probability of purchasing an item based on past purchases
1
I have some data on individual purchases.
In this data PANID is a person who bought a product on a particular week. In the sample I provide, there are 6 unique PANID's; so 6 people in total. I am trying to calculate the conditional probability that a PANID will repurchase a product a second time.
For example:
PANID 3104497 bought ITEM 7028 in WEEK 2010-01-11 and then again the same PANID bought the same ITEM in WEEK 2010-01-25. I am trying to figure out how to find the probability that they will buy that same item again (at any point in the data).
PANID WEEK ITEM
1 3104497 2010-01-11 526
2 3104497 2010-01-11 526
3 3104497 2010-01-11 526
4 3104497 2010-01-11 526
5 3104497 2010-01-11 526
6 3104497 2010-01-11 2890
...
705 3146217 2010-04-05 97
706 3146217 2010-04-05 132
707 3146217 2010-04-05 132
708 3146217 2010-04-05 132
709 3146217 2010-04-05 132
710 3146217 2010-04-05 132
Data:
df <- structure(list(PANID = c(3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3369710L, 3369710L, 3369710L, 3369710L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3138990L,
3138990L, 3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3138990L, 3138990L, 3138990L, 3138990L,
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3369710L, 3369710L, 3322156L, 3322156L, 3322156L, 3322156L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3322156L, 3322156L, 3322156L,
3322156L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3138990L,
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3104497L, 3322156L, 3322156L, 3322156L, 3322156L, 3138990L, 3138990L,
3138990L, 3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3322156L, 3322156L, 3322156L, 3322156L, 3369710L, 3369710L, 3369710L,
3369710L, 3369710L, 3369710L, 3369710L, 3369710L, 3138990L, 3138990L,
3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3138990L, 3138990L, 3138990L, 3138990L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L,
3369710L, 3369710L, 3369710L, 3369710L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3369710L, 3369710L, 3369710L, 3369710L, 3369710L, 3369710L, 3369710L,
3369710L, 3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3322156L, 3322156L, 3322156L, 3322156L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3138990L, 3138990L,
3138990L, 3138990L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3322156L,
3322156L, 3322156L, 3322156L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3322156L, 3322156L, 3322156L, 3322156L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L),
WEEK = structure(c(14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
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14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
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14704, 14704), class = "Date"), ITEM = c(526L, 526L, 526L,
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900L, 900L, 2202L, 2202L, 2202L, 2202L, 7363L, 7363L, 7363L,
7363L, 7362L, 7362L, 7362L, 7362L, 5995L, 5995L, 5995L, 5995L,
1251L, 1251L, 1251L, 1251L, 1251L, 76243L, 76243L, 76243L,
76243L, 76243L, 620L, 620L, 620L, 620L, 620L, 625L, 625L,
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626L, 626L, 626L, 14772L, 14772L, 14772L, 14772L, 14772L,
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13830L, 13830L, 13830L, 6080L, 6080L, 6080L, 6080L, 6L, 6L,
6L, 6L, 126L, 126L, 126L, 126L, 3637L, 3637L, 3637L, 3637L,
2202L, 2202L, 2202L, 2202L, 7357L, 7357L, 7357L, 7357L, 7357L,
7363L, 7363L, 7363L, 7363L, 7363L, 85121L, 85121L, 85121L,
85121L, 40268L, 40268L, 40268L, 40268L, 42238L, 42238L, 42238L,
42238L, 96166L, 96166L, 96166L, 96166L, 96166L, 80997L, 80997L,
80997L, 80997L, 80997L, 2891L, 2891L, 2891L, 2891L, 2891L,
5169L, 5169L, 5169L, 5169L, 5169L, 27536L, 27536L, 27536L,
27536L, 27536L, 6150L, 6150L, 6150L, 6150L, 6150L, 31846L,
31846L, 31846L, 31846L, 31846L, 42753L, 42753L, 42753L, 42753L,
42753L, 1302L, 1302L, 1302L, 1302L, 2663L, 2663L, 2663L,
2663L, 2663L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L,
900L, 2202L, 2202L, 2202L, 2202L, 2202L, 18285L, 18285L,
18285L, 18285L, 52531L, 52531L, 52531L, 52531L, 52531L, 7152L,
7152L, 7152L, 7152L, 1279L, 1279L, 1279L, 1279L, 1279L, 1425L,
1425L, 1425L, 1425L, 1425L, 13830L, 13830L, 13830L, 13830L,
6080L, 6080L, 6080L, 6080L, 3637L, 3637L, 3637L, 3637L, 2202L,
2202L, 2202L, 2202L, 26134L, 26134L, 26134L, 26134L, 600L,
600L, 600L, 600L, 1302L, 1302L, 1302L, 1302L, 1327L, 1327L,
1327L, 1327L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L,
900L, 900L, 900L, 900L, 900L, 900L, 96166L, 96166L, 96166L,
96166L, 96166L, 2395L, 2395L, 2395L, 2395L, 2395L, 2890L,
2890L, 2890L, 2890L, 2890L, 2891L, 2891L, 2891L, 2891L, 2891L,
75L, 75L, 75L, 75L, 75L, 5346L, 5346L, 5346L, 5346L, 5346L,
600L, 600L, 600L, 600L, 65020L, 65020L, 65020L, 65020L, 65020L,
1261L, 1261L, 1261L, 1261L, 668L, 668L, 668L, 668L, 668L,
1425L, 1425L, 1425L, 1425L, 1425L, 600L, 600L, 600L, 600L,
900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L,
900L, 900L, 900L, 900L, 362L, 362L, 362L, 362L, 40258L, 40258L,
40258L, 40258L, 40268L, 40268L, 40268L, 40268L, 2549L, 2549L,
2549L, 2549L, 94L, 94L, 94L, 94L, 94L, 96L, 96L, 96L, 96L,
96L, 97L, 97L, 97L, 97L, 97L, 132L, 132L, 132L, 132L, 132L
)), class = "data.frame", row.names = c(NA, -710L))
r probability
asked Nov 11 at 22:47
user113156
7911417
add a comment |
1
I have some data on individual purchases.
