Improving Prediction of Office Room Occupancy Through Random Sampling

In many cases, you may think that you have a Big Data problem, when in reality you just have a lot of data that a simple sampling can result in great accuracy. In todays blog, I decided to use office room occupancy dataset provided by"Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models. The dataset provided has 6 independent variables (predictors): date with timestamp; temperature of the room in Celsius; relative humidity in percent, light in Lux; CO2 in ppm, and humidity ratio or the ratio between temperature and humidity. The occupancy is a categorical variable with 2 levels: 0 for not occupied; and 1 for occupied. The occupancy has been measured every minutes, for the period of February 11, 2015 to February 18, 2015, and its dataset size is 9,752. The question I want to investigate is can a small random sample produce performance as good as large sample? For the model, I will build a Deep Feed ...