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Honestly, feature engineering is perhaps THE most important aspect of Kaggle competitions. A quick glance at previous winning solutions will show you how important feature engineering is. It’s often the difference between a top 20 percentile finish and a mid-leaderboard position. ...
but rather consistency, collaboration, and an obsession with learning. I studied top solutions, asked “why did this work,” and rebuilt models from scratch until I truly understood them. And I surrounded myself with people who were better than me. I didn’t try to win...
在Data Science Solutions book这本书里,描述了在解决一个竞赛问题时所需要做的具体工作流程: 问题的定义 获取训练数据以及测试数据 加工、准备以及清洗数据 分析、识别数据的模式,并对数据做可视化 建模、预测,并解决问题 对结果做可视化,生成报告,并且展示问题的解决步骤和最终的解决方案 ...
Learn about Kaggle Competitions and Tensorflow solutions in a different way✅ Sampling Strategies✅ Optimization Code✅ Statistical Modelling✅ Data Visualization✅ Machine Learning Modelling (Fine Tuning)CompetitionCompetition StrategyKaggleGitHub 1. Titanic 1 - Dynamic Imputation 2 - Sampling ...
Solutions By company size Enterprises Small and medium teams Startups Nonprofits By use case DevSecOps DevOps CI/CD View all use cases By industry Healthcare Financial services Manufacturing Government View all industries View all solutions Resources Topics AI DevOps...
Titanic Data Science Solutions 的学习笔记 1 解答竞赛问题的七个步骤定义问题 获取训练和测试数据 整理、准备、清洗数据 分析、定义模式,并且探索数据 建模,预测并解决问题 可视化,报告,并展示问题解决步骤…
在Data Science Solutions book这本书里,描述了在解决一个竞赛问题时所需要做的具体工作流程: 问题的定义 获取训练数据以及测试数据 加工、准备以及清洗数据 分析、识别数据的模式,并对数据做可视化 建模、预测,并解决问题 对结果做可视化,生成报告,并且展示问题的解决步骤和最终的解决方案 ...
EDA uses data visualization, statistics, and queries to find important variables, interesting relations among the variables, anomalies, patterns, and insights. You can examine how the data is distributed using summary statistics with the pandasdescribefunction. This function gives count,...