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images = [img, edge_roberts, edge_prewitt, edge_sobel]
names = ["input", "roberts", "prewitt", "sobel"]

fig = plt.figure(figsize = (8, 8))

for i in range(4):
  fig.add_subplot(2, 2, i + 1)
  plt.title(names[i])
  plt.imshow(images[i], cmap = 'gray')

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# Import necessary modules
from scipy.stats import randint
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import RandomizedSearchCV

# Setup the parameters and distributions to sample from: param_dist
param_dist = {"max_depth": [3, None],
              "max_features": randint(1, 9),
              "min_samples_leaf": randint(1, 9),
              "criterion": ["gini", "entropy"]}

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cols = ['EducationField']
data = pd.get_dummies(data, columns = cols, prefix = cols)

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from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
data[category_cols] = data[category_cols].apply(le.fit_transform)
data.head()

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# gender : Female : 0, Male : 1

data['Gender'] = data['Gender'].map({'Female' : 1, 'Male' : 0})
data.head()

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# lets scale the values.
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
data[numerical_cols] = scaler.fit_transform(data[numerical_cols])