import pandas    # 讀取數據集  titanic = pandas.read_csv('titanic_train.csv')  titanic.head(10)  

# Age 缺失值填充  titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())  print(titanic.describe())  

# print(titanic['Sex'].unique())    # Replace all the occurences of male with the number 0.  # 用數字0替換所有出現的男性。  titanic.loc[titanic["Sex"] == "male", "Sex"] = 0  titanic.loc[titanic["Sex"] == "female", "Sex"] = 1  

# print(titanic['Embarked'].unique())    # Embarked：上船港口，有三個取值，C/S/Q，是文字形式，不利於分析，  # 故可能需要映射到數值的值，而且有2個缺失值  titanic['Embarked'] = titanic['Embarked'].fillna('S') # 缺失值填充為這一列的眾數'S'  titanic.loc[titanic["Embarked"] == "S", "Embarked"] = 0  titanic.loc[titanic["Embarked"] == "C", "Embarked"] = 1  titanic.loc[titanic["Embarked"] == "Q", "Embarked"] = 2  

# Import the linear regression(回歸) class  # 注意不要導錯庫  from sklearn.linear_model import LinearRegression  from sklearn.model_selection import KFold    # The columns we'll use to predict the target  predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']    # Initialize our algorithm class  alg = LinearRegression()    # Generate cross validation folds for the titanic dataset. It return the row indices  # corresponding(相應的) to train and test.  # 為Titanic數據集生成交叉驗證摺疊。它返回與訓練和測試相對應的行索引。  # We set random_state to ensure we get the same splits every time we run this.  # kf = KFold(titanic.shape[0], n_folds=3, random_state=1) 寫法錯誤已被棄用    # 樣本平均分成3份，3折交叉驗證  kf = KFold(n_splits=3,shuffle=False, random_state=1)    # 注意這裡不是kf.split(titanic.shape[0])，會報如下錯誤：  # Singleton array array(891) cannot be considered a valid collection.    predictions = []
# 交叉驗證 劃分訓練集 驗證集  for train, test in  kf.split(titanic):      # The predictors we're using the train the algorithm. Note how we only take      # the rows in the train folds      # 注意我們只得到訓練集的rows      train_predictors = titanic[predictors].iloc[train, :]      # The target we're using to train the algorithm.      train_target = titanic['Survived'].iloc[train]      # Training the algorithm using the predictors and target      alg.fit(train_predictors, train_target)      # We can now make predictions on the test fold      test_predictions = alg.predict(titanic[predictors].iloc[test, :])      predictions.append(test_predictions)  

import numpy as np    # The predictions are in three separate numpy arrays. Concatenate them into one.  # We concatenate them on asix 0, as they only have one axis.  predictions = np.concatenate(predictions,axis=0)    # Map predictions to outcomes (only possible outcomes are 1 and 0)  predictions[predictions > .5] = 1  # 映射成分類結果 計算準確率  predictions[predictions <= .5] = 0    # 注意這一行與源代碼有出入  accuracy = sum(predictions==titanic['Survived'])/len(predictions)    # 驗證集的準確率  print(accuracy)  

from sklearn.model_selection import cross_val_score  from sklearn.linear_model import LogisticRegression    alg = LogisticRegression(random_state=1)  # Compute the accuracy score for all the cross validation folds,  # (much simper than what we did before!)  scores = cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=3)  # Take the mean of the scores (because we have one for each fold)  print(scores.mean())  

titanic_test = pandas.read_csv("test.csv")  titanic_test["Age"] = titanic_test["Age"].fillna(titanic["Age"].median())  titanic_test["Fare"] = titanic_test["Fare"].fillna(titanic_test["Fare"].median())  titanic_test.loc[titanic_test["Sex"] == "male", "Sex"] = 0  titanic_test.loc[titanic_test["Sex"] == "female", "Sex"] = 1  titanic_test["Embarked"] = titanic_test["Embarked"].fillna("S")    titanic_test.loc[titanic_test["Embarked"] == "S", "Embarked"] = 0  titanic_test.loc[titanic_test["Embarked"] == "C", "Embarked"] = 1  titanic_test.loc[titanic_test["Embarked"] == "Q", "Embarked"] = 2  

from sklearn.model_selection import cross_val_score  from sklearn.model_selection import KFold  from sklearn.ensemble import RandomForestClassifier    predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked"]    # Initialize our algorithm with the default paramters  # n_estimators is the number of trees we want to make  # min_samples_split is the minimum number of rows we need to make a split  # min_samples_leaf is the minimum number of samples we can have at the place where a  # tree branch(分支) ends (the bottom points of the tree)  alg = RandomForestClassifier(random_state=1,                               n_estimators=10,                               min_samples_split=2,                               min_samples_leaf=1)  # Compute the accuracy score for all the cross validation folds.  (much simpler than what we did before!)  kf = KFold(n_splits=3, shuffle=False, random_state=1)  scores = cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)    # Take the mean of the scores (because we have one for each fold)  print(scores.mean())  

alg = RandomForestClassifier(random_state=1,                               n_estimators=100,                               min_samples_split=4,                               min_samples_leaf=2)  # Compute the accuracy score for all the cross validation folds.  (much simpler than what we did before!)  kf = KFold(n_splits=3, shuffle=False, random_state=1)  scores = cross_val_score(alg, titanic[predictors], titanic["Survived"], cv=kf)    # Take the mean of the scores (because we have one for each fold)  print(scores.mean())