Over the past two weeks I was able to finalize and test out the ML model. The included adding and debugging some more code and using the RandomForestRegressor from SKLearn, as well as the fastai module. I then wrote code to use this model to predict the crowdedness values for the following week. Currently, I have managed to achieve a maximum of 60% accuracy, and am working to improve this to around 70%. I have also been writing the code to do a linear regression for the bench occupation times, which I will test tomorrow.<\/p>\n
Some of the hardware related challenges that I faced involved working with a Raspberry Pi for the first time. In order to better understand this, I watched a few of introductory youtube videos, and read reference code to understand how to build the people counter. Furthermore, I faced a similar issue while have to learn some of the new libraries for the machine learning analysis, particularly getting weather data through Openmeteo. Again, I was able to better implement this by using reference code and building upon it for my use. I also learnt some new machine learning analysis skills from other courses I was taking this semester.<\/p>\n
For the next week, I will be finalizing both the models and working on passing the predictions to the EC2 instance. I will also be working on our final presentation.<\/p>\n
With these updates, I am on track with our schedule.<\/p>\n","protected":false},"excerpt":{"rendered":"
Over the past two weeks I was able to finalize and test out the ML model. The included adding and debugging some more code and using the RandomForestRegressor from SKLearn, as well as the fastai module. I then wrote code to use this model to predict the crowdedness values for the following week. Currently, I … <\/p>\n