Kaggle泰坦尼克号-决策树Top 3%-0基础代码详解

        Titanic Disaster Kaggle，里的经典入门题目，因为在学决策树所以找了一个实例学习了一下，完全萌新零基础，所以基本每一句都做了注释。

        原文链接：Titanic: Simple Decision Tree model score(Top 3%) | Kaggle

目录

1. Preprocessing and EDA  #预处理和探索性数据分析

1.1. Missing Values  #缺失值

1.3. Fare column  #票价列

1.4. Embarked column #登船地

1.5. Cabin column #船舱列

2. Feature Extraction  #特征工程

2.1. SibSp and Parch column #兄弟姐妹和父母孩子

2.2. Ticket column #船票

2.3. Name Column #姓名

2.4. Woman or Child column #女人和孩子

2.4 Family Survived Rate column #家庭生存率

3. Modeling #训练模型

4. Conclutions #结论

5. References #参考文献

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Titanic Disaster

Improve your score to 82.78% (Top 3%)

In this work I have used some basic techniques to process of the easy way Titanic dataset.

1. Preprocessing and EDA  #预处理和探索性数据分析

Here, I reviewed the variables, impute missing values, found patterns and watched relationship between columns.

#第一部分的工作是查看变量，修补缺失值，通过观察数据之间的关系，进行特征工程。

1.1. Missing Values  #缺失值

Reading the dataset and merging Train and Test to get better results.

#读取数据集并合并训练和测试以获得更好的结果

# Libraries used

import numpy as np 
#运行速度非常快的数学库，主要用于数组计算

import pandas as pd 
#分析结构化数据的工具集，基础是 Numpy

import seaborn as sns
#可视化库 是对matplotlib进行二次封装

import matplotlib.pyplot as plt
#可视化库
​
#机器学习库
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler
​
from numpy.random import seed
​
seed(11111)
#随机种子 可以让每次的随机数都相同 保证程序可以复现

# Reading
#读取训练集和测试集
train = pd.read_csv("../input/titanic/train.csv")
test = pd.read_csv("../input/titanic/test.csv")
​
# Putting on index to each dataset before split it
#指定PassengerId列将被设置为索引
train = train.set_index("PassengerId")
test = test.set_index("PassengerId")
​
# dataframe 
#纵向合并两个DataFrame对象 axis=0纵向 sort=False列的顺序维持原样， 不进行重新排序。
df = pd.concat([train, test], axis=0, sort=False)
​
#输出df
df

	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
PassengerId
1	0.0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1.0	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1.0	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1.0	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0.0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
1305	NaN	3	Spector, Mr. Woolf	male	NaN	0	0	A.5. 3236	8.0500	NaN	S
1306	NaN	1	Oliva y Ocana, Dona. Fermina	female	39.0	0	0	PC 17758	108.9000	C105	C
1307	NaN	3	Saether, Mr. Simon Sivertsen	male	38.5	0	0	SOTON/O.Q. 3101262	7.2500	NaN	S
1308	NaN	3	Ware, Mr. Frederick	male	NaN	0	0	359309	8.0500	NaN	S
1309	NaN	3	Peter, Master. Michael J	male	NaN	1	1	2668	22.3583	NaN	C

1309 rows × 11 columns

#1309行 11列

As you can see Name, Sex, Ticket, Cabin, and Embarked column are objects, before processing each column we should know if there are NAs or missing values.

#姓名、性别、船票、客舱和登船地列都是对象，在预处理之前先查看一下数据的总体信息 判断是否有缺失数据

df.info()
#.info()函数用于获取 DataFrame 的简要摘要

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1309 entries, 1 to 1309
Data columns (total 11 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Survived  891 non-null    float64
 1   Pclass    1309 non-null   int64