Support Vector Machine - SVM - Classification Implementation for Beginners (using python) - Detailed
Steps followed are: ---------------------------------------- 1. Introduction to SVM Used SVM to build and train a model using human cell records, and classify cells to whether the samples are benign (mild state) or malignant (evil state). SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable (This gets done by kernel function of SVM classifier). A separator between the categories is found, then the data is transformed in such a way that the separator could be drawn as a hyperplane. ---------------------------------------- 2. Necessary imports ---------------------------------------- 3. About the Cancer data ---------------------------------------- Original Author - UCI Machine Learning Repository (Asuncion and Newman, 2007)[http://mlearn.ics.uci.edu/MLRepositor...] Public Source - https://s3-api.us-geo.objectstorage.s... ---------------------------------------- 4. Load Data From CSV File The characteristics of the cell samples from each patient are contained in fields Clump to Mit. The values are graded from 1 to 10, with 1 being the closest to benign. The Class field contains the diagnosis, as confirmed by separate medical procedures, as to whether the samples are benign (value = 2) or malignant (value = 4). ---------------------------------------- 5. Distribution of the classes ---------------------------------------- 6. Selection of unwanted columns ---------------------------------------- 7. Remove unwanted columns ---------------------------------------- 8. Divide the data as Train/Test dataset ---------------------------------------- 9. Modeling (SVM with Scikit-learn) ---------------------------------------- 10. Evaluation (Results) ----------------------------------------
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