train and test it

app.py

@@ -1,48 +1,22 @@
 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
+import matplotlib.pyplot as plt
 
-# Load the Iris dataset
-iris = load_iris()
+# Generate some sample binary data
+np.random.seed(0)
+X = np.random.randn(100, 1)
+y = np.where(np.dot(X, np.array([0.5, 0.5])) > 0, 1, 0)
 
-# 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
+# Create logistic regression object and fit the model to the data
 model = LogisticRegression()
+model.fit(X, y)
 
-# 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():
-    while True:
-        sepal_length = float(input("Enter Sepal Length: "))
-        sepal_width = float(input("Enter Sepal Width: "))
-        petal_length = float(input("Enter Petal Length: "))
-        petal_width = float(input("Enter Petal Width: "))
-
-        # Convert input to a numpy array
-        input_data = np.array([sepal_length, sepal_width, petal_length, petal_width]).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]]
+# Predict probabilities
+predictions = model.predict_proba(X)[:,1]
 
-        print(f'Predicted Iris class: {prediction_name}') # Corrected line
+# Plot the results
+plt.scatter(X, y)
+plt.plot(X, predictions, color='red', alpha=0.5)
+plt.xlabel('Feature')
+plt.ylabel('Probability')
+plt.show()