In this data PANID is a person who bought a product on a particular week. In the sample I provide, there are 6 unique PANID's; so 6 people in total. I am trying to calculate the conditional probability that a PANID will repurchase a product a second time.
For example:
PANID 3104497 bought ITEM 7028 in WEEK 2010-01-11 and then again the same PANID bought the same ITEM in WEEK 2010-01-25. I am trying to figure out how to find the probability that they will buy that same item again (at any point in the data).
PANID WEEK ITEM
1 3104497 2010-01-11 526
2 3104497 2010-01-11 526
3 3104497 2010-01-11 526
4 3104497 2010-01-11 526
5 3104497 2010-01-11 526
6 3104497 2010-01-11 2890
...
705 3146217 2010-04-05 97
706 3146217 2010-04-05 132
707 3146217 2010-04-05 132
708 3146217 2010-04-05 132
709 3146217 2010-04-05 132
710 3146217 2010-04-05 132
Data:
df <- structure(list(PANID = c(3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3369710L, 3369710L, 3369710L, 3369710L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3138990L,
3138990L, 3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3138990L, 3138990L, 3138990L, 3138990L,
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3138990L, 3369710L, 3369710L, 3369710L, 3369710L, 3369710L, 3369710L,
3369710L, 3369710L, 3322156L, 3322156L, 3322156L, 3322156L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3322156L, 3322156L, 3322156L,
3322156L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3138990L,
3138990L, 3138990L, 3138990L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3322156L, 3322156L, 3322156L, 3322156L, 3138990L, 3138990L,
3138990L, 3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3322156L, 3322156L, 3322156L, 3322156L, 3369710L, 3369710L, 3369710L,
3369710L, 3369710L, 3369710L, 3369710L, 3369710L, 3138990L, 3138990L,
3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3138990L, 3138990L, 3138990L, 3138990L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L,
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3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
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3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
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3369710L, 3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3322156L, 3322156L, 3322156L, 3322156L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3138990L, 3138990L,
3138990L, 3138990L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3322156L,
3322156L, 3322156L, 3322156L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3322156L, 3322156L, 3322156L, 3322156L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L),
WEEK = structure(c(14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
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14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
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14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
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14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634,
14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641,
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14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641,
14641, 14641, 14641, 14641, 14648, 14648, 14648, 14648, 14648,
14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648,
14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648,
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14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669,
14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669,
14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669,
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14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669,
14669, 14669, 14676, 14676, 14676, 14676, 14676, 14676, 14676,
14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676,
14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676,
14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676,
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14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676, 14676,
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14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683,
