Estimating box size from the contents












0












$begingroup$


I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.



Here's an example of the input data:



box # | contents                          | box size

----- | --------------------------------- | ---------

1     | a widget, a doodad, and a trinket | 20x12x8

2     | 3 widgets                         | 12x12x12


However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:



rank | object count | object type

---- | ------------ | -----------

1    | 500,000      | doodad

2    | 350,000      | trinket

3    | 300,000      | widget

---  | snip         | ---

50   | 6,000        | whatyoumacallits

---  | snip         | ---

300  | 5            | quarts of blinker fluid


Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).



It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.






machine-learning







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    $begingroup$


    I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.



    Here's an example of the input data:



    box # | contents                          | box size
    
----- | --------------------------------- | ---------
    
1     | a widget, a doodad, and a trinket | 20x12x8
    
2     | 3 widgets                         | 12x12x12


    However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:



    rank | object count | object type
    
---- | ------------ | -----------
    
1    | 500,000      | doodad
    
2    | 350,000      | trinket
    
3    | 300,000      | widget
    
---  | snip         | ---
    
50   | 6,000        | whatyoumacallits
    
---  | snip         | ---
    
300  | 5            | quarts of blinker fluid


    Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).



    It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.






    machine-learning







    share|improve this question







    New contributor




    Slothario is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      0












      0








      0





      $begingroup$


      I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.



      Here's an example of the input data:



      box # | contents                          | box size
      
----- | --------------------------------- | ---------
      
1     | a widget, a doodad, and a trinket | 20x12x8
      
2     | 3 widgets                         | 12x12x12


      However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:



      rank | object count | object type
      
---- | ------------ | -----------
      
1    | 500,000      | doodad
      
2    | 350,000      | trinket
      
3    | 300,000      | widget
      
---  | snip         | ---
      
50   | 6,000        | whatyoumacallits
      
---  | snip         | ---
      
300  | 5            | quarts of blinker fluid


      Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).



      It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.






      machine-learning







      share|improve this question







      New contributor




      Slothario is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I'm currently on week 4 of my Coursera course on ML, so I have much to learn about data science. However, I got the opportunity to apply what I've learned at work, and I'd like some guidance. Our company ships random objects to customers in boxes. We'd like to be able to estimate how big boxes will be, given the random objects inside.



      Here's an example of the input data:



      box # | contents                          | box size
      
----- | --------------------------------- | ---------
      
1     | a widget, a doodad, and a trinket | 20x12x8
      
2     | 3 widgets                         | 12x12x12


      However, our list of items has a long tail. I did a count of total items shipped by object type, ordered by count descending. Here's the result:



      rank | object count | object type
      
---- | ------------ | -----------
      
1    | 500,000      | doodad
      
2    | 350,000      | trinket
      
3    | 300,000      | widget
      
---  | snip         | ---
      
50   | 6,000        | whatyoumacallits
      
---  | snip         | ---
      
300  | 5            | quarts of blinker fluid


      Etc. By item number 340, the count is 1, and there are 360 distinct items. I think one way to approach this at first would be to only consider the top 50 items, and try to do a simple polynomial regression with 50 features to estimate L, W, and H (assuming each variable is less than the previous one).



      It won't be 100% accurate, but it will be better than wild guesses. But is there a better way to do this? Any advice is much appreciated.






      machine-learning




      machine-learning






      share|improve this question







      New contributor




      Slothario is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question







      New contributor




      Slothario is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question






      New contributor




      Slothario is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      asked 2 days ago









      SlotharioSlothario

      101




      101




      New contributor




      Slothario is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.





      New contributor





      Slothario is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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      Check out our Code of Conduct.






















