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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from sklearn.datasets import load_iris | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import StandardScaler | |
| from sklearn.metrics import accuracy_score, confusion_matrix | |
| from tensorflow.keras.models import Sequential | |
| from tensorflow.keras.layers import Dense | |
| # Load Iris dataset | |
| iris = load_iris() | |
| X = iris.data | |
| y = iris.target | |
| # Only use the first two classes for binary classification | |
| X = X[y != 2] | |
| y = y[y != 2] | |
| # 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) | |
| # Standardize the data | |
| scaler = StandardScaler() | |
| X_train = scaler.fit_transform(X_train) | |
| X_test = scaler.transform(X_test) | |
| # Build the logistic regression model using Keras | |
| model = Sequential() | |
| model.add(Dense(1, input_dim=4, activation='sigmoid')) | |
| model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) | |
| # Train the model | |
| model.fit(X_train, y_train, epochs=100, verbose=0) | |
| # Predict and evaluate the model | |
| y_pred_train = (model.predict(X_train) > 0.5).astype("int32") | |
| y_pred_test = (model.predict(X_test) > 0.5).astype("int32") | |
| train_accuracy = accuracy_score(y_train, y_pred_train) | |
| test_accuracy = accuracy_score(y_test, y_pred_test) | |
| conf_matrix = confusion_matrix(y_test, y_pred_test) | |
| # Streamlit interface | |
| st.title('Logistic Regression with Keras on Iris Dataset') | |
| st.write('## Model Performance') | |
| st.write(f'Training Accuracy: {train_accuracy:.2f}') | |
| st.write(f'Testing Accuracy: {test_accuracy:.2f}') | |
| st.write('## Confusion Matrix') | |
| fig, ax = plt.subplots() | |
| ax.matshow(conf_matrix, cmap=plt.cm.Blues, alpha=0.3) | |
| for i in range(conf_matrix.shape[0]): | |
| for j in range(conf_matrix.shape[1]): | |
| ax.text(x=j, y=i, s=conf_matrix[i, j], va='center', ha='center') | |
| plt.xlabel('Predicted Label') | |
| plt.ylabel('True Label') | |
| st.pyplot(fig) | |
| st.write('## Make a Prediction') | |
| sepal_length = st.number_input('Sepal Length (cm)', min_value=0.0, max_value=10.0, value=5.0) | |
| sepal_width = st.number_input('Sepal Width (cm)', min_value=0.0, max_value=10.0, value=3.5) | |
| petal_length = st.number_input('Petal Length (cm)', min_value=0.0, max_value=10.0, value=1.4) | |
| petal_width = st.number_input('Petal Width (cm)', min_value=0.0, max_value=10.0, value=0.2) | |
| if st.button('Predict'): | |
| input_data = np.array([[sepal_length, sepal_width, petal_length, petal_width]]) | |
| input_data_scaled = scaler.transform(input_data) | |
| prediction = (model.predict(input_data_scaled) > 0.5).astype("int32") | |
| st.write(f'Prediction: {"Setosa" if prediction[0][0] == 0 else "Versicolor"}') | |