The code implements logistic regression for binary classification using the breast cancer dataset. It starts by importing necessary libraries and reading the dataset from a CSV file. The dataset is preprocessed by dropping irrelevant columns, converting the target variable to binary values, and normalizing the feature values. The data is then split into training and testing sets. The logistic regression algorithm is implemented using gradient descent optimization. The weights and bias are initialized, and forward and backward propagation steps are performed iteratively to update the parameters. The code also includes a prediction function and evaluates the accuracy of the model on the test set. Additionally, a comparison is made with the logistic regression implementation from the scikit-learn library.