Rank features using logistic regression coefficients

You are given a binary classification dataset: - `X`: a 2D array of shape `(n_samples, n_features)` containing numeric features - `y`: a 1D binary array of shape `(n_samples,)` with values in {0,1} - `feature_names`: a list of length `n_features` with the name of each column in `X` ## Task 1. **Normalize** each feature column of `X` using z-score standardization: \[ X'_{:,j} = \frac{X_{:,j} - \mu_j}{\sigma_j} \] where \(\mu_j\) and \(\sigma_j\) are the mean and standard deviation of feature \(j\) computed on the training set. 2. Fit a **logistic regression** model on the normalized features. 3. Rank features by their learned coefficient values (largest to smallest), and return the **top 3 feature names**. ## Output Return a list of 3 strings: the names of the top-3 features. ## Notes - Assume binary logistic regression (one coefficient per feature). - Specify how you handle ties and how you deal with regularization defaults in common libraries.