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Ritvika Nandi's Projects

absenteeism icon absenteeism

Analysis and prediction of absenteeism at a work place using a secondary dataset of over 700+ different people, using python, pandas and logistic regression algorithm.

amazon_price_scraper icon amazon_price_scraper

A web chrome extension which scrapes the title, price, image and link of the wishlishted products on amazon and notifies whenever there is a price drop in any of the wishlishted products.

brilliant_brushes icon brilliant_brushes

A web application in Nodejs & Express with MongoDB as the database for painters with authentication, pagination, admin functionality, payment using Stripe and much more..

chat-app icon chat-app

Realtime chat application for users made using websockets and Socket.io in Nodejs with express server.

cpp icon cpp

Contains solutions to all my solved questions on competitive programming platforms like codeforces, codechef and atcoder in C++.

e-commerce_retail-customer_analysis icon e-commerce_retail-customer_analysis

Online Retail Market Customer Analysis of over 0.5M+ points to find the target customers based on Recency, Frequency and Total Revenue contributed by a customer using K-Means & Agglomerative Clustering.

happy-learning icon happy-learning

A learning platform for the campus, access to courses scraped by SWC, Blogs, Keeping track of learning, Bookmarking and a simple courseuera for campus. Social Learning, Effective learning

nextjs-basic-app icon nextjs-basic-app

This repository contains some basic practice on NextJS from the udemy course.

sms_spam_classification icon sms_spam_classification

Analysis of SMS tagged 5K+ messages collection to classify them as spam or ham. Used Natural Language Processing techniques to transform data into an understandable format.

swc-course_directory icon swc-course_directory

An open courseware to access all the BTech courses of IIT Guwahati using NodeJS, Express and mongoDB database storage.

titanic_classification icon titanic_classification

Analysis of what sorts of people were likely to survive by the sinking of RMS Titanic using Python and machine-learning classification model.

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