阿里巴巴笔试题：数据分析与建模测试.pdfVIP

阿⾥巴巴笔试题：数据分析与建模测试 阿⾥巴巴笔试题：数据分析与建模测试 请阅读以下⽂字答题。 Field Descriptions: isbuyer - Past purchaser of product buy_freq - How many times purchased in the past visit_freq - How many times visited website in the past buy_interval - Average time between purchases sv_interval - Average time between website visits expected_time_buy - ? expected_time_visit - ? last_buy - Days since last purchase. last_visit - Days since last website visit. multiple_buy - ? multiple_visit - ? uniq_url - Number of unique urls we observed web browser on. num_checkins - Number of times we observed web browser. y_buy - Outcome variable of interest, Did they purchase in period of interest. Question: Each observation in the provided training/test dataset is a web browser (or cookie) in our observed Universe. The goal is to model the behavior of a future purchase and classify cookies into those that will purchase in the future and those that will not. y_buy is the outcome variable that represents if a cookie made a purchase in the period of interest. All of the rest of the columns in the data set were recorded prior to this purchase and may be used to predict purchase. Please use ‘ads_train.csv’ as training data to create at least two different classes of models (e.g. logistic regression, random forest, etc.) to classify these cookies into future buyers or not. Explain your choice of model, how you did model selection, how you validated the quality of the model, and which variables are most informative of purchase. Also, comment on any general trends or anomalies in the data you can identify as well as propose a meaning for those fields not defined. The deliverable is a document with text and figures illustrating your thought process, how you began to explore the data, and a comparison of the models that you created. When evaluating your models, consider metrics such asAUC of Precision-Recall Curve, precision, recall