文章介绍了使用Python进行泰坦尼克号乘客生存预测的步骤，包括数据读取、数据清洗（如处理缺失值、提取特征）、模型创建（使用神经网络）、训练过程以及最终的预测。主要技术涉及数据预处理、特征工程和深度学习。
一、前言
- 任务目标：根据《泰坦尼克号登船人员名单》上的个人信息预测其是否生还
- 数据集：《泰坦尼克号登船人员名单》，自取https://download.youkuaiyun.com/download/weixin_43721000/87740848
- 数据集解释：
第一列age ，表示的是年龄（数值数据）
第二列cabin，表示客舱号（分类数据 ：字符串类型）
第三列embarked表示登船港口，S是Southampton南安普顿，C是Cherbourg法国瑟堡，Q是Queenstown爱尔兰昆士敦（分类数据：直接类别）
第四列fare，表示船票价格（数值数据）
第五列name，表示的是名字（分类数据：字符串类型）
第六列parch，船上父母数/子女数，不同代的直系亲属数，比如某人和他的女儿及父亲同在这个船上，则他的这个数值就是父母数（1）+子女数（1）=2
第七列passengerId，表示乘船编号
第八列pclass，表示客舱等级，这里有三级，1为一等舱，2为二等舱，3为三等舱（分类数据：直接类别）
第九列sex，表示性别male为男性，famale为女性（分类数据）
第十列sibsp，表示兄弟姐妹数/配偶数，同一代的直系亲属人数，比如某人和他的弟弟及妻子同在这个船上，则他的这个数值就是兄妹数（1）+配偶数（1）=2
第十一列surverved，表示是否存活，1为生存，2为死亡（分类数据：直接类别）
第十二列ticket，表示船票编号(数值数据)
二、实现方法
1.读取数据集
import numpy as np
import pandas as pd
dataset = pd.read_csv('/kaggle/input/titanic/train.csv')
X_test = pd.read_csv('/kaggle/input/titanic/test.csv')
print(dataset)
# PassengerId Survived Pclass \
# 0 1 0 3
# 1 2 1 1
# 2 3 1 3
# 3 4 1 1
# 4 5 0 3
# .. ... ... ...
# 886 887 0 2
# 887 888 1 1
# 888 889 0 3
# 889 890 1 1
# 890 891 0 3
#
# Name Sex Age SibSp \
# 0 Braund, Mr. Owen Harris male 22.0 1
# 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1
# 2 Heikkinen, Miss. Laina female 26.0 0
# 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1
# 4 Allen, Mr. William Henry male 35.0 0
# .. ... ... ... ...
# 886 Montvila, Rev. Juozas male 27.0 0
# 887 Graham, Miss. Margaret Edith female 19.0 0
# 888 Johnston, Miss. Catherine Helen "Carrie" female NaN 1
# 889 Behr, Mr. Karl Howell male 26.0 0
# 890 Dooley, Mr. Patrick male 32.0 0
#
# Parch Ticket Fare Cabin Embarked
# 0 0 A/5 21171 7.2500 NaN S
# 1 0 PC 17599 71.2833 C85 C
# 2 0 STON/O2. 3101282 7.9250 NaN S
# 3 0 113803 53.1000 C123 S
# 4 0 373450 8.0500 NaN S
# .. ... ... ... ... ...
# 886 0 211536 13.0000 NaN S
# 887 0 112053 30.0000 B42 S
# 888 2 W./C. 6607 23.4500 NaN S
# 889 0 111369 30.0000 C148 C
# 890 0 370376 7.7500 NaN Q
#
# [891 rows x 12 columns]
print(X_test)
