Identifying Iris Plants Using Machine Learning

1. TensorFlow provides the training data you need in the TensorFlow Datasets API. Using the Datasets API, download the training data and split it into a NumPy array of features and a NumPy array of labels.

2. TensorFlow also provides the testing data, but it is not available through the API. Download this test data, load it, and split it into a NumPy array of features and a NumPy array of labels.

3. Pandas provides a convenient way to visualize the data (the next objective), so combine the features and labels in a Pandas DataFrame for the training data, and a DataFrame for the testing data.

All of these steps will help you understand the data you are working with more fully:

1. Print counts of the number of samples in the training and test datasets.

2. View common statistics about the features in each dataset.

3. View the raw data from 15 examples.

4. Plot each feature against all other features to see if there are any natural groupings.

5. Plot the data to show how strongly each feature separates the data.

Create your model and make predictions!

1. Create and compile a Keras model for classifying the Iris data. Include accuracy as a model metric for easy evaluation.

2. Fit the Keras model to the training data.

3. Evaluate the model's accuracy on the test data set.

4. Show the model's predictions for the test data.