Dabbling with Machine Learning in Python using the Iris Flower Dataset
- Here is a visualization of a sample decision tree created via the program
- 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.
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Make sure to install the pandas library, which can be done via
pip install pandas
, the scikit-learn library, which can be done viapip install sklearn
, and the joblib library, which can be done viapip 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
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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 respectiveint
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
- I developed this piece of software by myself, with the aid of youtube tutorials
- This project is licensed through the MIT License
- 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.
- Thanks to Mosh Hamedani for his tutorial on Machine Learning in Python