Answers.Body: <h1>Straight answer:</h1>
  <p>There is no right answer as it depends on many factors. But here are some keywords you can look into:</p>
  <p>Keywords about your problem:</p>
  <ul>
  <li><strong>Time Series</strong></li>
  <li>Periodic</li>
  <li><strong>Forecasting</strong>.</li>
  <li>Uni-variable</li>
  </ul>
  <p>About the model, I'd recommend checking ARIMA. But before jumping into code.</p>
  <p>A good problem solving with Data Science is a dynamic process for deeply understanding your <strong>business</strong> while <em>testing hypothesis with</em> <strong>data</strong>.</p>
  <h4>Business:</h4>
  <ul>
  <li>If you are predicting visitors in a beach, for instance, it will probably depend on weather, weekdays and holidays.</li>
  <li>If it's a bank, you might have more visits on pay-day and people may choose the time with less expected line length.</li>
  <li>If it's an emergency hospital, you'll find a whole new situation.</li>
  </ul>
  <p>So I encourage you to think of your real problem before (or while) diving into math and programming.</p>
  <h4>Data:</h4>
  <p>Depending on your available data, you could test some hypothesis. For example: &quot;The weekday won't interfere much on the visitors&quot;. Once you are familiar with your data and your business, than you can even model it into a simple periodic regression. For example:</p>
  <ul>
  <li>Everyday my restaurant has 2 main peaks, one for lunch and other for dinner.</li>
  <li>I'll assume my data is well described by 2 Gaussian distribution across the day.</li>
  <li>Model it by a simple regression to find the best parameters for your Gaussian curves and than check how well it fits.</li>
  <li>Check the cases where the model fails and set new hypothesis.</li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-31T04:44:10.813Z
Answers.Text: Thank you! I'll keep that as a reference! It was very informative with concrete examples!
  I felt that factors such as the weather and temperature are also important, since in my case I have to take measurements in places where I am exposed to direct sunlight.
Answers.UserDisplayName: k_ele
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-20T04:08:48.373Z
Answers.LastActivityDate: 2021-08-20T04:08:48.373Z
Answers.UserDisplayName: Andre Goulart
Answers.Body: <p>There are various cheat sheets to recommend what predictive model to use in different situations. Here’s a couple to guide you on options for your dataset.</p>
  <p><a href="https://i.sstatic.net/9lf3e.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/9lf3e.jpg" alt="Scimitar Learn Cheat Sheet" /></a> <a href="https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html" rel="nofollow noreferrer">https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html</a></p>
  <p>For this first cheat sheet with your example, you would begin with the start icon. For the first question &gt; 50 samples, the answer is yes. For the second question predict category, the answer is no. For the third question predict quantity, the answer is yes. For the fourth question, &lt; 100K samples, the answer is yes . The suggested type of regression analysis to try from this cheat sheet is a SGD Regressor.</p>
  <p><a href="https://i.sstatic.net/2QSlx.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/2QSlx.jpg" alt="Microsoft Azure Machine Learning Cheat Sheet" /></a> <a href="https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet" rel="nofollow noreferrer">https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet</a></p>
  <p>For the cheat sheet you example begin with the question what do you want to do. The answer is predict values; so again we see this should be some type of regression analysis. There are a few options on this one that would be appropriate to try, such as a Poisson Regression.</p>
  <p>After deciding which model to use the next decision is how to implement the model. Kimball made a statement a good decade ago,  <em>In today’s environment, most organizations should use a vendor-supplied ETL tool as a general rule.</em> This was mainly due to maintenance costs. We’ve reached the same level of maturity with data science tools that same logic can be applied for common data science models.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-20T04:12:28.740Z
Answers.Text: Hi, @Christopher! Those are nice guidelines. But maybe you could try to be more specific. At least guiding the user through this mindmap and helping them finding their case.
Answers.UserDisplayName: Andre Goulart
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-08-19T23:44:17.943Z
Answers.LastActivityDate: 2021-08-20T06:05:29.393Z
Answers.LastEditDate: 2021-08-20T06:05:29.393Z
Answers.UserDisplayName: Christopher Klaus
Body: <p>I want to create a model to predict the number of visitors.</p>
  <p>Currently, I have a year's csv data for predicting the number of visitors, which is collected every 10 seconds.</p>
  <p>I would like to predict the number of future visitors on a daily basis based on this data for the past year.</p>
  <p>What kind of method or model can I use to achieve this? I can use a graphics board for learning.</p>
  <p>If you have any page of sample codes, it would be very helpful.</p>
  <p><a href="https://i.sstatic.net/YwjCs.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/YwjCs.jpg" alt="sample chart of one day" /></a></p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-19T10:53:13.353Z
Comments.Text: Do you have any other data attributes, or just the time-of-day?
Comments.UserDisplayName: Oliver Mason
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-08-20T02:52:14.240Z
Comments.Text: At the moment it is only time vs. count.
  I may add weather and other information in the future.
Comments.UserDisplayName: k_ele
Tags: <ai-design>
Title: How to create a model for predicting the number of visitors
UserDisplayName: k_ele
ThreadText: # How to create a model for predicting the number of visitors
  
  (Asked by: username_0 on 2021-08-19)
  
  I want to create a model to predict the number of visitors.
  
  Currently, I have a year's csv data for predicting the number of visitors, which is collected every 10 seconds.
  
  I would like to predict the number of future visitors on a daily basis based on this data for the past year.
  
  What kind of method or model can I use to achieve this? I can use a graphics board for learning.
  
  If you have any page of sample codes, it would be very helpful.
  
  [![sample chart of one day](https://i.sstatic.net/YwjCs.jpg)](https://i.sstatic.net/YwjCs.jpg)
  
  **Tags:** ai-design
  > **Comments:**
  > **username_1**: Do you have any other data attributes, or just the time-of-day?  
  > **username_0**: At the moment it is only time vs. count. I may add weather and other information in the future.  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2021-08-20. Score: 1 | Accepted answer
  
  # Straight answer:
  
  There is no right answer as it depends on many factors. But here are some keywords you can look into:
  
  Keywords about your problem:
  
  * **Time Series**
  * Periodic
  * **Forecasting**.
  * Uni-variable
  
  About the model, I'd recommend checking ARIMA. But before jumping into code.
  
  A good problem solving with Data Science is a dynamic process for deeply understanding your **business** while *testing hypothesis with* **data**.
  
  #### Business:
  
  * If you are predicting visitors in a beach, for instance, it will probably depend on weather, weekdays and holidays.
  * If it's a bank, you might have more visits on pay-day and people may choose the time with less expected line length.
  * If it's an emergency hospital, you'll find a whole new situation.
  
  So I encourage you to think of your real problem before (or while) diving into math and programming.
  
  #### Data:
  
  Depending on your available data, you could test some hypothesis. For example: "The weekday won't interfere much on the visitors". Once you are familiar with your data and your business, than you can even model it into a simple periodic regression. For example:
  
  * Everyday my restaurant has 2 main peaks, one for lunch and other for dinner.
  * I'll assume my data is well described by 2 Gaussian distribution across the day.
  * Model it by a simple regression to find the best parameters for your Gaussian curves and than check how well it fits.
  * Check the cases where the model fails and set new hypothesis.
  
  > **Comments:**
  > **username_0**: Thank you! I'll keep that as a reference! It was very informative with concrete examples! I felt that factors such as the weather and temperature are also important, since in my case I have to take measurements in places where I am exposed to direct sunlight.  
  
  ---
  
  ### Answer by username_3 on 2021-08-19. Score: 0
  
  There are various cheat sheets to recommend what predictive model to use in different situations. Here’s a couple to guide you on options for your dataset.
  
  [![Scimitar Learn Cheat Sheet](https://i.sstatic.net/9lf3e.jpg)](https://i.sstatic.net/9lf3e.jpg) <https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html>
  
  For this first cheat sheet with your example, you would begin with the start icon. For the first question > 50 samples, the answer is yes. For the second question predict category, the answer is no. For the third question predict quantity, the answer is yes. For the fourth question, < 100K samples, the answer is yes . The suggested type of regression analysis to try from this cheat sheet is a SGD Regressor.
  
  [![Microsoft Azure Machine Learning Cheat Sheet](https://i.sstatic.net/2QSlx.jpg)](https://i.sstatic.net/2QSlx.jpg) <https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet>
  
  For the cheat sheet you example begin with the question what do you want to do. The answer is predict values; so again we see this should be some type of regression analysis. There are a few options on this one that would be appropriate to try, such as a Poisson Regression.
  
  After deciding which model to use the next decision is how to implement the model. Kimball made a statement a good decade ago, *In today’s environment, most organizations should use a vendor-supplied ETL tool as a general rule.* This was mainly due to maintenance costs. We’ve reached the same level of maturity with data science tools that same logic can be applied for common data science models.
  
  > **Comments:**
  > **username_2**: Hi, @Christopher! Those are nice guidelines. But maybe you could try to be more specific. At least guiding the user through this mindmap and helping them finding their case.  
  
  ---
  
  