From 356dcfe0927e47dcaf446a6558f80c1c0aa78122 Mon Sep 17 00:00:00 2001
From: Pritimay Sarkar <pritimay@sminnovations.in>
Date: Sun, 24 Sep 2023 13:02:43 +0530
Subject: [PATCH] multiclass

---
 scripts/multiclass.py | 64 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 64 insertions(+)
 create mode 100644 scripts/multiclass.py

diff --git a/scripts/multiclass.py b/scripts/multiclass.py
new file mode 100644
index 0000000..1057821
--- /dev/null
+++ b/scripts/multiclass.py
@@ -0,0 +1,64 @@
+# 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
+  
+# 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)
+  
+# creating a confusion matrix
+cm = confusion_matrix(y_test, dtree_predictions)
+print("DescisionTreeClassifier", cm)
+
+
+# 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  
+accuracy = svm_model_linear.score(X_test, y_test)
+  
+# creating a confusion matrix
+cm = confusion_matrix(y_test, svm_predictions)
+print("linear SVM: ", cm)
+
+
+# training a KNN classifier
+from sklearn.neighbors import KNeighborsClassifier
+knn = KNeighborsClassifier(n_neighbors = 7).fit(X_train, y_train)
+  
+# accuracy on X_test
+accuracy = knn.score(X_test, y_test)
+print(accuracy)
+  
+# creating a confusion matrix
+knn_predictions = knn.predict(X_test) 
+cm = confusion_matrix(y_test, knn_predictions)
+print("KNN:\n", cm)
+
+
+# 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
+accuracy = gnb.score(X_test, y_test)
+print(accuracy)
+  
+# creating a confusion matrix
+cm = confusion_matrix(y_test, gnb_predictions)
+print("NB:\n", cm)