iris basic import numpy as np import pandas as pd import seaborn as sns sns.set_palette('husl') import matplotlib.pyplot as plt

from sklearn import metrics from sklearn.neighbors import KNeighborsClassifier from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split#to print precision recall etc from sklearn.metrics import classification_report,confusion_matrix

data = pd.read_csv("/content/drive/My Drive/datasets/Iris.csv") data.head()

#to check types of data and check if there exist any null values data.info()

data['Species'].value_counts()

tmp = data.drop('Id', axis=1) g = sns.pairplot(tmp, hue='Species') plt.show()

X = data.drop(['Id', 'Species'], axis=1) y = data['Species'] X_train, X_test, y_train, y_test = train_test_split(X,y, random_state=66)

#knn

from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(n_neighbors=9) knn.fit(X_train, y_train)

print('Accuracy of K-NN classifier on training set:',(knn.score(X_train, y_train))) print('Accuracy of K-NN classifier on test set :',(knn.score(X_test, y_test)))

y_pred=knn.predict(X_test) print(classification_report(y_test,y_pred))

training_accuracy = [] test_accuracy = []

neighbors_settings = range(1, 50)

for n_neighbors in neighbors_settings: # build the model knn = KNeighborsClassifier(n_neighbors=n_neighbors) knn.fit(X_train, y_train) # record training set accuracy training_accuracy.append(knn.score(X_train, y_train)) # record test set accuracy test_accuracy.append(knn.score(X_test, y_test)) plt.plot(neighbors_settings, training_accuracy, label="training accuracy") plt.plot(neighbors_settings, test_accuracy, label="test accuracy") plt.ylabel("Accuracy") plt.xlabel("n_neighbors") plt.legend()

from sklearn.linear_model import LogisticRegression

lr = LogisticRegression().fit(X_train, y_train)

print("Training set accuracy:",(lr.score(X_train, y_train))) print("Test set accuracy: ",(lr.score(X_test, y_test)))

y_pred=lr.predict(X_test) print(classification_report(y_test,y_pred)) ] from sklearn.tree import DecisionTreeClassifier

tree = DecisionTreeClassifier(random_state=0) tree.fit(X_train, y_train) print("Accuracy on training set: ",(tree.score(X_train, y_train))) print("Accuracy on test set: ",(tree.score(X_test, y_test))) prediction = knn.predict([[20, 4.3, 5.5,8]]) y_pred=tree.predict(X_test) print("Accuracy of the model:",metrics.accuracy_score(y_test, y_pred))

y_pred=tree.predict(X_test) print(classification_report(y_test,y_pred))

cm=confusion_matrix(y_test,y_pred) print(cm)

from sklearn.ensemble import RandomForestClassifier rdf=RandomForestClassifier(n_estimators=100) rdf.fit(X_train,y_train) from sklearn import metrics print("Accuracy on training set: ",(rdf.score(X_train, y_train))) print("Accuracy on test set: ",(rdf.score(X_test, y_test))) y_pred=rdf.predict(X_test)

print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

print(classification_report(y_test,y_pred))

SepalLength=input('enter SepalLength in Cm=') SepalWidth=input('enter SepalWidth in Cm=') PetalLength=input('enter PetalLength in Cm=') PetalWidth=input('enter PetalWidthCm in Cm=') answer = rdf.predict([[SepalLength, SepalWidth, PetalLength, PetalWidth]]) print('the flower is =',answer[0]) from sklearn import svm #------------------ Linear Kernel-------------------- model_linear = svm.SVC(kernel='linear') model_linear.fit(X_train, y_train)#Train the model using the training sets y_pred_linear = model_linear.predict(X_test)#Predict the response for test dataset #---------------sigmoid kernel------------- model_sigmoid = svm.SVC(kernel='sigmoid') model_sigmoid.fit(X_train, y_train) y_pred_sigmoid = model_sigmoid.predict(X_test) #-------------poly kernel----------- model_poly = svm.SVC(kernel='poly') model_poly.fit(X_train, y_train) y_pred_poly = model_poly.predict(X_test) #--------------rbf kernel-------------- model_rbf = svm.SVC(kernel='rbf') model_rbf.fit(X_train, y_train) y_pred_rbf = model_rbf.predict(X_test)

#accuracy acc_linear=np.mean(y_pred_linear==y_test) acc_sigmoid=np.mean(y_pred_sigmoid==y_test) acc_poly=np.mean(y_pred_poly==y_test) acc_rbf=np.mean(y_pred_rbf==y_test) print('acc using linear as kernel model is ={:.1%}'.format(acc_linear)) print('acc using sigmoid as kernel model is={:.1%}'.format(acc_sigmoid)) print('acc using poly as kernel model is={:.1%}'.format(acc_poly)) print('acc using rbf as kernel model is={:.1%}'.format(acc_rbf))