Create 6_Logistic_Regression.py

pages/6_Logistic_Regression.py

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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"}')
+