https://github.com/bryanhe24/iris-flower-classification-using-perceptron

https://github.com/bryanhe24/iris-flower-classification-using-perceptron

Last synced: 3 months ago
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• Host: GitHub
• URL: https://github.com/bryanhe24/iris-flower-classification-using-perceptron
• Owner: BryanHE24
• Created: 2024-11-23T23:16:11.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-11-23T23:18:29.000Z (7 months ago)
• Last Synced: 2025-02-04T10:58:44.090Z (4 months ago)
• Language: Jupyter Notebook
• Size: 5.86 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Iris Flower Classification using Perceptron

This Python script demonstrates how to use a perceptron model to classify iris flowers based on their petal length and width. 

The iris dataset is a popular dataset in machine learning, often used for classification tasks.

## Summary of what the code does

The program implements a machine learning workflow to classify iris flower species using a Perceptron model based on their petal measurements. 

Here are the key steps it performed:

  1) Imported Necessary Libraries:

       Utilized scikit-learn libraries for loading datasets, splitting data, creating a model, and evaluating model performance.

  

  2) Loaded the Iris Dataset:

       Retrieved the Iris dataset, which includes measurements of iris flowers and their species.

  

  4) Selected Features and Labels: 

      Chose petal length and petal width as features (X). 

      Used the species of the iris flowers as labels (y).

  5) Split the Data into Training and Testing Sets:

       Divided the dataset into training (70%) and testing (30%) sets to evaluate the model's performance on unseen data.

  6) Initialized and Trained the Perceptron Model: 

      Created a Perceptron model with specified parameters. 

      Trained the model on the training data.

  7) Made Predictions on the Test Set:

       Used the trained model to predict the species of flowers in the test set.

  

  8) Calculated and Printed the Model's Accuracy: 

      Evaluated the accuracy of the model by comparing its predictions to the actual species in the test set. 

      Printed the accuracy as a percentage, indicating how well the model performed.

9) Displayed Examples of Predictions:

      Printed examples of the predicted and actual species for some samples from the test set along with their petal measurements to illustrate the model's performance.