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14683, 14683, 14683, 14683, 14690, 14690, 14690, 14690, 14690,
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14690, 14690, 14690, 14690, 14690, 14690, 14690, 14690, 14690,
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14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704,
14704, 14704), class = "Date"), ITEM = c(526L, 526L, 526L,
526L, 526L, 2890L, 2890L, 2890L, 2890L, 2890L, 2933L, 2933L,
2933L, 2933L, 2933L, 548L, 548L, 548L, 548L, 548L, 106L,
106L, 106L, 106L, 106L, 6320L, 6320L, 6320L, 6320L, 6610L,
6610L, 6610L, 6610L, 7028L, 7028L, 7028L, 7028L, 7028L, 7414L,
7414L, 7414L, 7414L, 7414L, 1279L, 1279L, 1279L, 1279L, 1279L,
1425L, 1425L, 1425L, 1425L, 1425L, 6080L, 6080L, 6080L, 6080L,
1937L, 1937L, 1937L, 1937L, 1937L, 1L, 1L, 1L, 1L, 11321L,
11321L, 11321L, 11321L, 12064L, 12064L, 12064L, 12064L, 3L,
3L, 3L, 3L, 3448L, 3448L, 3448L, 3448L, 900L, 900L, 900L,
900L, 900L, 2202L, 2202L, 2202L, 2202L, 7363L, 7363L, 7363L,
7363L, 7362L, 7362L, 7362L, 7362L, 5995L, 5995L, 5995L, 5995L,
1251L, 1251L, 1251L, 1251L, 1251L, 76243L, 76243L, 76243L,
76243L, 76243L, 620L, 620L, 620L, 620L, 620L, 625L, 625L,
625L, 625L, 625L, 668L, 668L, 668L, 668L, 668L, 626L, 626L,
626L, 626L, 626L, 14772L, 14772L, 14772L, 14772L, 14772L,
27526L, 27526L, 27526L, 27526L, 27526L, 6320L, 6320L, 6320L,
6320L, 6500L, 6500L, 6500L, 6500L, 6560L, 6560L, 6560L, 6560L,
6610L, 6610L, 6610L, 6610L, 600L, 600L, 600L, 600L, 13902L,
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900L, 900L, 900L, 2202L, 2202L, 2202L, 2202L, 35202L, 35202L,
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540L, 540L, 540L, 7363L, 7363L, 7363L, 7363L, 8312L, 8312L,
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11L, 1251L, 1251L, 1251L, 1251L, 40268L, 40268L, 40268L,
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240L, 240L, 240L, 240L, 24444L, 24444L, 24444L, 24444L, 900L,
900L, 900L, 900L, 960L, 960L, 960L, 960L, 2202L, 2202L, 2202L,
2202L, 38249L, 38249L, 38249L, 38249L, 28350L, 28350L, 28350L,
28350L, 28350L, 8358L, 8358L, 8358L, 8358L, 8358L, 5995L,
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40230L, 40230L, 40230L, 40267L, 40267L, 40267L, 40267L, 40268L,
40268L, 40268L, 40268L, 42238L, 42238L, 42238L, 42238L, 42238L,
42274L, 42274L, 42274L, 42274L, 42274L, 42274L, 42274L, 42274L,
42274L, 42274L, 94L, 94L, 94L, 94L, 94L, 95L, 95L, 95L, 95L,
95L, 97L, 97L, 97L, 97L, 97L, 98L, 98L, 98L, 98L, 98L, 1278L,
1278L, 1278L, 1278L, 1278L, 1278L, 1278L, 1278L, 1278L, 1278L,
6346L, 6346L, 6346L, 6346L, 6346L, 6346L, 6346L, 6346L, 6346L,
6346L, 81014L, 81014L, 81014L, 81014L, 81014L, 15990L, 15990L,
15990L, 15990L, 15990L, 8321L, 8321L, 8321L, 8321L, 8321L,
8321L, 8321L, 8321L, 8321L, 8321L, 27509L, 27509L, 27509L,
27509L, 27512L, 27512L, 27512L, 27512L, 27551L, 27551L, 27551L,
27551L, 900L, 900L, 900L, 900L, 900L, 960L, 960L, 960L, 960L,
2202L, 2202L, 2202L, 2202L, 1111L, 1111L, 1111L, 1111L, 1081L,
1081L, 1081L, 1081L, 29422L, 29422L, 29422L, 29422L, 13830L,
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6L, 6L, 126L, 126L, 126L, 126L, 3637L, 3637L, 3637L, 3637L,
2202L, 2202L, 2202L, 2202L, 7357L, 7357L, 7357L, 7357L, 7357L,
7363L, 7363L, 7363L, 7363L, 7363L, 85121L, 85121L, 85121L,
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96L, 97L, 97L, 97L, 97L, 97L, 132L, 132L, 132L, 132L, 132L
)), class = "data.frame", row.names = c(NA, -710L))
r probability
asked Nov 11 at 22:47
user113156
7911417
add a comment |
1
1
1
1
I have some data on individual purchases.
In this data PANID is a person who bought a product on a particular week. In the sample I provide, there are 6 unique PANID's; so 6 people in total. I am trying to calculate the conditional probability that a PANID will repurchase a product a second time.
For example:
PANID 3104497 bought ITEM 7028 in WEEK 2010-01-11 and then again the same PANID bought the same ITEM in WEEK 2010-01-25. I am trying to figure out how to find the probability that they will buy that same item again (at any point in the data).
PANID WEEK ITEM
1 3104497 2010-01-11 526
2 3104497 2010-01-11 526
3 3104497 2010-01-11 526
4 3104497 2010-01-11 526
5 3104497 2010-01-11 526
6 3104497 2010-01-11 2890
...
