Training Classifiers

Training_Data/Classifiers/SciKit.py

-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-
-data = pd.read_csv('iris.csv')
-
-print(data)
-x= data.iloc[:,:-1].values
-y=data.iloc[:,-1].values
-
-print(x)
-print(y)
-
-from sklearn.preprocessing import LabelEncoder 
-ly = LabelEncoder()
-y = ly.fit_transform(y)
-
-
-sns.set()
-sns.pairplot(data[['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']],
-             hue="species", diag_kind="kde")
-
-
-
-from sklearn.model_selection import train_test_split
-x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2)
-
-
-from sklearn.naive_bayes import GaussianNB
-gnb = GaussianNB()
-gnb.fit(x_train,y_train)
-y_pred_test = gnb.predict(x_test)
-from sklearn.metrics import accuracy_score
-acc = accuracy_score(y_test,y_pred_test)
-
-from sklearn.linear_model import LogisticRegression
-logreg = LogisticRegression(solver = 'lbfgs',multi_class='auto')
-logreg.fit(x_train,y_train)
-y_pred = logreg.predict(x_test)
-from sklearn.metrics import accuracy_score
-acc1 = accuracy_score(y_test,y_pred)
-
-
-from sklearn.tree import DecisionTreeClassifier
-dt = DecisionTreeClassifier()
-dt.fit(x_train,y_train)
-y_pred2 = dt.predict(x_test)
-acc2 = accuracy_score(y_test,y_pred2)
-
-
-from sklearn.neighbors import KNeighborsClassifier
-clf = KNeighborsClassifier(n_neighbors=3,algorithm='ball_tree')
-clf.fit(x_train,y_train)
-y_pred3 = clf.predict(x_test)
-acc3 =   accuracy_score(y_test,y_pred3)
-
-
-from sklearn.svm import SVC
-svc1 = SVC(C=50,kernel='rbf',gamma=1)     
-svc1.fit(x_train,y_train)
-y_pred4 = svc1.predict(x_test)
-from sklearn.metrics import accuracy_score
-acc4=    accuracy_score(y_test,y_pred4)
-
-
-
-# print accuracy 
-print("Accuracy scores")
-print("Decision Tree Classifier: " ,acc2)
-print("KNN Classifier: " ,acc)
-print("SVM Classifier: " ,acc4)
-print("Naive Bayes Classifier: " , acc)
-print("Logistic Regression: " ,acc1)

Training_Data/Classifiers/SciKit2.py

-# importing necessary libraries 
-from sklearn import datasets 
-from sklearn.metrics import confusion_matrix 
-from sklearn.model_selection import train_test_split 
-
-# loading the iris dataset 
-iris = datasets.load_iris() 
-
-# X -> features, y -> label 
-X = iris.data 
-y = iris.target 
-
-print(X , y)
-
-# dividing X, y into train and test data 
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 0) 
-
-# training a DescisionTreeClassifier 
-from sklearn.tree import DecisionTreeClassifier 
-dtree_model = DecisionTreeClassifier(max_depth = 2).fit(X_train, y_train) 
-dtree_predictions = dtree_model.predict(X_test) 
-
-#accuracy
-DecisionAccuracy= dtree_model.score(X_test, y_test)
-
-# creating a confusion matrix 
-cm = confusion_matrix(y_test, dtree_predictions) 
-
-
-# training a linear SVM classifier 
-from sklearn.svm import SVC 
-svm_model_linear = SVC(kernel = 'linear', C = 1).fit(X_train, y_train) 
-svm_predictions = svm_model_linear.predict(X_test) 
-  
-# model accuracy for X_test   
-SVMaccuracy = svm_model_linear.score(X_test, y_test) 
-  
-# creating a confusion matrix 
-cm = confusion_matrix(y_test, svm_predictions) 
-
-
-# training a KNN classifier 
-from sklearn.neighbors import KNeighborsClassifier 
-knn = KNeighborsClassifier(n_neighbors = 7).fit(X_train, y_train) 
-  
-# accuracy on X_test 
-KNNaccuracy = knn.score(X_test, y_test) 
-  
-# creating a confusion matrix 
-knn_predictions = knn.predict(X_test)  
-cm = confusion_matrix(y_test, knn_predictions) 
-
-
-# training a Naive Bayes classifier 
-from sklearn.naive_bayes import GaussianNB 
-gnb = GaussianNB().fit(X_train, y_train) 
-gnb_predictions = gnb.predict(X_test) 
-  
-# accuracy on X_test 
-Bayesaccuracy = gnb.score(X_test, y_test) 
-  
-# creating a confusion matrix 
-cm = confusion_matrix(y_test, gnb_predictions)
-
-
-print("Accuracy scores")
-print("Decision Tree Classifier: " , DecisionAccuracy)
-print("KNN Classifier: " , KNNaccuracy)
-print("SVM Classifier: " , SVMaccuracy)
-print("Naive Bayes Classifier: " , Bayesaccuracy)
-         

