Calculating the number of songs of each genre popular_genre=data.groupby('Genre').size()# 根据类别分组,再统计每个类别多少首歌print(popular_genre)genre_list=data['Genre'].values.tolist()# 将每个类别转成列表形式 image-20200116125455420 Calculating the number of songs by each of the artists popu...
Kaggle竞赛入门(三):用Python处理过拟合和欠拟合,得到最佳模型 本文翻译自kaggle learn,也就是kaggle官方最快入门kaggle竞赛的教程,强调python编程实践和数学思想(而没有涉及数学细节),笔者在不影响算法和程序理解的基础上删除了一些不必要的废话,毕竟英文有的时候比较啰嗦。 一.什么是过拟合和欠拟合? 过拟合的含义就...
首先导入数据,将数据分为训练集和测试集: importpandas as pd#Load datamelbourne_file_path ='../input/melbourne-housing-snapshot/melb_data.csv'melbourne_data=pd.read_csv(melbourne_file_path)#Filter rows with missing valuesmelbourne_data = melbourne_data.dropna(axis=0)#Choose target and featuresy ...
一.什么是模型验证 模型验证在机器学习当中非常重要,因为有的时候拟合出来的模型误差非常大而自己却不知道,就会造成很大的失误。在kaggle竞赛入门(二)当中,我们利用决策树算法已经拟合出来了一个模型,那么如何去验证这个模型的准确性呢?那就是使用真实值和预测值的差值的绝对值来进行衡量,衡量一个点的误差的代码如下:...
Kaggle竞赛入门(一):决策树算法的Python实现 本文翻译自kaggle learn,也就是kaggle官方最快入门kaggle竞赛的教程,强调python编程实践和数学思想(而没有涉及数学细节),笔者在不影响算法和程序理解的基础上删除了一些不必要的废话,毕竟英文有的时候比较啰嗦。 一.决策树算法基本原理...
Explore and run machine learning code with Kaggle Notebooks | Using data from Top Songs of the World
Explore and run machine learning code with Kaggle Notebooks | Using data from 1980s Classic Hits (with Spotify Data)
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Explore and run machine learning code with Kaggle Notebooks | Using data from Billboard Hits Songs Dataset
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