https://github.com/abhipatel35/automated-machine-learning-pipeline-for-iris-dataset-classification

Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.
https://github.com/abhipatel35/automated-machine-learning-pipeline-for-iris-dataset-classification

automated-machine-learning classsification data-preprocessing descision-tree dimentionality-reduction end-to-end-ml-workflows iris-dataset machine-learning-pipeline python random-forest scikit-learn

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Automated ML pipeline for Iris dataset classification using Decision Tree. Features PCA dimensionality reduction and standard scaling.

• Host: GitHub
• URL: https://github.com/abhipatel35/automated-machine-learning-pipeline-for-iris-dataset-classification
• Owner: abhipatel35
• Created: 2024-02-14T01:17:12.000Z (12 months ago)
• Default Branch: main
• Last Pushed: 2024-02-14T01:24:22.000Z (12 months ago)
• Last Synced: 2024-11-07T21:37:55.859Z (3 months ago)
• Topics: automated-machine-learning, classsification, data-preprocessing, descision-tree, dimentionality-reduction, end-to-end-ml-workflows, iris-dataset, machine-learning-pipeline, python, random-forest, scikit-learn
• Language: Python
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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:

```bash

pip install scikit-learn numpy

```

## Usage

1. Clone the repository:

```bash

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

```

2. Navigate to the project directory:

```bash

cd automated-ml-pipeline-iris

```

3. Run the script:

```bash

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.