Pranav Shukla's repositories
Mental-Health-Analyzer
Mental Health Analyzing platform with questionnaire and Chatbot!
Code-Helper
This is a fun little Chrome extension that reminds you to study every time you open a new tab, if u have the focus span of a 3 year old.
Social-Media-Campaign-Management
Audience analysis , trend analysis and also generates content for small businesses ! This project has AI / ML tools for Social Media, it can help different small businesses create effective social media marketing campaigns!
pranavshuklaa
My personal repository.
browser-history-analyzer
The repo for a digital forensic software analyzing browser history
flink-data-streaming-from-kafka-to-postgres
This project demonstrates a real-time data streaming pipeline using Kafka, Apache Flink, and PostgreSQL. It continuously generates weather data for a city and processes this data through a Kafka-Flink pipeline before storing the results in a PostgreSQL database
forage-jpmc-swe-task-1
Starter repo for task 1 of the JPMC software engineering program
hypertrons-crx
A browser extension to get more insights into projects and developers on GitHub.
Grievance-Management-System
We have created a digitalized grievance management system where the students of VJTI can send their grievance online using our website and get the required solution by the admin. We have given utmost importance to the privacy of the student and maintained transparency in the complaints
hindsight
Web browser forensics for Google Chrome/Chromium
ML-ProjectKart
🙌Kart of 210+ projects based on machine learning, deep learning, computer vision, natural language processing and all. Show your support by ✨ this repository.
Sentiment-Analysis-of-Text-Data-Tweets-
This project addresses the problem of sentiment analysis on Twitter. The goal of this project was to predict sentiment for the given Twitter post using Python. Sentiment analysis can predict many different emotions attached to the text, but in this report, only 3 major were considered: positive, negative and neutral. The training dataset was small