import mglearn from numpy import int64 from sklearn import metrics from sklearn.calibration import CalibratedClassifierCV from sklearn.model_selection import GridSearchCV, KFold from sklearn.model_selection import cross_val_score import matplotlib.pyplot as plt import pandas as pd import numpy as np from sklearn.naive_bayes import GaussianNB from sklearn.neural_network import MLPClassifier from sklearn.pipeline import make_pipeline from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression, Lasso, LinearRegression, LogisticRegressionCV from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVR, SVC from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report import seaborn as sns from sklearn.preprocessing import PolynomialFeatures from sklearn.decomposition import PCA, TruncatedSVD from sklearn.feature_selection import SelectKBest, chi2, f_classif, RFECV from sklearn.metrics import roc_auc_score import warnings warnings.filterwarnings('always') warnings.filterwarnings('ignore') sns.set(style="darkgrid") plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示 # 字段说明 # # NO 字段名称 数据类型 字段描述 # 1 ID Int 客户唯一标识 # 2 age Int 客户年龄 # 3 job String 客户的职业 # 4 marital String 婚姻状况 # 5 education String 受教育水平 # 6 default String 是否有违约记录 # 7 balance Int 每年账户的平均余额 # 8 housing String 是否有住房贷款 # 9 loan String 是否有个人贷款 # 10 contact String 与客户联系的沟通方式 # 11 day Int 最后一次联系的时间（几号） # 12 month String 最后一次联系的时间（月份） # 13 duration Int 最后一次联系的交流时长 # 14 campaign Int 在本次活动中，与该客户交流过的次数 # 15 pdays Int 距离上次活动最后一次联系该客户，过去了多久（999表示没有联系过） # 16 previous Int 在本次活动之前，与该客户交流过的次数 # 17 poutcome String 上一次活动的结果 # 18 y Int 预测客户是否会订购定期存款业务 from sklearn.tree import DecisionTreeClassifier data_train = pd.read_csv('train_set.csv') data_test = pd.read_csv('test_set.csv') ids_test = data_test['ID'] print(data_train.shape[0]) # data_train['cppv']=data_train['campaign']+data_train['previous'] # data_test['cppv']=data_test['campaign']+data_test['previous'] # data_train.drop(['campaign','previous'], axis=1, inplace=True) # data_test.drop(['campaign','previous'], axis=1, inplace=True) # Rela_grouped=data_train.groupby(['cp']) # Rela_Survival_Rate=(Rela_grouped.sum()/Rela_grouped.count())['y'] # Rela_count=Rela_grouped.count()['y'] # # ax1=Rela_count.plot(kind='bar',color='g') # ax2=ax1.twinx() # ax2.plot(Rela_Survival_Rate.values,color='r') # ax1.set_xlabel('Relatives') # ax1.set_ylabel('Number') # ax2.set_ylabel('Survival Rate') # plt.title('Survival Rate by Relatives') # plt.grid(True,linestyle='-',color='0.7') # plt.show() # g = sns.FacetGrid(data_train, col='y') # g.map(plt.hist, 'day', bins=30) # plt.show() print("数值处理1：标签指标one-hot编码处理") data_train.drop(['ID'], axis=1, inplace=True) data_test.drop(['ID'], axis=1, inplace=True) dummy = pd.get_dummies(data_train[['month','job','marital','education','default','housing','loan','contact','poutcome']]) dummyTest = pd.get_dummies(data_test[['month','job','marital','education','default','housing','loan','contact','poutcome']]) data_train = pd.concat([dummy, data_train], axis=1) data_train.drop(['job','marital','education','default','housing','loan','contact','poutcome'], inplace=True, axis=1) data_test = pd.concat([dummyTest, data_test], axis=1) data_test.drop(['job','marital','education','default','housing','loan','contact','poutcome'], inplace=True, axis=1) data_train['day'].replace([30,13,15,4,14,12,18],4,inplace=True) data_train['day'].replace([5,20,21,11,8,16,2,3],3,inplace=True) data_train['day'].replace([17,9,6,27,7,22,28],2,inplace=True) data_train['day'].replace([23,25,26,10,29,19],1,inplace=True) data_train['day'].replace([1,24,31],0,inplace=True) data_test['day'].replace([30,13,15,4,14,12,18],4,inplace=True) data_test['day'].replace([5,20,21,11,8,16,2,3],3,inplace=True) data_test['day'].replace([17,9,6,27,7,22,28],2,inplace=True) data_test['day'].replace([23,25,26,10,29,19],1,inplace=True) data_test['day'].replace([1,24,31],0,inplace=True) # data_train['month1'] = data_train.month.apply(lambda x: 4 if x in ['may'] else 0) # data_train['month1'] = data_train.month.apply(lambda x: 3 if x in ['aug','jul','apr'] else 0) # data_train['month1'] = data_train.month.apply(lambda x: 2 if x in ['jun','feb','nov','oct'] else 0) # data_train['month1'] = data_train.month.apply(lambda x: 1 if x in ['sep','mar'] else 0) # # data_test['month1'] = data_test.month.apply(lambda x: 4 if x in ['may'] else 0) # data_test['month1'] = data_test.month.apply(lambda x: 3 if x in ['aug','jul','apr'] else 0) # data_test['month1'] = data_test.month.apply(lambda x: 2 if x in ['jun','feb','nov','oct'] else 0) # data_test['month1'] = data_test.month.apply(lambda x: 1 if x in ['sep','mar'] else 0) # # data_train.drop(['month'], inplace=True, axis=1) data_test.drop(['month'], inplace=True, axis=1) # data_train.drop(['day','job_management','marital_single'], axis=1, inplace=True) # data_test.drop(['day','job_management','marital_single'], axis=1, inplace=True) # data_train['month'].replace(['may'],4,inplace=True) # data_train['month'].replace(['aug','jul','apr'],3,inplace=True) # data_train['month'].replace(['jun','feb','nov','oct'],2,inplace=True) # data_train['month'].replace(['sep','mar'],1,inplace=True) # data_train['month'].replace(['jan','dec'],0,inplace=True) # 多删特征 # data_train.drop(['age','balance','duration','pdays','previous','day','month','job','marital','education','default','housing','loan','contact','poutcome'], inplace=True, axis=1) # data_test.drop(['age','balance','duration','pdays','previous','day','month','job','marital','education','default','housing','loan','contact','poutcome'], inplace=True, axis=1) #default、housing、loan都是2分类的指标，删除其中一个即可 # data_train.drop(['default_no','housing_no','loan_no'], inplace=True, axis=1) # data_test.drop(['default_no','housing_no','loan_no'], inplace=True, axis=1) ################################ ########### 数据整理 ########### ################################ data_train['pdays'].replace(-1,9999,inplace=True) data_test['pdays'].replace(-1,9999,inplace=True) print("数值处理2：pdays将-1替换为999") # data_train.drop(['pdays'], inplace=True, axis=1) # data_test.drop(['pdays'], inplace=True, axis=1) # g = sns.FacetGrid(data_train, col='y') # g.map(plt.hist, 'pdays', bins=20) # plt.show() # data_train.drop(['pdays'], inplace=True, axis=1) # data_test.drop(['pdays'], inplace=True, axis=1) y = data_train['y'] X = data_train[data_train.columns[: -1]] # # X.info() # pdays的平均值先前看到是45，而-1距离45很近，距离max值854很远，故还是需要将所有的-1替换为999 #数据预处理： #数据中pdays=-1表示从未联络过，替换为999 #对方差较大的数据指标进行变换，MinMaxScaler或者StandardScaler print("数值处理3：数值指标Scaler变换") scaler = MinMaxScaler() # numerical = ['age','balance', 'duration', 'pdays', 'previous'] # X[numerical] = scaler.fit_transform(X[numerical]) # data_test[numerical] = scaler.fit_transform(data_test[numerical]) print(data_test.shape) X = scaler.fit_transform(X) data_test = scaler.fit_transform(data_test) # tsvd = TruncatedSVD(n_components=46) # data_test = tsvd.fit_transform(data_test) #数据分割，用于测试 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.06, random_state=1) # X_train = tsvd.fit_transform(X_train) # X_test = tsvd.fit_transform(X_test) # print(X_train.shape) #增加二项式特征 # polynomial_interaction = PolynomialFeatures(degree=2,include_bias=False) # #增加二项式特征，仅仅是交叉特征 # polynomial_interaction = PolynomialFeatures(degree=2,interaction_only=True,include_bias=False) # X_train = polynomial_interaction.fit_transform(X_train) # X_test = polynomial_interaction.fit_transform(X_test) # data_test = polynomial_interaction.fit_transform(data_test) # print('after Polynomial:',X_train.shape) # # # #保留99%的信息，进行朱成本分析 # pca = PCA(n_components=100,whiten=True) # X_train = pca.fit_transform(X_train) # X_test = pca.fit_transform(X_test) # data_test = pca.fit_transform(data_test) # print('after PCA:',X_train.shape) # #卡方分类筛选 # selector = SelectKBest(f_classif,k=300) # X_train = selector.fit_transform(X_train,y_train) # X_test = selector.fit_transform(X_test,y_test) # print('after SelectKBest:',X_train.shape) # print(X_train['pdays']) ################################ ########### 性能计算 ########### ################################ # print('决策树，分数不理想') # clf = DecisionTreeClassifier(random_state=11) # clf.fit(X_train, y_train) # predictions = clf.predict(X_test) # print(classification_report(y_test, predictions)) # print(cross_val_score(clf,X_train, y_train,scoring='f1')) # print(cross_val_score(clf,X_test, y_test,scoring='f1')) # print(clf.score(X_test, y_test)) # # y_predprob = clf.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) # print('随机森林，0.919203') clf = RandomForestClassifier(n_estimators=90, random_state=0,oob_score=True,n_jobs=-1) clf.fit(X_train, y_train) predictions = clf.predict(X_test) print(classification_report(y_test, predictions)) # print(cross_val_score(clf,X_train, y_train,scoring='f1')) # print(cross_val_score(clf,X_test, y_test,scoring='f1')) print(clf.score(X_test, y_test)) y_predprob = clf.predict_proba(X_test) y_predprob = y_predprob[:, 1] print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) #穷举随机森林的最佳参数,答案：90 # param_test1 ={'n_estimators':range(10,100,5)} # gsearch1= GridSearchCV(estimator =RandomForestClassifier(min_samples_split=100, # min_samples_leaf=20,max_depth=8,max_features='sqrt',random_state=10), # param_grid =param_test1,scoring='roc_auc',cv=5) # gsearch1.fit(X_train, y_train) # print(gsearch1.best_params_) # y_predprob = gsearch1.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # predictions = gsearch1.predict(X_test) # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) # # print('逻辑回归,0.904655,0.915316') # # print(X_train) # #clf = Lasso(alpha=0.5) # clf = LogisticRegression(random_state=0,solver='newton-cg',class_weight='balanced',penalty='l2',n_jobs=-1) # # solver : str, {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’}, optional (default=’liblinear’). # clf.fit(X_train, y_train) # # clf.fit(X_train, y_train) # predictions = clf.predict(X_test) # # print(classification_report(y_test, predictions)) # # print(cross_val_score(clf,X_train, y_train,scoring='f1')) # # print(cross_val_score(clf,X_test, y_test,scoring='f1')) # print(clf.score(X_test, y_test)) # y_predprob = clf.