This project focuses on applying Support Vector Machine (SVM) classifiers to classify datasets obtained from the UCI Machine Learning Repository. The SVM classifier is utilized to perform classification tasks on three distinct datasets: Iris, Ecoli, and Malware Detection.
1. Iris Dataset
The Iris dataset is a well-known dataset in the field of machine learning. It contains measurements of various iris flowers, with each sample belonging to one of three classes: Setosa, Versicolor, or Virginica. Features include sepal length, sepal width, petal length, and petal width.
2. Ecoli Dataset The Ecoli dataset consists of various attributes of protein localization sites in Escherichia coli bacteria. The dataset includes features such as sequence name, DNA-binding domain, amino acid composition, and more. The classification task involves predicting the localization site of the protein.
3. Malware Detection Dataset The Malware Detection dataset comprises features extracted from API call sequences of Windows executable files. Each sample is labeled as either malware or benign. Features include frequency counts of different API calls and statistical properties of the API call sequences.# UCI_Datasets_Classification SVM Classifier applied on Iris dataset, ecoli dataset and malware detection dataset