Automated Machine Learning Pipeline for Iris Dataset Classification

This project implements an automated machine learning pipeline for classifying the Iris dataset using a Decision Tree classifier. The pipeline includes dimensionality reduction using Principal Component Analysis (PCA), standard scaling of features, and training the classifier. The project serves as a demonstration of how to create an end-to-end machine learning workflow using scikit-learn pipelines.

Requirements

• Python 3.x
• scikit-learn
• numpy

Installation

You can install the required packages using pip:

pip install scikit-learn numpy

Usage

1. Clone the repository:

git clone https://github.com/abhipatel35/Automated-Machine-Learning-Pipeline-for-Iris-Dataset-Classification.git

2. Navigate to the project directory:

cd automated-ml-pipeline-iris

3. Run the script:

python main.py

Pipeline Overview

1. Data Loading: The Iris dataset is loaded using scikit-learn's datasets module.
2. Data Splitting: The dataset is split into training and testing sets.
3. Pipeline Creation: A scikit-learn pipeline is created, which includes:
   • Dimensionality reduction using PCA.
   • Standard scaling of features.
   • Training a Decision Tree classifier.
4. Model Training: The pipeline is fitted to the training data.
5. Model Evaluation: The accuracy score of the model on the test set is computed.