# PassengerId Pclass Name \
# 0 892 3 Kelly, Mr. James
# 1 893 3 Wilkes, Mrs. James (Ellen Needs)
# 2 894 2 Myles, Mr. Thomas Francis
# 3 895 3 Wirz, Mr. Albert
# 4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist)
# .. ... ... ...
# 413 1305 3 Spector, Mr. Woolf
# 414 1306 1 Oliva y Ocana, Dona. Fermina
# 415 1307 3 Saether, Mr. Simon Sivertsen
# 416 1308 3 Ware, Mr. Frederick
# 417 1309 3 Peter, Master. Michael J
#
# Sex Age SibSp Parch Ticket Fare Cabin Embarked
# 0 male 34.5 0 0 330911 7.8292 NaN Q
# 1 female 47.0 1 0 363272 7.0000 NaN S
# 2 male 62.0 0 0 240276 9.6875 NaN Q
# 3 male 27.0 0 0 315154 8.6625 NaN S
# 4 female 22.0 1 1 3101298 12.2875 NaN S
# .. ... ... ... ... ... ... ... ...
# 413 male NaN 0 0 A.5. 3236 8.0500 NaN S
# 414 female 39.0 0 0 PC 17758 108.9000 C105 C
# 415 male 38.5 0 0 SOTON/O.Q. 3101262 7.2500 NaN S
# 416 male NaN 0 0 359309 8.0500 NaN S
# 417 male NaN 1 1 2668 22.3583 NaN C
#
# [418 rows x 11 columns]
2.数据清洗
- 清洗称谓
# 从 Name 字段中提取称谓，并将较少的称谓统一替换为 ‘rare’ 减少数据噪声
dataset_title = [i.split(',')[1].split('.')[0].strip() for i in dataset['Name']] # 提取称谓
dataset['Title'] = pd.Series(dataset_title) # 插入新列
print(dataset['Title'].value_counts())
# # Mr 517
# Miss 182
# Mrs 125
# Master 40
# Dr 7
# Rev 6
# Mlle 2
# Major 2
# Col 2
# the Countess 1
# Capt 1
# Ms 1
# Sir 1
# Lady 1
# Mme 1
# Don 1
# Jonkheer 1
# Name: Title, dtype: int64
# 查看称谓数量分布
dataset['Title'] = dataset['Title'].replace(['Lady', 'the Countess', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona', 'Ms', 'Mme', 'Mlle'], 'Rare') # 替换数量较少的称谓为 ‘rare’
print(dataset['Title'].value_counts())
# Mr 517
# Miss 182
# Mrs 125
# Master 40
# Rare 27
# Name: Title, dtype: int64
dataset_title = [i.split(',')[1].split('.')[0].strip() for i in X_test['Name']]
X_test['Title'] = pd.Series(dataset_title)
print(X_test['Title'].value_counts())
# Mr 240
# Miss 78
# Mrs 72
# Master 21
# Col 2
# Rev 2
# Ms 1
# Dr 1
# Dona 1
# Name: Title, dtype: int64
X_test['Title'] = X_test['Title'].replace(['Lady', 'the Countess', 'Countess', 'Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona', 'Ms', 'Mme', 'Mlle'], 'Rare')
print(X_test['Title'].value_counts())
# Mr 240
# Miss 78
# Mrs 72
# Master 21
# Rare 7
# Name: Title, dtype: int64
- 家庭成员数量清洗
# 从 SibSp（同代家属：兄弟姐妹或配偶）和 Parch（不同代家属：父母或子女）中提取上船的家庭人数=SibSp+Parch+1，并将家庭人数标签化
dataset['FamilyS'] = dataset['SibSp'] + dataset['Parch'] + 1
X_test['FamilyS'] = X_test['SibSp'] + X_test['Parch'] + 1
def family(x):
if x < 2:
return 'Single'
elif x == 2:
return 'Couple'
elif x <= 4:
return 'InterM'
else:
return 'Large'
dataset['FamilyS'] = dataset['FamilyS'].apply(family)
X_test['FamilyS'] = X_test['FamilyS'].apply(family)
print(dataset['FamilyS'].value_counts())
# Single 537
# Couple 161
# InterM 131
# Large 62
# Name: FamilyS, dtype: int64
- 缺失数据填充
# 将 登船港口列 缺失的值替换为该列的众数（如有多个众数取第一个）
dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace=True)
X_test['Embarked'].fillna(X_test['Embarked'].mode()[0], inplace=True)
# 将 年龄列 缺失的值替换为该列的平均数
dataset['Age'].fillna(dataset['Age'].median(), inplace=True)
X_test['Age'].fillna(X_test['Age'].median(), inplace=True)
# 将 船票价格 缺失的值替换为该列的平均数
dataset['Fare'].fillna(dataset['Fare'].median(), inplace=True)
X_test['Fare'].fillna(X_test['Fare'].median(), inplace=True)
- 不使用数据清除
# 删掉不使用的列
dataset = dataset.drop(['PassengerId', 'Cabin', 'Name', 'SibSp', 'Parch', 'Ticket'], axis=1)
X_test_passengers = X_test['PassengerId']
X_test = X_test.drop(['PassengerId', 'Cabin', 'Name', 'SibSp', 'Parch', 'Ticket'], axis=1)
print(dataset)
