import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split [] from keras.datasets import fashion_mnist (train_X, train_y), (test_X, test y) = fashion_mnist.load_data() fig = plt.figure(figsize=(10,7)) for i in range (15): ax fig.add_subplot (3, 5, i+1) ax.imshow (train_X[i], cmap= 'viridis') ax.set_title('Label (y): {y}'.format(y=train_y[i])) plt.axis ('off') Label (y): 9 Label (y): 2 Label (y): 0 Label (y): 3 TIXX Label (y): 0 Label (y): 7 Label (y): 2 Label (y): 5 Label (y): 0 Label (y): 5

89. Using OQL, you may do which of the following? a. Return an entire collection of elements including duplicates. b. Return a collection of elements without duplicates. c. Return a specific subset of elements using a given criteria. d. All of the above.

TODO 12 Let's now split our input data X and labels y into a train and test set using the train_test_split() function (docs). Here we'll use the 80-20 split rule where we use 80% of the data for training and 20% for testing. Lastly, we'll seed our split using the random_state keyword argument which will make sure we create the same split every time we run the function. Use the train_test_split() function to get a train and test split. Store the output into X_train, X_test, y_train, and y_test. Pass the required arguments X and y. Further specify we want to use 20% of the training data by setting the test_size keyword argument. Lastly, pass the keyword argument random_state=42 to set the random seed so we get the same split every time we run this code. Print the shape for X_train. Print the shape for y_train. Print the shape for X_test. Print the shape for y_test. # TODO 12.1X_train, X_test, y_train, y_test =  todo_check([    (X_train.shape == (413, 29), 'X_train does not have the…

PEAR:  sort the data according to the employees salary (the last column). Use the Schwartzian  transform method.

PYTHON / DATA ORGANIZATION Import the following reaction mechanism for syngas production from the course repository data/syngas-short-rxn.txt, keep the order of the reactions the same, and display the reactions.The reaction mechanism is given below kindly write code assuming its to be extracted. # Corrected ChatGPT##   Pyrolysis (Destruction of Feedstock):C(s) -> C(g)#   Primary Oxidation (Partial Combustion):2 C(g) + O2 -> 2 CO#   Secondary Oxidation (Complete Combustion):CO + 0.5 O2 -> CO2#   Methanation:C(g) + H2O -> CO + H2#   Shift Methane Reaction (Methane Decomposition):CH4 <=> C(g) + 2 H2

The following statement tells the decomposition of the people survived a.k.a column Survived as of 0 (not survived) or 1 (survived). Create a similar statement using groupby function to further breakdown by Sex. In other words, add additional sex dimension. The desired output should be similar to the following: Sex Survided female 0 81 1 233 male 0 468 1 109Name: Survived, dtype: int64 In [16]: