Reflection – Learning Launch 

 Challenge 

 Being an engineering student , I face a wide variety of people every day . Some are seniors in   college some are my guides for projects or internships . Conveying my ideas to them in the exact   way that I think is not easy . Each one of them has different experiences and views in life . Every   time we work on projects , we are expected to give our best output to the companies . Each project   has a different approach . The project I recently worked on was loan default prediction using   machine learning . The topic itself had a wide scope hence it was n’t as easy as we thought it to   be . In this project , we had to find out if a particular loan applicant will default a loan or not using   machine learning techniques . This can be done by building a machine learning model , fitting the   data into the model and testing it . Once the model is tested to give the desired output , it can be   used by the banks . We had to find appropriate data and fit it in the right kind of model that would   give us the maximum accuracy within a month . The challenge here was that we do n’t know   which model would best fit our data and give it to the bank . It would highly impractical to fit any   random model and give the project to the bank as it would not give the desired output n that   might lead to huge losses for the bank . Hence learning launch came into the picture . 

 Selection 

 For our challenge , we had to choose the model that gave us the best results for the right kind of   data and that would be beneficial for the bank . My teammates and I applied various techniques to   the data that we found so that it would fit the model in the best way . We also found out various   models in which the data could be fit in but we did n’t have the time to fit the data into each   model and test it because the data is huge . Hence we used learning launch technique . We choose   a very small sample of the data . And fit it into every model and tested it for accuracy . 

 Application 

 As mentioned in module 4 ,   the key is to do experiments that are fast and cheap . Because you   want to do lots of experiments . Similar to that , we have a lot of models and we have to build the   best one out of them . So we experiment / test smaller versions of data on different models . This   did not take us much time but the results were unexpected . The model that we expected to give   maximum accuracy gave the least accuracy . Like in tradition thoughts , we thought that the   problem was with the model . But when we worked on the models , we realised the models were   perfectly fine . The problem lied in the root i.e. the data . The sample data we used was incorrect   and hence gave wrong results . Hence we fixed the data and fit it into the model again and boom !   It gave us the best ever results . Thus with the implementation of learning launch tool we found   out the best model for our system and also fixed the data in no time . Unlike in the traditional way 

 in which we would have built all the models on the whole data just to find out the best one would   have taken us a year or more , here we implemented small samples of the models , tested them for   their accuracies and chose the best out of them in a very small span of time . Thus saving time ,   effort and money . 

 Insights 

 Thus by using launch learning , we conducted short and quick experiments on the different types   of models using sample data thus finding out that the best model gave the worst results . But   unlike the traditional way in which we would have tweaked the model , mind mapping helped us   to recognise that the problem lied in the data and not in the model . Thus failing early helped us to   rectify our problem and helped us to succeed in the end . 

 Approach 

 The approach that we used her helped us a lot when compared to any other way . If I ever have to   do it again , ill use a similar approach however I would try to use various other tools I have   learned about from this course . Thus coming up with an even better model . 


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: Reflection – Learning Launch 

 Challenge 

 Being an engineering student , I face a wide variety of people every day . Some are seniors in   college some are my guides for projects or internships . Conveying my ideas to them in the exact   way that I think is not easy . Each one of them has different experiences and views in life . Every   time we work on projects , we are expected to give our best output to the companies . Each project   has a different approach . The project I recently worked on was loan default prediction using   machine learning . The topic itself had a wide scope hence it was n’t as easy as we thought it to   be . In this project , we had to find out if a particular loan applicant will default a loan or not using   machine learning techniques . This can be done by building a machine learning model , fitting the   data into the model and testing it . Once the model is tested to give the desired output , it can be   used by the banks . We had to find appropriate data and fit it in the right kind of model that would   give us the maximum accuracy within a month . The challenge here was that we do n’t know   which model would best fit our data and give it to the bank . It would highly impractical to fit any   random model and give the project to the bank as it would not give the desired output n that   might lead to huge losses for the bank . Hence learning launch came into the picture . 

 Selection 

 For our challenge , we had to choose the model that gave us the best results for the right kind of   data and that would be beneficial for the bank . My teammates and I applied various techniques to   the data that we found so that it would fit the model in the best way . We also found out various   models in which the data could be fit in but we did n’t have the time to fit the data into each   model and test it because the data is huge . Hence we used learning launch technique . We choose   a very small sample of the data . And fit it into every model and tested it for accuracy . 

 Application 

 As mentioned in module 4 ,   the key is to do experiments that are fast and cheap . Because you   want to do lots of experiments . Similar to that , we have a lot of models and we have to build the   best one out of them . So we experiment / test smaller versions of data on different models . This   did not take us much time but the results were unexpected . The model that we expected to give   maximum accuracy gave the least accuracy . Like in tradition thoughts , we thought that the   problem was with the model . But when we worked on the models , we realised the models were   perfectly fine . The problem lied in the root i.e. the data . The sample data we used was incorrect   and hence gave wrong results . Hence we fixed the data and fit it into the model again and boom !   It gave us the best ever results . Thus with the implementation of learning launch tool we found   out the best model for our system and also fixed the data in no time . Unlike in the traditional way 

 in which we would have built all the models on the whole data just to find out the best one would   have taken us a year or more , here we implemented small samples of the models , tested them for   their accuracies and chose the best out of them in a very small span of time . Thus saving time ,   effort and money . 

 Insights 

 Thus by using launch learning , we conducted short and quick experiments on the different types   of models using sample data thus finding out that the best model gave the worst results . But   unlike the traditional way in which we would have tweaked the model , mind mapping helped us   to recognise that the problem lied in the data and not in the model . Thus failing early helped us to   rectify our problem and helped us to succeed in the end . 

 Approach 

 The approach that we used her helped us a lot when compared to any other way . If I ever have to   do it again , ill use a similar approach however I would try to use various other tools I have   learned about from this course . Thus coming up with an even better model .
### Output: 