          1 Answer
          1






          active

          oldest

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          0












          $begingroup$

          This is known as "3D Bin Packing Problem" in literature.



          https://en.wikipedia.org/wiki/Bin_packing_problem



          Since this is NP-Hard; Some of the approaches are :




          1. Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem

          2. Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930

          3. Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc






          share|improve this answer









          $endgroup$









          • 1




            $begingroup$
            Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
            $endgroup$
            – Slothario
            2 days ago










          • $begingroup$
            Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
            $endgroup$
            – Slothario
            2 days ago













          Your Answer





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          1 Answer
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          1 Answer
          1






          active

          oldest

          votes









          active

          oldest

          votes






          active

          oldest

          votes









          0












          $begingroup$

          This is known as "3D Bin Packing Problem" in literature.



          https://en.wikipedia.org/wiki/Bin_packing_problem



          Since this is NP-Hard; Some of the approaches are :




          1. Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem

          2. Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930

          3. Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc






          share|improve this answer









          $endgroup$









          • 1




            $begingroup$
            Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
            $endgroup$
            – Slothario
            2 days ago










          • $begingroup$
            Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
            $endgroup$
            – Slothario
            2 days ago


















          0












          $begingroup$

          This is known as "3D Bin Packing Problem" in literature.



          https://en.wikipedia.org/wiki/Bin_packing_problem



          Since this is NP-Hard; Some of the approaches are :




          1. Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem

          2. Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930

          3. Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc






          share|improve this answer









          $endgroup$









          • 1




            $begingroup$
            Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
            $endgroup$
            – Slothario
            2 days ago










          • $begingroup$
            Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
            $endgroup$
            – Slothario
            2 days ago
















          0












          0








          0





          $begingroup$

          This is known as "3D Bin Packing Problem" in literature.



          https://en.wikipedia.org/wiki/Bin_packing_problem



          Since this is NP-Hard; Some of the approaches are :




          1. Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem

          2. Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930

          3. Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc






          share|improve this answer









          $endgroup$



          This is known as "3D Bin Packing Problem" in literature.



          https://en.wikipedia.org/wiki/Bin_packing_problem



          Since this is NP-Hard; Some of the approaches are :




          1. Heuristics : https://www.researchgate.net/publication/226249396_A_New_Heuristic_Algorithm_for_the_3D_Bin_Packing_Problem

          2. Deep Reinforcement Learning : https://arxiv.org/abs/1708.05930

          3. Ensemble : https://medium.com/@alitech_2017/alibabas-ai-solution-for-the-3d-bin-packing-problem-3ce66d730ecc







          share|improve this answer












          share|improve this answer



          share|improve this answer










          answered 2 days ago









          Shamit VermaShamit Verma

          78426




          78426








          • 1




            $begingroup$
            Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
            $endgroup$
            – Slothario
            2 days ago










          • $begingroup$
            Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
            $endgroup$
            – Slothario
            2 days ago
















          • 1




            $begingroup$
            Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
            $endgroup$
            – Slothario
            2 days ago










          • $begingroup$
            Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
            $endgroup$
            – Slothario
            2 days ago










          1




          1




          $begingroup$
          Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
          $endgroup$
          – Slothario
          2 days ago




          $begingroup$
          Well, we aren't trying to find the optimal way to pack bins. We're asking, if a human packs a number of items into a bin, what will be the size of the bin based on past data? The past data of course is messy, too, and we don't always have reliable data on object size. It would be ideal to say "In the past, we've had three widgets and two trinkets, and the box size is typically LxWxH." Although this is a useful approach to the problem I'll consider.
          $endgroup$
          – Slothario
          2 days ago












          $begingroup$
          Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
          $endgroup$
          – Slothario
          2 days ago






          $begingroup$
          Actually, come to think of it, I believe a useful approach would be to run a bin packing algorithm on the input data but make it configurable by a few parameters (like padding, alternate placements, etc). And then I could create a cost function that I would try to minimize so that my bin packing algorithm matches the data available as closely as possible. Is that kind of what you're suggesting?
          $endgroup$
          – Slothario
          2 days ago












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