Regression Model Development on Mixed Data

I'm seeking a proficient machine learning expert to assist with a regression task.
Tasks:

1. Apply data preprocessing

2. Train Logistic Regression model on the train set

3. Test the trained model on the test set

4. Evaluate the performance on the test set using the following metrics:

a. Accuracy

b. Confusion Matrix

c. Precision

d. Recall

e. F1 Score

5. Plot the following learning curves:

a. Accuracy (y-axis) vs Solver (x-axis)

a. Accuracy (y-axis) vs Max_iter (x-axis)

Submission type: Notebook file with output (Name must be the group name)

Dataset: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/4VGTDRLinks to an external site.

Two models for two targets- Injury Type and Patient Status
Note:
Solver = {'lbfgs', 'liblinear', 'newton_cg', 'newton-cholesky', 'sag', 'saga'}
Max_iter = {50, 100, 150, 200, 250, 300}