completed 2 clustering parts of unsupervised learning section

python/Unsupervised Learning/Clustering/tests2.py

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+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from sklearn.cluster import KMeans
+from IPython.display import Image
+from sklearn.datasets.samples_generator import make_blobs
+
+
+
+def check_q1(stuff):
+    a = 0
+    b = 60
+    c = 22.9
+    d = 4.53
+    e = 511.7
+
+    q1_dict = {
+    'number of missing values': a,
+    'the mean 5k time in minutes': c,
+    'the mean test score as a raw value': e,
+    'number of individuals in the dataset': b
+    }
+
+    if stuff == q1_dict:
+        print("That looks right!")
+
+    else:
+        print("Oops!  That doesn't look quite right! Try again.")
+
+
+def check_q5(stuff):
+    a = 'We should always use normalizing'
+    b = 'We should always scale our variables between 0 and 1.'
+    c = 'Variable scale will frequently influence your results, so it is important to standardize for all of these algorithms.'
+    d = 'Scaling will not change the results of your output.'
+
+    if stuff == c:
+        return Image(filename="./giphy.gif")
+    else:
+        print("Oops!  That doesn't look quite right.  Try again!")