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正如網(wǎng)站的名字，插畫目錄，在照片流之外，目錄頁(yè)中按插畫師姓氏收錄著作品。
在近年金融類電詐案中，投資理財(cái)類詐騙通常涉案金額最高、人均損失最大。盡管銀監(jiān)會(huì)、證監(jiān)會(huì)多次提示投資者警惕“莊家”“大V”聯(lián)合誘騙和“殺豬盤”風(fēng)險(xiǎn)，多地警方也在加大對(duì)非法投資理財(cái)、證券期貨交易詐騙的打擊，但由于非法證券期貨交易具有流動(dòng)性強(qiáng)、隱蔽性高、法律適用性存在分歧（涉及跨國(guó)犯罪）等特點(diǎn)，打擊難度非常大。
Artand一個(gè)免費(fèi)入駐的藝術(shù)社區(qū)。網(wǎng)站展示了不少中國(guó)插畫師的作品，藝術(shù)家在這里分享原創(chuàng)的當(dāng)代藝術(shù)作品。
Pclass，艙室類別，也許 1 表示經(jīng)濟(jì)艙，2 表示商務(wù)艙，3 表示頭等艙；
Python 機(jī)器學(xué)習(xí)項(xiàng)目
視覺中國(guó)設(shè)計(jì)師區(qū)
1、Booooooom
2、Colossal
3、Mirador
4、Identity Designed
5、Illustration Age
魔獸動(dòng)作條兩邊的鷹還是獅鷲怎么去掉 - 櫻花動(dòng)漫
6、Illustrators Lounge
7、Contemporary Art Daily
8、A Secret
9、Ain't Bad Magazine
10、SHOWstudio
正是這一套“風(fēng)控兜底”的手段，讓許多受害人在受騙后都很難認(rèn)清這個(gè)騙局的本質(zhì)。一旦受害人虧損達(dá)到50％后，業(yè)務(wù)員通常先是百般拖延，再轉(zhuǎn)移給風(fēng)控人員跟進(jìn)，對(duì)受害人逐步勸退，還要對(duì)虧損較多、可能報(bào)警的受害人進(jìn)行安撫，必要時(shí)還會(huì)退還部分傭金，讓受害人簽署和解協(xié)議，并要求其刪除相關(guān)聊天記錄，避免受到公安機(jī)關(guān)打擊處理。
首先當(dāng)然是 Python。第一次安裝 Python 需要從官網(wǎng)上安裝。你要安裝 3.6 以上的版本，這樣才能跟最新版本的庫(kù)保持同步。
探索數(shù)據(jù)是第一步。你需要從 Kaggle 的 Titanic 頁(yè)面下載數(shù)據(jù)，然后將下載的數(shù)據(jù)放到你啟動(dòng) Jupyter 筆記本的文件夾中。
def missingdata(data): total = data.isnull().sum().sort_values(ascending = False) percent = (data.isnull().sum()/data.isnull().count()*100).sort_values(ascending = False) ms=pd.concat([total, percent], axis=1, keys=['Total', 'Percent']) ms= ms[ms["Percent"] > 0] f,ax =plt.subplots(figsize=(8,6)) plt.xticks(rotation='90') fig=sns.barplot(ms.index, ms["Percent"],color="green",alpha=0.8) plt.xlabel('Features', fontsize=15) plt.ylabel('Percent of missing values', fontsize=15) plt.title('Percent missing data by feature', fontsize=15) return ms missingdata(train_df)
drop_column = ['Cabin'] train_df.drop(drop_column, axis=1, inplace = True)
all_data = train_df for dataset in all_data : dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1 import re # Define function to extract titles from passenger names def get_title(name): title_search = re.search(' ([A-Za-z]+)\.', name) # If the title exists, extract and return it. if title_search: return title_search.group(1) return "" # Create a new feature Title, containing the titles of passenger names for dataset in all_data: dataset['Title'] = dataset['Name'].apply(get_title) # Group all non-common titles into one single grouping "Rare" for dataset in all_data: dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare') dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss') dataset['Title'] = dataset['Title'].replace('Ms', 'Miss') dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs') for dataset in all_data: dataset['Age_bin'] = pd.cut(dataset['Age'], bins=[0,14,20,40,120], labels=['Children','Teenage','Adult','Elder']) for dataset in all_data: dataset['Fare_bin'] = pd.cut(dataset['Fare'], bins=[0,7.91,14.45,31,120], labels ['Low_fare','median_fare', 'Average_fare','high_fare']) traindf=train_df for dataset in traindf: drop_column = ['Age','Fare','Name','Ticket'] dataset.drop(drop_column, axis=1, inplace = True) drop_column = ['PassengerId'] traindf.drop(drop_column, axis=1, inplace = True) traindf = pd.get_dummies(traindf, columns = ["Sex","Title","Age_bin","Embarked","Fare_bin"], prefix=["Sex","Title","Age_type","Em_type","Fare_type"])
from sklearn.model_selection import train_test_split #for split the data from sklearn.metrics import accuracy_score #for accuracy_score from sklearn.model_selection import KFold #for K-fold cross validation from sklearn.model_selection import cross_val_score #score evaluation from sklearn.model_selection import cross_val_predict #prediction from sklearn.metrics import confusion_matrix #for confusion matrix all_features = traindf.drop("Survived",axis=1) Targeted_feature = traindf["Survived"] X_train,X_test,y_train,y_test = train_test_split(all_features,Targeted_feature,test_size=0.3,random_state=42) X_train.shape,X_test.shape,y_train.shape,y_test.shape
from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(criterion='gini', n_estimators=700, min_samples_split=10,min_samples_leaf=1, max_features='auto',oob_score=True, random_state=1,n_jobs=-1) model.fit(X_train,y_train) prediction_rm=model.predict(X_test) print('--------------The Accuracy of the model----------------------------') print('The accuracy of the Random Forest Classifier is', round(accuracy_score(prediction_rm,y_test)*100,2)) kfold = KFold(n_splits=10, random_state=22) # k=10, split the data into 10 equal parts result_rm=cross_val_score(model,all_features,Targeted_feature,cv=10,scoring='accuracy') print('The cross validated score for Random Forest Classifier is:',round(result_rm.mean()*100,2)) y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10) sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap="summer") plt.title('Confusion_matrix', y=1.05, size=15)
哇哦！準(zhǔn)確率高達(dá) 83%。就第一次嘗試而言，這個(gè)結(jié)果已經(jīng)很好了。
交叉驗(yàn)證分?jǐn)?shù)的意思是 K 折驗(yàn)證方法。如果 K=10，就是說要把數(shù)據(jù)分成 10 個(gè)變量，計(jì)算所有分?jǐn)?shù)的均值，并將它們作為最終分?jǐn)?shù)。
model = RandomForestClassifier(criterion='gini', n_estimators=700, min_samples_split=10,min_samples_leaf=1, max_features='auto',oob_score=True, random_state=1,n_jobs=-1)
# Random Forest Classifier Parameters tunning model = RandomForestClassifier() n_estim=range(100,1000,100) ## Search grid for optimal parameters param_grid = {"n_estimators" :n_estim} model_rf = GridSearchCV(model,param_grid = param_grid, cv=5, scoring="accuracy", n_jobs= 4, verbose = 1) model_rf.fit(train_X,train_Y) # Best score print(model_rf.best_score_) #best estimator model_rf.best_estimator_