一、简介 ELO商户类别推荐有助于了解客户忠诚度
Elo Merchant Category Recommendation Help understand customer loyalty (ELO商户类别推荐有助于了解客户忠诚度)
竞赛描述:
想象一下,在一个陌生的地方饿着肚子,在适当的时候,根据你的个人喜好,提供餐馆推荐。推荐信附带了您的信用卡提供商提供的拐角处当地饭店的折扣!
目前,作为巴西最大的支付品牌之一,ELO已经与商家建立了合作关系,为持卡人提供促销或折扣。但是,这些促销活动对消费者或商家都有效吗?客户喜欢他们的体验吗?商人看到重复的生意了吗?个性化是关键。
ELO建立了机器学习模型,以了解客户生命周期中从食品到购物的最重要方面和偏好。但到目前为止,没有一个是专门为个人或个人资料定制的。这就是你进来的地方。
在这场竞争中,Kaggers将通过发现客户忠诚度中的信号,开发算法来识别并为个人提供最相关的机会。您的输入将改善客户的生活,帮助ELO减少不必要的活动,为客户创造正确的体验。
评价指标
提交的数据按均方根误差(root mean squared error)计分。RMSE定义为:
where y^ is the predicted loyalty score for each card_id
, and y is the actual loyalty score assigned to a card_id
.
提交文件格式:
card_id, target C_ID_9e86007114,0 C_ID_1c9f77086c,0.5 C_ID_07b20e9908,0 C_ID_63d6bac69a,0 C_ID_bbc26a86eb,0 C_ID_f749aad790,0 C_ID_7b5c15ff41,-0.25 C_ID_ec6b0f2d30,0 C_ID_0a11e759c5,0
Timeline
February 19, 2019 - External Data Disclosure deadline. All external data used in the competition must be disclosed in the forums by this date.
数据
需要的文件
- train.csv and test.csv 文件包含用于训练和预测的
card_id
s - The historical_transactions.csv and new_merchant_transactions.csv files 包含有关每张卡交易的信息。
- historical_transactions.csv 包含在任何提供的商户ID上的每张卡最多3个月的交易价值。
- new_merchant_transactions.csv 包含两个月内新商户的交易(该卡尚未访问的商户ID)。
- merchants.csv 包含数据集中表示的每个商户ID的汇总信息。
- The historical_transactions.csv and new_merchant_transactions.csv files contain information about each card's transactions.
- historical_transactions.csv contains up to 3 months' worth of transactions for every card at any of the provided
merchant_id
s. - new_merchant.csv contains the transactions at new merchants (merchant_ids that this particular card_id has not yet visited) over a period of two months.
- merchants.csv contains aggregate information for each
merchant_id
represented in the data set.
数据格式如下:
- train.csv和test.csv包含卡ID和卡本身的信息-卡激活的第一个月( the first month),等等。train.csv也包含目标。
- historical_transactions.csv and new_merchant_transactions.csv are designed to be joined with train.csv, test.csv, and merchants.csv.它们包含有关每张卡的交易信息。
- merchants can be joined with the transaction sets to provide additional merchant-level information. 可以将商家与交易集结合起来,以提供额外的商家级别信息。
预测:
You are predicting a loyalty score for each card_id
represented in test.csv and sample_submission.csv.
文件描述:
- train.csv - the training set
- test.csv - the test set
- sample_submission.csv - a sample submission file in the correct format - contains all
card_id
s you are expected to predict for. - historical_transactions.csv - up to 3 months' worth of historical transactions for each
card_id
- merchants.csv - additional information about all merchants /
merchant_id
s in the dataset. - new_merchant_transactions.csv - two months' worth of data for each
card_id
containing ALL purchases thatcard_id
made atmerchant_id
s that were not visited in the historical data.