Implemented Support Vector Machine, Logistic Regression, K-Nearest Neighbors, Decision Tree Classifier and Random Forest Classifier Machine Learning algorithms and developed RMI Scoring System to process the Ovarian Cancer tumor dataset into benign, malignant, and borderline cases and predict the accuracy of the models using Python in Google Collaboratory and Jupyter Notebook.
Implemented various preprocessing methods to clean and optimize the dataset which helped in achieving an accuracy above 85% using NumPy, Pandas, scikit-learn, Multiple Imputation libraries.
Analyzed the performance of the utilized Machine Learning algorithms using Performance Metrics, implementing, and visualizing AUC-ROC curve using Matplotlib, Seaborn, roccurve libraries.