Create Cancer-Identifying-AI.py

Cancer-Identifying-AI.py

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+import numpy as np
+from sklearn.datasets import load_breast_cancer
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import accuracy_score
+import tensorflow as tf
+
+# Load the Breast Cancer dataset
+data = load_breast_cancer()
+X = data.data
+y = data.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)
+
+# Standardize the features
+scaler = StandardScaler()
+X_train = scaler.fit_transform(X_train)
+X_test = scaler.transform(X_test)
+
+# Build the neural network model
+model = tf.keras.Sequential([
+    tf.keras.layers.Dense(30, activation='relu', input_shape=(X_train.shape[1],)),
+    tf.keras.layers.Dense(15, activation='relu'),
+    tf.keras.layers.Dense(1, activation='sigmoid')  # Binary classification
+])
+
+# Compile the model
+model.compile(optimizer='adam', 
+              loss='binary_crossentropy', 
+              metrics=['accuracy'])
+
+# Train the model
+model.fit(X_train, y_train, epochs=50, batch_size=16, verbose=1)
+
+# Evaluate the model
+y_pred = (model.predict(X_test) > 0.5).astype(int).flatten()
+accuracy = accuracy_score(y_test, y_pred)
+
+print(f"Model Accuracy: {accuracy * 100:.2f}%")