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) # # raletion = pd.DataFrame({"columns":list(data_train.columns)[0:-1], "coef":list(clf.coef_.T)}) # print('相关性：',raletion) # #穷举逻辑回归的最佳参数,答案: # # best C : LogisticRegression(C=7.742636826811269, class_weight=None, dual=False, # # fit_intercept=True, intercept_scaling=1, l1_ratio=None, # # max_iter=100, multi_class='warn', n_jobs=None, penalty='l2', # # random_state=None, solver='warn', tol=0.0001, verbose=0, # # warm_start=False) # penalty = ['l1','l2'] # C=np.logspace(0,4,10) # hyperparameters = dict(C=C,penalty=penalty) # gridsearch = GridSearchCV(clf,hyperparameters,cv=5,verbose=0) # best_clf= gridsearch.fit(X_train, y_train) # print('best C :',best_clf.best_estimator_) # print(gridsearch.best_params_) # y_predprob = gridsearch.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # predictions = gridsearch.predict(X_test) # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) # print('AdaBoost') # clf = AdaBoostClassifier(n_estimators=60, random_state=90) # # clf.fit(X_train, y_train) # predictionsByadaBoost = clf.predict(X_test) # print(classification_report(y_test, predictionsByadaBoost)) # print(cross_val_score(clf,X_train, y_train,scoring='f1')) # print(cross_val_score(clf,X_test, y_test,scoring='f1')) # print(clf.score(X_test, y_test)) # pred = clf.predict_proba(X_test) # dataPred = pd.DataFrame(pred, columns=['pred0', 'pred']) # dataPred.drop('pred0', axis=1, inplace=True) # print(dataPred) # # y_predprob = clf.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # # predictions_train = clf.predict(X_train) # y_predprob_train = clf.predict_proba(X_train) # y_predprob_train = y_predprob_train[:, 1] # # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictionsByadaBoost)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) # print("Accuracy y_train : %.4g" % metrics.accuracy_score(y_train, predictions_train)) # print("AUC Score (Train): %f" % metrics.roc_auc_score(y_train, y_predprob_train)) # # # # # # # # print('神经网络') # # ‘lbfgs’ is an optimizer in the family of quasi-Newton methods. # # ‘sgd’ refers to stochastic gradient descent. # # ‘adam’ refers to a stochastic gradient-based optimizer proposed by Kingma, Diederik, and Jimmy Ba # clf = MLPClassifier(solver='adam', hidden_layer_sizes=(80,80), # random_state=1) # clf.fit(X_train, y_train) # predictions = clf.predict(X_test) # print(clf.score(X_test, y_test)) # y_predprob = clf.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) # print('神经网络 end') # # #导出结果 ID = list(range(25318,36170)) submission = pd.DataFrame(ID) submission.rename(columns = {0: 'ID'}, inplace = True) # 将pred_y从array转化成DataFrame y_predprob_test = clf.predict_proba(data_test) y_predprob_test = y_predprob_test[:, 1] y_predprob_DataFrame = pd.DataFrame(y_predprob_test) submission['pred'] =y_predprob_DataFrame submission.to_csv('Result.csv', index = False) #为防止过拟合而减半步长，最大迭代次数加倍 # gbm1 = GradientBoostingClassifier(learning_rate=0.001, n_estimators=10000, max_depth=7, min_samples_leaf=70, # min_samples_split=1300, subsample=0.8, random_state=10) # gbm1.fit(X_train, y_train) # # y_pred = gbm1.predict(X_test) # y_predprob = gbm1.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, y_pred)) # print("AUC Score (Train): %f" % metrics.roc_auc_score(y_test, y_predprob)) # print('KNN近邻，分数不理想') # clf = KNeighborsClassifier(n_neighbors=5) # clf.fit(X_train,y_train) # predictions = clf.predict(X_test) # print(classification_report(y_test, predictions)) # y_predprob = clf.