kaggle 泰坦尼克号数据分析 笔记

#泰坦尼克号 / 数据来源importpandas#读取数据titanic = pandas.read_csv("D:/panana/taitan/titanic_train.csv")#查看数据前五行#print(titanic.head())#进行统计#print (titanic.describe())#对缺失值进行填写 把缺失的值按照平均值填充 fillna 缺失值填充 titanic["Age"].median 求出age的均值titanic["Age"] = titanic["Age"].fillna(titanic["Age"].median())#print(titanic.describe())#数据预处理 把不能处理的值改成可以处理的 把字母改成数字#先查看 字段 Sex 下有哪几种可能#print(titanic["Sex"].unique())#因为性别是str类型 所以要转换成int male 转换成0 female 转换成1#loc 进行定位 把字段Sex ==male 改成 Sex==0titanic.loc[titanic["Sex"]=="male","Sex"] = 0titanic.loc[titanic["Sex"]=="female","Sex"] = 1#上船地点 把str类型处理成int#查看Embarked 上船地点有哪几种类型：print(titanic["Embarked"].unique())#得出“S”,"C","Q",nan#因Embarked 有缺失值 所以需要补全缺失值 把所有的缺失值补成Stitanic["Embarked"] = titanic["Embarked"].fillna('S')#把数据进行转换 S,C,Q 转换成0,1,2titanic.loc[titanic["Embarked"]=="S","Embarked"] = 0titanic.loc[titanic["Embarked"]=="C","Embarked"] = 1titanic.loc[titanic["Embarked"]=="Q","Embarked"] = 2#导入机器学习裤子 LinearRegression 线性回归算法fromsklearn.linear_modelimportLinearRegression#引入交叉验证fromsklearn.cross_validationimportKFold#选择要搞的特征predictors = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"]#导入线性回归alg = LinearRegression()#进行交叉验证的选择 n_folds =3 表示选择3倍的交叉验证 titanic.shape[0]表示m个样本#含义就是 把m个样本平均分成三份进行交叉验证kf = KFold(titanic.shape[0],n_folds=3,random_state=1)

predictions =[]for train, test in kf:#取到原始数据 train 代表训练数据 先把训练数据提取出来train_predictors = (titanic[predictors].iloc[train,:])train_target = titanic["Survived"].iloc[train]#.fit 表示把选择的算法应用在当前的数据上alg.fit(train_predictors, train_target)#训练完后查看模型的好坏程度 alg.predict() 对测试集数据进行预测test_predictions = alg.predict(titanic[predictors].iloc[test,:])#把测试结果导入到predictors里面predictions.append(test_predictions)

import numpy as nppredictions = np.concatenate(predictions,axis=0)#类别归属 当大于0.5 存放到1 小于0.5存放到0predictions[predictions > .5] =1predictions[predictions <= .5]=0#查看模型准确率accuracy = sum(predictions[predictions == titanic["Survived"]]) /len(predictions)print(accuracy)

最终准确率只有百分之20 --换种方法

0.2615039281705948

from sklearn import cross_validation#LogisticRegression 逻辑回归from sklearn.linear_model import LogisticRegressionalg = LogisticRegression(random_state = 1)scores = cross_validation.cross_val_score(alg,titanic[predictors],titanic["Survived"],cv = 3 )print(scores.mean())

最终准确率

0.7878787878787877

使用集成算法 随机森林

#使用随机森林进行预测 防止过拟合

from sklearn import cross_validation

#调用随机森林的包

from sklearn.ensemble import RandomForestClassifier

predictors = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"]

#指定随机森林的参数 n_estimators设置决策树的个数 min_samples_split最小的样本个数 min_samples_leaf 最小叶子节点的个数

alg = RandomForestClassifier(random_state=1,n_estimators=10,min_samples_split=2,min_samples_leaf=1)

#进行三次交叉验证

kf =cross_validation.KFold(titanic.shape[0],n_folds=3,random_state=1)

scores = cross_validation.cross_val_score(alg,titanic[predictors],titanic["Survived"],cv=kf)

print(scores.mean())

最终准确率

0.7856341189674523

#对参数进行修改#更改参数像后在进行查看 把决策树个数改成50 最小样本个数提升到4 最小叶子节点提升到2 alg = RandomForestClassifier(random_state=1,n_estimators=50,min_samples_split=4,min_samples_leaf=2)kf =cross_validation.KFold(titanic.shape[0],n_folds=3,random_state=1)scores = cross_validation.cross_val_score(alg,titanic[predictors],titanic["Survived"],cv=kf)print(scores.mean())

最终准确率

0.8159371492704826

未完--