数据来源: kaggle的Titanic 生存模型:titanic_train.csv。 引入的库: import numpy as npimport pandas as pdimport sysreload(sys)sys.setdefaultencoding('gbk')import matplotlib.pyplot as pltimport seaborn as sns
titanic, prediction using sklearn after EDA, we can now preprocess the training data and learn a model to predict using scikit-learn (sklearn) ml library 做完上面的分析,可以选定几个特征进行使用,然后选择模型。 我们使用scikit-lea
数据可视化分析 import pandas as pdimport matplotlib.pyplot as pltimport seaborn as snsimport numpy as nptitanic=pd.read_csv('train.csv')#print(titanic.head())#设置某一列为索引#print(titanic.set_index('Passe
代码所需数据集:https://github.com/jsusu/Titanic_Passenger_Survival_Prediction_2/tree/master/titanic_data import reimport numpy as npimport pandas as pdimport matplotlib.pyplot as pltimport seaborn as sn
A Data Science Framework: To Achieve 99% Accuracy 学习data scientist的思考方式,而不是如何编码。 目录 A Data Science Framework: To Achieve 99% Accuracy 1 怎样处理问题 2 数据科学基本框架 2.1 问题定义 2.2 数据收集 2.3 可用数据准备与