This project focuses on using Support Vector Machines (SVM) to classify human cell records as benign or malignant, aiding in early detection of potentially cancerous cells. SVM is a powerful supervised learning algorithm that maps data into a high-dimensional space to find an optimal hyperplane separating different classes. Objectives Develop an SVM-based model for cell classification Train the model using human cell sample data Optimize model performance through hyperparameter tuning Evaluate accuracy using key metrics like precision, recall, and confusion matrix
Methodology The dataset used in this project contains labeled human cell samples with various features that help differentiate benign from malignant cases. The workflow involves:
Data Preprocessing – Cleaning and preparing the dataset for training Feature Selection – Identifying key attributes that impact classification Model Training – Implementing an SVM classifier to learn from training data Hyperparameter Tuning – Optimizing kernel functions and regularization parameters Evaluation – Assessing model accuracy using various performance metrics