mnisar Goto Github PK

followers: 8.0 following: 20.0 repos: 25.0 gists: 0.0

Name: Monil Nisar

Type: User

Company: Student

Bio: Software Engineer

Location: Tempe

Hi there 👋

Hi 👋, I'm Monil Nisar

I completed my Masters's Degree in Computer Science at Arizona State University in May 2021. I currently work in Access Security domain for Workspace @ Google (formerly known as GSuite).

🌱 I’m currently learning Docker & Kubernetes
💬 Ask me about Python, MongoDB
📫 How to reach me [email protected]

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Monil Nisar's Projects

awesomescripts

A Collection of Awesome Scripts in Python to Ease Daily-Life. Create an issue If you have some great idea to the new script

clinical-entity-normalization

Named Entity Linking - Clinical Entity Normalization - for medical entities in clinical text

dashdemo

A web application to demonstrate ue of DASH, an interactive library built on top of plotly.

emailspamdetection

emal classification problem using dataset on kaggle.

github-profile-readme-generator

🚀 Generate GitHub profile README easily with the latest add-ons like visitors count, GitHub stats, etc using minimal UI.

handwrittendigitrecog

Kaggle Kannad hand written digit reoginition

pitcher

Pitcher is an online pitching platform where you can convert your raw idea into product with the help of investors and developers.

predicting-dummy-emergency-call-

Using Data-Capeston project dataset to predict the nature of the calls, if dummy or not.

python-flask-microservices

Demonstrates how to use Python, Flask, and Docker to quickly prototype and build microservices

python_and_the_web

Build Bots, Scrape a website or use an API to solve a problem.

relational-database-engine

A extension to Minibase: a relational database in Java. Added Skyline, Joins, GroupBy operators along with indexes (hash & b-tree) and the database catalog with query executor.

ru102py

The example project for Redis University's RU102PY course.

survellience_system_using_raspberry_pi_and_aws

The Application detects motion and starts recording using the camera connected to the raspberry pi. The videos are processed to detects object using the darknet model. The application balances the processing load between raspberry pi and EC2 instances. We have hard coded the auto-scaling feature to control the number of instances that are launched.