Project VI : Bank Note Authenctication

Aim of the Project

To build an End-to-End Web App using Data Science & Machine Learning to predict whether a Bank Note is Legit or not

Life Cycle of the Project

  1. Collected the dataset from Kaggle
  2. Preprocessed the data well and built a High Performance ML model with 100% Accuracy, Precision, Recall & F1score using KNN Classifier
  3. Saved the ML model in a Pickle file
  4. Built a Web App using Python at the Backend with Flask API and HTML & Bootstrap serving at the Frontend.
  5. Deployed the Web App on Heroku Cloud Platform
  6. The Web App receives the data from the User Input, makes predictions with the saved ML model and sends a Prediction text indicating whether a Bank Note is Legit or not

Results from the Project

  • If the note is legit
  • If the note is not legit

Technologies Used

| Python | Sci-kit Learn | Flask | Heroku | Gunicorn |

| Pickle | Pandas | Numpy | Matplotlib | Seaborn |

Created a Custom Transformer to handle the Outliers

Heroku Web App

GitHub Repository