705 3146217 2010-04-05 97
706 3146217 2010-04-05 132
707 3146217 2010-04-05 132
708 3146217 2010-04-05 132
709 3146217 2010-04-05 132
710 3146217 2010-04-05 132
Data:
df <- structure(list(PANID = c(3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
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3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
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3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3369710L, 3369710L, 3369710L, 3369710L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3138990L,
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3146217L, 3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L,
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3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3322156L,
3322156L, 3322156L, 3322156L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3322156L, 3322156L, 3322156L, 3322156L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L),
WEEK = structure(c(14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
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14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
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14641, 14641, 14641, 14641, 14648, 14648, 14648, 14648, 14648,
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14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648,
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14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669,
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14669, 14669, 14676, 14676, 14676, 14676, 14676, 14676, 14676,
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14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683,
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14690, 14690, 14690, 14690, 14690, 14690, 14690, 14690, 14697,
14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697,
14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697,
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14697, 14697, 14697, 14697, 14697, 14697, 14704, 14704, 14704,
14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704,
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14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704,
14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704,
14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704, 14704,
14704, 14704), class = "Date"), ITEM = c(526L, 526L, 526L,
526L, 526L, 2890L, 2890L, 2890L, 2890L, 2890L, 2933L, 2933L,
2933L, 2933L, 2933L, 548L, 548L, 548L, 548L, 548L, 106L,
106L, 106L, 106L, 106L, 6320L, 6320L, 6320L, 6320L, 6610L,
6610L, 6610L, 6610L, 7028L, 7028L, 7028L, 7028L, 7028L, 7414L,
7414L, 7414L, 7414L, 7414L, 1279L, 1279L, 1279L, 1279L, 1279L,
1425L, 1425L, 1425L, 1425L, 1425L, 6080L, 6080L, 6080L, 6080L,
1937L, 1937L, 1937L, 1937L, 1937L, 1L, 1L, 1L, 1L, 11321L,
11321L, 11321L, 11321L, 12064L, 12064L, 12064L, 12064L, 3L,
3L, 3L, 3L, 3448L, 3448L, 3448L, 3448L, 900L, 900L, 900L,
900L, 900L, 2202L, 2202L, 2202L, 2202L, 7363L, 7363L, 7363L,
7363L, 7362L, 7362L, 7362L, 7362L, 5995L, 5995L, 5995L, 5995L,
1251L, 1251L, 1251L, 1251L, 1251L, 76243L, 76243L, 76243L,
76243L, 76243L, 620L, 620L, 620L, 620L, 620L, 625L, 625L,
625L, 625L, 625L, 668L, 668L, 668L, 668L, 668L, 626L, 626L,
626L, 626L, 626L, 14772L, 14772L, 14772L, 14772L, 14772L,
27526L, 27526L, 27526L, 27526L, 27526L, 6320L, 6320L, 6320L,
6320L, 6500L, 6500L, 6500L, 6500L, 6560L, 6560L, 6560L, 6560L,
6610L, 6610L, 6610L, 6610L, 600L, 600L, 600L, 600L, 13902L,
13902L, 13902L, 13902L, 822L, 822L, 822L, 822L, 822L, 2178L,
2178L, 2178L, 2178L, 900L, 900L, 900L, 900L, 900L, 900L,
900L, 900L, 900L, 2202L, 2202L, 2202L, 2202L, 35202L, 35202L,
35202L, 35202L, 540L, 540L, 540L, 540L, 540L, 540L, 540L,
540L, 540L, 540L, 7363L, 7363L, 7363L, 7363L, 8312L, 8312L,
8312L, 8312L, 7362L, 7362L, 7362L, 7362L, 11L, 11L, 11L,
11L, 1251L, 1251L, 1251L, 1251L, 40268L, 40268L, 40268L,
40268L, 26037L, 26037L, 26037L, 26037L, 26037L, 26037L, 26037L,
26037L, 26037L, 4116L, 4116L, 4116L, 4116L, 4116L, 7789L,
7789L, 7789L, 7789L, 7028L, 7028L, 7028L, 7028L, 7028L, 1302L,
1302L, 1302L, 1302L, 13301L, 13301L, 13301L, 13301L, 240L,
240L, 240L, 240L, 240L, 24444L, 24444L, 24444L, 24444L, 900L,
900L, 900L, 900L, 960L, 960L, 960L, 960L, 2202L, 2202L, 2202L,
2202L, 38249L, 38249L, 38249L, 38249L, 28350L, 28350L, 28350L,
28350L, 28350L, 8358L, 8358L, 8358L, 8358L, 8358L, 5995L,
5995L, 5995L, 5995L, 40224L, 40224L, 40224L, 40224L, 40230L,
40230L, 40230L, 40230L, 40267L, 40267L, 40267L, 40267L, 40268L,
40268L, 40268L, 40268L, 42238L, 42238L, 42238L, 42238L, 42238L,
42274L, 42274L, 42274L, 42274L, 42274L, 42274L, 42274L, 42274L,
42274L, 42274L, 94L, 94L, 94L, 94L, 94L, 95L, 95L, 95L, 95L,
95L, 97L, 97L, 97L, 97L, 97L, 98L, 98L, 98L, 98L, 98L, 1278L,
1278L, 1278L, 1278L, 1278L, 1278L, 1278L, 1278L, 1278L, 1278L,
6346L, 6346L, 6346L, 6346L, 6346L, 6346L, 6346L, 6346L, 6346L,
6346L, 81014L, 81014L, 81014L, 81014L, 81014L, 15990L, 15990L,
15990L, 15990L, 15990L, 8321L, 8321L, 8321L, 8321L, 8321L,
8321L, 8321L, 8321L, 8321L, 8321L, 27509L, 27509L, 27509L,
27509L, 27512L, 27512L, 27512L, 27512L, 27551L, 27551L, 27551L,
27551L, 900L, 900L, 900L, 900L, 900L, 960L, 960L, 960L, 960L,
2202L, 2202L, 2202L, 2202L, 1111L, 1111L, 1111L, 1111L, 1081L,
1081L, 1081L, 1081L, 29422L, 29422L, 29422L, 29422L, 13830L,
13830L, 13830L, 13830L, 6080L, 6080L, 6080L, 6080L, 6L, 6L,
6L, 6L, 126L, 126L, 126L, 126L, 3637L, 3637L, 3637L, 3637L,
