Create Ml-Classification-Model.py

Ml-Classification-Model.py

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+import numpy as np
+from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import accuracy_score, classification_report
+
+# Load the Iris dataset
+iris = load_iris()
+X = iris.data  # Features (sepal and petal measurements)
+y = iris.target  # Target (species)
+
+# Split the dataset into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Initialize the Decision Tree Classifier
+classifier = DecisionTreeClassifier(random_state=42)
+
+# Train the model
+classifier.fit(X_train, y_train)
+
+# Make predictions
+y_pred = classifier.predict(X_test)
+
+# Evaluate the model
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Accuracy: {accuracy:.2f}")
+
+# Classification report
+print("\nClassification Report:")
+print(classification_report(y_test, y_pred, target_names=iris.target_names))
+
+# Optional: Predict a new sample
+new_sample = np.array([[5.0, 3.5, 1.3, 0.3]])  # Example flower measurements
+prediction = classifier.predict(new_sample)
+print(f"\nPredicted class for new sample: {iris.target_names[prediction[0]]}")