Breast cancer is an uncontrolled growth of breast cells and the most common invasive cancer in women, the second leading cause of cancer death in women next to lung cancer. Cancer starts from breast and spreads to other parts of the body. People are unable to identify the disease before it becomes dangerous. It can be cured if the disease is identified at an earlier stage. Awareness of breast cancer, public attentiveness, and advancement in breast imaging has made a positive impact on the identification and screening of breast cancer. The interpretation of a tumor image is taken from patients and stored in datasets. This study suggests a feature extraction method such as PCA (Principal Component Analysis) which is used for pre-processing the data and extracting the most relevant features. Several classifiers like K-Nearest Neighbour (KNN), Naïve Bayes(NB), Linear Support Vector Machine(L-SVM), Gaussian Kernel Support Vector Machine(K-SVM), Logistic Regression(LR) are used to build machine learning model, among these classifiers Linear kernel Support Vector Machine (L-SVM) gives better accuracy. The proposed system uses a Linear kernel Support vector machine(L-SVM) to perform staging. The objective of the project is to carry out dimensionality reduction on cancer datasets and to build a predictive model for multi-class cancer stage classification using a linear kernel SVM classifier

Classification Techniques, Feature extraction, Principal Component Analysis(PCA) k-Nearest Neighbor (KNN), Linear Support Vector Machine (L-SVM), Gaussian Kernel Support Vector Machine(K-SVM) , Naïve Bayes (NB), Decision Tree (DT), Logistic Regression (LR)

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