# Survived Pclass Sex Age Fare Embarked Title FamilyS
# 0 0 3 male 22.0 7.2500 S Mr Couple
# 1 1 1 female 38.0 71.2833 C Mrs Couple
# 2 1 3 female 26.0 7.9250 S Miss Single
# 3 1 1 female 35.0 53.1000 S Mrs Couple
# 4 0 3 male 35.0 8.0500 S Mr Single
# .. ... ... ... ... ... ... ... ...
# 886 0 2 male 27.0 13.0000 S Rare Single
# 887 1 1 female 19.0 30.0000 S Miss Single
# 888 0 3 female 28.0 23.4500 S Miss InterM
# 889 1 1 male 26.0 30.0000 C Mr Single
# 890 0 3 male 32.0 7.7500 Q Mr Single
3.划分训练集、验证集 和 测试集
- 样本和标签分离
# 拆分训练集的样本和标签
X_train = dataset.iloc[:, 1:9].values
Y_train = dataset.iloc[:, 0].values
# 测试集只有样本没有标签
X_test = X_test.values
- 文本标签转为 one-hot 编码
# 文本标签直接转为 one-hot 编码
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer
print(X_train[0], X_train[0].shape)
# [3 'male' 22.0 7.25 'S' 'Mr' 'Couple'] (7,)
one_hot_encoder = ColumnTransformer(
[(
'one_hot_encoder', # 转换器名称（任意）
OneHotEncoder(categories='auto'), # 编码类型
[0, 1, 4, 5, 6] # 对哪些列进行编码
)],
remainder='passthrough' # 保留未编码的列
)
X_train = one_hot_encoder.fit_transform(X_train).tolist()
X_test = one_hot_encoder.fit_transform(X_test).tolist()
print(X_train[0], len(X_train[0]))
# [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 22.0, 7.25] 19
# 7列变为19列的稀疏矩阵
- 从训练集中分离 1/10 的数据作为验证集
# 拆分训练集和验证集
from sklearn.model_selection import train_test_split
x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size = 0.1)
print(len(x_train))
# 801
print(len(x_val))
# 90
print(len(y_train))
# 801
print(len(y_val))
# 90
4.创建模型
# 创建神经网络
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(19, 270) # 上采样到270
self.fc2 = nn.Linear(270, 2) # 2分类
def forward(self, x):
x = self.fc1(x)
x = F.dropout(x, p=0.1)
x = F.elu(x)
x = self.fc2(x)
# x = torch.sigmoid(x) # 输出结果映射到（0,1）之间
return x
net = Net()
5.指定训练参数
# 训练参数
batch_size = 50
num_epochs = 50
learning_rate = 0.01
batch_no = len(x_train) // batch_size
6.定义损失函数、优化器
# 损失函数、优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)
7.训练
# 训练
from sklearn.utils import shuffle
from torch.autograd import Variable
for epoch in range(num_epochs):
if epoch % 5 == 0:
print('Epoch {}'.format(epoch+1))
x_train, y_train = shuffle(x_train, y_train) # 乱序
# Mini batch learning
for i in range(batch_no):
start = i * batch_size
end = start + batch_size
x_var = Variable(torch.FloatTensor(x_train[start:end]))
y_var = Variable(torch.LongTensor(y_train[start:end]))
# Forward + Backward + Optimize
optimizer.zero_grad()
ypred_var = net(x_var)
loss =criterion(ypred_var, y_var)
loss.backward()
optimizer.step()
8.验证准确率
# 验证准确率
test_var = Variable(torch.FloatTensor(x_val), requires_grad=True)
with torch.no_grad():
result = net(test_var)
# values, labels = torch.max(result, 1)
# print(values, labels)
labels = torch.argmax(result, dim=1)
print(labels)
# tensor([1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0,
# 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,
# 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,
# 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1])
num_right = np.sum(labels.data.numpy() == y_val)
print('Accuracy {:.2f}'.format(num_right / len(y_val)))
# Accuracy 0.81
9.预测
# 预测
X_test_var = Variable(torch.FloatTensor(X_test), requires_grad=True)
with torch.no_grad():
test_result = net(X_test_var)
# print(test_result)
values, labels = torch.max(test_result, 1)
survived = labels.data.numpy()
print(f"预测结果：{survived}")
# 预测结果：[0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 0 0 1
# 1 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 0
# 1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0
# 1 1 1 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0
# 1 0 1 0 0 1 0 0 1 1 0 1 1 1 1 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1
# 0 1 0 0 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0
# 1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1
# 0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0
# 1 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0
# 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0
# 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0
# 1 1 1 1 1 1 0 1 0 0 1]
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