Hello, below is the assignment details. This is written in Python. Thank You.

Description: Linear regression models are powerful but have limited performance on classification tasks. Logistic regression is on the other hand that works very well for binary classification tasks. For this assignment, you are going to use the scikit-learn breast cancer wisconsin dataset to build a logistic regression model for breast cancer diagnosis. Purpose: - Build logistic regression models for a given task. - Apply more evaluation metrics. Directions: For this assignment, you need to build a logistic regression model. Below is a detailed instruction of what you may need to do. - Dataset Preparation - You need to load the dataset using sklearn.datasets.load_breast_cancer: - After loading the dataset, randomly shuffle the dataset to split the dataset to train/dev/test sets. - Use the 70% of data for the train set, 15% for the dev set, and 15% for the test set - Logistic Regression Development - You could use the scikit-learn build-in functions to fit the multivariate model. - (Optional) You could develop the multivariate regression model from scratch, and using gradient descent to solve the parameters. - Bonus points will be given if you build your model from scratch. - Test the Model - Test the model using the test set. - Submission - You need to submit a written report for this assignment. - For this report, you need to: - Explain what you have done - Report the best performance on the test set (in terms of Accuracy and AUC score) - Include your code as an appendix - You could save your Colab code as a PDF file and attach it to your report, or you could copy and paste your code into the report