Create A\\app.py

A////app.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.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+
+# Load the Iris dataset
+iris = load_iris()
+
+# Split the dataset into features (X) and target (y)
+X = iris.data
+y = iris.target
+
+# Split the data 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)
+
+# Create a Logistic Regression model
+model = LogisticRegression(max_iter=200)
+
+# Fit the model to the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the test data
+y_pred = model.predict(X_test)
+
+# Calculate the accuracy of the model
+accuracy = accuracy_score(y_test, y_pred)
+
+print(f'Model Accuracy on Test Data: {accuracy*100:.2f}%')
+
+# Define a function to make predictions based on user input
+def predict_iris(sepal_length, sepal_width, petal_length, petal_width):
+  # Convert input to a numpy array
+  input_data = np.array([sepal_length, sepal_width, petal_length, petal_width])
+
+  # Reshape the input to match the model's input shape
+  input_data = input_data.reshape(1, -1)
+
+  # Make a prediction using the trained model
+  prediction = model.predict(input_data)
+
+  # Get the name of the predicted class
+  prediction_name = iris.target_names[prediction[0]]
+
+  return prediction_name
+
+# User input loop for continuous predictions
+while True:
+  try:
+    # Get user input for flower features
+    sepal_length = float(input("Enter Sepal Length (cm): "))
+    sepal_width = float(input("Enter Sepal Width (cm): "))
+    petal_length = float(input("Enter Petal Length (cm): "))
+    petal_width = float(input("Enter Petal Width (cm): "))
+
+    # Make prediction and display results
+    prediction = predict_iris(sepal_length, sepal_width, petal_length, petal_width)
+    print(f"\nPredicted Iris Class: {prediction}")
+
+  except ValueError:
+    print("\nInvalid input. Please enter numerical values for flower features.")
+
+  # Ask user if they want to continue
+  choice = input("\nDo you want to make another prediction? (y/n): ")
+  if choice.lower() != 'y':
+    break
+
+print("\nExiting the program.")
+