Logistic Regression for bones fracture prediction We consider the dataset "Fracture.xlsx" containing information on some patients and their bone fracture states; fractured and unfractured. Some information on each patient (indicated by his id) is provided: age, sex, weight, height, and bmd (bone mineral density). We want to find the best logistic regression model that fits the dataset and predict the bone state based on the following features: age, sex, bmd, and bmi (body mass index). The value of the bmi feature can be computed as follows: bmi=height2weight Where weight must be in kg and height in meters. Requirement for the assignment: Write a python code to implement the following tasks: 1. Read the data from the provided excel sheet of the dataset 2. Use data visualization techniques to explain the relationship between features 3. Find the logistic regression model that fits the data and predicts the bone fracture (all the steps required to build the model must be detailed). Use the logistic function regression from sklearn library. https://scikit-leam.org/stable/modules/generated/sklearn. linear model.Logistic Regression.html 4. Use cross-validation while building the model 5. Apply the selected model to test data and compute the accuracy, precision, recall, and F1 score. Comment on the obtained results