2202L, 2202L, 2202L, 2202L, 7357L, 7357L, 7357L, 7357L, 7357L,
7363L, 7363L, 7363L, 7363L, 7363L, 85121L, 85121L, 85121L,
85121L, 40268L, 40268L, 40268L, 40268L, 42238L, 42238L, 42238L,
42238L, 96166L, 96166L, 96166L, 96166L, 96166L, 80997L, 80997L,
80997L, 80997L, 80997L, 2891L, 2891L, 2891L, 2891L, 2891L,
5169L, 5169L, 5169L, 5169L, 5169L, 27536L, 27536L, 27536L,
27536L, 27536L, 6150L, 6150L, 6150L, 6150L, 6150L, 31846L,
31846L, 31846L, 31846L, 31846L, 42753L, 42753L, 42753L, 42753L,
42753L, 1302L, 1302L, 1302L, 1302L, 2663L, 2663L, 2663L,
2663L, 2663L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L,
900L, 2202L, 2202L, 2202L, 2202L, 2202L, 18285L, 18285L,
18285L, 18285L, 52531L, 52531L, 52531L, 52531L, 52531L, 7152L,
7152L, 7152L, 7152L, 1279L, 1279L, 1279L, 1279L, 1279L, 1425L,
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2202L, 2202L, 2202L, 26134L, 26134L, 26134L, 26134L, 600L,
600L, 600L, 600L, 1302L, 1302L, 1302L, 1302L, 1327L, 1327L,
1327L, 1327L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L,
900L, 900L, 900L, 900L, 900L, 900L, 96166L, 96166L, 96166L,
96166L, 96166L, 2395L, 2395L, 2395L, 2395L, 2395L, 2890L,
2890L, 2890L, 2890L, 2890L, 2891L, 2891L, 2891L, 2891L, 2891L,
75L, 75L, 75L, 75L, 75L, 5346L, 5346L, 5346L, 5346L, 5346L,
600L, 600L, 600L, 600L, 65020L, 65020L, 65020L, 65020L, 65020L,
1261L, 1261L, 1261L, 1261L, 668L, 668L, 668L, 668L, 668L,
1425L, 1425L, 1425L, 1425L, 1425L, 600L, 600L, 600L, 600L,
900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L, 900L,
900L, 900L, 900L, 900L, 362L, 362L, 362L, 362L, 40258L, 40258L,
40258L, 40258L, 40268L, 40268L, 40268L, 40268L, 2549L, 2549L,
2549L, 2549L, 94L, 94L, 94L, 94L, 94L, 96L, 96L, 96L, 96L,
96L, 97L, 97L, 97L, 97L, 97L, 132L, 132L, 132L, 132L, 132L
)), class = "data.frame", row.names = c(NA, -710L))
r probability
asked Nov 11 at 22:47
user113156
7911417
I have some data on individual purchases.
In this data PANID is a person who bought a product on a particular week. In the sample I provide, there are 6 unique PANID's; so 6 people in total. I am trying to calculate the conditional probability that a PANID will repurchase a product a second time.
For example:
PANID 3104497 bought ITEM 7028 in WEEK 2010-01-11 and then again the same PANID bought the same ITEM in WEEK 2010-01-25. I am trying to figure out how to find the probability that they will buy that same item again (at any point in the data).
PANID WEEK ITEM
1 3104497 2010-01-11 526
2 3104497 2010-01-11 526
3 3104497 2010-01-11 526
4 3104497 2010-01-11 526
5 3104497 2010-01-11 526
6 3104497 2010-01-11 2890
...
705 3146217 2010-04-05 97
706 3146217 2010-04-05 132
707 3146217 2010-04-05 132
708 3146217 2010-04-05 132
709 3146217 2010-04-05 132
710 3146217 2010-04-05 132
Data:
df <- structure(list(PANID = c(3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3369710L, 3369710L, 3369710L, 3369710L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3138990L,
3138990L, 3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
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3104497L, 3322156L, 3322156L, 3322156L, 3322156L, 3138990L, 3138990L,
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3322156L, 3322156L, 3322156L, 3322156L, 3369710L, 3369710L, 3369710L,
3369710L, 3369710L, 3369710L, 3369710L, 3369710L, 3138990L, 3138990L,
3138990L, 3138990L, 3369710L, 3369710L, 3369710L, 3369710L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3138990L, 3138990L, 3138990L, 3138990L, 3322156L,
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3322156L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3146217L, 3146217L, 3146217L,
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3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3104497L, 3138990L,
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3146217L, 3146217L, 3146217L, 3322156L, 3322156L, 3322156L, 3322156L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3138990L, 3138990L,
3138990L, 3138990L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L,
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3322156L, 3322156L, 3322156L, 3322156L, 3104497L, 3104497L, 3104497L,
3104497L, 3104497L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3322156L,
3322156L, 3322156L, 3322156L, 3816413L, 3816413L, 3816413L, 3816413L,
3816413L, 3322156L, 3322156L, 3322156L, 3322156L, 3816413L, 3816413L,
3816413L, 3816413L, 3816413L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L, 3322156L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L,
3146217L, 3146217L, 3146217L, 3146217L, 3146217L, 3146217L),
WEEK = structure(c(14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
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14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
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14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620, 14620,
14620, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627, 14627,
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)), class = "data.frame", row.names = c(NA, -710L))
r probability
r probability
asked Nov 11 at 22:47
user113156
7911417
asked Nov 11 at 22:47
user113156
7911417
edited Nov 12 at 17:43
asked Nov 11 at 22:47
user113156
7911417
asked Nov 11 at 22:47
user113156
7911417
asked Nov 11 at 22:47
user113156
7911417
7911417
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
1
In the most general case, you expect something to happen X times in a given timeframe if it, well, happened X times in a comparable timeframe in the past.
