Group data without losing information The Next CEO of Stack Overflow2019 Community Moderator ElectionEfficient solution of fmincg without providing gradient?Getting GitHub repository information by different criteriaIs it ethical to claim experience with big data when the data isn't a part of the new advertising/social media/retail fad?Optimizing Weka for large data setsWhat's an efficient way to compare and group millions of store names?Memory efficient structure for membership checking without false positiveUser activity representation for Prediction/MLHow to preprocess Acoustic DataDoes discretization of continuous features also lose information about distance?Loading Data into DW: Direct Insert from PreProcessing vs PreProcess and then loading from CSV files
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Group data without losing information
The Next CEO of Stack Overflow2019 Community Moderator ElectionEfficient solution of fmincg without providing gradient?Getting GitHub repository information by different criteriaIs it ethical to claim experience with big data when the data isn't a part of the new advertising/social media/retail fad?Optimizing Weka for large data setsWhat's an efficient way to compare and group millions of store names?Memory efficient structure for membership checking without false positiveUser activity representation for Prediction/MLHow to preprocess Acoustic DataDoes discretization of continuous features also lose information about distance?Loading Data into DW: Direct Insert from PreProcessing vs PreProcess and then loading from CSV files
$begingroup$
Context
Imagine that I have a dataset about sending messages. Each row as user_id, a batch_id, a is_open field (boolean) and a is_clicked field (boolean).
So one row means that one message was sent. It might have been open (is_open is true) or not (is_open is false). Same for clicked.
For this question, all corner use cases (what if a message is clicked without being opened?) are not relevant.
I want to graph open rate vs. click rate.
Question
How can I group these rows in a valid way, without discarding most of them?
Long version
The crux of my problem is that every single message has an open (and click) rate of exactly 0 or 100%.
I could first group messages per user, but then I would have to discard users having received less than at least 5 or 10 messages, to not have a peak a 0/20/40/60/80/100 %. This is a lot of data to drop, which is perfectly valid (and furthermore, I would like to compute things like median time to open, which does not lend itself well to multi-step calculation). It would take as well a while to get have enough historical data.
I could group by batch. But I could have for instance one batch per month, of 500k users. After a year, I would only have 12 points on my graph, whereas I already sent 6M messages.
My naive idea would be to just take rows by bunches of eg. 1000, and compute the open and click rate for this random bunch. It does not seem intellectually correct to me.
The actual language/implementation does not matter. I want to understand how to do this, actually doing it will come later.
bigdata preprocessing
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
$endgroup$
add a comment |
$begingroup$
Context
Imagine that I have a dataset about sending messages. Each row as user_id, a batch_id, a is_open field (boolean) and a is_clicked field (boolean).
So one row means that one message was sent. It might have been open (is_open is true) or not (is_open is false). Same for clicked.
For this question, all corner use cases (what if a message is clicked without being opened?) are not relevant.
I want to graph open rate vs. click rate.
Question
How can I group these rows in a valid way, without discarding most of them?
Long version
The crux of my problem is that every single message has an open (and click) rate of exactly 0 or 100%.
I could first group messages per user, but then I would have to discard users having received less than at least 5 or 10 messages, to not have a peak a 0/20/40/60/80/100 %. This is a lot of data to drop, which is perfectly valid (and furthermore, I would like to compute things like median time to open, which does not lend itself well to multi-step calculation). It would take as well a while to get have enough historical data.
I could group by batch. But I could have for instance one batch per month, of 500k users. After a year, I would only have 12 points on my graph, whereas I already sent 6M messages.
My naive idea would be to just take rows by bunches of eg. 1000, and compute the open and click rate for this random bunch. It does not seem intellectually correct to me.
The actual language/implementation does not matter. I want to understand how to do this, actually doing it will come later.
bigdata preprocessing
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
$endgroup$
add a comment |
$begingroup$
Context
Imagine that I have a dataset about sending messages. Each row as user_id, a batch_id, a is_open field (boolean) and a is_clicked field (boolean).
So one row means that one message was sent. It might have been open (is_open is true) or not (is_open is false). Same for clicked.
For this question, all corner use cases (what if a message is clicked without being opened?) are not relevant.
I want to graph open rate vs. click rate.
Question
How can I group these rows in a valid way, without discarding most of them?
Long version
The crux of my problem is that every single message has an open (and click) rate of exactly 0 or 100%.
I could first group messages per user, but then I would have to discard users having received less than at least 5 or 10 messages, to not have a peak a 0/20/40/60/80/100 %. This is a lot of data to drop, which is perfectly valid (and furthermore, I would like to compute things like median time to open, which does not lend itself well to multi-step calculation). It would take as well a while to get have enough historical data.
I could group by batch. But I could have for instance one batch per month, of 500k users. After a year, I would only have 12 points on my graph, whereas I already sent 6M messages.
My naive idea would be to just take rows by bunches of eg. 1000, and compute the open and click rate for this random bunch. It does not seem intellectually correct to me.
The actual language/implementation does not matter. I want to understand how to do this, actually doing it will come later.
bigdata preprocessing
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
$endgroup$
Context
Imagine that I have a dataset about sending messages. Each row as user_id, a batch_id, a is_open field (boolean) and a is_clicked field (boolean).
So one row means that one message was sent. It might have been open (is_open is true) or not (is_open is false). Same for clicked.
For this question, all corner use cases (what if a message is clicked without being opened?) are not relevant.
I want to graph open rate vs. click rate.
Question
How can I group these rows in a valid way, without discarding most of them?
Long version
The crux of my problem is that every single message has an open (and click) rate of exactly 0 or 100%.
I could first group messages per user, but then I would have to discard users having received less than at least 5 or 10 messages, to not have a peak a 0/20/40/60/80/100 %. This is a lot of data to drop, which is perfectly valid (and furthermore, I would like to compute things like median time to open, which does not lend itself well to multi-step calculation). It would take as well a while to get have enough historical data.
I could group by batch. But I could have for instance one batch per month, of 500k users. After a year, I would only have 12 points on my graph, whereas I already sent 6M messages.
My naive idea would be to just take rows by bunches of eg. 1000, and compute the open and click rate for this random bunch. It does not seem intellectually correct to me.
The actual language/implementation does not matter. I want to understand how to do this, actually doing it will come later.
bigdata preprocessing
bigdata preprocessing
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
edited Mar 26 at 6:04
Guillaume
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
asked Mar 25 at 13:45
GuillaumeGuillaume
1063
1063
add a comment |
add a comment |
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