I am a Data Scientist
Motivated and detail-oriented data scientist with a master's degree, skilled in data analytics, visualizations, predictive analytics, and machine learning, enthusiastic about Deep Learning, collaborative, positive attitude and always exploring and learning.
I am passionate about leveraging data to drive business decisions and continuously seek new ways to optimize processes and enhance performance. Currently, I work as a Data Scientist in the retail FMCG industry, focusing on building models and providing data-driven insights to help businesses make informed commercial investments, stay competitive, and drive revenue growth. Previously, I worked on projects within the BFSI sector, where I contributed to developing AI-based solutions for a debt collection platform. I thrive in collaborative environments and enjoy working with cross-functional teams to achieve shared goals. I am also always committed to learning and staying current with the latest technologies and methodologies. I am excited to bring my skills and experience to make a positive impact.
Depression Detection based on Sentiment Analysis in Social Media using Deep Learning
A non-invasive technique used for stress detection in tomato plants using deep learning and thermal imaging.
Subtitulo - A deep learning based android application which takes an image as a input and provides automatic caption for the following image.
Web application deployed on docker to find out the sentiment of the movie review
All projects related to udacity computer vision nanodegree: Facial Keypoint Detection, Image Captioning, Landmark Detection & Tracking (SLAM)
All projects related to udacity data foundations nanodegree: Analyze Survey Data, Query a Digital Music Store Database, Data Visulalization Project- Tableau
This paper proposes to implement a system that will help identify depressive tweets along with depressive user i.e. it is a two-step depression detection process that utilizes deep learning algorithms in conjunction with additional classification for emotion recognition of tweets.
This chapter discusses the thermal imaging and deep learning can be used for stress detection in tomato plants and indirectly in the field of agriculture.
This paper discusses about the different methods and techniques used by the researchers for stress detection in plants and an improved method is designed for the process of stress detection in plants by using a combination of both numeric values obtained in the form of temperature measures as well as the thermal images obtained that is using multimodal analysis.