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Israel Showell

Start Date: 2-9-2024

End Date: 2-13-2024

Project: Iris Flowers Predictor

Version: 1.0.0

Description:

This is a medium-sized C++ program that will predict what kind of Iris Flower you have. It does this by taking the length and width of the Sepal and Petal that you have and the Iris-Dataset csv file for data to power its predictions. This program exercises Machine Learning by taking in values for a new Iris flower. It uses data from the famous iris dataset, and then processes the new input. Afterwards, it will show how confidient it is in its prediction, and then add the new flower in as the type it is most sure that it is.

C++ Concepts Used:

  • Control Structures
  • Memory Management

Machine Learning Concepts Used:

  • Statistical Analysis
  • Gaussian (Normal) Distribution
  • Likelihood Estimation
  • Confidence Calculation
  • Feature Extraction
  • Predictive Modeling

NOTE:

This repo is no longer going to be updated in the forseeable future, as the 2.0.0 version is much easier to use and update!

Version History:

V-1.00: (2-9-13-24)

Initial Version Uploaded to Github

V-2.00: (4-9-24)

Found Here: https://github.com/IsraelShowell/Iris-Flower-Predictor-Vector

Current Features as of V-1.00:

  • Takes 4 variable values from the user about an Iris flower type
  • Uses the data from the famous Iris flower dataset to predict the likeihood of what type of flower the user may have
  • Provides an accurate prediction of the user's flower type

Future Features to Implement:

  • Dynamic addition of new Iris types
  • Dynamic allocation of memory for data structures
  • Implement a cleaner UI

NOTE:

The readability on this is pretty rough, this is intentional, as I was more focused on getting a working prototype than making it run as best it can.

I used the knowledge of C++ that I best was familar with to create this, but I plan on refactoring this code later on. Project is currently over 500 lines long.

About

This is a medium-sized C++ program that will predict what kind of Iris Flower you have. It does this by taking the length and width of the Sepal and Petal that you have and the Iris-Dataset csv file for data to power its predictions.

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