AliRZ-02 / IrisClassification

Dabbling with Machine Learning in Python

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Iris Classification

Dabbling with Machine Learning in Python using the Iris Flower Dataset

Final Result

  • Here is a visualization of a sample decision tree created via the program

Decision Tree

Motivation

  • I created this project in December 2020. After learning about decision trees, the ID3 algorithm and machine learning in general while in school, I decided to learn how to use Python for its machine learning applications through this video by Programming with Mosh.

Getting Started

Prerequisites

  • Make sure to install the pandas library, which can be done via pip install pandas, the scikit-learn library, which can be done via pip install sklearn, and the joblib library, which can be done via pip install joblib.

  • The dataset from which the model was trained is included in the files, but it was collected from the UCI Machine Learning Repository

Running the Classifier

  • All that is required to run this classifier is to open up a terminal window and type print(flower_model.predict([[int,int,int,int]])) where each of the respective int values represent the sepal-length, sepal-width, petal-length and petal-width respectively.

  • As for running a similar classifier for your own projects, the video by Programming with Mosh linked above is extremely helpful

Authors

  • I developed this piece of software by myself, with the aid of youtube tutorials

License

  • This project is licensed through the MIT License

Learning Process

  • Although I was aware of the concepts of decision trees and machine learning as I had studied these in school, I learned the industry applications and variations of these methods.

Acknowledgements

  • Thanks to Mosh Hamedani for his tutorial on Machine Learning in Python

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Dabbling with Machine Learning in Python

License:MIT License


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