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) # print('SVM支持向量机') # clf = SVC(kernel='rbf',C=1,gamma='auto',probability=True).fit(X_train, y_train) # predictions = clf.predict(X_test) # print(classification_report(y_test, predictions)) # y_predprob = clf.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) #朴素贝叶斯 # print('朴素贝叶斯') # clf = GaussianNB() # # clf_sigmoid = CalibratedClassifierCV(clf,cv=5) # clf_sigmoid.fit(X_train,y_train) # predictions = clf_sigmoid.predict(X_test) # y_predprob = clf_sigmoid.predict_proba(X_test) # y_predprob = y_predprob[:, 1] # # print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions)) # print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob)) ################################ # AdaBoost选为第一次使用的算法,提交数据 ################################ # print('AdaBoost') # adaBoost = AdaBoostClassifier(n_estimators=50, random_state=11) # adaBoost.fit(X_train, y_train) # # age_null = pd.isnull(data_test['age']) # data_null = data_test[age_null == True] # # print(data_null) # # id = data_test["ID"] # print(id) # X_test.drop(['ID'], axis=1, inplace=True) # # submission = pd.DataFrame({ # "ID": id # }) # # submission[['ID']].astype(int) # # submission[['ID']] = submission[['ID']].astype(int) # submission.to_csv('submission.csv', index=False) # data_test.dropna(inplace=True) # print(np.isnan(data_test).any()) # submission.replace(np.nan, 0, inplace=True) # predictionsByadaBoost = adaBoost.predict_proba(X_test) # # submission = pd.DataFrame({ # "ID": id, # "pred": predictionsByadaBoost # }) # submission.to_csv('submission.csv', index=False)
第一次提交，没做什么特征工程，分数还不太理想
0.9157894736842105
Accuracy : 0.9158
AUC Score (Test): 0.932477
过程分析
from numpy import int64
from sklearn import metrics
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
import seaborn as sns
from sklearn.preprocessing import PolynomialFeatures
from sklearn.decomposition import PCA
from sklearn.feature_selection import SelectKBest,chi2,f_classif
from sklearn.metrics import roc_auc_score
data_train = pd.read_csv('/home/kesci/input/firstdata1587/train_set.csv')
data_test = pd.read_csv('/home/kesci/input/firstdata1587/test_set.csv')
data_train.describe()
Out[4]:
总计记录25317人。 年龄分布：18-95； balance（存款）分布：-8019 - 102127，balance的标准差2999.822811，比较大，看到平均存款1357，上四分位1435，下四分位才只有73元，存款的差距还是蛮大的，万恶的资本主义； day（最后一次联系是几号）：1-31，很明显一个月从1号开始，从31号结束，这个特征很可能和预测无关联； duration（交流时长）：0-3881，这个猜测是持续的天数； campaign（交流次数）：1-55 pdays（上次联系后过了多久）：-1 - 854，这里没有999，应该是-1为没有联系，>-1就是期间几天前曾联系过； previous(活动前交流次数)：0-275，平均0.591737，不到1次；
In [5]:
#工作和购买理财的关系
y_0 = data_train.job[data_train.y == 0].value_counts()
y_1 = data_train.job[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"job to buy")
plt.ylabel(u"counts")
plt.show()
In [14]:
#婚姻和购买理财的关系
#看不出啥结果
y_0 = data_train.marital[data_train.y == 0].value_counts()
y_1 = data_train.marital[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"marital to buy")
plt.ylabel(u"counts")
plt.show()
In [15]:
#教育和购买理财的关系
y_0 = data_train.education[data_train.y == 0].value_counts()
y_1 = data_train.education[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"education to buy")
plt.ylabel(u"counts")
plt.show()
In [24]:
#上次活动结果和购买理财的关系
#发现poutcome指标相当重要，上次活动成功的客户这次也购买理财的比例非常高
y_0 = data_train.poutcome[data_train.y == 0].value_counts()
y_1 = data_train.poutcome[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"poutcome to buy")
plt.ylabel(u"counts")
