Overview: This project delves into the in-depth exploration of Support Vector Machine (SVM) classification techniques. The primary objective is to gain a comprehensive understanding of SVM and its application on the provided Loan dataset. The analysis involves utilizing three different types of kernels to classify and compare their performances.
SVM Classification:
Objective: Understand the intricacies of SVM classification and apply it to predict outcomes in the Loan dataset. Explore the impact of three distinct kernel types on the classification performance.
Implementation:
- Utilize Python for SVM implementation, leveraging appropriate libraries.
- Apply three different kernel types (linear, polynomial, and radial basis function) for classification.
- Evaluate and compare the performance of each kernel through metrics and visualizations.