<p>I am a newbie in the field of AI/ML. I am trying to implement predictive analytics model on the data generated and collected every minute from a device with sensors.</p>

<p>I have two questions: </p>

<ol>
<li>What are various ML algorithms I can use to predict the number of
people in a room given temperature, humidity, luminosity, and motion
{ 0 | 1 }?</li>
<li>What other things can we predict using the above data from the
sensor that is deployed in a closed room?</li>
</ol>

<p><strong>Context:</strong></p>

<p>The device sends temperature, humidity, luminosity, and motion(yes/no) in real-time. I deployed this device in a closed room and started collecting data. Now I want to use this data to predict the number of people in the room using the data collected. I believe a multiple (linear/poly) regression model will help me in achieving this but, wanted to know if there are any other algorithms or any other use cases I can look into.</p>

CC BY-SA 3.0
2017-11-21T22:30:39.060Z
I expect there will be a lot of error margin (as I know people who can't sit still, and other who are extremely sedentary; some sweat a lot and other's not so much) but it's an extraordinarily interesting problem.  Welcome to AI!
DukeZhou
CC BY-SA 3.0
2017-11-22T01:36:53.747Z
What about sound?  Humans make a lot of sound (breathing/heartbeat vibrations etc) depending on how good your sensors are and where they are located...?  Just an idea...
solarflare
CC BY-SA 3.0
2017-11-23T09:45:17.783Z
@DukeZhou one here, is trying to broaden the scope of the question.
quintumnia
CC BY-SA 3.0
2017-11-23T19:36:28.530Z
@DenizC That's a great idea.  Dogs will generally pant, or their collars give a little jingle.  Wonder if the sensors can pick up things like heartbeat though...
DukeZhou
CC BY-SA 3.0
2017-11-23T22:53:47.483Z
Google remote heartbeat sensors - military has been using them for years to detect heartbeats through walls.
solarflare
<machine-learning><prediction><linear-regression>
What are suitable predictive analytics models for data from multiple sensors?
Jaya S Parimi
# What are suitable predictive analytics models for data from multiple sensors?

(Asked by: username_0 on 2017-11-21)

I am a newbie in the field of AI/ML. I am trying to implement predictive analytics model on the data generated and collected every minute from a device with sensors.

I have two questions:

1. What are various ML algorithms I can use to predict the number of
   people in a room given temperature, humidity, luminosity, and motion
   { 0 | 1 }?
2. What other things can we predict using the above data from the
   sensor that is deployed in a closed room?

**Context:**

The device sends temperature, humidity, luminosity, and motion(yes/no) in real-time. I deployed this device in a closed room and started collecting data. Now I want to use this data to predict the number of people in the room using the data collected. I believe a multiple (linear/poly) regression model will help me in achieving this but, wanted to know if there are any other algorithms or any other use cases I can look into.

**Tags:** machine-learning, prediction, linear-regression

> **Comments:**
> **username_1**: I expect there will be a lot of error margin (as I know people who can't sit still, and other who are extremely sedentary; some sweat a lot and other's not so much) but it's an extraordinarily interesting problem.  Welcome to AI!  
> **username_2**: What about sound?  Humans make a lot of sound (breathing/heartbeat vibrations etc) depending on how good your sensors are and where they are located...?  Just an idea...  
> **username_3**: @username_1 one here, is trying to broaden the scope of the question.  
> **username_1**: @DenizC That's a great idea.  Dogs will generally pant, or their collars give a little jingle.  Wonder if the sensors can pick up things like heartbeat though...  
> **username_2**: Google remote heartbeat sensors - military has been using them for years to detect heartbeats through walls.  

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## 0 answers