plt.show()
判断day、month是和客户交流的月份和日份，很容易被当成噪音特征。用统计来说话。
In [3]:
#月份对结果的影响
y_0 = data_train.month[data_train.y == 0].value_counts()
y_1 = data_train.month[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"poutcome to buy")
plt.ylabel(u"counts")
plt.show()
print(y_1/data_train.shape[0])
#我们发现may（0.019789）和dec （0.001896）相差10倍，所以这个特征还是蛮重要的
In [4]:
#日对结果的影响
y_0 = data_train.day[data_train.y == 0].value_counts()
y_1 = data_train.day[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"poutcome to buy")
plt.ylabel(u"counts")
plt.show()
print(y_1/data_train.shape[0])
#发现30号最容易出单，31号极不容易出单
In [7]:
#'job','marital','education','default','housing','loan','contact','poutcome'这8个字段都
#要做one-hot编码预处理，暂时先将unknown作为一个特征项。
dummy = pd.get_dummies(data_train[['day','month','job','marital','education','default','housing','loan','contact','poutcome']])
dummyTest = pd.get_dummies(data_test[['day','month','job','marital','education','default','housing','loan','contact','poutcome']])
data_train = pd.concat([dummy, data_train], axis=1)
data_train.drop(['day','month','job','marital','education','default','housing','loan','contact','poutcome'], inplace=True, axis=1)
data_test = pd.concat([dummyTest, data_test], axis=1)
data_test.drop(['day','month','job','marital','education','default','housing','loan','contact','poutcome'], inplace=True, axis=1)
print("数值处理1：标签指标one-hot编码处理")
#default、housing、loan都是2分类的指标，删除其中一个即可
#data_train.drop(['default_no','housing_no','loan_no'], inplace=True, axis=1)
#data_test.drop(['default_no','housing_no','loan_no'], inplace=True, axis=1)
data_train['pdays'].replace(-1,999,inplace=True)
data_test['pdays'].replace(-1,999,inplace=True)
print("数值处理2：pdays将-1替换为999")
In [20]:
data_train.head()
Out[20]:
In [6]:
#测试单一特征和目标的关系
#print('无违约：',data_train[data_train['default_yes']==0].count())
#print('有违约：',data_train[data_train['default_yes']==1].count())
print(data_train['default_yes'].value_counts())
print(data_test['default_yes'].value_counts())
#data_train.groupby(["default_yes"], as_index=False)['y'].count()
Out[6]:
In [8]:
#违约记录&订购理财的关系
fig = plt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
y_0 = data_atrain.default_yes[data_train.y == 0].value_counts()
y_1 = data_train.default_yes[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"buy or not")
plt.xlabel(u"default")
plt.ylabel(u"counts")
plt.show()
In [9]:
#住房贷款&订购理财的关系
#可以看出没有房贷购买理财的比例稍微高一些，但不明显，可能是还房贷的人资金压力稍大
fig = plt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
y_0 = data_train.housing_yes[data_train.y == 0].value_counts()
y_1 = data_train.housing_yes[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"buy or not")
plt.xlabel(u"housing")
plt.ylabel(u"counts")
plt.show()
#发现没有违约的人买理财比例略高
In [19]:
#个人贷款&订购理财的关系
#可以看出两种情况差别不大
fig = plt.figure()
fig.set(alpha=0.2) # 设定图表颜色alpha参数
y_0 = data_train.loan_yes[data_train.y == 0].value_counts()
y_1 = data_train.loan_yes[data_train.y == 1].value_counts()
df=pd.DataFrame({u'buy':y_1, u'not buy':y_0})
df.plot(kind='bar', stacked=True)
plt.title(u"buy or not")
plt.xlabel(u"loan")
plt.ylabel(u"counts")
plt.show()
data_train[["loan_yes", "y"]].groupby(['loan_yes'], as_index=False).mean().sort_values(by='y', ascending=False)
#可以看出12.6%的无个人贷的人买了理财，有贷款的只有6.89%买了理财
#说明无个贷买理财的机会比较大
Out[19]:
In [7]:
#使用直方图来看看那个区段年龄的人最多购买或不购买
g = sns.FacetGrid(data_train, col='y')
g.map(plt.hist, 'age', bins=20)
plt.show()
#貌似看不出什么问题，只能说明买理财的年龄不大集中，不买的集中在30-40岁之间
