上次將數(shù)據(jù)訓練了模型

由于數(shù)據(jù)中的大多數(shù)候選人都有70%以上的機會，許多不成功的候選人都沒有很好的預測。

df["Chance of Admit"].plot(kind = 'hist',bins = 200,figsize = (6,6)) plt.title("Chance of Admit") plt.xlabel("Chance of Admit") plt.ylabel("Frequency") plt.show()

為分類準備數(shù)據(jù)

如果候選人的錄取機會大于80%，則該候選人將獲得1個標簽。
如果候選人的錄取機會小于或等于80%，則該候選人將獲得0標簽。

# reading the dataset df = pd.read_csv("../input/Admission_Predict.csv",sep = ",")# it may be needed in the future. serialNo = df["Serial No."].values df.drop(["Serial No."],axis=1,inplace = True)y = df["Chance of Admit"].values x = df.drop(["Chance of Admit"],axis=1)# separating train (80%) and test (%20) sets from sklearn.model_selection import train_test_split x_train, x_test,y_train, y_test = train_test_split(x,y,test_size = 0.20,random_state = 42)# normalization from sklearn.preprocessing import MinMaxScaler scalerX = MinMaxScaler(feature_range=(0, 1)) x_train[x_train.columns] = scalerX.fit_transform(x_train[x_train.columns]) x_test[x_test.columns] = scalerX.transform(x_test[x_test.columns])y_train_01 = [1 if each > 0.8 else 0 for each in y_train] y_test_01 = [1 if each > 0.8 else 0 for each in y_test]# list to array y_train_01 = np.array(y_train_01) y_test_01 = np.array(y_test_01)

邏輯回歸

from sklearn.linear_model import LogisticRegression lrc = LogisticRegression() lrc.fit(x_train,y_train_01) print("score: ", lrc.score(x_test,y_test_01)) print("real value of y_test_01[1]: " + str(y_test_01[1]) + " -> the predict: " + str(lrc.predict(x_test.iloc[[1],:]))) print("real value of y_test_01[2]: " + str(y_test_01[2]) + " -> the predict: " + str(lrc.predict(x_test.iloc[[2],:])))# confusion matrix from sklearn.metrics import confusion_matrix cm_lrc = confusion_matrix(y_test_01,lrc.predict(x_test)) # print("y_test_01 == 1 :" + str(len(y_test_01[y_test_01==1]))) # 29# cm visualization import seaborn as sns import matplotlib.pyplot as plt f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_lrc,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.title("Test for Test Dataset") plt.xlabel("predicted y values") plt.ylabel("real y values") plt.show()from sklearn.metrics import precision_score, recall_score print("precision_score: ", precision_score(y_test_01,lrc.predict(x_test))) print("recall_score: ", recall_score(y_test_01,lrc.predict(x_test)))from sklearn.metrics import f1_score print("f1_score: ",f1_score(y_test_01,lrc.predict(x_test)))

score: 0.9
real value of y_test_01[1]: 0 -> the predict: [0]
real value of y_test_01[2]: 1 -> the predict: [1]

precision_score: 0.9565217391304348
recall_score: 0.7586206896551724
f1_score: 0.8461538461538461

Test for Train Dataset:

cm_lrc_train = confusion_matrix(y_train_01,lrc.predict(x_train)) f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_lrc_train,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.xlabel("predicted y values") plt.ylabel("real y values") plt.title("Test for Train Dataset") plt.show()

SVC

from sklearn.svm import SVC svm = SVC(random_state = 1) svm.fit(x_train,y_train_01) print("score: ", svm.score(x_test,y_test_01)) print("real value of y_test_01[1]: " + str(y_test_01[1]) + " -> the predict: " + str(svm.predict(x_test.iloc[[1],:]))) print("real value of y_test_01[2]: " + str(y_test_01[2]) + " -> the predict: " + str(svm.predict(x_test.iloc[[2],:])))# confusion matrix from sklearn.metrics import confusion_matrix cm_svm = confusion_matrix(y_test_01,svm.predict(x_test)) # print("y_test_01 == 1 :" + str(len(y_test_01[y_test_01==1]))) # 29# cm visualization import seaborn as sns import matplotlib.pyplot as plt f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_svm,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.title("Test for Test Dataset") plt.xlabel("predicted y values") plt.ylabel("real y values") plt.show()from sklearn.metrics import precision_score, recall_score print("precision_score: ", precision_score(y_test_01,svm.predict(x_test))) print("recall_score: ", recall_score(y_test_01,svm.predict(x_test)))from sklearn.metrics import f1_score print("f1_score: ",f1_score(y_test_01,svm.predict(x_test)))

score: 0.9
real value of y_test_01[1]: 0 -> the predict: [0]
real value of y_test_01[2]: 1 -> the predict: [1]

precision_score: 0.9565217391304348
recall_score: 0.7586206896551724
f1_score: 0.8461538461538461

