Cancer-Identifying-AI.py

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}%")