Training_Data/Classifiers/iris.csv

-sepal_length,sepal_width,petal_length,petal_width,species
-5.1,3.5,1.4,0.2,setosa
-4.9,3,1.4,0.2,setosa
-4.7,3.2,1.3,0.2,setosa
-4.6,3.1,1.5,0.2,setosa
-5,3.6,1.4,0.2,setosa
-5.4,3.9,1.7,0.4,setosa
-4.6,3.4,1.4,0.3,setosa
-5,3.4,1.5,0.2,setosa
-4.4,2.9,1.4,0.2,setosa
-4.9,3.1,1.5,0.1,setosa
-5.4,3.7,1.5,0.2,setosa
-4.8,3.4,1.6,0.2,setosa
-4.8,3,1.4,0.1,setosa
-4.3,3,1.1,0.1,setosa
-5.8,4,1.2,0.2,setosa
-5.7,4.4,1.5,0.4,setosa
-5.4,3.9,1.3,0.4,setosa
-5.1,3.5,1.4,0.3,setosa
-5.7,3.8,1.7,0.3,setosa
-5.1,3.8,1.5,0.3,setosa
-5.4,3.4,1.7,0.2,setosa
-5.1,3.7,1.5,0.4,setosa
-4.6,3.6,1,0.2,setosa
-5.1,3.3,1.7,0.5,setosa
-4.8,3.4,1.9,0.2,setosa
-5,3,1.6,0.2,setosa
-5,3.4,1.6,0.4,setosa
-5.2,3.5,1.5,0.2,setosa
-5.2,3.4,1.4,0.2,setosa
-4.7,3.2,1.6,0.2,setosa
-4.8,3.1,1.6,0.2,setosa
-5.4,3.4,1.5,0.4,setosa
-5.2,4.1,1.5,0.1,setosa
-5.5,4.2,1.4,0.2,setosa
-4.9,3.1,1.5,0.1,setosa
-5,3.2,1.2,0.2,setosa
-5.5,3.5,1.3,0.2,setosa
-4.9,3.1,1.5,0.1,setosa
-4.4,3,1.3,0.2,setosa
-5.1,3.4,1.5,0.2,setosa
-5,3.5,1.3,0.3,setosa
-4.5,2.3,1.3,0.3,setosa
-4.4,3.2,1.3,0.2,setosa
-5,3.5,1.6,0.6,setosa
-5.1,3.8,1.9,0.4,setosa
-4.8,3,1.4,0.3,setosa
-5.1,3.8,1.6,0.2,setosa
-4.6,3.2,1.4,0.2,setosa
-5.3,3.7,1.5,0.2,setosa
-5,3.3,1.4,0.2,setosa
-7,3.2,4.7,1.4,versicolor
-6.4,3.2,4.5,1.5,versicolor
-6.9,3.1,4.9,1.5,versicolor
-5.5,2.3,4,1.3,versicolor
-6.5,2.8,4.6,1.5,versicolor
-5.7,2.8,4.5,1.3,versicolor
-6.3,3.3,4.7,1.6,versicolor
-4.9,2.4,3.3,1,versicolor
-6.6,2.9,4.6,1.3,versicolor
-5.2,2.7,3.9,1.4,versicolor
-5,2,3.5,1,versicolor
-5.9,3,4.2,1.5,versicolor
-6,2.2,4,1,versicolor
-6.1,2.9,4.7,1.4,versicolor
-5.6,2.9,3.6,1.3,versicolor
-6.7,3.1,4.4,1.4,versicolor
-5.6,3,4.5,1.5,versicolor
-5.8,2.7,4.1,1,versicolor
-6.2,2.2,4.5,1.5,versicolor
-5.6,2.5,3.9,1.1,versicolor
-5.9,3.2,4.8,1.8,versicolor
-6.1,2.8,4,1.3,versicolor
-6.3,2.5,4.9,1.5,versicolor
-6.1,2.8,4.7,1.2,versicolor
-6.4,2.9,4.3,1.3,versicolor
-6.6,3,4.4,1.4,versicolor
