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Manjunath's Projects

angular8-demo icon angular8-demo

In this tutorial, we will be developing an Angular 8 application and perform CRUD operation on a user entity. We will be developing a full stack app with REST API integration. We will have a login, list user, add and edit user component and based on the routing configurations these pages will be served. We will have HTTP interceptor to intercept our HTTP request and inject JWT token in the header. The backend server code is implemented using Spring Boot.

bootstrap icon bootstrap

The most popular HTML, CSS, and JavaScript framework for developing responsive, mobile first projects on the web.

coviddiseaseassistant icon coviddiseaseassistant

A system which uses a computer vision model to keep the hygiene in public places in check and a natural language processing chatbot that suggests the best course of action or a doctor’s appointment in case of high risk of infection. Also developed a full stack web server which enables users to book medical appointments.

e-commerce-bookstore icon e-commerce-bookstore

An online bookstore web application built in Java with Spring Boot, Spring MVC, and Hibernate.

jdbc-java-crud-operations icon jdbc-java-crud-operations

This repository contains code for the tutorial I authored at ( https://www.youtube.com/user/vigneshjava ). In this repository as of now I am adding just source code for what I explained the tutorial. More enhancements will be made to it in future.

jupyter_webapp_demo icon jupyter_webapp_demo

Code for the article "How to Create an Interactive Web Application using Jupyter Notebook".

library icon library

Online Library Management. User can search, check in, checkout book. System adds fines automatically if the book is not checked in by due date

linkedin-skill-assessments-quizzes icon linkedin-skill-assessments-quizzes

Full reference of LinkedIn answers 2023 for skill assessments (aws-lambda, rest-api, javascript, react, git, html, jquery, mongodb, java, Go, python, machine-learning, power-point) linkedin excel test lösungen, linkedin machine learning test LinkedIn test questions and answers

movie-recommendation-chatbot icon movie-recommendation-chatbot

Movie Recommendation Chatbot provides information about a movie like plot, genre, revenue, budget, imdb rating, imdb links, etc. The model was trained with Kaggle’s movies metadata dataset. To give a recommendation of similar movies, Cosine Similarity and TFID vectorizer were used. Slack API was used to provide a Front End for the chatbot. IBM Watson was used to link the Python code for Natural Language Processing with the front end hosted on Slack API. Libraries like nltk, sklearn, pandas and nlp were used to perform Natural Language Processing and cater to user queries and responses.

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