In [8]:
#使用直方图来看看“距离上次活动最后一次联系该客户，过去了多久”的人最多购买或不购买
#看来是时间越短，购买率越高，说明pdays是相当重要的指标
g = sns.FacetGrid(data_train, col='y')
g.map(plt.hist, 'pdays', bins=20)
plt.show()
#pdays指标让人读不懂，以后重点解决
In [9]:
y = data_train['y']
X = data_train[data_train.columns[: -1]]
X.info()
In [ ]:
#查看相关矩阵，连带y也作为指标
#data_train.corr()
#查看相关矩阵热图
#colormap = plt.cm.RdBu
#plt.figure(figsize=(39,37))
#plt.title('Correlation of Features', y=1.05, size=37)
#sns.heatmap(data_train.astype(float).corr(),linewidths=0.1,vmax=1.0,
# square=True, cmap=colormap, linecolor='white', annot=True)
#plt.show()
In [11]:
print("数值处理3：数值指标Scaler变换")
scaler = StandardScaler()
X = scaler.fit_transform(X)
data_test = scaler.fit_transform(data_test)
#数据分割，用于测试
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=90)
In [12]:
# print('决策树')
# clf = DecisionTreeClassifier(random_state=11)
# clf.fit(X_train, y_train)
# predictions = clf.predict(X_test)
# print(classification_report(y_test, predictions))
# print(cross_val_score(clf,X_train, y_train,scoring='f1'))
# print(cross_val_score(clf,X_test, y_test,scoring='f1'))
# print(clf.score(X_test, y_test))
#
# y_predprob = clf.predict_proba(X_test)
# y_predprob = y_predprob[:, 1]
#
# print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions))
# print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob))
#
# print('随机森林')
# clf = RandomForestClassifier(n_estimators=10, random_state=11)
# clf.fit(X_train, y_train)
# predictions = clf.predict(X_test)
# print(classification_report(y_test, predictions))
# print(cross_val_score(clf,X_train, y_train,scoring='f1'))
# print(cross_val_score(clf,X_test, y_test,scoring='f1'))
# print(clf.score(X_test, y_test))
# y_predprob = clf.predict_proba(X_test)
# y_predprob = y_predprob[:, 1]
#
# print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions))
# print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob))
# print('逻辑回归')
# clf = LogisticRegression()
# clf.fit(X_train, y_train)
# predictions = clf.predict(X_test)
# print(classification_report(y_test, predictions))
# print(cross_val_score(clf,X_train, y_train,scoring='f1'))
# print(cross_val_score(clf,X_test, y_test,scoring='f1'))
# print(clf.score(X_test, y_test))
# y_predprob = clf.predict_proba(X_test)
# y_predprob = y_predprob[:, 1]
#
# print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictions))
# print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob))
print('AdaBoost')
adaBoost = AdaBoostClassifier(n_estimators=50, random_state=11)
adaBoost.fit(X_train, y_train)
predictionsByadaBoost = adaBoost.predict(X_test)
print(classification_report(y_test, predictionsByadaBoost))
print(cross_val_score(adaBoost,X_train, y_train,scoring='f1'))
print(cross_val_score(adaBoost,X_test, y_test,scoring='f1'))
print(adaBoost.score(X_test, y_test))
pred = adaBoost.predict_proba(X_test)
dataPred = pd.DataFrame(pred, columns=['pred0', 'pred'])
dataPred.drop('pred0', axis=1, inplace=True)
print(dataPred)
y_predprob = adaBoost.predict_proba(X_test)
y_predprob = y_predprob[:, 1]
predictions_train = adaBoost.predict(X_train)
y_predprob_train = adaBoost.predict_proba(X_train)
y_predprob_train = y_predprob_train[:, 1]
print("Accuracy : %.4g" % metrics.accuracy_score(y_test, predictionsByadaBoost))
print("AUC Score (Test): %f" % metrics.roc_auc_score(y_test, y_predprob))
print("Accuracy y_train : %.4g" % metrics.accuracy_score(y_train, predictions_train))