Project is about predicting Class Of Beans using Supervised Learning Models

This repository contains code for parameter optimization of Support Vector Machines (SVM) using the Dry Bean Dataset. The code is implemented in Python using scikit-learn library. The goal of this project is to find the best parameters for the SVM model in order to achieve the highest accuracy possible for the Dry Bean Dataset.

This project implements a multiclass classification system to predict dry bean varieties using machine learning techniques. The system classifies beans into 7 different classes: Seker, Barbunya, Bombay, Cali, Dermason, Horoz, and Sira based on their physical characteristics.

Machine learning pipeline on Dry Bean Dataset including preprocessing, feature extraction (PCA & LDA), classification models, and nested cross-validation evaluation.