Over the past week, I worked towards improving the machine learning model, and working on the final project presentation that we had on Wednesday. For the models, I wrote and tested all the code for the 6-dimension polynomial regression to determine the likelihood of availability of benches at the gym. Furthermore, for the crowd prediction, I tried a few different Machine Learning models and tested different parameters to increase our accuracy values. I also passed on the predicted results to the EC2 instance to be integrated into the app, and added details for the models in the project presentation.<\/p>\n
For testing the regression model, the dataset was divided into a training and validation dataset with a 80\/20 split.\u00a0<\/span>On the validation dataset, MSE, RMSE, and Accuracy were calculated. A maximum accuracy value of 60% was achieved with an RMSE value of 0.65.\u00a0<\/span>The model was also tested against our measured values for the crowd, achieving a 65% accuracy in predicting crowdedness.\u00a0For the polynomial model, data was created by summing the time for which a bench was occupied over the hour, and averaging over days For the next week, I will be testing our models with a larger dataset to include all the measured values from the past month, which I hope would increase the predictive accuracy. I will also make some changes to the polynomial model to calculate bench availability for shorter time intervals. Lastly, I will ensure smooth functionality for the people counter.<\/p>\n With these updates, I am on track with our schedule. I look forward to showing our work at the demo!<\/p>\n","protected":false},"excerpt":{"rendered":" Over the past week, I worked towards improving the machine learning model, and working on the final project presentation that we had on Wednesday. For the models, I wrote and tested all the code for the 6-dimension polynomial regression to determine the likelihood of availability of benches at the gym. Furthermore, for the crowd prediction, … <\/p>\n
\nThe coefficient of determination (R2 value) for the fit was calculated to be 0.92 using sklearn_metrics built-in calculator.
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