Implement logistic regression in Python. Take the data set as input of your choice.

Logistic Regression using the Iris dataset from sklearn. Logistic Regression is commonly used for binary or multi-class classification problems. The Iris dataset is a good example because it is a multi-class classification problem with three classes of iris flowers.

Steps for Implementation:

1. Load the Dataset: Use the Iris dataset, which contains 150 samples of iris flowers.
2. Preprocess the Data: Split the data into training and testing sets, and standardize the features.
3. Train the Logistic Regression Model: Use LogisticRegression from sklearn.
4. Evaluate the Model: Assess the model's performance using accuracy and confusion matrix.
5. Visualize Results: Optionally, visualize some predictions.

Python Code:

import numpy as np
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