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/MLRepository.html
Public Source - https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/cell_samples.cs
----------------------------------------
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)
----------------------------------------
Subscribe: https://www.youtube.com/@cloudandmlonline8949/featured
