Answers.Body: <p>"Regression", is to estimate a continuous value as a function of some other parameters.</p>
  
  <p>There are different forms of regression, such as linear regression and logistic regression, that differ in the assumptions they make on the data.</p>
  
  <p>You could try looking up those for a start.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-12-05T13:48:45.597Z
Answers.Text: I used Accord.net's Multivariate Linear Regression for it and it beautifully and very accurately predicts the numbers, even on the weekends when there it predicts accurately less people in given hours. Thank you again for the answer!
Answers.UserDisplayName: Ákos Nikházy
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2017-12-01T16:39:25.707Z
Answers.LastActivityDate: 2017-12-01T16:39:25.707Z
Answers.UserDisplayName: k.c. sayz 'k.c sayz'
Body: <p>I have a MySQL database that contains datetime for every check-in with an RFID card. I have millions of records in it. It shouts machine learning prediction for me.</p>
  
  <p>So I'd like to predict popular times to see when most people use the terminal. I'd like to represent it the same way as Google does at places:</p>
  
  <p><a href="https://i.sstatic.net/J5ARp.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/J5ARp.jpg" alt="Google&#39;s popular times panel at location search"></a></p>
  
  <p>This is the rare case when I do not ask for code, but keywords.</p>
  
  <p>I assume I have to predict number of check-ins between time ranges. Also I am sure I have to make a dataset like this:</p>
  
  <pre><code>                        (These are the number of check-ins in hour ranges)
  month | day | day-name | 0-1 | 1-2 | ... | 6-7 etc. 
  jan   | 1   | Monday   |  0  |  1  |     |  12
  jan   | 2   | Tuesday  |  1  |  3  |     |  15
  </code></pre>
  
  <p>So it can predict today's popular times based on what day it is, also by day of given month (as Saturdays and Sundays will be dead, also 25th of December. A good prediction should know these from the dataset).</p>
  
  <p>As I wrote this question I solved most of it it seems. <strong>The only thing I need is a keyword</strong> as I have little experience in this. <strong>What model fits this best?</strong></p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2017-12-01T18:45:37.903Z
Comments.Text: An average hourly check-in on the day of the week would not be enough for a forecast?
Comments.UserDisplayName: GIA
Tags: <machine-learning><prediction>
Title: Predicting popular times
UserDisplayName: Ákos Nikházy
ThreadText: # Predicting popular times
  
  (Asked by: username_0 on 2017-12-01)
  
  I have a MySQL database that contains datetime for every check-in with an RFID card. I have millions of records in it. It shouts machine learning prediction for me.
  
  So I'd like to predict popular times to see when most people use the terminal. I'd like to represent it the same way as Google does at places:
  
  [![Google's popular times panel at location search](https://i.sstatic.net/J5ARp.jpg)](https://i.sstatic.net/J5ARp.jpg)
  
  This is the rare case when I do not ask for code, but keywords.
  
  I assume I have to predict number of check-ins between time ranges. Also I am sure I have to make a dataset like this:
  
  ```
                          (These are the number of check-ins in hour ranges)
  month | day | day-name | 0-1 | 1-2 | ... | 6-7 etc. 
  jan   | 1   | Monday   |  0  |  1  |     |  12
  jan   | 2   | Tuesday  |  1  |  3  |     |  15
  
  ```
  
  So it can predict today's popular times based on what day it is, also by day of given month (as Saturdays and Sundays will be dead, also 25th of December. A good prediction should know these from the dataset).
  
  As I wrote this question I solved most of it it seems. **The only thing I need is a keyword** as I have little experience in this. **What model fits this best?**
  
  **Tags:** machine-learning, prediction
  
  > **Comments:**
  > **username_1**: An average hourly check-in on the day of the week would not be enough for a forecast?  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2017-12-01. Score: 2 | Accepted answer
  
  "Regression", is to estimate a continuous value as a function of some other parameters.
  
  There are different forms of regression, such as linear regression and logistic regression, that differ in the assumptions they make on the data.
  
  You could try looking up those for a start.
  
  > **Comments:**
  > **username_0**: I used Accord.net's Multivariate Linear Regression for it and it beautifully and very accurately predicts the numbers, even on the weekends when there it predicts accurately less people in given hours. Thank you again for the answer!  
  
  ---
  
  