Feature engineering suggestion required












4












$begingroup$


I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"










share|improve this question









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  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago
















4












$begingroup$


I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"










share|improve this question









New contributor




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







$endgroup$








  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago














4












4








4





$begingroup$


I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"










share|improve this question









New contributor




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







$endgroup$




I am having a problem during feature engineering. Looking for some suggestions. Problem statement: I have usage data of multiple customers for 3 days. Some have just 1 day usage some 2 and some 3. Data is related to number of emails sent / contacts added on each day etc.



I am converting this time series data to column-wise ie., number of emails sent by a customer on day1 as one feature, number of emails sent by a customer on day2 as one feature and so on. But problem is that, the usage can be of either increasing order or decreasing order for different customers.



ie., example 1: customer 'A' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=0



example 2: customer 'B' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=100



example 3: customer 'C' --> 'number of emails sent on 1st . day' = 0 . ' number of emails sent on 2nd day'=0



example 4: customer 'D' --> 'number of emails sent on 1st . day' = 100 . ' number of emails sent on 2nd day'=100



In the first two cases => My new feature will have "-100" and "100" as values. Which I guess is good for differentiating. But the problem arises for 3rd and 4th columns when the new feature value will be "0" in both scenarios Can anyone suggest a way to handle this.



One way to handle this:



I can add "No change" in those scenarios, but I am confused about one thing. If I do that, I will have to make the new feature as categorical, which is not ideal as the other values will be continuous.



Instead, I can have absolute values in the new feature and indicate the trend as "+1" or increasing "-1" for decreasing "no change" for no change and "0" if both the values have been "0". Would that be a good approach though?



The end goal is to predict if a user would continue using the application or not. So it basically would be a two-class model. And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day"







machine-learning feature-engineering data-science-model






share|improve this question









New contributor




SSuram 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




SSuram 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








edited 1 hour ago







SSuram













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asked 2 hours ago









SSuramSSuram

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New contributor





SSuram 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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  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago














  • 1




    $begingroup$
    could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
    $endgroup$
    – Pedro Henrique Monforte
    2 hours ago










  • $begingroup$
    I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    Yes, just add it to your question and it will be perfect
    $endgroup$
    – Pedro Henrique Monforte
    1 hour ago








1




1




$begingroup$
could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
$endgroup$
– Pedro Henrique Monforte
2 hours ago




$begingroup$
could you explain a bit better what are you trying to predict? Your question is pretty well explained but the kind of model you plan do train might give some of us better ideas.
$endgroup$
– Pedro Henrique Monforte
2 hours ago












$begingroup$
I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
$endgroup$
– SSuram
1 hour ago




$begingroup$
I would want to predict if a user would continue using the application or not. So it basically would be a two-class model. Does that answer?
$endgroup$
– SSuram
1 hour ago












$begingroup$
Yes, just add it to your question and it will be perfect
$endgroup$
– Pedro Henrique Monforte
1 hour ago




$begingroup$
Yes, just add it to your question and it will be perfect
$endgroup$
– Pedro Henrique Monforte
1 hour ago










1 Answer
1






active

oldest

votes


















2












$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago












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1 Answer
1






active

oldest

votes








1 Answer
1






active

oldest

votes









active

oldest

votes






active

oldest

votes









2












$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago
















2












$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago














2












2








2





$begingroup$

Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.






share|improve this answer











$endgroup$



Well, you want to identify change in usage you could try something like:



$$ f(day_1,day2) = frac{day_2-day_1 + delta}{||day_2-day_1+delta||} times Biggr|Biggr|frac{day_2+day_1}{(day_2+day_1+1)(day_2-day_1+1)}Biggl|Biggl| $$



where $delta$ is the eps of your machine (minimum value needed to be summed to differ it from other floats)



that will give you
$$f(100,0) approx -98.02$$
$$f(0,100) = 100$$
$$f(100,100) approx 0.995$$
$$f(0,0) = 0$$



You can look at my experiment here



This will map all non-changes from $[0,1]$ where $f(0,0)$ maps to $0$ and $f(infty,infty)$ maps to $1$



Where is it from? Just tuned the function manually. But I think this might suffice for your application



Explaining the ideia



You want to have a feature that packs a lot of information:
- Is the usage greater than zero?
- Is it increasing or decreasing?
- If it is stalled, how much is the usage?



Well, your usage vary in integer values so you can map the entire non-changing but above 0 case to a previously non-used interval.



The function above will map in $[0,1]$ all non-changing possibilities, in a exponential kind of way ($a^{(-frac{1}{usage})}$) also you can extract the actual value from positive changes and the approximate value for negative change (been a better approximation when the drop is high)



This is not the perfect scenario but it is the maximum information I could compress into 1 variable with little loss.







share|improve this answer














share|improve this answer



share|improve this answer








edited 44 mins ago


























community wiki





2 revs
Pedro Henrique Monforte













  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago


















  • $begingroup$
    I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
    $endgroup$
    – SSuram
    1 hour ago










  • $begingroup$
    What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
    $endgroup$
    – SSuram
    53 mins ago












  • $begingroup$
    that will actually return zero for near every case
    $endgroup$
    – Pedro Henrique Monforte
    42 mins ago
















$begingroup$
I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
$endgroup$
– SSuram
1 hour ago




$begingroup$
I am not sure if it would answer --- "'''And I would want to capture even the scale of usage i.e., "A user sending 100 emails every day" should be different from "B user sending 10000 emails every day ""''---- part of the question. Could you please explain?
$endgroup$
– SSuram
1 hour ago












$begingroup$
What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
$endgroup$
– SSuram
53 mins ago






$begingroup$
What would you say about adding the below info to it f = (((d2-d1+eps)/abs(d2-d1+eps))*abs((d2+d1)/(d1+d2+1)*(d2-d1+1)))*(d2/1000)*(d1/1000) where "1000"-- would be max(usage).
$endgroup$
– SSuram
53 mins ago














$begingroup$
that will actually return zero for near every case
$endgroup$
– Pedro Henrique Monforte
42 mins ago




$begingroup$
that will actually return zero for near every case
$endgroup$
– Pedro Henrique Monforte
42 mins ago










SSuram is a new contributor. Be nice, and check out our Code of Conduct.










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SSuram is a new contributor. Be nice, and check out our Code of Conduct.













SSuram is a new contributor. Be nice, and check out our Code of Conduct.












SSuram is a new contributor. Be nice, and check out our Code of Conduct.
















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