#StackBounty: #machine-learning #poisson-distribution #survey Probability of successful charging based on independent historical or sam…

Bounty: 100

I tried my best to find a solution, but failed to find a decent one.
Imagine I want to charge a customer and I have last three days of random charge try data:

  +----   Date     ------   Time  ------ Amount  ------  Status ------+
  |     2018/05/05    |     08:00    |     500      |       --        |  
  |     2018/05/05    |     12:00    |     500      |       --        |  
  |     2018/05/05    |     16:00    |     500      |       --        |  
  |     2018/05/05    |     20:00    |     500      |       OK        | <-
  +-------------------+--------------+--------------+-----------------+
  |     2018/05/06    |     08:00    |     500      |       --        |  
  |     2018/05/06    |     12:00    |     500      |       --        |  
  |     2018/05/06    |     16:00    |     500      |       OK        |  
  +-------------------+--------------+--------------+-----------------+
  |     2018/05/07    |     08:00    |     500      |       --        |  
  |     2018/05/07    |     12:00    |     500      |       --        |  
  |     2018/05/07    |     16:00    |     500      |       OK        |  <-
  +-------------------+--------------+--------------+-----------------+
  |     2018/05/08    |     08:00    |     500      |       --        |  
  |     2018/05/08    |     12:00    |     500      |       --        |  
  |     2018/05/08    |     20:00    |     500      |       --        |
  |     2018/05/08    |     22:00    |     500      |       OK        |  <-
  +-------------------+--------------+--------------+-----------------+

1- What is the best way to find probability of a success charge on 11:00 O’clock tomorrow?

2- I also have access to 2K user’ historical data. How can I use that data to improve probability accuracy?


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!

#StackBounty: #machine-learning #neural-network #recommender-system #supervised-learning #k-nn Taking Neural Network's false positi…

Bounty: 50

I am creating a recommendation system and considering two parallel ways of formalizing the problem. One classical, using proximity (recommend the product to the customer if a majority vote of 2k+1 customers closest by has the product), and another one that I have trouble understanding but seems valid to some extent.

The approach I’m thinking about is:

1) Fit a highly regularized neural network (to make sure it doesn’t overfit the training set) for a classification task that can predict if the person does or doesn’t have given product

2) Make sure test accuracy is as close to train accuracy as possible

3) Take false positives (customers who don’t have the product originally but the NN predicted that they have it) predicted on the whole dataset (the training set as well) as the result – the people I should recommend the product to

Now, I am aware of why in general one wouldn’t want to take that approach but I also can’t exactly explain why it wouldn’t return people ‘close by’ to each other that ‘should’ have given product in a similar sense like the KNN-based approach. I’m not sure how to analyse this problem exactly to validate, modify or reject the idea altogether.


Get this bounty!!!