对titanic.csv数据进行预测生死

数据集：http://download.csdn.net/detail/u010343650/9844427

survived:乘客最后的生存情况，这个是我们预测的目标变量 （0代表否，1代表是）

pclass:社会经济地位 （1代表上层阶级，2代表中层阶级，3代表底层阶级）

name:姓名

sex:性别

age:年纪

sibsp:船上兄弟姐妹或者配偶的数

parch:船上父母或者孩子的数量

ticket:船票的号码

fare:船票价格

检查数据的完整性

    # coding:GBK  
    __author__ = 'Mouse'  
    import pandas as pd  
    full_data = pd.read_csv('titanic_dataset.csv')  
    print full_data.info()  

结果显示数据很完整：

代码一：正确率60%-70%之间

# coding:GBK
__author__ = 'Mouse'
import sys
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelEncoder
reload(sys)
sys.setdefaultencoding('utf8')
from sklearn_test import *

def read_data(data_file):
    data = pd.read_csv(data_file)
    data = data.drop('ticket', axis=1) #删除票信息
    data = data.drop('name', axis=1) #删除姓名

    #one-hot编码
    le_sex = LabelEncoder().fit(data['sex'])
    sex_label = le_sex.transform(data['sex'])
    ohe_sex = OneHotEncoder(sparse=False).fit(sex_label.reshape(-1, 1))
    sex_ohe = ohe_sex.transform(sex_label.reshape(-1, 1))

    data['sex_0'] = sex_ohe[:, 0]
    data['sex_1'] = sex_ohe[:, 1]

    data = data.drop('sex', axis=1)

    train = data[:int(len(data)*0.6)]
    test = data[int(len(data)*0.6):]
    train_y = train.survived
    train_x = train.drop('survived', axis=1)
    test_y = test.survived
    test_x = test.drop('survived', axis=1)
    return train_x, train_y, test_x, test_y

if __name__ == '__main__':
    data_file = "titanic_dataset.csv"
    thresh = 0.5
    model_save_file = None
    model_save = {}
    test_classifiers = ['NB', 'KNN', 'LR', 'RF', 'DT', 'SVM', 'SVMCV', 'GBDT']
    classifiers = {'NB': naive_bayes_classifier,
                   'KNN': knn_classifier,
                   'LR': logistic_regression_classifier,
                   'RF': random_forest_classifier,
                   'DT': decision_tree_classifier,
                   'SVM': svm_classifier,
                   'SVMCV': svm_cross_validation,
                   'GBDT': gradient_boosting_classifier
    }

    print 'reading training and testing data...'
    train_x, train_y, test_x, test_y = read_data(data_file)

    for classifier in test_classifiers:
        print '----------------------- %s ----------------------' % classifier
        start_time = time.time()
        model = classifiers[classifier](train_x, train_y)
        print 'training took %fs!' % (time.time() - start_time)
        predict = model.predict(test_x)
        if model_save_file != None:
             model_save[classifier] = model
        #if is_binary_class:
        precision = metrics.precision_score(test_y, predict)
        recall = metrics.recall_score(test_y, predict)
        print 'precision: %.2f%%, recall: %.2f%%' % (100 * precision, 100 * recall)
        accuracy = metrics.accuracy_score(test_y, predict)
        print 'accuracy: %.2f%%' % (100 * accuracy)

    if model_save_file != None:
        pickle.dump(model_save, open(model_save_file, 'wb'))

代码二：正确率68%-76%之间

# coding: utf-8
__author__ = 'Mouse'
import pandas as pd
from sklearn_test import *
full_data = pd.read_csv('titanic_dataset.csv', header =0 )
# 统计家庭成员的个数
full_data["familysize"] = full_data["sibsp"] + full_data["parch"] + 1
# 家庭成员（包括自己）等于一个人的为1，大于1人为0
full_data['is_alone'] = 0
full_data.loc[full_data['familysize'] == 1, 'is_alone'] = 1

# 替换性别，女 0，男 1
full_data['sex'] = full_data['sex'].map({'female': 0, 'male': 1}).astype(int)

# 费用化简
full_data.loc[full_data['fare'] <= 7.91, 'fare'] = 0
full_data.loc[(full_data['fare'] > 7.91) & (full_data['fare'] <= 14.454), 'fare'] = 1
full_data.loc[(full_data['fare'] > 14.454) & (full_data['fare'] <= 31), 'fare'] = 2
full_data.loc[full_data['fare'] > 31, 'fare'] = 3
full_data['fare'] = full_data['fare'].astype(int)

# 替换年龄
full_data.loc[full_data['age'] <= 16, 'age'] = 0
full_data.loc[(full_data['age'] > 16) & (full_data['age'] <= 32), 'age'] = 1
full_data.loc[(full_data['age'] > 32) & (full_data['age'] <= 48), 'age'] = 2
full_data.loc[(full_data['age'] > 48) & (full_data['age'] <= 64), 'age'] = 3
full_data.loc[full_data['age'] > 64, 'age'] = 4
full_data['age'] = full_data['age'].astype(int)

# 选出我认为重要的特征作为数据集和测试集
drop_elements = ['name', 'ticket', 'sibsp', 'parch', 'familysize']
full_data = full_data.drop(drop_elements, axis=1)

# 划分数据集 前百分之60的数据用于训练数据，后百分之40的数据用于测试
train = full_data[:int(len(full_data) * 0.6)]
test = full_data[int(len(full_data) * 0.6):]

X_train = train.values[:, 1::]  # 除去survived
y_train = train.values[:, 0]  # 只有survived
X_test = test.values[:, 1::]
y_test = test.values[:, 0]

model_save_file = None
model_save = {}
test_classifiers = ['NB', 'KNN', 'LR', 'RF', 'DT', 'SVM', 'SVMCV', 'GBDT']
classifiers = {'NB': naive_bayes_classifier,  # 朴素贝叶斯分类
               'KNN': knn_classifier,  # 邻近算法
               'LR': logistic_regression_classifier,  # LR分类器
               'RF': random_forest_classifier,  # 随机森林
               'DT': decision_tree_classifier,  # 决策树
               'SVM': svm_classifier,  # 向量机
               'SVMCV': svm_cross_validation,  # 交叉验证
               'GBDT': gradient_boosting_classifier  # 梯度提升决策树
 }
print('reading training and testing data...')
for classifier in test_classifiers:
    print('-------------------- %s -------------------' % classifier)
    start_time = time.time()
    model = classifiers[classifier](X_train, y_train)
    print('training took %fs!' % (time.time() - start_time))
    predict = model.predict(X_test)
    if model_save_file != None:
        model_save[classifier] = model
    # 正确率 = 提取出的正确信息条数 /  提取出的信息条数
    # P = TP/(TP+FP)
    precision = metrics.precision_score(y_test, predict, average='macro')
    # 召回率 = 提取出的正确信息条数 /  样本中的信息条数
    # R = TP/(TP+FN) = 1 - FN/T
    recall = metrics.recall_score(y_test, predict, average='macro')
    print('precision: %.2f%%, recall: %.2f%%' % (100 * precision, 100 * recall))
    # A = (TP + TN)/(P+N)
    accuracy = metrics.accuracy_score(y_test, predict)
    print('accuracy: %.2f%%' % (100 * accuracy))