In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.
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In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.
bhavya840/ML-Classification
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In this project the data is been used from UCI Machinery Repository. Main aim of this project is to predict telling tumor of each patient is Benign (class – 2) or Malignant (class – 4) the models used are – Decision tree Classification, Logistic Regression, K-Nearest Neighbors, SVM, Kernel SVM, Naïve-Bayes and Random Forest Classification.
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