Cancer Diagnosis Prediction

This project aims to predict cancer diagnosis (malignant or benign) using machine learning techniques. The dataset used in this project is cancer.csv.

Dataset

The dataset cancer.csv contains information regarding various features related to tumors and their diagnosis. The target variable is diagnosis, where 1 represents malignant tumors and 0 represents benign tumors.

Setup

To run this project, ensure you have Python installed along with the following libraries:

• pandas
• scikit-learn
• TensorFlow

You can install these dependencies using pip: pip install pandas scikit-learn tensorflow

Usage

1. Clone this repository to your local machine.
2. Navigate to the project directory.
3. Run the Python script to execute the model.

python cancer_prediction.py

Implementation Details

• The dataset is loaded using pandas.
• Features (x) and target (y) variables are separated.
• The dataset is split into training and testing sets using train_test_split from scikit-learn.
• A Sequential model is created using TensorFlow's Keras API.
• The model architecture consists of Dense layers with sigmoid activation.
• The model is compiled with Adam optimizer and binary crossentropy loss.
• Training is performed for 2000 epochs.

Contributors

Gonçalo Alves

License

This project is licensed under the MIT License.