output <- aggregate(df$PANID, by = list(df$ITEM, df$PANID), length)
colnames(output) <- c('ITEM', 'PANID', 'COUNT')
k <- as.integer(max(df$WEEK) - min(df$WEEK)) / 7 # number of weeks in the data
output$EXPECTATION <- output$COUNT / k
head(output)
#ITEM PANID COUNT EXPECTATION
#1 106 3104497 5 0.4166667
#2 240 3104497 5 0.4166667
#3 526 3104497 5 0.4166667
#4 548 3104497 5 0.4166667
#5 900 3104497 5 0.4166667
#6 1251 3104497 5 0.4166667
That said, it's a very back-of-the-envelope calculation. With more data (e.g. with longer timeframes and greater temporal resolution), you could look into seasonality (it's not very reasonable to expect the sales to remain constant from month to month, right). If you had actual features describing PANIDs and ITEMs, you could look into possible relationships between those features and purchase counts. Really, there's hardly a limit to how sophisticated one could get with such analysis.
answered Nov 11 at 23:08
12b345b6b78
767115
Thanks for your comment! Yes I have more time frame data, I am looking into seasonality of the data also (I know there exists some for some products, such as beer etc. - think Christmas, superbowl, 4th July - all spikes). I have data onPANID's such as how many cats, dogs,TV's, sex, age, race etc. (Its a rich dataset). I also have product information also. I was just looking for a begining point. My plan was to apply a Naive Bayes and then get a little more complicated with NN or which ever ML method would be suitable.
– user113156
Nov 11 at 23:13
1
If you have more data, you can totally try building a model of sorts. Those are very vast topics though! Look into forecasting with ARIMA models, this may help with the seasonality. And by all means this problem sounds like one amenable to ML; there's tons of relevant tutorials online. And if you get stuck, post a specific request for help!
– 12b345b6b78
Nov 11 at 23:19
At this stage I just wanted to get aYvariable because at the moment I have this rich panel level dataset but do not know exactly how to find an outcome variable for it. I think its super interesting to find out whichPANELIDs / People are more likely to buy certain products based on past purchases but I just could not think of a way to find thisYvariable... if this makes sense.
– user113156
Nov 11 at 23:26
1
I think it makes perfect sense to define the outcome variable as precisely the thing that you are trying to find out: probability of purchase (y) given customer profile and item (x)
– 12b345b6b78
Nov 11 at 23:30
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
1
In the most general case, you expect something to happen X times in a given timeframe if it, well, happened X times in a comparable timeframe in the past.
output <- aggregate(df$PANID, by = list(df$ITEM, df$PANID), length)
colnames(output) <- c('ITEM', 'PANID', 'COUNT')
k <- as.integer(max(df$WEEK) - min(df$WEEK)) / 7 # number of weeks in the data
output$EXPECTATION <- output$COUNT / k
head(output)
#ITEM PANID COUNT EXPECTATION
#1 106 3104497 5 0.4166667
#2 240 3104497 5 0.4166667
#3 526 3104497 5 0.4166667
#4 548 3104497 5 0.4166667
#5 900 3104497 5 0.4166667
#6 1251 3104497 5 0.4166667
That said, it's a very back-of-the-envelope calculation. With more data (e.g. with longer timeframes and greater temporal resolution), you could look into seasonality (it's not very reasonable to expect the sales to remain constant from month to month, right). If you had actual features describing PANIDs and ITEMs, you could look into possible relationships between those features and purchase counts. Really, there's hardly a limit to how sophisticated one could get with such analysis.
answered Nov 11 at 23:08
12b345b6b78
767115
Thanks for your comment! Yes I have more time frame data, I am looking into seasonality of the data also (I know there exists some for some products, such as beer etc. - think Christmas, superbowl, 4th July - all spikes). I have data onPANID's such as how many cats, dogs,TV's, sex, age, race etc. (Its a rich dataset). I also have product information also. I was just looking for a begining point. My plan was to apply a Naive Bayes and then get a little more complicated with NN or which ever ML method would be suitable.
– user113156
Nov 11 at 23:13
1
If you have more data, you can totally try building a model of sorts. Those are very vast topics though! Look into forecasting with ARIMA models, this may help with the seasonality. And by all means this problem sounds like one amenable to ML; there's tons of relevant tutorials online. And if you get stuck, post a specific request for help!
– 12b345b6b78
Nov 11 at 23:19
At this stage I just wanted to get aYvariable because at the moment I have this rich panel level dataset but do not know exactly how to find an outcome variable for it. I think its super interesting to find out whichPANELIDs / People are more likely to buy certain products based on past purchases but I just could not think of a way to find thisYvariable... if this makes sense.
