Iris Dataset Classification with SVM

This Jupyter Notebook classifies the Iris dataset using a Support Vector Machine (SVM) model. It covers data loading, preprocessing, model training, and evaluation.

Requirements

• Python 3.x
• Jupyter Notebook
• Required Libraries:
  • pandas
  • numpy
  • scikit-learn
  • matplotlib
• The Iris dataset is sourced from sklearn.datasets. It includes three species of iris flowers with 50 samples each and measurements for sepal length, sepal width, petal length, and petal width.

Model Used

Support Vector Machine (SVM)

Different configurations for parameters like gamma and kernel types (linear, polynomial) are tested to determine the best setup.

Structure

Data Loading - Loads the Iris dataset using sklearn.datasets.load_iris. Exploratory Data Analysis - Provides a brief overview and visualization of the dataset. Model Training - Trains and evaluates the SVM model with various parameters. Evaluation - Evaluates model performance with accuracy scores on test data.

Usage

1. Open the Jupyter Notebook and execute each cell in sequence.
2. Adjust SVM parameters as desired to observe different results.

Results

The notebook displays model performance based on accuracy scores, providing insights into the impact of parameter variations on classification accuracy.