Test for Train Dataset

cm_svm_train = confusion_matrix(y_train_01,svm.predict(x_train)) f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_svm_train,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.xlabel("predicted y values") plt.ylabel("real y values") plt.title("Test for Train Dataset") plt.show()

樸素貝葉斯

from sklearn.naive_bayes import GaussianNB nb = GaussianNB() nb.fit(x_train,y_train_01) print("score: ", nb.score(x_test,y_test_01)) print("real value of y_test_01[1]: " + str(y_test_01[1]) + " -> the predict: " + str(nb.predict(x_test.iloc[[1],:]))) print("real value of y_test_01[2]: " + str(y_test_01[2]) + " -> the predict: " + str(nb.predict(x_test.iloc[[2],:])))# confusion matrix from sklearn.metrics import confusion_matrix cm_nb = confusion_matrix(y_test_01,nb.predict(x_test)) # print("y_test_01 == 1 :" + str(len(y_test_01[y_test_01==1]))) # 29 # cm visualization import seaborn as sns import matplotlib.pyplot as plt f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_nb,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.title("Test for Test Dataset") plt.xlabel("predicted y values") plt.ylabel("real y values") plt.show()from sklearn.metrics import precision_score, recall_score print("precision_score: ", precision_score(y_test_01,nb.predict(x_test))) print("recall_score: ", recall_score(y_test_01,nb.predict(x_test)))from sklearn.metrics import f1_score print("f1_score: ",f1_score(y_test_01,nb.predict(x_test)))

score: 0.9625
real value of y_test_01[1]: 0 -> the predict: [0]
real value of y_test_01[2]: 1 -> the predict: [1]

precision_score: 0.9333333333333333
recall_score: 0.9655172413793104
f1_score: 0.9491525423728815

Test for Train Dataset:

cm_nb_train = confusion_matrix(y_train_01,nb.predict(x_train)) f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_nb_train,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.xlabel("predicted y values") plt.ylabel("real y values") plt.title("Test for Train Dataset") plt.show()

決策樹

from sklearn.tree import DecisionTreeClassifier dtc = DecisionTreeClassifier() dtc.fit(x_train,y_train_01) print("score: ", dtc.score(x_test,y_test_01)) print("real value of y_test_01[1]: " + str(y_test_01[1]) + " -> the predict: " + str(dtc.predict(x_test.iloc[[1],:]))) print("real value of y_test_01[2]: " + str(y_test_01[2]) + " -> the predict: " + str(dtc.predict(x_test.iloc[[2],:])))# confusion matrix from sklearn.metrics import confusion_matrix cm_dtc = confusion_matrix(y_test_01,dtc.predict(x_test)) # print("y_test_01 == 1 :" + str(len(y_test_01[y_test_01==1]))) # 29# cm visualization import seaborn as sns import matplotlib.pyplot as plt f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_dtc,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.title("Test for Test Dataset") plt.xlabel("predicted y values") plt.ylabel("real y values") plt.show()from sklearn.metrics import precision_score, recall_score print("precision_score: ", precision_score(y_test_01,dtc.predict(x_test))) print("recall_score: ", recall_score(y_test_01,dtc.predict(x_test)))from sklearn.metrics import f1_score print("f1_score: ",f1_score(y_test_01,dtc.predict(x_test)))

score: 0.9375
real value of y_test_01[1]: 0 -> the predict: [0]
real value of y_test_01[2]: 1 -> the predict: [1]

precision_score: 0.9615384615384616
recall_score: 0.8620689655172413
f1_score: 0.9090909090909091

Test for Train Dataset

cm_dtc_train = confusion_matrix(y_train_01,dtc.predict(x_train)) f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_dtc_train,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.xlabel("predicted y values") plt.ylabel("real y values") plt.title("Test for Train Dataset") plt.show()

