機器學習實戰之Titanic(Kaggle)
阿新 • • 發佈：2019-02-02
一、船員資料分析
- PassengerId :每一個乘客的標誌符
- Survived:Lable值，代表是否獲救
- Pclass:船員倉庫等級
- Name:姓名
- Sex:性別
- Age:年齡
- SibSp:兄弟姐妹有幾個
- Parch:老人孩子的數量
- Ticket:船票的編號
- Fare:船票價格
- Cabin:船艙位置，此列出現大量缺失，可以不要
- Embarked:上船地點
二、資料預處理
1.匯入需要的包
import pandas as pa import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestClassifier from sklearn.cross_validation import KFold
2.觀察資料的前幾行
filename = "train.csv"
titanic = pa.read_csv(filename)
titanic.head()
3.觀察資料的簡單資料特徵
print titanic.describe()結果：
PassengerId Survived Pclass Age SibSp \
count 891.000000 891.000000 891.000000 714.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008
std 257.353842 0.486592 0.836071 14.526497 1.102743
min 1.000000 0.000000 1.000000 0.420000 0.000000
25% 223.500000 0.000000 2.000000 NaN 0.000000
50% 446.000000 0.000000 3.000000 NaN 0.000000
75% 668.500000 1.000000 3.000000 NaN 1.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000
Parch Fare
count 891.000000 891.000000
mean 0.381594 32.204208
std 0.806057 49.693429
min 0.000000 0.000000
25% 0.000000 7.910400
50% 0.000000 14.454200
75% 0.000000 31.000000
max 6.000000 512.329200 - 可以看到Age列資料只有714個，其餘列均有891個，因此此列需要對缺失值進行填充
titanic["Age"]=titanic["Age"].fillna(titanic["Age"].median()) print titanic.describe()結果：
PassengerId Survived Pclass Age SibSp \ count 891.000000 891.000000 891.000000 891.000000 891.000000 mean 446.000000 0.383838 2.308642 29.361582 0.523008 std 257.353842 0.486592 0.836071 13.019697 1.102743 min 1.000000 0.000000 1.000000 0.420000 0.000000 25% 223.500000 0.000000 2.000000 22.000000 0.000000 50% 446.000000 0.000000 3.000000 28.000000 0.000000 75% 668.500000 1.000000 3.000000 35.000000 1.000000 max 891.000000 1.000000 3.000000 80.000000 8.000000 Parch Fare count 891.000000 891.000000 mean 0.381594 32.204208 std 0.806057 49.693429 min 0.000000 0.000000 25% 0.000000 7.910400 50% 0.000000 14.454200 75% 0.000000 31.000000 max 6.000000 512.329200 - 將string值轉為int/float值
1) 首先，觀察相應列有幾種字串print titanic["Sex"].unique() print titanic["Embarked"].unique()結果：
2) 然後，將相應字串的位置附上對應的Int/float值['male' 'female'] ['S' 'C' 'Q' nan]titanic.loc[titanic["Sex"]=="male","Sex"] = 0; titanic.loc[titanic["Sex"]=="female","Sex"] = 1; titanic.loc[titanic["Embarked"]=="S","Embarked"] = 0; titanic.loc[titanic["Embarked"]=="C","Embarked"] = 1; titanic.loc[titanic["Embarked"]=="Q","Embarked"] = 2; titanic.head()結果：
替換成功
三、分類
def data_proprocess():
import pandas as pa
import numpy as np
import matplotlib.pyplot as plt
filename = "train.csv"
titanic = pa.read_csv(filename)
#titanic.head()
#print titanic.describe()
titanic["Age"]=titanic["Age"].fillna(titanic["Age"].median())
titanic['Embarked'] = titanic['Embarked'].fillna('S')
#print titanic["Sex"].unique()
#print titanic["Embarked"].unique()
titanic.loc[titanic["Sex"]=="male","Sex"] = 0;
titanic.loc[titanic["Sex"]=="female","Sex"] = 1;
titanic.loc[titanic["Embarked"]=="S","Embarked"] = 0;
titanic.loc[titanic["Embarked"]=="C","Embarked"] = 1;
titanic.loc[titanic["Embarked"]=="Q","Embarked"] = 2;
#titanic.head()
return titanic
def classify_LinearRegression(titanic):
import pandas as pa
import numpy as np
import matplotlib.pyplot as plt
from sklearn.cross_validation import KFold
from sklearn.linear_model import LinearRegression
predictors = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"]#特徵
alg = LinearRegression()#線性迴歸
kf = KFold(titanic.shape[0],n_folds=3,random_state=1)#交叉驗證集
predictions = []
for train,test in kf:
train_predictors = (titanic[predictors].iloc[train,:])
train_target = titanic["Survived"].iloc[train]
alg.fit(train_predictors,train_target)
test_predictions = alg.predict(titanic[predictors].iloc[test,:])
predictions.append(test_predictions)
predictions = np.concatenate(predictions,axis=0)
predictions[predictions > 0.5] =1
predictions[predictions <= 0.5] =0
accuracy = sum(predictions[predictions == titanic['Survived']])/len(predictions)
return accuracy
def classify_LogisticRegression(titanic):
import pandas as pa
import numpy as np
import matplotlib.pyplot as plt
from sklearn import cross_validation
from sklearn.linear_model import LogisticRegression
predictors = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"]#特徵
alg = LogisticRegression(random_state=1)
scores = cross_validation.cross_val_score(alg,titanic[predictors],titanic["Survived"],cv=3)
return scores.mean()
print "LinearRegression Classification result is ："
print classify_LinearRegression(data_proprocess())
print "LogisticRegression Classification result is ："
print classify_LogisticRegression(data_proprocess())LinearRegression Classification result is ：
0.261503928171
LogisticRegression Classification result is ：
0.787878787879從結果可以看出，還是用邏輯迴歸做分類問題精度更高。
四、使用隨機森林提高分類精度並將結果傳到kaggle
def classify_RandomForestClassifier(train_data,test_data):
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
import pandas as pa
import numpy as np
predictors = ["Pclass","Sex","Age","SibSp","Parch","Fare","Embarked"]
clf = RandomForestClassifier(n_estimators=10, max_depth=None,min_samples_split=2, random_state=0)
scores = cross_validation.cross_val_score(clf,train_data[predictors],train_data["Survived"],cv=3)
clf .fit(train_data[predictors],train_data["Survived"])
predict_result= clf.predict(test_data[predictors])
result = pa.DataFrame({'PassengerId':test_data['PassengerId'].as_matrix(), 'Survived':predict_result.astype(np.int32)})
result.to_csv("logistic_regression_predictions.csv", index=False)
return scores.mean()
print "train"
titanic_train=data_proprocess("train.csv")
print "test"
titanic_test=data_proprocess("test.csv")
classify_RandomForestClassifier(titanic_train,titanic_test)