and result to the values , characters and   emotions   of management through story telling . Additionally , through story telling , I sought to solve   the problem in accordance with corporate ideals as well as integrating other stakeholders ’   interests .     Application     Historic data on client credit history was available and so I started with that . I pointed to   management the outstanding reasons why clients come to contract loans and explained to them   how each of us who find ourselves in similar situation are very likely to seek financial assistance like   the clients do .   Also , as per the data analytics results , I pointed out to management   the key causes   of defaults on credit facilities . I also commented on the Machine Learning algorithm I used and its   efficiency in predicting default on loans , given information about the client . Finally , i told   management that it turned out that among clients who defaulted were those who were at lower   levels of the socio - economic strata and actually sought   to address very basic and essential needs   that everyone should have access to . In as much as management was determined to making profits   they were also empathic about their clients . So I suggested to them that for such clients who will   not qualify for credit facilities based of the algorithm , management should still go ahead to extend   the credit to them with a little premium for insurance cover against default . In this way everybody   wins !       Insight     When I finished the presentation , management was wowed by the fact that I did n’t only provide   a   working algorithm for solving the problem of predicting defaults on loans but I was empathetic   about the vulnerable and I was thinking systemically and holistically in a way that benefits all   stakeholders ; customers , managers , shareholders as well as external stakeholders like the insurance   company . According to them , I stretched the limit of providing an efficient and effective algorithm   but altogether a lop - sided solution , where just management would have won .           Approach 

 I feel story telling for this project was effective and efficient but there ’s room for a blended   approach . I envisage the possibility and the potency of using story telling with visualization as well .   With this we could achieve connecting the information with the emotions and ideals of   management while incorporating the interests of other stakeholders plus presenting a pictorial flow   of concepts , ideas and solutions , which is what Machine Learning and Data Sscience is about . 


EMAIL: 
ID_NUM: 
NAME_STUDENT: 
PHONE_NUM: 
STREET_ADDRESS: 
URL_PERSONAL: 
USERNAME: 

### Task: You are a PII (Personal Identity Information) Detection. Given a essays written by students, you have to identify ONLY the following:
NAME_STUDENT - The full or partial name of a student that is not necessarily the author of the essay. This excludes instructors, authors, and other person names.
EMAIL - A student’s email address.
USERNAME - A student's username on any platform.
ID_NUM - A number or sequence of characters that could be used to identify a student, such as a student ID or a social security number.
PHONE_NUM - A phone number associated with a student.
URL_PERSONAL - A URL that might be used to identify a student.
STREET_ADDRESS - A full or partial street address that is associated with the student, such as their home address.
### Input: Challenge     I had the problem of developing a working , effective and efficient tool for predicting whether a   prospective customer is likely to default on a loan facility or not , using a Machine Learning algorithm .   Knowing very well that management had series of meetings , seminars and presentations every now   and then where figures and data were hurled at them , I had the duty to arouse their interests ,   sustain those interests and finally get their buy - in into my idea of predicting customer default   probability .     Selection     To do this I fell on story telling . The idea was to not just put across a convincing argument for my   proffered solution to the problem , but to connect the data and result to the values , characters and   emotions   of management through story telling . Additionally , through story telling , I sought to solve   the problem in accordance with corporate ideals as well as integrating other stakeholders ’   interests .     Application     Historic data on client credit history was available and so I started with that . I pointed to   management the outstanding reasons why clients come to contract loans and explained to them   how each of us who find ourselves in similar situation are very likely to seek financial assistance like   the clients do .   Also , as per the data analytics results , I pointed out to management   the key causes   of defaults on credit facilities . I also commented on the Machine Learning algorithm I used and its   efficiency in predicting default on loans , given information about the client . Finally , i told   management that it turned out that among clients who defaulted were those who were at lower   levels of the socio - economic strata and actually sought   to address very basic and essential needs   that everyone should have access to . In as much as management was determined to making profits   they were also empathic about their clients . So I suggested to them that for such clients who will   not qualify for credit facilities based of the algorithm , management should still go ahead to extend   the credit to them with a little premium for insurance cover against default . In this way everybody   wins !       Insight     When I finished the presentation , management was wowed by the fact that I did n’t only provide   a   working algorithm for solving the problem of predicting defaults on loans but I was empathetic   about the vulnerable and I was thinking systemically and holistically in a way that benefits all   stakeholders ; customers , managers , shareholders as well as external stakeholders like the insurance   company . According to them , I stretched the limit of providing an efficient and effective algorithm   but altogether a lop - sided solution , where just management would have won .           Approach 

 I feel story telling for this project was effective and efficient but there ’s room for a blended   approach . I envisage the possibility and the potency of using story telling with visualization as well .   With this we could achieve connecting the information with the emotions and ideals of   management while incorporating the interests of other stakeholders plus presenting a pictorial flow   of concepts , ideas and solutions , which is what Machine Learning and Data Sscience is about .
### Output: 