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Predicting Customer Activity Absence

1

$begingroup$

Could you please assist me with to following question?

I have a customer activity dataframe that looks like this:

It contains at least 500.000 customers and a "timeseries" of 42 months. The ones and zeroes represent customer activity. If a customer was active during a particular month then there will be a 1, if not - 0. I need determine those customers that most likely (+ probability) will not be active during the next 6 months (2018 July-December).

Could you please direct me what approach/models should i use in order to predict this? I use Python.

Thanks in advance!

python pandas prediction dataframe data-analysis

share|improve this question

asked Jun 16 '18 at 9:15

AndreiAndrei

61

$endgroup$

bumped to the homepage by Community♦ 31 mins ago

This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  This question is way too broad. What have you tried already? Have you done any basic statistical analysis? Are the data any different from random 1s and 0s? Have you seen any patterns? Is your outcome "any activity (ie at least one "1") in the next six months"?
  $endgroup$
  – Spacedman
  Jun 16 '18 at 9:44
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @Spacedman: Thanks for your input. To be honest i'm a bit lost (+ very new to this field) and haven't done anything yet. No, the data is only 1 and 0. The outcome would be, as you mentioned, "any activity". If there is at least one 1 in the next 6 months then I am not interested in this customer.
  $endgroup$
  – Andrei
  Jun 16 '18 at 9:58
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Is there seasonality (a yearly pattern)? Is there serial correlation in the 1s (ie you are more likely to get 10 1s in a row than if they are completely random)? Do 1s "tail off" for all customers? Do some basic descriptive summaries before thinking all you need to do is throw it into a magic machine learning algorithm.
  $endgroup$
  – Spacedman
  Jun 16 '18 at 15:40
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  There pretty much might be a seasonality pattern, that's what i'm trying to figure out. Some of them might have had 1's for a few years and afterwards are just trailing 0's that pretty much mean (based on the data available) that the customer will not be active anymore. Some of them have only a few 1's in the middle of the selected daterange (these are, as well, not very likely to produce new activity). Some might bring up recurring activity based on seasonality (these are the one's id like to determine). There might not have been any sight from them recently but the might be active again soon
  $endgroup$
  – Andrei
  Jun 16 '18 at 15:47
  

  
  
  
  

add a comment | 

1

$begingroup$

Could you please assist me with to following question?

I have a customer activity dataframe that looks like this:

It contains at least 500.000 customers and a "timeseries" of 42 months. The ones and zeroes represent customer activity. If a customer was active during a particular month then there will be a 1, if not - 0. I need determine those customers that most likely (+ probability) will not be active during the next 6 months (2018 July-December).

Could you please direct me what approach/models should i use in order to predict this? I use Python.

Thanks in advance!

python pandas prediction dataframe data-analysis

share|improve this question

asked Jun 16 '18 at 9:15

AndreiAndrei

61

$endgroup$

bumped to the homepage by Community♦ 31 mins ago

This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  This question is way too broad. What have you tried already? Have you done any basic statistical analysis? Are the data any different from random 1s and 0s? Have you seen any patterns? Is your outcome "any activity (ie at least one "1") in the next six months"?
  $endgroup$
  – Spacedman
  Jun 16 '18 at 9:44
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @Spacedman: Thanks for your input. To be honest i'm a bit lost (+ very new to this field) and haven't done anything yet. No, the data is only 1 and 0. The outcome would be, as you mentioned, "any activity". If there is at least one 1 in the next 6 months then I am not interested in this customer.
  $endgroup$
  – Andrei
  Jun 16 '18 at 9:58
  
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Is there seasonality (a yearly pattern)? Is there serial correlation in the 1s (ie you are more likely to get 10 1s in a row than if they are completely random)? Do 1s "tail off" for all customers? Do some basic descriptive summaries before thinking all you need to do is throw it into a magic machine learning algorithm.
  $endgroup$
  – Spacedman
  Jun 16 '18 at 15:40
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  There pretty much might be a seasonality pattern, that's what i'm trying to figure out. Some of them might have had 1's for a few years and afterwards are just trailing 0's that pretty much mean (based on the data available) that the customer will not be active anymore. Some of them have only a few 1's in the middle of the selected daterange (these are, as well, not very likely to produce new activity). Some might bring up recurring activity based on seasonality (these are the one's id like to determine). There might not have been any sight from them recently but the might be active again soon
  $endgroup$
  – Andrei
  Jun 16 '18 at 15:47
  

  
  
  
  

add a comment | 

$begingroup$

Could you please assist me with to following question?

I have a customer activity dataframe that looks like this:

It contains at least 500.000 customers and a "timeseries" of 42 months. The ones and zeroes represent customer activity. If a customer was active during a particular month then there will be a 1, if not - 0. I need determine those customers that most likely (+ probability) will not be active during the next 6 months (2018 July-December).

Could you please direct me what approach/models should i use in order to predict this? I use Python.

Thanks in advance!

python pandas prediction dataframe data-analysis

share|improve this question

asked Jun 16 '18 at 9:15

AndreiAndrei

61

$endgroup$

share|improve this question

asked Jun 16 '18 at 9:15

AndreiAndrei

61

share|improve this question

asked Jun 16 '18 at 9:15

AndreiAndrei

61

asked Jun 16 '18 at 9:15

AndreiAndrei

61

asked Jun 16 '18 at 9:15

AndreiAndrei

61

1 Answer
1

active

oldest

votes

0

$begingroup$

First issue in your model building method is that you have data in the binary form for each month and you are trying to predict into a 6 month time period. You gotta define what will be considered active in the next 6 months?

1. Active in 6 months = Active in each individual months?
   or


2. Active in 6 months = Active in any given month?

If you want to first predict for individual months, you can use logistic regression. It is useful for a binary classification of this kind.

