Overview

The Iris dataset is a classic in machine learning, used to classify species of iris flowers based on measurements of their petals and sepals. In this challenge, your goal is to build a model that can accurately classify an iris flower into one of three species: Setosa, Versicolor, or Virginica.

You'll work with a small but clean and well-structured dataset, making it ideal for beginners to practice data analysis, visualization, and classification.

Practice Skills

In this challenge, you will gain hands-on experience with:

• Python
• Classification with:
  • Logistic regression
  • Decision trees
  • SVMs

Evaluation

Goal

Train a multi-class classification model that predicts the species of an iris flower.

Metric

Models will be evaluated based on the Accuracy metric.

• Accuracy = Correct Predictions / Total Predictions

Submission Format

Submission guide

The submission file has two fields:

• index: The unique identifier corresponding to each sample.
• species: The predicted label for the corresponding iris flower.

index	species
123	virginica
124	setosa

The challenge includes two types of submissions:

Public Submission

• Use the public testing dataset (provided in the Data section) to predict your submission file.

Private Submission

• You must submit your model.
• It will be evaluated on a private testing dataset.
• Important: Your code must recursively scan the entire data directory (Code section for details).