根据过去的购买情况购买物品的概率答案

【问题标题】：probability of purchasing an item based on past purchases根据过去的购买情况购买物品的概率
【发布时间】：2018-11-11 22:47:04
【问题描述】：

我有一些个人购买的数据。

在此数据中，PANID 是在特定周购买产品的人。在我提供的示例中，有 6 个唯一的PANID；所以一共6个人。我正在尝试计算PANID 第二次回购产品的条件概率。

例如：

PANID 3104497 在WEEK 2010-01-11 中购买了ITEM 7028，然后又在WEEK 2010-01-25 中购买了相同的PANID 相同的ITEM。我试图弄清楚如何找到他们再次购买同一商品的概率（在数据中的任何点）。

    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

数据：

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, 
3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 3138990L, 
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, 
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, 
    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, 
    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, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 14634, 
    14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 
    14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 
    14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 14641, 
    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, 
    14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648, 14648, 
    14648, 14648, 14648, 14648, 14648, 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, 14669, 14669, 14669, 14669, 
    14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669, 14669, 
    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, 
    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, 14683, 14683, 14683, 
    14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 
    14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 
    14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 14683, 
    14683, 14683, 14683, 14683, 14690, 14690, 14690, 14690, 14690, 
    14690, 14690, 14690, 14690, 14690, 14690, 14690, 14690, 14690, 
    14690, 14690, 14690, 14690, 14690, 14690, 14690, 14690, 14690, 
    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, 
    14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697, 
    14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697, 14697, 
    14697, 14697, 14697, 14697, 14697, 14697, 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, 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, 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, 
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    )), class = "data.frame", row.names = c(NA, -710L))

【问题讨论】：

标签： r probability

【解决方案1】：

在最一般的情况下，如果某件事在过去的可比时间范围内发生了 X 次，那么您预计某件事会在给定的时间范围内发生 X 次。

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

也就是说，这是一个非常粗略的计算。有了更多数据（例如，更长的时间范围和更高的时间分辨率），您可以研究季节性（期望销售额每个月保持不变是不太合理的，对吧）。如果您有描述PANIDs 和ITEMs 的实际功能，您可以查看这些功能和购买次数之间可能存在的关系。确实，这种分析的复杂程度几乎没有限制。

【讨论】：

感谢您的评论！是的，我有更多的时间框架数据，我也在研究数据的季节性（我知道某些产品存在一些数据，例如啤酒等 - 想想圣诞节，超级碗，7 月 4 日 - 所有峰值）。我有PANID 的数据，例如有多少猫、狗、电视、性别、年龄、种族等（它是一个丰富的数据集）。我也有产品信息。我只是在寻找一个起点。我的计划是应用朴素贝叶斯，然后使用 NN 或使用哪种 ML 方法变得更复杂一些。
如果你有更多的数据，你完全可以尝试建立一个模型。这些都是非常广泛的话题！使用 ARIMA 模型进行预测，这可能有助于季节性。无论如何，这个问题听起来像是一个适合 ML 的问题。网上有大量相关教程。如果您遇到困难，请发布特定的帮助请求！
在这个阶段我只是想得到一个Y 变量，因为目前我有这个丰富的面板级数据集，但不知道如何找到它的结果变量。我认为找出哪些PANELIDs / 人们更有可能根据过去的购买购买某些产品非常有趣，但我只是想不出一种方法来找到这个Y 变量......如果这是有道理的。
我认为将结果变量精确定义为您想要找出的东西是非常有意义的：给定客户资料和商品 (x) 的购买概率 (y)