Creating a Decision Tree Classifier with Scikit-learn's toy dataset module's Iris flowers datasets

Author: DevOpsThinh

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,63 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import pydotplus
+
+from sklearn import datasets
+from sklearn.metrics import accuracy_score
+from sklearn.model_selection import train_test_split
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_graphviz
+
+from IPython.display import Image
+from six import StringIO
+
+# Load the iris flowers dataset
+iris_dataset = datasets.load_iris()
+
+df_iris = pd.DataFrame(data=np.c_[iris_dataset['data'], iris_dataset['target']],
+                       columns=iris_dataset['feature_names'] + ['target'])
+# Verify the shape of the dataset
+print('df_iris has ' + str(df_iris.shape[0]) + ' rows and ' + str(df_iris.shape[1]) + ' columns')
+# Inspect the first few rows (5) of the dataframe
+pd.set_option('display.max_columns', None)
+df_iris.head()
+df_iris.info()
+# Histogram of target attribute
+# df_iris['target'].hist(figsize=(7, 4))
+
+# Change the data type of the target column to string
+df_iris[['target']] = df_iris[['target']].astype(np.uint8)
+df_iris['target'] = df_iris['target'].apply(str)
+
+df_iris.isnull().sum()
+# Split the df_iris dataframe into two, one with the features and the other with the target
+df_iris_target = df_iris.loc[:, ['target']]
+df_iris_features = df_iris.drop(['target'], axis=1)
+# Create training &  test sets
+iris_split = train_test_split(df_iris_features, df_iris_target, test_size=0.25, random_state=17, stratify=df_iris_target['target'])
+df_iris_features_train = iris_split[0]
+df_iris_features_test = iris_split[1]
+df_iris_target_train = iris_split[2]
+df_iris_target_test = iris_split[3]
+# Create the Decision Tree Classification Model (DTM) with Scikit-learn
+# https:/scikit-learn.org/stable/modules;generated/sklearn.tree.DecisionTreeClassifier.html
+# Train a DTM
+model = DecisionTreeClassifier(random_state=17)
+model.fit(df_iris_features_train, df_iris_target_train.values.ravel())
+print(model.feature_importances_)
+# Get predictions from model, and compute accuracy
+predictions = model.predict(df_iris_features_test)
+print(predictions)
+accuracy = accuracy_score(df_iris_target_test, predictions)
+print(accuracy)
+# Visualize the Decision Tree
+dot_data = StringIO()
+export_graphviz(model, out_file=dot_data, filled=True, rounded=True,
+                special_characters=True, feature_names=df_iris_features.columns)
+graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
+graph.write_png("iris_flow_dtm.png")
+Image(graph.create_png())
+
+
+

.idea/misc.xml

@@ -5,7 +5,6 @@
 # composition. The model will be trained on the popular UCI ML wine dataset and export the dataset to CSV files.
 
 import numpy as np
-import matplotlib
 import pandas as pd
 
 from sklearn import datasets