使用逻辑回归对泰坦尼克号数据 生存死亡情况预测_探索泰坦尼克号乘客存活情况 逻辑回归模型

泰坦尼克号数据集介绍

Titanic数据集是非常适合数据科学和机器学习新手入门练习的数据集。
数据集为1912年泰坦尼克号沉船事件中一些船员的个人信息以及存活状况。这些历史数据已经非分为训练集和测试集，你可以根据训练集训练出合适的模型并预测测试集中的存活状况。

数据描述

使用算法为逻辑回归
整个预测代码如下：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 导入数据
data_train = pd.read_csv('train.csv')
pd.set_option('display.max_columns', None)
data_train.describe()
data_train.info()

# 数据划分，并对特征进行初步筛选
X = data_train.iloc[:,[2,4,5,6,7,11]].values
y = data_train.iloc[:,1].values

# 缺失值处理
from sklearn.impute import SimpleImputer
imp_mean = SimpleImputer(missing_values=np.nan, strategy='mean')
imp_mean.fit(X[:,2:3])
X[:,2:3] = imp_mean.transform(X[:,2:3])
imp_mean = SimpleImputer(missing_val8ues=np.nan, strategy='most_frequent')
imp_mean.fit(X[:,5:6])
X[:,5:6] = imp_mean.transform(X[:,5:6])

# 虚拟编码
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelEncoder = LabelEncoder()
X[:,1] = labelEncoder.fit_transform(X[:,1])
X[:,5] = labelEncoder.fit_transform(X[:,5])

oneHotEncoder = OneHotEncoder(categorical_features=[5])
X = oneHotEncoder.fit_transform(X).toarray()
X = X[:,1:]

# 划分数据
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# 特征缩放
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# 建模
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=0)
classifier.fit(X_train, y_train)

# 测试
y_pred = classifier.predict(X_test)

# 计算正确值
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
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