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alt_dataset #1

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s474137 wants to merge 7 commits from alt_dataset into tree_increase
pull from: alt_dataset
merge into: s474137:tree_increase
Conversation 0 Commits 7 Files Changed 8 +606 -304
8 changed files with 606 additions and 304 deletions
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3
.gitignore vendored
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@ -59,3 +59,6 @@ Pipfile
Pipfile.lock
decision_tree
decision_tree.pdf
Source.gv.pdf
Source.gv
decision_tree.txt

31
TreeConcept.py
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@ -5,35 +5,34 @@ import graphviz
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz/bin/'
# Define the training dataset with 8 attributes and corresponding labels
#train_data_m = pd.read_csv("dataset.csv") #importing the dataset from the disk
train_data_m=np.genfromtxt("dataset.csv", delimiter=",",skip_header=1);
#print(train_data_m)
# print(train_data_m) #viewing some row of the dataset
#importing the dataset from the disk
train_data_m=np.genfromtxt("dataset/converted_dataset.csv", delimiter=",",skip_header=1);
# Separate the attributes and labels
#X_train = [data[:-1] for data in training_data]
#y_train = [data[-1] for data in training_data]
X_train = [data[:-1] for data in train_data_m]
y_train = [data[-1] for data in train_data_m]
#X_train = pd.get_dummies(data[:-1] for data in train_data_m)
#print(X_train)
#print(y_train)
# Create the decision tree classifier using the ID3 algorithm
clf = tree.DecisionTreeClassifier(criterion='entropy')
#clf = tree.DecisionTreeClassifier(criterion='gini')
# Train the decision tree on the training data
clf.fit(X_train, y_train)
# Visualize the trained decision tree
dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Attr1', 'Attr2', 'Attr3', 'Attr4', 'Attr5', 'Attr6', 'Attr7'], class_names=['YES', 'NO'], filled=True)
tree_text = tree.export_text(clf,feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'])
with open('decision_tree.txt', 'w') as f:
f.write(tree_text) # Save the visualization as a text file
dot_data = tree.export_graphviz(clf, out_file=None, feature_names=['Battery Charge', 'Fullness', 'Ready orders', 'Waiting tables','Availability', 'Cleanliness', 'Error'], class_names=['NO', 'YES'], filled=True,rounded=True)
graph = graphviz.Source(dot_data)
graph.render("decision_tree") # Save the visualization as a PDF file
# Test the decision tree with a new example
#new_example = [1, 0, 0, 1, 1, 0, 0, 1] # Example with 8 attributes
new_example = [2, 0, 0, 1, 1 ,2, 1] # Example with 8 attributes
#Battery Charge,Fullness,Ready orders,Waiting tables,Availability,Cleanliness,Error
new_example = [2, 0, 1, 1, 1 ,2, 0]
predicted_label = clf.predict([new_example])
print("Predicted Label:", predicted_label[0])
if predicted_label[0]>0:
result="YES"
else:
result="NO"
print("Predicted Label:", result)

288
dataset.csv
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@ -1,288 +0,0 @@
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