– user113156
Nov 11 at 23:26
1
I think it makes perfect sense to define the outcome variable as precisely the thing that you are trying to find out: probability of purchase (y) given customer profile and item (x)
– 12b345b6b78
Nov 11 at 23:30
add a comment |
1
In the most general case, you expect something to happen X times in a given timeframe if it, well, happened X times in a comparable timeframe in the past.
output <- aggregate(df$PANID, by = list(df$ITEM, df$PANID), length)
colnames(output) <- c('ITEM', 'PANID', 'COUNT')
k <- as.integer(max(df$WEEK) - min(df$WEEK)) / 7 # number of weeks in the data
output$EXPECTATION <- output$COUNT / k
head(output)
#ITEM PANID COUNT EXPECTATION
#1 106 3104497 5 0.4166667
#2 240 3104497 5 0.4166667
#3 526 3104497 5 0.4166667
#4 548 3104497 5 0.4166667
#5 900 3104497 5 0.4166667
#6 1251 3104497 5 0.4166667
That said, it's a very back-of-the-envelope calculation. With more data (e.g. with longer timeframes and greater temporal resolution), you could look into seasonality (it's not very reasonable to expect the sales to remain constant from month to month, right). If you had actual features describing PANIDs and ITEMs, you could look into possible relationships between those features and purchase counts. Really, there's hardly a limit to how sophisticated one could get with such analysis.
answered Nov 11 at 23:08
12b345b6b78
767115
Thanks for your comment! Yes I have more time frame data, I am looking into seasonality of the data also (I know there exists some for some products, such as beer etc. - think Christmas, superbowl, 4th July - all spikes). I have data onPANID's such as how many cats, dogs,TV's, sex, age, race etc. (Its a rich dataset). I also have product information also. I was just looking for a begining point. My plan was to apply a Naive Bayes and then get a little more complicated with NN or which ever ML method would be suitable.
– user113156
Nov 11 at 23:13
1
If you have more data, you can totally try building a model of sorts. Those are very vast topics though! Look into forecasting with ARIMA models, this may help with the seasonality. And by all means this problem sounds like one amenable to ML; there's tons of relevant tutorials online. And if you get stuck, post a specific request for help!
– 12b345b6b78
Nov 11 at 23:19
At this stage I just wanted to get aYvariable because at the moment I have this rich panel level dataset but do not know exactly how to find an outcome variable for it. I think its super interesting to find out whichPANELIDs / People are more likely to buy certain products based on past purchases but I just could not think of a way to find thisYvariable... if this makes sense.
– user113156
Nov 11 at 23:26
1
I think it makes perfect sense to define the outcome variable as precisely the thing that you are trying to find out: probability of purchase (y) given customer profile and item (x)
– 12b345b6b78
Nov 11 at 23:30
add a comment |
1
1
1
In the most general case, you expect something to happen X times in a given timeframe if it, well, happened X times in a comparable timeframe in the past.
output <- aggregate(df$PANID, by = list(df$ITEM, df$PANID), length)
colnames(output) <- c('ITEM', 'PANID', 'COUNT')
k <- as.integer(max(df$WEEK) - min(df$WEEK)) / 7 # number of weeks in the data
output$EXPECTATION <- output$COUNT / k
head(output)
#ITEM PANID COUNT EXPECTATION
#1 106 3104497 5 0.4166667
#2 240 3104497 5 0.4166667
#3 526 3104497 5 0.4166667
#4 548 3104497 5 0.4166667
#5 900 3104497 5 0.4166667
#6 1251 3104497 5 0.4166667
That said, it's a very back-of-the-envelope calculation. With more data (e.g. with longer timeframes and greater temporal resolution), you could look into seasonality (it's not very reasonable to expect the sales to remain constant from month to month, right). If you had actual features describing PANIDs and ITEMs, you could look into possible relationships between those features and purchase counts. Really, there's hardly a limit to how sophisticated one could get with such analysis.
answered Nov 11 at 23:08
12b345b6b78
767115
In the most general case, you expect something to happen X times in a given timeframe if it, well, happened X times in a comparable timeframe in the past.
output <- aggregate(df$PANID, by = list(df$ITEM, df$PANID), length)
colnames(output) <- c('ITEM', 'PANID', 'COUNT')
k <- as.integer(max(df$WEEK) - min(df$WEEK)) / 7 # number of weeks in the data
output$EXPECTATION <- output$COUNT / k
head(output)
#ITEM PANID COUNT EXPECTATION
#1 106 3104497 5 0.4166667
#2 240 3104497 5 0.4166667
#3 526 3104497 5 0.4166667
#4 548 3104497 5 0.4166667
#5 900 3104497 5 0.4166667
#6 1251 3104497 5 0.4166667
That said, it's a very back-of-the-envelope calculation. With more data (e.g. with longer timeframes and greater temporal resolution), you could look into seasonality (it's not very reasonable to expect the sales to remain constant from month to month, right). If you had actual features describing PANIDs and ITEMs, you could look into possible relationships between those features and purchase counts. Really, there's hardly a limit to how sophisticated one could get with such analysis.
answered Nov 11 at 23:08
12b345b6b78
767115
answered Nov 11 at 23:08
12b345b6b78
767115
answered Nov 11 at 23:08
12b345b6b78
767115
answered Nov 11 at 23:08
12b345b6b78
767115
767115
Thanks for your comment! Yes I have more time frame data, I am looking into seasonality of the data also (I know there exists some for some products, such as beer etc. - think Christmas, superbowl, 4th July - all spikes). I have data onPANID's such as how many cats, dogs,TV's, sex, age, race etc. (Its a rich dataset). I also have product information also. I was just looking for a begining point. My plan was to apply a Naive Bayes and then get a little more complicated with NN or which ever ML method would be suitable.
