https://classifier-ml.herokuapp.com/
https://prediction-on-iris-dataset.herokuapp.com/ (basic version: for iris dataset)
Python(version 3.7) IDE (Anaconda recommended)
FLASK
Gunicorn
Libraries: numpy, pandas, os, seaborn, scikit-learn, matplotlib, pickle
After installing packages in requirements and setting up virtual env,
run this command in the directory containing code:
python app.py
After executing the command above, visit http://localhost:5000/ in your browser to see your app
To train and deploy ML classification algorithms on Dataset uploaded by user.
Algorithms used here are:
Logistic Regression,Decision Tree, KNN, SVM & Random Forest Classifier.
The deployed website has the following provisions:
•Upload datasets
•Select current dataset and:
->Add new data over the current dataset:
User can add input data over the current datset.
->Train the current dataset on model of user's choice(from the 5) and retain the model
->Test the current model:
The species is predicted by the trained model of user's choice.
->View the dataset
Anaconda: https://docs.anaconda.com/anaconda/install/
ML scikit-learn classification models: https://stackabuse.com/overview-of-classification-methods-in-python-with-scikit-learn/
Integrating ML models with flask: https://www.analyticsvidhya.com/blog/2020/09/integrating-machine-learning-into-web-applications-with-flask/
https://hidenobu-tokuda.com/how-to-build-a-hello-world-web-application-using-flask-and-deploy-it-to-heroku/ https://stackabuse.com/deploying-a-flask-application-to-heroku/