I am a beginner in ML and I want to create a smart thermostat, that after collecting enough data from the interaction with the user, it will start to set the home temperature by itself.
What I got so far is the hardware prototype that lets the user set the temperature, and in the same time it posts the Environment and the UserSetTemperature to ThingSpeak (to easily store the data for later access)
The other part is a python algorithm that gets the data from ThingSpeak and it converts it into a Pandas DataFrame.
The data frame looks like bellow:
timeStamp environment_temp user_set_temp 2018-05-27T00:12:43Z 20 21 2018-05-27T00:17:27Z 20 22 2018-05-27T00:17:59Z 20 24 2018-05-27T00:20:01Z 20 21 2018-05-27T00:23:14Z 20 24 2018-05-28T09:39:07Z 20 22 2018-05-28T10:40:17Z 20 23 2018-05-28T20:12:47Z 20 25 2018-05-28T20:14:16Z 23 25 2018-05-30T20:29:30Z 18 24
And here is where I got stuck. I don't know how to use this data with the ML libraries in order to make predictions on how the temperature should be set when the environment temperature is x.
I tried to use the sklearn train_test_split() and LinearRegression(), but with no significant result. I really don't know how to use this data
Every suggestion will be highly appreciated!!