I am working on a indoor localization based on magnetometer.
I have 9 separate time-series datasets of sensor readings taken from coordinates 00, 01, 02, 10, 11, and so on until 22. Basically I am using my own coordinate system and gathered data. The coordinate system looks like this:
0,0 | 0,1 | 0,2 1,0 | 1,1 | 1,2 2,0 | 2,1 | 2,2
The dataset has columns
Magnitude. Here's what all my dataset looks like:
I collected a sensor readings data at one place with coordinate say 0,0 and saved the file as
(coor00.csv) then at another place with coordinate say 0,1 and saved the file as
(coor01.csv) and so on.
What I want to predict is coordinates. I want to build a simple classifier that predicts the coordinate/place/area based on sensor readings.
Since I don't have any labels/tags so I thought of creating one myself by simply adding a column named
label. This column will be my target variable. So in
coor00.csv file I will add a column
label which will have details of what coordinates the sensr readings were taken from and I will do the same for other datasets. Then I will combine 9 datasets into a dataframe and then run a classifier after splitting the dataset into train and test.
There are plenty resources out there, but I just want to know how/where to start. I want to know if this is the right way.
I plan on using RandomForest classifier but I would appreciate any suggestions on what kind of classifier algorithms should be used?