隨機森林

from sklearn.ensemble import RandomForestClassifier rfc = RandomForestClassifier(n_estimators = 100,random_state = 1) rfc.fit(x_train,y_train_01) print("score: ", rfc.score(x_test,y_test_01)) print("real value of y_test_01[1]: " + str(y_test_01[1]) + " -> the predict: " + str(rfc.predict(x_test.iloc[[1],:]))) print("real value of y_test_01[2]: " + str(y_test_01[2]) + " -> the predict: " + str(rfc.predict(x_test.iloc[[2],:])))# confusion matrix from sklearn.metrics import confusion_matrix cm_rfc = confusion_matrix(y_test_01,rfc.predict(x_test)) # print("y_test_01 == 1 :" + str(len(y_test_01[y_test_01==1]))) # 29 # cm visualization import seaborn as sns import matplotlib.pyplot as plt f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_rfc,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.title("Test for Test Dataset") plt.xlabel("predicted y values") plt.ylabel("real y values") plt.show()from sklearn.metrics import precision_score, recall_score print("precision_score: ", precision_score(y_test_01,rfc.predict(x_test))) print("recall_score: ", recall_score(y_test_01,rfc.predict(x_test)))from sklearn.metrics import f1_score print("f1_score: ",f1_score(y_test_01,rfc.predict(x_test)))

score: 0.9375
real value of y_test_01[1]: 0 -> the predict: [0]
real value of y_test_01[2]: 1 -> the predict: [1]

precision_score: 0.9615384615384616
recall_score: 0.8620689655172413
f1_score: 0.9090909090909091

Test for Train Dataset

cm_rfc_train = confusion_matrix(y_train_01,rfc.predict(x_train)) f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_rfc_train,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.xlabel("predicted y values") plt.ylabel("real y values") plt.title("Test for Train Dataset") plt.show()

kNN

from sklearn.neighbors import KNeighborsClassifier# finding k value scores = [] for each in range(1,50):knn_n = KNeighborsClassifier(n_neighbors = each)knn_n.fit(x_train,y_train_01)scores.append(knn_n.score(x_test,y_test_01))plt.plot(range(1,50),scores) plt.xlabel("k") plt.ylabel("accuracy") plt.show()knn = KNeighborsClassifier(n_neighbors = 3) # n_neighbors = k knn.fit(x_train,y_train_01) print("score of 3 :",knn.score(x_test,y_test_01)) print("real value of y_test_01[1]: " + str(y_test_01[1]) + " -> the predict: " + str(knn.predict(x_test.iloc[[1],:]))) print("real value of y_test_01[2]: " + str(y_test_01[2]) + " -> the predict: " + str(knn.predict(x_test.iloc[[2],:])))# confusion matrix from sklearn.metrics import confusion_matrix cm_knn = confusion_matrix(y_test_01,knn.predict(x_test)) # print("y_test_01 == 1 :" + str(len(y_test_01[y_test_01==1]))) # 29# cm visualization import seaborn as sns import matplotlib.pyplot as plt f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_knn,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.title("Test for Test Dataset") plt.xlabel("predicted y values") plt.ylabel("real y values") plt.show()from sklearn.metrics import precision_score, recall_score print("precision_score: ", precision_score(y_test_01,knn.predict(x_test))) print("recall_score: ", recall_score(y_test_01,knn.predict(x_test)))from sklearn.metrics import f1_score print("f1_score: ",f1_score(y_test_01,knn.predict(x_test)))

score of 3 : 0.9375
real value of y_test_01[1]: 0 -> the predict: [0]
real value of y_test_01[2]: 1 -> the predict: [1]

precision_score: 0.9285714285714286
recall_score: 0.896551724137931
f1_score: 0.912280701754386

Test for Train Dataset:

cm_knn_train = confusion_matrix(y_train_01,knn.predict(x_train)) f, ax = plt.subplots(figsize =(5,5)) sns.heatmap(cm_knn_train,annot = True,linewidths=0.5,linecolor="red",fmt = ".0f",ax=ax) plt.xlabel("predicted y values") plt.ylabel("real y values") plt.title("Test for Train Dataset") plt.show()

所有分類算法都取得了大約90%的成功。最成功的是高斯樸素貝葉斯，得分為96%。

y = np.array([lrc.score(x_test,y_test_01),svm.score(x_test,y_test_01),nb.score(x_test,y_test_01),dtc.score(x_test,y_test_01),rfc.score(x_test,y_test_01),knn.score(x_test,y_test_01)]) #x = ["LogisticRegression","SVM","GaussianNB","DecisionTreeClassifier","RandomForestClassifier","KNeighborsClassifier"] x = ["LogisticReg.","SVM","GNB","Dec.Tree","Ran.Forest","KNN"]plt.bar(x,y) plt.title("Comparison of Classification Algorithms") plt.xlabel("Classfication") plt.ylabel("Score") plt.show()

上文是回歸算法，此文分類

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