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 wasn’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 don’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 didn’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.

