GitHub - chris1129/DataMining-Project-Clustering: Implemented classification algorithms: K Nearest Neighbor, Decision Tree, Naïve Bayes, Random forest and Boosting Decision Tree, and Adopt 10-fold Cross Validation to evaluate the performance of all Algorithm.

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Implemented classification algorithms: K Nearest Neighbor, Decision Tree, Naïve Bayes, Random forest and Boosting Decision Tree, and Adopt 10-fold Cross Validation to evaluate the performance of all Algorithm.

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
NaiveBayes.py		NaiveBayes.py
Naive_bayes_divider.py		Naive_bayes_divider.py
README.txt		README.txt
boosting.py		boosting.py
decision_tree.py		decision_tree.py
decision_tree_diver.py		decision_tree_diver.py
kNN.py		kNN.py
kfold.py		kfold.py
perform_eval.py		perform_eval.py
project3_dataset1.txt		project3_dataset1.txt
project3_dataset2.txt		project3_dataset2.txt
random_forest.py		random_forest.py

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K Nearest Neighbor:
in KNN.py
1.set the file path on line 93
2.set the K value on line 92
3.run KNN.py

Decision Tree:
1.set the file path on line 30 in kfold.py
in decision_tree_diver.py
2.set the depth for the tree on line 8
3.run decision_tree_diver.py

Naive Bayes:
1.set the file path on line 30 in kfold.py
2.run Naive_bayes_divider.py

Random Forests:
1.set the file path on line 30 in kfold.py
in random_forest.py
2.set tree number on line 101
3.set sample number on line 107
4.set feature number on line 108
5.run random_forest.py

Adaboost Decision Tree:
1.set the file path on line 30 in kfold.py
in boosting.py
2.set sample number on line 79
3.set tree number on line 80
run boosting.py