Please read the goal and data sets attached "pima-indian-diabetes" and write Python code for the question.

Goal: In this question, you will implement Logistic Regression, Regularized (L2) Logistic Regression. The goal of this question is to give you experience in implementation of Logistic Regression and analyze the hyperparameter tuning in case of Regularized Logistic Regression.

Data sets: The dataset that you use in this project is pima-indians-diabetes. Please find the attachments.

This dataset describes the medical records for Pima Indians and whether or not each patient will have an onset of diabetes within year.

Fields description follow:

preg = Number of times pregnant plas = Plasma glucose concentration a 2 hours in an oral glucose tolerance test pres = Diastolic blood pressure (mm Hg) skin = Triceps skin fold thickness (mm) test = 2-Hour serum insulin (mu U/ml) mass = Body mass index (weight in kg/(height in m)^2) pedi = Diabetes pedigree function age = Age (years) class = Class variable (1:tested positive for diabetes, 0: tested negative for diabetes)

Your Task:

Question: Using attached data sets "pima-indian-diabetes". Implement Regularized Logistic Regression. For hyperparameter tuning, divide the data into training, validation and test set by 70%, 15% and 15% respectively. Find the best hyperparameter on validation set and test on test set.

2 4 5 10 11 12 13 14