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Co-authored-by: github-actions <${GITHUB_ACTOR}@users.noreply.github.com>

machine_learning/gaussian_naive_bayes.py

@@ -1,10 +1,9 @@
 # Gaussian Naive Bayes Example
-
-from sklearn.naive_bayes import GaussianNB
-from sklearn.metrics import plot_confusion_matrix
+from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
+from sklearn.metrics import plot_confusion_matrix
 from sklearn.model_selection import train_test_split
-import matplotlib.pyplot as plt
+from sklearn.naive_bayes import GaussianNB
 
 
 def main():

machine_learning/k_means_clust.py

@@ -52,11 +52,12 @@ Usage:
 
 
 """
+import warnings
+
 import numpy as np
 import pandas as pd
 from matplotlib import pyplot as plt
 from sklearn.metrics import pairwise_distances
-import warnings
 
 warnings.filterwarnings("ignore")
 
@@ -193,7 +194,7 @@ def kmeans(
 
 # Mock test below
 if False:  # change to true to run this test case.
-    import sklearn.datasets as ds
+    from sklearn import datasets as ds
 
     dataset = ds.load_iris()
     k = 3

machine_learning/k_nearest_neighbours.py

@@ -1,5 +1,6 @@
-import numpy as np
 from collections import Counter
+
+import numpy as np
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
 

machine_learning/knn_sklearn.py

@@ -1,5 +1,5 @@
-from sklearn.model_selection import train_test_split
 from sklearn.datasets import load_iris
+from sklearn.model_selection import train_test_split
 from sklearn.neighbors import KNeighborsClassifier
 
 # Load iris file

machine_learning/linear_discriminant_analysis.py

@@ -41,11 +41,9 @@
 
     Author: @EverLookNeverSee
 """
-
 from math import log
 from os import name, system
-from random import gauss
-from random import seed
+from random import gauss, seed
 
 
 # Make a training dataset drawn from a gaussian distribution

machine_learning/linear_regression.py

@@ -7,8 +7,8 @@ We try to set the weight of these features, over many iterations, so that they b
 fit our dataset. In this particular code, I had used a CSGO dataset (ADR vs
 Rating). We try to best fit a line through dataset and estimate the parameters.
 """
-import requests
 import numpy as np
+import requests
 
 
 def collect_dataset():

machine_learning/logistic_regression.py

@@ -14,14 +14,12 @@ Helpful resources:
 Coursera ML course
 https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
 """
-
 import numpy as np
-import matplotlib.pyplot as plt
+from matplotlib import pyplot as plt
+from sklearn import datasets
 
 # get_ipython().run_line_magic('matplotlib', 'inline')
 
-from sklearn import datasets
-
 
 # In[67]:
 

machine_learning/lstm/lstm_prediction.py

@@ -4,14 +4,12 @@
     * http://colah.github.io/posts/2015-08-Understanding-LSTMs
     * https://en.wikipedia.org/wiki/Long_short-term_memory
 """
-
-from keras.layers import Dense, LSTM
-from keras.models import Sequential
 import numpy as np
 import pandas as pd
+from keras.layers import LSTM, Dense
+from keras.models import Sequential
 from sklearn.preprocessing import MinMaxScaler
 
-
 if __name__ == "__main__":
     """
     First part of building a model is to get the data and prepare

machine_learning/multilayer_perceptron_classifier.py

@@ -1,6 +1,5 @@
 from sklearn.neural_network import MLPClassifier
 
-
 X = [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
 y = [0, 1, 0, 0]
 

machine_learning/polymonial_regression.py

@@ -1,5 +1,5 @@
-import matplotlib.pyplot as plt
 import pandas as pd
+from matplotlib import pyplot as plt
 from sklearn.linear_model import LinearRegression
 
 # Splitting the dataset into the Training set and Test set

machine_learning/random_forest_classifier.py

@@ -1,10 +1,9 @@
 # Random Forest Classifier Example
-
+from matplotlib import pyplot as plt
 from sklearn.datasets import load_iris
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import plot_confusion_matrix
-import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
 
 
 def main():

machine_learning/random_forest_regressor.py

@@ -1,10 +1,8 @@
 # Random Forest Regressor Example
-
 from sklearn.datasets import load_boston
-from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
-from sklearn.metrics import mean_absolute_error
-from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error, mean_squared_error
+from sklearn.model_selection import train_test_split
 
 
 def main():

machine_learning/sequential_minimum_optimization.py

@@ -36,9 +36,9 @@ import os
 import sys
 import urllib.request
 
-import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
+from matplotlib import pyplot as plt
 from sklearn.datasets import make_blobs, make_circles
 from sklearn.preprocessing import StandardScaler
 

machine_learning/support_vector_machines.py

@@ -1,5 +1,5 @@
-from sklearn.datasets import load_iris
 from sklearn import svm
+from sklearn.datasets import load_iris
 from sklearn.model_selection import train_test_split