I'm on my first (real), data, programming job. As everyone can imagine, this can be quite hard and I learn a lot from it, given I am a data science student in university. However, I am completely stuck and I want some help.
To give you some background information, this is what is going on: I am expected to build a model which can cluster people (based on care needed) given data, which is called a 'care-level'. This data are profile settings of users, sensor data (movement, doors, smoke, etc.), and alarm data (panic, smoke, inactivity, etc.). The alarm and sensor data has the timestamp of the event. I have data for 2 months available. In other words, I am expected to build a clustering model of people within those 2 months. Additionally, I don't have any data on their current care-level.
I restructered the data to be grouped on ID, year, month, time-of-day (night, morning, afternoon, evening). This grouped data is linked to every type of sensor, one-hot encoded, and then counted for every ID, year, month, time-of-day respectively. This looks like the following: structure of data, shape: (4688, 10) Disclaimer: for privacy concerns, I have blurred the entries. The prefix 'type_' stands for the type of sensor.
Right now, I have troubles to continue. I have my eyes on the goal, however, I have no clue how to get there anymore. I tried fitting a KMeans model, but I am just not able to interpret the model and if the clusters it 'finds' make any sense. You can basically say it is useless. What do you guys recommend to help me? It would be great if someone can give me some advice!