Answers.Body: <p>This is possible.... but there's no reason to use a neural network! Your best bet on a problem like this is likely to use a logistical regression for the yes/no aspect of the question and a linear regression (or combination of linear regressions) to answer the pricing question - there are also ways of simply using linear regressions and setting up cutoffs to answer the yes/no question.</p>
  
  <p>The reality is that the accuracy of such a model/series of models would depend entirely on the quality and quantity of the data, but it's unlikely that in this case a neural network would provide a better result than smart usage of simpler models.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-05-13T22:31:59.270Z
Answers.LastActivityDate: 2019-05-13T22:31:59.270Z
Answers.UserDisplayName: skant
Body: <p>Our customer runs a tour agency. He has an excel spreadsheet containing the following information for people that have contacted them:</p>
  
  <p>Customer name, country, tour duration (requested by customer), tour date, number of people in the tour (usually from 1 to 3), price given to the customer, answer: accepted/rejected (indicates if customer accepted or rejected the price given by the tour agency).</p>
  
  <p>My customer wants a predictor or tool that can let him enter the details given by future customers, e.g:</p>
  
  <p>Number of participants,
  Tour duration,
  Country (not sure if necessary?)</p>
  
  <p>And the system will return the best price to charge the customer (so he won't reject the proposal but pay the maximum possible). </p>
  
  <p>Another option would be that the tour agency owner will enter the price and the system will answer "Customer will accept that price" or "Customer will reject that price".</p>
  
  <p>Is this even possible? I think it may be done using neural networks trained with the previous answers from customers that the tour agency owner has in his excel spreadsheet? </p>
  
Tags: <neural-networks><deep-learning><prediction>
Title: Predict best price using neural network?
UserDisplayName: Stephen H. Anderson
ThreadText: # Predict best price using neural network?
  
  (Asked by: username_0 on 2019-05-13)
  
  Our customer runs a tour agency. He has an excel spreadsheet containing the following information for people that have contacted them:
  
  Customer name, country, tour duration (requested by customer), tour date, number of people in the tour (usually from 1 to 3), price given to the customer, answer: accepted/rejected (indicates if customer accepted or rejected the price given by the tour agency).
  
  My customer wants a predictor or tool that can let him enter the details given by future customers, e.g:
  
  Number of participants,
  Tour duration,
  Country (not sure if necessary?)
  
  And the system will return the best price to charge the customer (so he won't reject the proposal but pay the maximum possible).
  
  Another option would be that the tour agency owner will enter the price and the system will answer "Customer will accept that price" or "Customer will reject that price".
  
  Is this even possible? I think it may be done using neural networks trained with the previous answers from customers that the tour agency owner has in his excel spreadsheet?
  
  **Tags:** neural-networks, deep-learning, prediction
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-05-13. Score: 1
  
  This is possible.... but there's no reason to use a neural network! Your best bet on a problem like this is likely to use a logistical regression for the yes/no aspect of the question and a linear regression (or combination of linear regressions) to answer the pricing question - there are also ways of simply using linear regressions and setting up cutoffs to answer the yes/no question.
  
  The reality is that the accuracy of such a model/series of models would depend entirely on the quality and quantity of the data, but it's unlikely that in this case a neural network would provide a better result than smart usage of simpler models.
  
  
  ---
  
  