Use LogisticRegression from sklearn.linear_model to train and fit the data.
Then, use confusion matrix and classification_report from sklearn.metrics to test the performance of your model.

After having predictions for next 6 months, you can create a new column that checks if there are any 1's in the last 6 months and stores 1 else stores 0

share|improve this answer

edited Jun 16 '18 at 13:35

answered Jun 16 '18 at 13:26

Sharvari GcSharvari Gc

262

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  A logistic regression model has no other covariates for this data, so it can only predict a mean for each customer, so you may as well count the fraction of 1s for each customer and use that as a percentage probability of a 1 in any month. Then combinatorics and probability theory gets you the P(>0 1s in the next six months). Basic probability theory, no need to "learn" anything. The next level would be to assume some kind of time trend, but that requires a model as a function of time, and an assumed correlation structure and so on.
  $endgroup$
  – Spacedman
  Jun 16 '18 at 14:57
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Individual months actually do not matter. I'm interested in the 6-month period a s whole. I think seasonality plays a big role here.
  $endgroup$
  – Andrei
  Jun 16 '18 at 15:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @Spacedman You are right. Should delete this answer?
  $endgroup$
  – Sharvari Gc
  Jun 17 '18 at 14:12
  

  
  
  
  

add a comment | 

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0

$begingroup$

First issue in your model building method is that you have data in the binary form for each month and you are trying to predict into a 6 month time period. You gotta define what will be considered active in the next 6 months?

1. Active in 6 months = Active in each individual months?
   or


2. Active in 6 months = Active in any given month?

If you want to first predict for individual months, you can use logistic regression. It is useful for a binary classification of this kind.

Use LogisticRegression from sklearn.linear_model to train and fit the data.
Then, use confusion matrix and classification_report from sklearn.metrics to test the performance of your model.

After having predictions for next 6 months, you can create a new column that checks if there are any 1's in the last 6 months and stores 1 else stores 0

share|improve this answer

edited Jun 16 '18 at 13:35

answered Jun 16 '18 at 13:26

Sharvari GcSharvari Gc

262

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  A logistic regression model has no other covariates for this data, so it can only predict a mean for each customer, so you may as well count the fraction of 1s for each customer and use that as a percentage probability of a 1 in any month. Then combinatorics and probability theory gets you the P(>0 1s in the next six months). Basic probability theory, no need to "learn" anything. The next level would be to assume some kind of time trend, but that requires a model as a function of time, and an assumed correlation structure and so on.
  $endgroup$
  – Spacedman
  Jun 16 '18 at 14:57
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Individual months actually do not matter. I'm interested in the 6-month period a s whole. I think seasonality plays a big role here.
  $endgroup$
  – Andrei
  Jun 16 '18 at 15:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @Spacedman You are right. Should delete this answer?
  $endgroup$
  – Sharvari Gc
  Jun 17 '18 at 14:12
  

  
  
  
  

add a comment | 

0

$begingroup$

First issue in your model building method is that you have data in the binary form for each month and you are trying to predict into a 6 month time period. You gotta define what will be considered active in the next 6 months?

1. Active in 6 months = Active in each individual months?
   or


2. Active in 6 months = Active in any given month?

If you want to first predict for individual months, you can use logistic regression. It is useful for a binary classification of this kind.

Use LogisticRegression from sklearn.linear_model to train and fit the data.
Then, use confusion matrix and classification_report from sklearn.metrics to test the performance of your model.

After having predictions for next 6 months, you can create a new column that checks if there are any 1's in the last 6 months and stores 1 else stores 0

share|improve this answer

edited Jun 16 '18 at 13:35

answered Jun 16 '18 at 13:26

Sharvari GcSharvari Gc

262

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  A logistic regression model has no other covariates for this data, so it can only predict a mean for each customer, so you may as well count the fraction of 1s for each customer and use that as a percentage probability of a 1 in any month. Then combinatorics and probability theory gets you the P(>0 1s in the next six months). Basic probability theory, no need to "learn" anything. The next level would be to assume some kind of time trend, but that requires a model as a function of time, and an assumed correlation structure and so on.
  $endgroup$
  – Spacedman
  Jun 16 '18 at 14:57
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  Individual months actually do not matter. I'm interested in the 6-month period a s whole. I think seasonality plays a big role here.
  $endgroup$
  – Andrei
  Jun 16 '18 at 15:51
  

  
  
  
  


• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  @Spacedman You are right. Should delete this answer?
  $endgroup$
  – Sharvari Gc
  Jun 17 '18 at 14:12
  

  
  
  
  

add a comment | 

$begingroup$

First issue in your model building method is that you have data in the binary form for each month and you are trying to predict into a 6 month time period. You gotta define what will be considered active in the next 6 months?

1. Active in 6 months = Active in each individual months?
   or


2. Active in 6 months = Active in any given month?

If you want to first predict for individual months, you can use logistic regression. It is useful for a binary classification of this kind.

Use LogisticRegression from sklearn.linear_model to train and fit the data.
Then, use confusion matrix and classification_report from sklearn.metrics to test the performance of your model.

After having predictions for next 6 months, you can create a new column that checks if there are any 1's in the last 6 months and stores 1 else stores 0

share|improve this answer

edited Jun 16 '18 at 13:35

answered Jun 16 '18 at 13:26

Sharvari GcSharvari Gc

262

$endgroup$

share|improve this answer

edited Jun 16 '18 at 13:35

answered Jun 16 '18 at 13:26

Sharvari GcSharvari Gc

262

answered Jun 16 '18 at 13:26

Sharvari GcSharvari Gc

262

answered Jun 16 '18 at 13:26

Sharvari GcSharvari Gc

262