Backend engineer

github

medium

Abdulmateen Pitodia  • Peerlist

> Has advanced features such as grouping sub-tasks under a major task group
> Implemented local storage
> Minimal UI with Sass

Advanced Todo App using Sass and Javascript

Developed a Basic email registeration website which is connected to a MySQL database.
The website provides live count of how many users currently exist on the database.
Used Node.js, Express.js and EJS for making a basic website wherein a user can Register with their email and the count is updated.
Made queries using MySQL and node package and Express.js for post and send requests to be sent on the server.



Basic Login Page using Node.js and Express.js

Created a gesture based arduino controlled cleaning robot.

Gesture controlled wireless robot

In-depth analysis( bivariate and multivariate)  and visualisation of the Heart Disease UCI dataset, which contains 303 samples of patients involving heart diseases.
> Performed extensive data analysis by using popular python libraries such as Pandas, Seaborn , Sci-kit Learn and Matplotlib.
> Analysed the datasets and the multiple relations of different features and plotted distribution plots, histograms and heatmaps
> Chose an appropriate Logistic Regression model for the classification problem using SK Learn library.
> Performed cross-validation on the dataset to get an average accuracy score of 83%

Heart Disease - Data Visualisation, EDA, Logistic Regression

> Made using microservices architecture using spring boot
> User can add products, place orders and update inventory.
> Implemented circuit breaker and used apache Kafka for message streaming

Inventory Management Spring Boot Microservices App

Used random library of python to create a basic random password generator.

Password generator using Python

> User can write something memorable about their day and record it to the journal
> User can add custom tags to tag the memory of the day
> Built using Spring Boot, Spring Security and Angular-Typescript stack.

Personal Journal App

A real time sign language gesture recognizer made using the advancements in computer vision and deep learning, the system is able to capture the gestures made by differently abled people and convert them to english language to be interpreted by normal people, thereby allowing fluent communication between the two.

Tech used : OpenCV, TensorFlow, Keras, Python

Real time Sign language Gesture recognition using Deep learn

A simple web app that allows user to add tasks and reminders.
Tech used : React JS