Naive Bayes Gaussian (Tugas 4)

from google.colab import drive
drive.mount('/content/drive')

KeyboardInterruptTraceback (most recent call last)
<ipython-input-1-d5df0069828e> in <module>
      1 from google.colab import drive
----> 2 drive.mount('/content/drive')

/usr/local/lib/python3.7/dist-packages/google/colab/drive.py in mount(mountpoint, force_remount, timeout_ms, readonly)
    104       timeout_ms=timeout_ms,
    105       ephemeral=True,
--> 106       readonly=readonly)
    107 
    108 

/usr/local/lib/python3.7/dist-packages/google/colab/drive.py in _mount(mountpoint, force_remount, timeout_ms, ephemeral, readonly)
    123   if ephemeral:
    124     _message.blocking_request(
--> 125         'request_auth', request={'authType': 'dfs_ephemeral'}, timeout_sec=None)
    126 
    127   mountpoint = _os.path.expanduser(mountpoint)

/usr/local/lib/python3.7/dist-packages/google/colab/_message.py in blocking_request(request_type, request, timeout_sec, parent)
    169   request_id = send_request(
    170       request_type, request, parent=parent, expect_reply=True)
--> 171   return read_reply_from_input(request_id, timeout_sec)

/usr/local/lib/python3.7/dist-packages/google/colab/_message.py in read_reply_from_input(message_id, timeout_sec)
     95     reply = _read_next_input_message()
     96     if reply == _NOT_READY or not isinstance(reply, dict):
---> 97       time.sleep(0.025)
     98       continue
     99     if (reply.get('type') == 'colab_reply' and

KeyboardInterrupt: 

#Metrics
from sklearn.metrics import make_scorer, accuracy_score,precision_score
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score ,precision_score,recall_score,f1_score

#Model Select
from sklearn.model_selection import KFold,train_test_split,cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.linear_model import  LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn import linear_model
from sklearn.linear_model import SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC, LinearSVC
from sklearn.naive_bayes import GaussianNB

Import library

import pandas as pd

Ambil data

%cd /content/drive/MyDrive/datamining/tugas/Iris/

/content/drive/MyDrive/datamining/tugas/Iris

df = pd.read_csv("Iris.csv")
df.head()

	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm	Species
0	1	5.1	3.5	1.4	0.2	Iris-setosa
1	2	4.9	3.0	1.4	0.2	Iris-setosa
2	3	4.7	3.2	1.3	0.2	Iris-setosa
3	4	4.6	3.1	1.5	0.2	Iris-setosa
4	5	5.0	3.6	1.4	0.2	Iris-setosa

#create a dataframe with all training data except the target column
X = df.drop(columns=['Species'])
#check that the target variable has been removed
X.head()

	Id	SepalLengthCm	SepalWidthCm	PetalLengthCm	PetalWidthCm
0	1	5.1	3.5	1.4	0.2
1	2	4.9	3.0	1.4	0.2
2	3	4.7	3.2	1.3	0.2
3	4	4.6	3.1	1.5	0.2
4	5	5.0	3.6	1.4	0.2

#separate target values
y = df['Species'].values
#view target values
y[0:5]

array(['Iris-setosa', 'Iris-setosa', 'Iris-setosa', 'Iris-setosa',
       'Iris-setosa'], dtype=object)

#Train and Test split
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=0)

gaussian = GaussianNB()
gaussian.fit(X_train, y_train)
Y_pred = gaussian.predict(X_test) 
accuracy_nb=round(accuracy_score(y_test,Y_pred)* 100, 2)
acc_gaussian = round(gaussian.score(X_train, y_train) * 100, 2)

cm = confusion_matrix(y_test, Y_pred)
accuracy = accuracy_score(y_test,Y_pred)
precision =precision_score(y_test, Y_pred,average='micro')
recall =  recall_score(y_test, Y_pred,average='micro')
f1 = f1_score(y_test,Y_pred,average='micro')
print('Confusion matrix for Naive Bayes\n',cm)
print('accuracy_Naive Bayes: %.3f' %accuracy)
print('precision_Naive Bayes: %.3f' %precision)
print('recall_Naive Bayes: %.3f' %recall)
print('f1-score_Naive Bayes : %.3f' %f1)

Confusion matrix for Naive Bayes
 [[16  0  0]
 [ 0 18  0]
 [ 0  0 11]]
accuracy_Naive Bayes: 1.000
precision_Naive Bayes: 1.000
recall_Naive Bayes: 1.000
f1-score_Naive Bayes : 1.000