https://github.com/shaik-sohail-72/iris-flower-type-prediction-and-classification-with-ml-and-mern

Iris flower type prediction and classification with machine learning and MERN web I/O system. This project predict the type of iris flower by using machine learning . K-NN algorithm is used for multiclass classification.
https://github.com/shaik-sohail-72/iris-flower-type-prediction-and-classification-with-ml-and-mern

css ejs expressjs google-oauth2 heroku-deployment html iris-dataset javascript knn-algorithm knn-classification knn-classifier machine-learning nodejs pandas python sklearn-library

Last synced: 27 days ago
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Iris flower type prediction and classification with machine learning and MERN web I/O system. This project predict the type of iris flower by using machine learning . K-NN algorithm is used for multiclass classification.

• Host: GitHub
• URL: https://github.com/shaik-sohail-72/iris-flower-type-prediction-and-classification-with-ml-and-mern
• Owner: Shaik-Sohail-72
• Created: 2022-10-31T18:54:04.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2022-11-01T13:16:39.000Z (about 2 years ago)
• Last Synced: 2023-04-01T07:32:06.752Z (over 1 year ago)
• Topics: css, ejs, expressjs, google-oauth2, heroku-deployment, html, iris-dataset, javascript, knn-algorithm, knn-classification, knn-classifier, machine-learning, nodejs, pandas, python, sklearn-library
• Language: EJS
• Homepage: https://iris-flower-72.herokuapp.com/
• Size: 283 KB
• Stars: 2
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Iris Flower Type prediction and Classification with Machine Learning and MERN Web I/O System.

## Description : 

This project predicts the type of iris flower by using machine learning and iris dataset. K-Nearest-Neighbor algorithm used for multi class classification.

https://iris-flower-72.herokuapp.com/

![Screenshot (600)](https://user-images.githubusercontent.com/106341416/198973350-e043537d-0aa7-44a4-8279-03dfe83c6905.png)

![Screenshot (601)](https://user-images.githubusercontent.com/106341416/198973372-28da553b-c4a3-4b13-8ffb-86e545d6d7f6.png)

![Screenshot (602)](https://user-images.githubusercontent.com/106341416/198973400-78261276-7751-442c-b11a-09c90dfa093f.png)

![Screenshot (603)](https://user-images.githubusercontent.com/106341416/198973432-12e5575e-5452-4a1b-87d7-92393b8a9379.png)

![Screenshot (604)](https://user-images.githubusercontent.com/106341416/198973481-c0b6a39a-6eef-47be-8b6c-b037342f1ae1.png)

![Screenshot (605)](https://user-images.githubusercontent.com/106341416/198973506-be4d6328-a1fb-4a6b-84fa-3f678836173e.png)

The user enter the parameters (sepal length, sepal width, petal length, petal width) in the front end which is designed by using embedded javascript. The model predicts the type of flower and gives the information about the Accuracy, Confussion matrix to the user. MongoDB is used for storing the users data, NodeJS is serverd as backend framework and expressJS is used for user authentication.

![d](https://user-images.githubusercontent.com/106341416/198971052-6ec62524-377d-4975-9ad4-65c5d010bcaa.png)

The project is fully responsive and completely based on session and cookies concepts. Once the user authenticated and logged-in It will not ask the user to enter the login parameters again and again (next visit). It ask login parameters only when user click on logout button. And also using google oauth 2.0 for user authentication and storing user details in salted hash in the mongoDB.

#python #machine-learning #knn-algorithm #iris-dataset #sklearn #pandas #html #css #ejs #javascript #nodejs #expressjs #google-oauth2.0 #heroku-deployment