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Iris says!:cherry_blossom:

A minimalist platform for learning, understanding and realising Iris Flower Classification.

This is an educational tool to encourage learning Iris Flower Classification with inbuilt graphical visualisations and on-spot prediction system. We have loaded the platform with 7 highly optimized, pretrained models of different algorithms namely,

  • Decision Tree Classifier
  • Gaussian Naive Bayes
  • K-Neighbors Classifier
  • Logistic Regression
  • Random Forest Classifier
  • Support Vector Machine
  • Multinomial Naive Bayes.

The architecture of the model are saved and are reused for faster prediction.

Algorithm 🌸

Due to continuous prediction calls, we devised a simple algorithm for isolated prediction.

  • Each time a user wants to predict with a particular model, the measurands along with the model key is sent to the server.
  • The model is searched and when found, loaded as an object file with all the architecture expanded and ready for prediction.
  • The expanded object takes in the measurands via the predict function. (The functions are stored within the object's architecture).
  • Values are predicted and then return to the DOM.
  • Then a javascript function call deletes all the prior data, to avoid unexpected object expansion errors during the process.

To Get Started:cherry_blossom:

Production

git clone https://github.com/j0fiN/Iris-Says.git
cd Iris-Says
pip install -r requirements.txt
python run.py

Testing (algorithm test)

python -m unittest discover tests

Learn it!:cherry_blossom:

A full description available about the dataset and the models.

Home page

Interact with it!:cherry_blossom:

Loaded with major graphs which are useful and not very complex to grasp. Simplicity had been maintained!

GIF

Realise how it works!:cherry_blossom:

With robust, yet flexible configurations, users can select his own settings and get wonderful predictions.

Predict page

Major Reach:cherry_blossom:

This platform is majorily developed for beginners in Data Analytics/Machine Learning. Giving a strong foundation in these topics enhances them to move forward faster in this ever-growing field. They will understand how to approach any data and analyze them and then use it to build powerful machine learning models.
The platform can play a major part in showcasing AI and machine learning for students in high schools and other bootcamps.

Higher Optimizations:cherry_blossom:

  • The tool can grow in size to explore various other famous datasets and the usage of machine learning in each of them and not only iris(A good example would be Boston says!).
  • The tool can become a platform for users to develop their own models on that dataset and upload them. They can also write content about the database.
  • The Graphical visualisations can be enhanced using various tools of javascript.

Tools used to develop the project:cherry_blossom:

  • Flask (Python)
  • Scikit-Learn (Python)
  • Plotly-Express (Python)
  • Basic webtools(HTML, CSS, JS(some JQuery too!))

Contribution:cherry_blossom:

Do contribute if you have ideas, ⭐ the repo if you find it impressive!

Made with ❤️ => PYTHON