泰坦尼克船员获救分析泰坦尼克船员获救分析

1.导入数据

import pandas #ipython notebook titanic = pandas.read_csv("titanic_train.csv") titanic.head(5)

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数据预览
由上可知，该数据共有12个字段，各个字段含义如下：

PassengerId 整型变量，标识乘客的ID，递增变量，对预测无帮助
Survived 整型变量，标识该乘客是否幸存。0表示遇难，1表示幸存。将其转换为factor变量比较方便处理
Pclass 整型变量，标识乘客的社会-经济状态，1代表Upper，2代表Middle，3代表Lower
Name 字符型变量，除包含姓和名以外，还包含Mr.
Mrs. Dr.这样的具有西方文化特点的信息
Sex 字符型变量，标识乘客性别，适合转换为factor类型变量
Age 整型变量，标识乘客年龄，有缺失值
SibSp 整型变量，代表兄弟姐妹及配偶的个数。其中Sib代表Sibling也即兄弟姐妹，Sp代表Spouse也即配偶
Parch 整型变量，代表父母或子女的个数。其中Par代表Parent也即父母，Ch代表Child也即子女
Ticket 字符型变量，代表乘客的船票号 Fare 数值型，代表乘客的船票价
Cabin 字符型，代表乘客所在的舱位，有缺失值
Embarked 字符型，代表乘客登船口岸，适合转换为factor型变量

2.数据预处理 2.1数据描述性统计

titanic.describe()

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描述性统计
可以知道，字段Age有缺失值，将平均值作为填充

titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())

2.2非数字型数据转换为数字表示类型

# 男性用0表示，女性用1表示 titanic.loc[titanic["Sex"] == "male", "Sex"] = 0 titanic.loc[titanic["Sex"] == "female", "Sex"] = 1 print(titanic["Embarked"].unique()) # 将Embarked字段缺失值填充为数量最多的S titanic["Embarked"] = titanic["Embarked"].fillna('S') # 把S、C、Q分别用数字0、1、2表示 titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0 titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1 titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2

处理后的数据预览：

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处理后数据 2.3对训练数据进行划分，进行交叉验证

from sklearn.model_selection import KFold# cross_validation 已经被model_selection 代替 # 七个特征值 predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]

3.逻辑回归算法实现

# 从sklearn中导入逻辑回归算法 from sklearn import model_selection from sklearn.linear_model import LogisticRegression alg = LogisticRegression(random_state=1) scores = model_selection.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3) print(scores.mean())

输出结果：

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逻辑回归算法预测分数
可知，该算法预测准确率达到78%，预测效果不错
4.随机森林算法实现

from sklearn import model_selection from sklearn.ensemble import RandomForestClassifierpredictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]# Initialize our algorithm with the default paramters # n_estimators is the number of trees we want to make # min_samples_split is the minimum number of rows we need to make a split # min_samples_leaf is the minimum number of samples we can have at the place where a tree branch ends (the bottom points of the tree) alg = RandomForestClassifier(random_state=1, n_estimators=10, min_samples_split=2, min_samples_leaf=1) # Compute the accuracy score for all the cross validation folds.(much simpler than what we did before!) kf = model_selection.KFold( n_splits=3, random_state=1).split(titanic) scores = model_selection.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)# Take the mean of the scores (because we have one for each fold) print(scores.mean())

输出结果：

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随机森林算法预测分数
【泰坦尼克船员获救分析】可知，随机森林算法预测准确率达到80%，预测效果好于逻辑回归算法
4.1调整随机森林算法参数

# 调参 alg = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=4, min_samples_leaf=2) # Compute the accuracy score for all the cross validation folds.(much simpler than what we did before!) kf = model_selection.KFold(n_splits=3, random_state=1).split(titanic) scores = model_selection.cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)# Take the mean of the scores (because we have one for each fold) print(scores.mean())

输出结果：