From eddd4dbc908abc2b0ced7697b03c7bf1690fae1a Mon Sep 17 00:00:00 2001
From: amritsingh047 <68320490+amritsingh047@users.noreply.github.com>
Date: Fri, 31 Oct 2025 01:17:34 +0530
Subject: [PATCH] Add iris flower classification script using SVM

This script classifies iris flowers into different species using support vector machines based on petal and sepal measurements. It includes data loading, visualization, model training, and accuracy evaluation.
---
 iris flowers | 23 +++++++++++++++++++++++
 1 file changed, 23 insertions(+)
 create mode 100644 iris flowers

diff --git a/iris flowers b/iris flowers
new file mode 100644
index 0000000..2b4ffb0
--- /dev/null
+++ b/iris flowers	
@@ -0,0 +1,23 @@
+​#Classify iris flowers into different species based on their petal and sepal measurements using
+#support vector machines technique.
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score
+iris = sns.load_dataset('iris')
+print(iris.head())
+print(iris.describe())
+sns.pairplot(iris, hue='species')
+plt.suptitle('Pairplot of Iris Dataset', y=1.02)
+plt.show()
+x= iris.drop(columns='species')
+y=
+iris['species']
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size =0.3, random_state =42)
+model = SVC(kernel='linear') # You can experiment with 'rbf', 'poly', 'sigmoid' as well
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+accuracy = accuracy_score(y_test, y_pred)
+print(f'Accuracy: {accuracy * 100:.2f}%')