This repository contains code for the detection of breast cancer using an artificial neural network. The model is implemented in Python and makes use of the following libraries:
Pandas
Numpy
Matplotlib
Sklearn
TensorFlow
The dataset used for training and testing the model is a CSV file taken from "https://www.kaggle.com/datasets/merishnasuwal/breast-cancer-prediction-dataset" , which contains various attributes related to breast cancer diagnosis. The dataset is pre-processed to obtain relevant information for the model to make predictions.
The model is implemented using TensorFlow and is a feedforward neural network. It consists of multiple dense layers, with each layer having a certain number of neurons. The model is trained using the backpropagation algorithm, where the model's parameters are updated to minimize the loss function.
To run the code, simply clone the repository and run the main.py file. The code will load the dataset, pre-process the data, train the model, and make predictions on the test data. You can visualize the model's performance by plotting the accuracy and loss curves.
The model's accuracy on the test data is quite good and demonstrates the power of artificial neural networks in solving complex problems. This code can be used as a reference for developing more advanced models for breast cancer detection.
Note: The accuracy of the model may vary depending on the dataset used for training. It is recommended to try different neural network architectures and hyperparameters to obtain the best results.