Steps followed are:
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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.
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2. Necessary imports
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3. About the Cancer data
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Original Author - UCI Machine Learning Repository (Asuncion and Newman, 2007)[mlearn.ics.uci....]
Public Source - s3-api.us-geo....
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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).
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5. Distribution of the classes
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6. Selection of unwanted columns
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7. Remove unwanted columns
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8. Divide the data as Train/Test dataset
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9. Modeling (SVM with Scikit-learn)
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10. Evaluation (Results)
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24 сен 2024
