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Hi there šŸ‘‹

  • šŸ”­ Iā€™m currently working on Cloud-native applications driven with Big Data and Machine Learning
  • šŸ‘Æ Iā€™m looking to collaborate on ideas around Applied ML and NLP
  • šŸ‘Øā€šŸ’» I primarily work with Python, Java and C++

Hardik Srivastava's Projects

ai-advertisement-platform-dl icon ai-advertisement-platform-dl

This is an AI-powered advertisement platform that performs Face Detection using Haar Cascade Frontal Face and Wide ResNet to detect your age and gender via an external camera and then show you video advertisements related to the products that suit your corresponding age and gender in real-time.

child-abuse-server icon child-abuse-server

AI powered Child Grievance Prioritization using a Decision support grievance redressal system using sentence sentiment analysis

curae icon curae

AI-powered telemedicine app that reduces the hassle of offline appointments by bridging the gap between the doctor and patient by transforming the entire appointment lifecycle online.

customer_segmentation icon customer_segmentation

A customer segmentation analysis on a dataset provided by kaggle to segment customers based upon their labelled behavior.

dbms-mini-project icon dbms-mini-project

Mini-Project submission for Hardik Srivastava (RA1911027010021) and Snehith Reddy Kondakalla (RA1911027010055)

dieten icon dieten

A diet management app with an activity tracker, diet recommendation and an image classification model to tell your dishes' nutrient content.

electrical_substation_detection icon electrical_substation_detection

Machine Learning based feature extraction of electrical substations from satellite data. Powered by IEEE-ICETCI, RRSC-Central, NRSC, and ISRO, this project incorporates instance segmentation of substations using UNet, Albumentations for image augmentation, and OpenCV for computer vision tasks.

food-delivery-rn icon food-delivery-rn

A restaurant searching app with segregation upon the searched dishes and full details of the requested restaurants.

food-on icon food-on

Food On is an end-to-end food ordering application with real-time inventory management serving three different dashboards, one for the user, one for the chef, and another for the delivery executive. Deployed using Firebase, this app also comes with in-built cloud functions for an effective user experience.

forum-posts-clustering icon forum-posts-clustering

This project incorporates Hierarchical document clustering of the Kaggle forum posts using data from Meta Kaggle. Includes fine-tuned vectors using GoogleNews embeddings.

location-tracker-rn icon location-tracker-rn

Realtime Location Tracker is an application where you can see your current location and save your traversed path between any 2 points on a map. It can be used for reference in the future if you wish to locate the way on a google map to a place you already visited before.

memorable-places icon memorable-places

Memorable Places lets you save all your memorable places that you might wish to visit again on a google map and lets you access them later using a simple and materialistic user interface developed on android.

nmt-english-to-hindi icon nmt-english-to-hindi

This is an NLP pipeline that performs neural machine translation using Encoder-Decoder architecture to translate text from the English language to the Hindi language. Deployed using TensorFlow.js in a mobile application, the pipeline can be expanded to include translation amongst multiple languages.

nutrical icon nutrical

A health management app which keeps tracks of your daily diet where a user can add meals which he consumes daily in accordance to the calories that he wishes to burn. Nutrical comes with a workout manager where a user can burn the excess fat and keep track of his health. It also comes with all the features of an activity tracker.

odin-ai-flutter icon odin-ai-flutter

ODIN-AI or Object detection and identification ai is a flutter based mobile application that can detect up-to 9 objects in the camera and assign them with their predicted labels. The deep learning technique used is transfer learning using OpenCV and the SSD MobileNet model.

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