Challenge 

 I work in the business intelligence department of my organization , and the main task of this   department is to analyze the key business processes that occur in our and complementary   markets . In fact , it all comes down to building profiles of consumers and the market as a whole ,   forecasting our operating indicators , and taking into account risks . In addition , my last task in this   department was to create a unified system for predicting consumer behaviour based on various   data ( both our private and public ) . Of course , internal resources ( budget and time ) limit the   project . The implementation period should not exceed 50 days , and the solution itself should   provide a high forecast accuracy . The core of the project is to find optimal indicators for   describing consumers and this is the most bottleneck in the development . 

 Selection 

 My team has already gone through several activities and design thinking tools , so we are ready   to try the learning launcher tool . A training launch is an experiment designed to quickly test an   idea and prototype . This is what you need in the face of a large number of indicator alternatives .   It is possible to understand which mathematical model is suitable for our company ( for our   market ) . Alexander Funk made it clear in his video in the fourth week that it is a great tool for such   purposes . 

 Application 

 When we first started training , we focused on objective indicators , on all sorts of numbers and   time series . In particular , we wanted to understand what objective indicators would allow us to   best describe the situation ( for example , the state of the stock market , operating indicators , etc . ) .   This tested our assumption that objective indicators can not give a high forecast accuracy ,   regardless of the algorithm used . Then we decided to involve the psychological research   department in our company . They helped us develop our models . In fact , we have built their   psychological portrait and the method of obtaining this portrait based on numerical and   qualitative indicators . 

 During the second launch of training , we focused on the implementation of this subjective   module of the forecasting system . In addition , this gave a result , having implemented this system   for several weeks ; we saw that our self - learning system , which combined all the previous   developments , made it possible to predict real behaviour . This improved the efficiency of our   company by 40 % and attracted many new customers . 

 Insight 

 Using a training launch tool , our team identified our assumptions and designed launches for   testing . In addition , we thought about the expected results , hoping to find that a flexible   approach even in the construction of rigorous mathematical models gave its result . Our actual   results initially did not differ from what we anticipated . However , after a few more months it   became noticeable that something was missing - a certain psychological market portrait . Moving   forward , our team will review this understanding and incorporate it into future launch projects .   Continued incorporating insights is especially important at this stage , as launches occur before   full deployment in the design thinking timeline . 

 Approach 

 Considering that our team has already gone through several activities and tools of design thinking   and is in this on the timeline , launching the training is a suitable tool . As mentioned earlier , our   team needs to analyze the information from our first two launches and incorporate it into future   launch designs . Future launch plans should reflect the knowledge gained , the progress made , and   move towards a convincing solution ready for full deployment . 


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 work in the business intelligence department of my organization , and the main task of this   department is to analyze the key business processes that occur in our and complementary   markets . In fact , it all comes down to building profiles of consumers and the market as a whole ,   forecasting our operating indicators , and taking into account risks . In addition , my last task in this   department was to create a unified system for predicting consumer behaviour based on various   data ( both our private and public ) . Of course , internal resources ( budget and time ) limit the   project . The implementation period should not exceed 50 days , and the solution itself should   provide a high forecast accuracy . The core of the project is to find optimal indicators for   describing consumers and this is the most bottleneck in the development . 

 Selection 

 My team has already gone through several activities and design thinking tools , so we are ready   to try the learning launcher tool . A training launch is an experiment designed to quickly test an   idea and prototype . This is what you need in the face of a large number of indicator alternatives .   It is possible to understand which mathematical model is suitable for our company ( for our   market ) . Alexander Funk made it clear in his video in the fourth week that it is a great tool for such   purposes . 

 Application 

 When we first started training , we focused on objective indicators , on all sorts of numbers and   time series . In particular , we wanted to understand what objective indicators would allow us to   best describe the situation ( for example , the state of the stock market , operating indicators , etc . ) .   This tested our assumption that objective indicators can not give a high forecast accuracy ,   regardless of the algorithm used . Then we decided to involve the psychological research   department in our company . They helped us develop our models . In fact , we have built their   psychological portrait and the method of obtaining this portrait based on numerical and   qualitative indicators . 

 During the second launch of training , we focused on the implementation of this subjective   module of the forecasting system . In addition , this gave a result , having implemented this system   for several weeks ; we saw that our self - learning system , which combined all the previous   developments , made it possible to predict real behaviour . This improved the efficiency of our   company by 40 % and attracted many new customers . 

 Insight 

 Using a training launch tool , our team identified our assumptions and designed launches for   testing . In addition , we thought about the expected results , hoping to find that a flexible   approach even in the construction of rigorous mathematical models gave its result . Our actual   results initially did not differ from what we anticipated . However , after a few more months it   became noticeable that something was missing - a certain psychological market portrait . Moving   forward , our team will review this understanding and incorporate it into future launch projects .   Continued incorporating insights is especially important at this stage , as launches occur before   full deployment in the design thinking timeline . 

 Approach 

 Considering that our team has already gone through several activities and tools of design thinking   and is in this on the timeline , launching the training is a suitable tool . As mentioned earlier , our   team needs to analyze the information from our first two launches and incorporate it into future   launch designs . Future launch plans should reflect the knowledge gained , the progress made , and   move towards a convincing solution ready for full deployment .
### Output: 