Welcome to the Iris Dataset Analysis Repository! This repository contains code and documentation for analyzing the Iris dataset using machine learning techniques. The primary focus is on data visualization, training Support Vector Machine (SVM) models, and building a neural network for classification tasks.
The Iris dataset is a classic dataset in the field of machine learning. It consists of 150 samples of iris flowers, with each sample containing measurements of sepal and petal length and width, as well as the species of the iris. There are three species in the dataset: Iris-setosa, Iris-versicolor, and Iris-virginica.
- Data visualization using t-SNE.
- Training Support Vector Machine (SVM) models.
- Building a neural network for classification tasks.
Clone the repository:
git clone https://github.com/yourusername/iris-dataset-analysis.git
Load the raw data and visualize it using t-SNE. Train SVM models for classification tasks. Build a neural network and evaluate its performance.
SVM models accuracy: 96% (linear), 100% (non-linear). Neural network accuracy: 97.77%.
This project is licensed under the MIT License.