挑战排行前4%--Titanic: Machine Learning from Disaster--kaggle入门（40）

最新推荐文章于 2024-09-06 16:38:31 发布

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最新推荐文章于 2024-09-06 16:38:31 发布

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分类专栏：机器学习机器学习及应用文章标签：数据挖掘机器学习

本文链接：https://blog.youkuaiyun.com/LieQueov/article/details/87904619

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1.题目链接

Titanic: Machine Learning from Disaster

2.参考资料

1.Titanic [0.82] - [0.83]

3.线上成绩

截至2019年2月24日

排名:413/9909 前4%

线上成绩:0.82296

4.流程及代码

4.1 载入数据

import pandas as pd 
import numpy as np
import warnings
warnings.filterwarnings('ignore')
train_data = pd.read_csv('./data/train.csv')
test_data = pd.read_csv('./data/test.csv')
train_y = train_data["Survived"]
train_x = train_data
# 将训练集和测试集合成
all_data = pd.concat((train_x, test_data)).reset_index(drop=True)

4.2 缺失值填充

## 根据计算不同仓位的在不同港口的中位数售价，最接近票价的港口,填充Embarked
P1 = all_data[all_data['Pclass']==1][['Fare','Embarked']]
P1.groupby('Embarked')['Fare'].median()

all_data.loc[829,'Embarked'] = 'C'
all_data.loc[61,'Embarked'] = 'C'

P3 = all_data[all_data['Pclass']==3][['Fare','Embarked']]
P3.groupby('Embarked')['Fare'].median()
all_data.loc[1043,'Fare'] = 8.05

all_data_Name = all_data['Name']
all_data_Family = all_data_Name.str.split(',',expand=True)[0]
all_data['Family'] = all_data_Family 
only_have_Sibsp = all_data[all_data['Age'].isnull()==True][(all_data['SibSp'] > 0) & (all_data['Parch']==0)]
Sibsp_ticket = only_have_Sibsp['Ticket'].unique()

only_have_Sibsp = all_data[all_data['Age'].isnull()==True][(all_data['SibSp'] > 0) & (all_data['Parch']==0)]
Sibsp_ticket = only_have_Sibsp['Ticket'].unique()

for f in Sibsp_ticket:
    tmp = all_data[all_data['Ticket'] =&#

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