Decision Tree Classification Algorithm in Telugu

#decisiontree #decisiontreeclassifieralgorithm

## import dependencies
from sklearn import tree #For our Decision Tree
import pandas as pd # For our DataFrame
import pydotplus # To create our Decision Tree Graph
from IPython.display import Image # To Display a image of our graph
#Create the dataset
#create empty data frame
golf_df = pd.DataFrame()
#add outlook
golf_df['Outlook'] = ['sunny', 'sunny', 'overcast', 'rainy', 'rainy', 'rainy',
'overcast', 'sunny', 'sunny', 'rainy', 'sunny', 'overcast',
'overcast', 'rainy']
#add temperature
golf_df['Temperature'] = ['hot', 'hot', 'hot', 'mild', 'cool', 'cool', 'cool',
'mild', 'cool', 'mild', 'mild', 'mild', 'hot', 'mild']
#add humidity
golf_df['Humidity'] = ['high', 'high', 'high', 'high', 'normal', 'normal', 'normal',
'high', 'normal', 'normal', 'normal', 'high', 'normal', 'high']
#add windy
golf_df['Windy'] = ['false', 'true', 'false', 'false', 'false', 'true', 'true',
'false', 'false', 'false', 'true', 'true', 'false', 'true']
#finally add play
golf_df['Play'] = ['no', 'no', 'yes', 'yes', 'yes', 'no', 'yes', 'no', 'yes', 'yes', 'yes',
'yes', 'yes', 'no']
#Print/show the new data
print(golf_df)
# Convert categorical variable into dummy/indicator variables or (binary vairbles) essentialy 1's and 0's
# I chose the variable name one_hot_data bescause in ML one-hot is a group of bits among which the legal combinations of values are only those with a single high (1) bit and all the others low (0)

# The decision tree classifier.
clf = tree.DecisionTreeClassifier()
# Training the Decision Tree
clf_train = clf.fit(one_hot_data, golf_df['Play'])
one_hot_data = pd.get_dummies(golf_df[ ['Outlook', 'Temperature', 'Humidity', 'Windy'] ])
#print the new dummy data
one_hot_data

# Export/Print a decision tree in DOT format.
print(tree.export_graphviz(clf_train, None))

#Create Dot Data
dot_data = tree.export_graphviz(clf_train, out_file=None, feature_names=list(one_hot_data.columns.values),
class_names=['Not_Play', 'Play'], rounded=True, filled=True) #Gini decides which attribute/feature should be placed at the root node, which features will act as internal nodes or leaf nodes
#Create Graph from DOT data
graph = pydotplus.graph_from_dot_data(dot_data)
# Show graph
Image(graph.create_png())

Видео Decision Tree Classification Algorithm in Telugu канала CSE & IT Tutorials 4u