-6.8,2.8,4.8,1.4,versicolor
-6.7,3,5,1.7,versicolor
-6,2.9,4.5,1.5,versicolor
-5.7,2.6,3.5,1,versicolor
-5.5,2.4,3.8,1.1,versicolor
-5.5,2.4,3.7,1,versicolor
-5.8,2.7,3.9,1.2,versicolor
-6,2.7,5.1,1.6,versicolor
-5.4,3,4.5,1.5,versicolor
-6,3.4,4.5,1.6,versicolor
-6.7,3.1,4.7,1.5,versicolor
-6.3,2.3,4.4,1.3,versicolor
-5.6,3,4.1,1.3,versicolor
-5.5,2.5,4,1.3,versicolor
-5.5,2.6,4.4,1.2,versicolor
-6.1,3,4.6,1.4,versicolor
-5.8,2.6,4,1.2,versicolor
-5,2.3,3.3,1,versicolor
-5.6,2.7,4.2,1.3,versicolor
-5.7,3,4.2,1.2,versicolor
-5.7,2.9,4.2,1.3,versicolor
-6.2,2.9,4.3,1.3,versicolor
-5.1,2.5,3,1.1,versicolor
-5.7,2.8,4.1,1.3,versicolor
-6.3,3.3,6,2.5,virginica
-5.8,2.7,5.1,1.9,virginica
-7.1,3,5.9,2.1,virginica
-6.3,2.9,5.6,1.8,virginica
-6.5,3,5.8,2.2,virginica
-7.6,3,6.6,2.1,virginica
-4.9,2.5,4.5,1.7,virginica
-7.3,2.9,6.3,1.8,virginica
-6.7,2.5,5.8,1.8,virginica
-7.2,3.6,6.1,2.5,virginica
-6.5,3.2,5.1,2,virginica
-6.4,2.7,5.3,1.9,virginica
-6.8,3,5.5,2.1,virginica
-5.7,2.5,5,2,virginica
-5.8,2.8,5.1,2.4,virginica
-6.4,3.2,5.3,2.3,virginica
-6.5,3,5.5,1.8,virginica
-7.7,3.8,6.7,2.2,virginica
-7.7,2.6,6.9,2.3,virginica
-6,2.2,5,1.5,virginica
-6.9,3.2,5.7,2.3,virginica
-5.6,2.8,4.9,2,virginica
-7.7,2.8,6.7,2,virginica
-6.3,2.7,4.9,1.8,virginica
-6.7,3.3,5.7,2.1,virginica
-7.2,3.2,6,1.8,virginica
-6.2,2.8,4.8,1.8,virginica
-6.1,3,4.9,1.8,virginica
-6.4,2.8,5.6,2.1,virginica
-7.2,3,5.8,1.6,virginica
-7.4,2.8,6.1,1.9,virginica
-7.9,3.8,6.4,2,virginica
-6.4,2.8,5.6,2.2,virginica
-6.3,2.8,5.1,1.5,virginica
-6.1,2.6,5.6,1.4,virginica
-7.7,3,6.1,2.3,virginica
-6.3,3.4,5.6,2.4,virginica
-6.4,3.1,5.5,1.8,virginica
-6,3,4.8,1.8,virginica
-6.9,3.1,5.4,2.1,virginica
-6.7,3.1,5.6,2.4,virginica
-6.9,3.1,5.1,2.3,virginica
-5.8,2.7,5.1,1.9,virginica
-6.8,3.2,5.9,2.3,virginica
-6.7,3.3,5.7,2.5,virginica
-6.7,3,5.2,2.3,virginica
-6.3,2.5,5,1.9,virginica
-6.5,3,5.2,2,virginica
-6.2,3.4,5.4,2.3,virginica
-5.9,3,5.1,1.8,virginica