Lavi Moolchandani

Lavi Moolchandani

Software Engineer
A full-stack developer who loves Algorithms and Data Structures and an Enthusiast that craves for new technologies.

The most amazing...

... I have created a Web App based on machine learning that predicts the possible outcomes or estimations as inputs by a user using .csv or .xls files and compares all the regression algorithms for the data and outputs the best suitable algorithm along with the prediction data.

Interest & Expertise

  • UI
  • Object-oriented design
  • Dashboard Applications
  • Complex Data Structure and Algorithms
  • Challenging competitive problem-solving
  • Adaptive algorithms
  • Machine Learning

Achievements

  • CodeChef Certified Coder for Data Structure and Algorithm
  • Winner of all three coding events of State level Tech fest of Rajasthan Technical University
  • Certified in Basic machine learning by online course of Stanford University on Coursera: Secured 98% marks in online course by Prof. Andrew NG

Skills

Backend Framework & Library

Laravel, Express.js, Objection.js

Databases

MySQL

Frontend Framework & Library

React.js, Ant Design

Languages

HTML / CSS, JavaScript, PHP, Java, C, Python, C++, Perl

Version Control

Git, GitLab, GitHub

Project Management

Trello

Editors

Sublime Text, VS Code

State Management

Redux, Redux Saga, Thunk

Libraries/APIs

Eslint, Formik

Projects

A Multi Tenant application for organisation management and communications

Portal to manage companies' progress contribution and all based on team, end-user. It is built with MYSQL, Expressjs, & React

An App To Improve Effectiveness of HealthCare Organisation

Organisation Management API’s that keep track of patient-focused initiatives by individual team, the contribution of an individual with each organization having some objective to achieve using Express.js, MySQL.

Housing Cost Prediction Using Machine Learning

A web App based on machine learning that predicts the possible outcomes or estimations using .csv or .xls files input by the user and compares all the regression algorithms for the data and outputs the best suitable algorithm along with prediction data.

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