How to analyse move kindness in python (Logistic regression, neural network, etc.)? With a team of researchers we were given the assignment to make a scale for the move kindness (how inviting a room or place is for exercise--e.g., a gym). In order to get objective results we were asked to make a measurement device. This device receives input through various sensors and maybe some true/false questions, and then analyze it with training information in mind. After the analysis it returns a number from, say: 1 to 10, indicating the move kindness. So a gym gets, for example an eight and a classroom a three. The problem with this is, that move kindness is very subjective, so we have conducted some surveys. For example one of the criteria is the temperature of the room/place. While the survey was being conducted we measured the temperature:


  From a scale from 1 to 10, what is your opinion about the temperature?


And then we measured the temperature. We have put all these information into some  spreadsheets:

Rating (Move Kindness): 8
Temperature: 18 degrees Celsius

At the end of this survey we asked them to give the move kindness a rating.
So we have this, for example: 
Temperature: 8, 18 
Light: 7, 300 
Humidity: 8, 50 
.... 
Rating (Move Kindness): 8

So my question is, what's the best way to analyse these data for a reliable measurement device using python?
We were thinking of using neural networks, because they can be trained, but logistic regression or some other machine learning algorithm is also an option. Can anyone give me some direction on this?
Okay, so from what I understand, you have a regression problem taking into account a variety of physical features. The reason I say that this is a regression problem, verses a classification problem is because the scale you are trying to predict is an ordinal scale.
There are a couple approaches to this. If your features are discriminative and linear enough, a simple least squares linear regression might work. If you believe the problem you have is to complicated for linear regressions, a simple vanilla neural network with one single output. I would recommend using the scikit-learn library in python for all models that are not neural networks. Here is a link to the generalized linear regression page.
That link has code samples and mathematical explanations. If you decide to use neural networks, and you don't have a great amount of samples or a need to use the GPU, the pyBrain library is great.
I wouldn't recommend using a logistic regression (since you mentioned it in your question), simply because a logistic regression is a classification problem, and I believe you would be better off approaching this from a regression standpoint.