Technology: Python (along with pandas, matplotlib, sklearn libraries).
• Applied SVM, K-Nearest Neighbors, Logistic Regression, Naïve Bayes and Random Forest algorithms to the Wisconsin Breast Cancer dataset from the UCI ML Repository (Kaggle)
• To predict whether the breast cancer tumor is malignant or benign.
• Compared the performance results of all the algorithms based on the accuracy and ROC values.
Dataset:
https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
Implemented SVM, K-Nearest Neighbors, Logistic Regression and Naïve Bayes as well as Random Forest algorithm for classification and showed why it has proved better than the other algorithms.
Used Wisconsin Breast Cancer (Diagnostic) dataset from the UCI Machine Learning Repository (Kaggle) and Python scripting language for this project. Before running the algorithms on the dataset, data preprocessing has also been done on it.
In our results we have compared the different algorithms based on the accuracy and ROC values and showed that random forest classifier is the best among all in determining benign and malignant tumors.
Steps to execute the program :
- Open any Python IDE and run the code.
- The graph and the results will be shown in the console.