[{"data":1,"prerenderedAt":98},["ShallowReactive",2],{"lang-switch-post-\u002Fen\u002Fprojects":3,"projects-index-en":4},null,[5],{"id":6,"title":7,"body":8,"cover":3,"description":83,"extension":84,"meta":85,"navigation":86,"order":87,"path":88,"seo":89,"status":90,"stem":91,"tags":92,"__hash__":97},"projects\u002Fen\u002Fprojects\u002Folist-ecommerce\u002Findex.md","Olist: an End-to-End Brazilian E-Commerce Case Study",{"type":9,"value":10,"toc":78},"minimark",[11,20,31,36,39,67,70],[12,13,14,15,19],"p",{},"This project is different from any playlist here. A playlist is a class, one concept at a time, each post independent from the last. This is an ",[16,17,18],"strong",{},"end-to-end data science case study",": I take a real, messy dataset with nine tables tied together, and go all the way through, EDA, visualization, feature engineering, model training, interpretability, and a business conclusion. The chapters build on each other, chapter 3 assumes you've already read chapters 1 and 2.",[12,21,22,23,30],{},"The dataset is the ",[24,25,29],"a",{"href":26,"rel":27},"https:\u002F\u002Fwww.kaggle.com\u002Fdatasets\u002Folistbr\u002Fbrazilian-ecommerce",[28],"nofollow","Brazilian E-Commerce Public Dataset, by Olist",", available on Kaggle. Real (anonymized) data from over 100 thousand orders placed between 2016 and 2018 on an actual Brazilian marketplace, customer, seller, product, payment, review, all tied together by keys. All the heavy processing (pandas, scikit-learn, XGBoost, SHAP) runs in a notebook I execute on Google Colab, with a T4 GPU when a chapter needs one. The real results of those notebooks become the interactive charts here on the blog, never an invented number.",[32,33,35],"h2",{"id":34},"the-four-scenarios","The four scenarios",[12,37,38],{},"Defined right in the first chapter, because they steer the whole project:",[40,41,42,49,55,61],"ol",{},[43,44,45,48],"li",{},[16,46,47],{},"Delivery delay prediction"," (binary classification): does the order arrive after the estimated date or not?",[43,50,51,54],{},[16,52,53],{},"Review score prediction"," (classification\u002Fregression): how many stars will the customer give?",[43,56,57,60],{},[16,58,59],{},"Freight or order value prediction"," (regression): how much will it cost?",[43,62,63,66],{},[16,64,65],{},"Customer segmentation"," (clustering, RFM): what kinds of customers exist in this base?",[12,68,69],{},"Each pulls a different slice of the same dataframe, and the goal is to show you how the same base dataset serves very different business questions.",[12,71,72,73,77],{},"Start with ",[24,74,76],{"href":75},"\u002Fen\u002Fprojects\u002Folist-ecommerce\u002F01-intro-relational-model-eda","Chapter 1: relational model and first exploration",".",{"title":79,"searchDepth":80,"depth":80,"links":81},"",2,[82],{"id":34,"depth":80,"text":35},"A real data science case study on the Brazilian marketplace Olist. From raw CSV to trained model, covering EDA, feature engineering, training four different models, and interpretability.","md",{},true,1,"\u002Fen\u002Fprojects\u002Folist-ecommerce",{"title":7,"description":83},"published","en\u002Fprojects\u002Folist-ecommerce\u002Findex",[93,94,95,96],"pandas","scikit-learn","xgboost","shap","Zyg5at_SeJfe3mUFl8y1jpFOae7KlXUDgPBPFUpuapU",1787605213207]