数据分析领域都有一个经典的入门题目，泰坦尼克号生还者预测。数据集可以去kaggle下载。

　　

import pandas as pdimport numpy as npimport matplotlib.pyplot as plttitanic = pd.read_csv('D:/train.csv')# print(titanic.head())# print(titanic.describe())#通过describe发现age数据不完整，需要填充titanic['Age'] = titanic['Age'].fillna(titanic['Age'].median())# print(titanic.describe())# print(titanic['Sex'].unique())#将字符值转换成数值titanic.loc[titanic['Sex'] == 'male', 'Sex'] = 0titanic.loc[titanic['Sex'] == 'female', 'Sex'] = 1#登船地址# print(titanic['Embarked'].unique())titanic['Embarked'] = titanic['Embarked'].fillna('S')titanic.loc[titanic['Embarked'] == 'S', 'Embarked'] = 0titanic.loc[titanic['Embarked'] == 'C', 'Embarked'] = 1titanic.loc[titanic['Embarked'] == 'Q', 'Embarked'] = 2#用线性回归来预测from sklearn.linear_model import LinearRegressionfrom sklearn.cross_validation import KFoldpredictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']alg = LinearRegression()kf = KFold(titanic.shape[0], n_folds=3, random_state=1)#预测结果predictions = []#训练集，测试集，交叉验证for train, test in kf:    train_predictors = (titanic[predictors].iloc[train, :])    train_target = titanic['Survived'].iloc[train]    #训练数据的X，Y => 让他进行判断    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] = 1predictions[predictions <= 0.5] = 0#进行模型评估accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)# print(accuracy)#用逻辑回归来预测from sklearn import cross_validationfrom 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())#用随机森林来做from sklearn.ensemble import RandomForestClassifierpredictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']alg = RandomForestClassifier(random_state=1, n_estimators=1000, min_samples_split=8, min_samples_leaf=8)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())#关于特征提取问题 (非常关键)#尽可能多的提取特征#看不同特征的效果#特征提取是数据挖掘里很- 要的一部分#以上使用的特征都是数据里已经有的了，在真实的数据挖掘里我们常常没有合适的特征，需要我们自己取提取#合并数据：自己生成一个特征，家庭成员的大小：兄弟姐妹+老人孩子titanic['FamilySize'] = titanic['SibSp'] + titanic['Parch']#名字的长度titanic['NameLength'] = titanic['Name'].apply(lambda x : len(x))import redef get_title(name):    #使用正则表达式匹配出Mr等    title_search = re.search(' ([A-Za-z]+)\.', name)    if title_search:        return title_search.group(1)    return ""titles = titanic['Name'].apply(get_title)# print(pd.value_counts(titles))#国外不同阶层的人都有不同的称呼title_mapping = {
   "Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8,                 "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2 }for k, v in title_mapping.items():    #将不同的称呼替换成机器可以计算的数字    titles[titles == k] = vprint(pd.value_counts(titles))titanic['Title'] = titles# 进行特征选择# 特征重要性分析# 分析 不同特征对 最终结果的影响# 例如 衡量age列的重要程度时，什么也不干，得到一个错误率error1，# 加入一些噪音数据，替换原来的值(注意，此时其他列的数据不变)，又得到一个一个错误率error2# 两个错误率的差值 可以体现这一个特征的重要性from sklearn.feature_selection import SelectKBest, f_classifimport matplotlib.pylab as plt# 选中一些特征predictors = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', "Embarked",             'FamilySize', 'Title', 'NameLength']#选择特征selector = SelectKBest(f_classif, k = 5)selector.fit(titanic[predictors], titanic['Survived'])scores= -np.log10(selector.pvalues_)plt.bar(range(len(predictors)), scores)plt.xticks(range(len(predictors)), predictors, rotation = 'vertical')# plt.show()#通过以上特征重要性分析，选择出4个最重要的特征，重新进行随机森林的算法predictors = ['Pclass', 'Sex', 'Fare', 'Title']alg = RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=8, min_samples_leaf=8)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())#可以使用多种算法一起来训练，最后取平均值防止过拟合from sklearn.ensemble import GradientBoostingClassifieralgorithms = [    [LogisticRegression(random_state=1),     ['Pclass', 'Sex', 'Fare', 'Title']],    [RandomForestClassifier(random_state=1, n_estimators=100, min_samples_split=8, min_samples_leaf=8), ['Pclass', 'Sex', 'Fare', 'Title']]]# Initialize the cross validation foldskf = KFold(titanic.shape[0], n_folds=3, random_state=1)predictions = []for train, test in kf:    train_target = titanic['Survived'].iloc[train]    full_test_predictions = []    # Make predictions for each algorithm on each folds    for alg, predictors in algorithms:        # Fit the algorithm on the training data.        alg.fit(titanic[predictors].iloc[train, :], train_target)        # Select and predict on the test fold.        # The astype(float) is necessary to convert the dataframe        test_predictions = alg.predict_proba(titanic[predictors].iloc[test, :].astype(float))[:, 1]        full_test_predictions.append(test_predictions)    # Use a simple ensembling scheme - just average the predictions to get the final classification    # 两个算法, 分别算出来的 预测值, 取平均    test_predictions = (full_test_predictions[0] * 3 + full_test_predictions[1]) / 2    # Any value over 5 is assumed to be a 1 prediction, and below 5 is a 0 prediction    test_predictions[test_predictions <= 0.5] = 0    test_predictions[test_predictions > .5] = 1    predictions.append(test_predictions)# Put all the predictions together into one arraypredictions = np.concatenate(predictions, axis=0)accuracy = sum(predictions[predictions == titanic['Survived']]) / len(predictions)print(accuracy)