jelendu / Breast-Cancer-Detection-using-TensorFlow

This repository showcases a TensorFlow and Keras-based machine learning model, developed using Pandas for data preprocessing. The project focuses on breast cancer diagnosis, highlighting the Keras API for neural network architecture and TensorFlow for model training and evaluation.

import pandas as pd import tensorflow as tf from sklearn.model_selection import train_test_split

Load the dataset

dataset = pd.read_csv('cancer.csv')

Split the dataset into input (x) and output (y) variables

x = dataset.drop(columns=['diagnosis(1=m, 0=b)']) y = dataset['diagnosis(1=m, 0=b)']

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)

Define the model architecture

model = tf.keras.models.Sequential([ tf.keras.layers.Dense(256, input_shape=(30,), activation='sigmoid'), tf.keras.layers.Dense(256, activation='sigmoid'), tf.keras.layers.Dense(1, activation='sigmoid') ])

Compile the model

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

Train the model

model.fit(x_train, y_train, epochs=1000)

Evaluate the model on the testing data

loss, accuracy = model.evaluate(x_test, y_test) print(f'Test Loss: {loss:.4f}') print(f'Test Accuracy: {accuracy:.4f}')