In this R$-$focused exercise, you will apply feature engineering to the Pima Indians Diabetes dataset to enhance the predictive power of a logistic regression model. You are required to carry out at least $3$ of the following steps:

$1.$ Scale Features:

Use the scale$()$ function in R to standardize continuous variables in the dataset. Explain why scaling the features is important for logistic regression.

$2.$ Create Interaction Terms:

Manually create interaction terms between features that you think might have a significant relationship. For example, consider interactions between BMI and Glucose. Explain how these interaction terms might affect the predictive model.

$3.$ Bin Continuous Features:

Bin the Age and BMI features into categorical variables $($e$.$g$.,$ age ranges or BMI categories$).$ Describe how binning these features could impact the model's predictions.

$4.$ Feature Transformation:

Apply appropriate transformations $($such as log or square root$)$ to handle skewed features, particularly Insulin and Skin

Goal:

After performing feature engineering, train a logistic regression model to predict whether a patient has diabetes $($Outcome variable$)$ using the enhanced dataset. Be sure to evaluate the model's performance

Deliverables:

$1.$ R code implementing the above steps

$2.$ A summary of your findings and explanations for the feature engineering techniques you applied

$3.$ Evaluation of the logistic regression model performance