– user113156
Nov 11 at 23:13
1
If you have more data, you can totally try building a model of sorts. Those are very vast topics though! Look into forecasting with ARIMA models, this may help with the seasonality. And by all means this problem sounds like one amenable to ML; there's tons of relevant tutorials online. And if you get stuck, post a specific request for help!
– 12b345b6b78
Nov 11 at 23:19
At this stage I just wanted to get aYvariable because at the moment I have this rich panel level dataset but do not know exactly how to find an outcome variable for it. I think its super interesting to find out whichPANELIDs / People are more likely to buy certain products based on past purchases but I just could not think of a way to find thisYvariable... if this makes sense.
– user113156
Nov 11 at 23:26
1
I think it makes perfect sense to define the outcome variable as precisely the thing that you are trying to find out: probability of purchase (y) given customer profile and item (x)
– 12b345b6b78
Nov 11 at 23:30
add a comment |
Thanks for your comment! Yes I have more time frame data, I am looking into seasonality of the data also (I know there exists some for some products, such as beer etc. - think Christmas, superbowl, 4th July - all spikes). I have data onPANID's such as how many cats, dogs,TV's, sex, age, race etc. (Its a rich dataset). I also have product information also. I was just looking for a begining point. My plan was to apply a Naive Bayes and then get a little more complicated with NN or which ever ML method would be suitable.
– user113156
Nov 11 at 23:13
1
If you have more data, you can totally try building a model of sorts. Those are very vast topics though! Look into forecasting with ARIMA models, this may help with the seasonality. And by all means this problem sounds like one amenable to ML; there's tons of relevant tutorials online. And if you get stuck, post a specific request for help!
– 12b345b6b78
Nov 11 at 23:19
At this stage I just wanted to get aYvariable because at the moment I have this rich panel level dataset but do not know exactly how to find an outcome variable for it. I think its super interesting to find out whichPANELIDs / People are more likely to buy certain products based on past purchases but I just could not think of a way to find thisYvariable... if this makes sense.
– user113156
Nov 11 at 23:26
1
I think it makes perfect sense to define the outcome variable as precisely the thing that you are trying to find out: probability of purchase (y) given customer profile and item (x)
– 12b345b6b78
Nov 11 at 23:30
Thanks for your comment! Yes I have more time frame data, I am looking into seasonality of the data also (I know there exists some for some products, such as beer etc. - think Christmas, superbowl, 4th July - all spikes). I have data on
PANID's such as how many cats, dogs,TV's, sex, age, race etc. (Its a rich dataset). I also have product information also. I was just looking for a begining point. My plan was to apply a Naive Bayes and then get a little more complicated with NN or which ever ML method would be suitable.– user113156
Nov 11 at 23:13
Thanks for your comment! Yes I have more time frame data, I am looking into seasonality of the data also (I know there exists some for some products, such as beer etc. - think Christmas, superbowl, 4th July - all spikes). I have data on
PANID's such as how many cats, dogs,TV's, sex, age, race etc. (Its a rich dataset). I also have product information also. I was just looking for a begining point. My plan was to apply a Naive Bayes and then get a little more complicated with NN or which ever ML method would be suitable.– user113156
Nov 11 at 23:13
1
1
If you have more data, you can totally try building a model of sorts. Those are very vast topics though! Look into forecasting with ARIMA models, this may help with the seasonality. And by all means this problem sounds like one amenable to ML; there's tons of relevant tutorials online. And if you get stuck, post a specific request for help!
– 12b345b6b78
Nov 11 at 23:19
If you have more data, you can totally try building a model of sorts. Those are very vast topics though! Look into forecasting with ARIMA models, this may help with the seasonality. And by all means this problem sounds like one amenable to ML; there's tons of relevant tutorials online. And if you get stuck, post a specific request for help!
– 12b345b6b78
Nov 11 at 23:19
At this stage I just wanted to get a
Y variable because at the moment I have this rich panel level dataset but do not know exactly how to find an outcome variable for it. I think its super interesting to find out which PANELIDs / People are more likely to buy certain products based on past purchases but I just could not think of a way to find this Y variable... if this makes sense.– user113156
Nov 11 at 23:26
At this stage I just wanted to get a
Y variable because at the moment I have this rich panel level dataset but do not know exactly how to find an outcome variable for it. I think its super interesting to find out which PANELIDs / People are more likely to buy certain products based on past purchases but I just could not think of a way to find this Y variable... if this makes sense.– user113156
Nov 11 at 23:26
1
1
I think it makes perfect sense to define the outcome variable as precisely the thing that you are trying to find out: probability of purchase (y) given customer profile and item (x)
– 12b345b6b78
Nov 11 at 23:30
I think it makes perfect sense to define the outcome variable as precisely the thing that you are trying to find out: probability of purchase (y) given customer profile and item (x)
– 12b345b6b78
Nov 11 at 23:30
add a comment |
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