<class'pandas.core.frame.DataFrame'>RangeIndex:418entries,0to417Datacolumns (total11columns):PassengerId418non-nullint64Pclass418non-nullint64Name418non-nullobjectSex418non-nullobjectAge332non-nullfloat64SibSp418non-nullint64Parch418non-nullint64Ticket418non-nullobjectFare417non-nullfloat64Cabin91non-...
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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 提交结果 以上的工作流程仅仅...
Data-Science-Competitionsis a Github repository, presents solutions thatwon the Competitiontopic by topic (I just checked it out that 11 months ago was the last commit). The winning solution is technology-based at the time, so we need to see if we have better technology today. ...
It is particularly well suited to handling the huge and very fast-changing datasets which are used in Big Data operations, including artificial intelligence and machine learning applications, thanks to integration with a large number of advanced database solutions including Hadoop, Amazon AWS, My SQL...
Data Visualization Make great data visualizations. A great way to see the power of coding! Estimated time: 4 hours Join 8K monthly users Intro to Machine Learning Learn the core ideas in machine learning, and build your first models.
Kaggle, the home of data science, provides a global platform for competitions, customer solutions and job board. Here’s the Kaggle catch, these competitions not only make you think out of the box, but also offers a handsome prize money....
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,...
接着就是按照教程来:https://www.kaggle.com/startupsci/titanic-data-science-solutions 调用matplotlib又出现了版本问题: sudo pip install matplotlib==2.2.0 1. 因为版本问题太多,大多是是因为版本不够新,而新版本的包多只支持python3,所以系统安装python3,并在python3环境下操作,使用pip3安装相应包。
While the generic solutions are likely good to start with, in a real project I would try to collect a real dataset of questions and answers from the domain experts and the intended users of the RAG solution. As the LLM is typically expected to generate a natural language response, this can...