AIML engineering student | Exploring Deep Learning, NLP & Chatbots | Building hands-on projects with Python & Streamlit | Always learning.
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Student
Backend Engineer
June 18, 2026 – Present
face-shape-skin-tone-app
June 8, 2025 – June 12, 2025
Face Shape & Skin Tone Detection app with personalized makeup, hairstyle, and skincare suggestions — built with Streamlit and MediaPipe.
View Projectdrowsiness_detector
June 8, 2025 – June 30, 2025
> A lightweight drowsiness detection app built with Streamlit and MediaPipe.
View Projectspeech-emotion-app
June 6, 2025 – June 6, 2025
This project is a web-based application built with Streamlit that detects human emotions from speech using machine learning. It uses MFCC features extracted from audio files and a trained Random Forest classifier to predict the emotional state of the speaker.
View ProjectAi_Therapist_Chatbot
June 5, 2025 – June 6, 2025
The AI Therapist Chatbot is an intelligent tool that offers emotional support using natural language processing and machine learning. It understands your feelings, detects your emotions, and provides empathetic responses to help manage stress, anxiety, and other challenges.
View ProjectCultural Fit Analysis
The candidate's personal projects show a strong interest in AI/ML, particularly in areas like emotion detection, facial analysis, and chatbots. This indicates a proactive learning attitude and a drive to build practical applications. The diversity of these projects suggests an exploratory mindset. However, without professional experience or team-based projects, assessing collaboration or adaptability for cultural fit is limited.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's profile indicates an 'experienceLevel' of 0 and lists 'Student' as their current role, which limits the ability to evaluate professional soft skills or operational experience.