valkyron/ML-Model-deployment-using-Flask

Training iris data based KNN Logistic Regression model and visualizing data using Plotly-express sunburst tree charts, and deploying on localhost using Flask.

ML-Model-deployment-using-Flask

A project that focuses on ML model deployment and data visualization, with python-based web framework Flask and Plotly-express.

Repo Objective

An ML model trained on a large dataset, with a accuracy greater than 90 percent, isn't fully complete until it's deployed for the world to experience. Hence, in this project rather than making a great model, the focus has been shifted to deploying a basic ML model as a user friendly web project using Flask.

Also, I had seen a post showing a beautiful visualization of data in a sunburst chart using plotly. So created one of my own charts and added it in the project as a jewel in the crown!
Feedback is appreciated!

Prerequisites

• Install dependencies using [requirements.txt] (/requirements.txt)
  pip install requirements.txt
• Imported 2 datasets:
  • PUBG Finish Placement Prediction Data
  • Iris Data

Features

The website consists of 3 tabs: Home, Predict, Visualize
Home: Gives a clear description of the website and its working.

Predict: With its earier-to-use interface lets end user to input prediction attributes for the pretrained-model to predict, in this case, a KNN Logistic Regression Model claassifying flower species accurately.

Visualization: Shows data visulizations and an interactive interface.