Final-year Computer Science Engineering student with a robust foundation in software engineering, specializing in AI and Machine Learning. Proven ability to develop user-centric applications and optimize systems for performance, demonstrating expertise in AI/ML model development, web application design, and data classification. Eager to leverage strong problem-solving skills and technical proficiency to create innovative solutions in a dynamic engineering environment.
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Bachelor of Technology
Computer Science Engineering
Grade: CGPA: 8.87
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Higher Secondary Education
Intermediate (BIE)
Grade: Marks: 936/1000
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High School Diploma
Secondary Education (BSEAP)
Grade: CGPA: 9.2
Issued By
AICTE AWS
Issued By
N/A
Issued By
N/A
C++, Python, HTML, CSS.
Machine Learning, Deep Learning, TensorFlow, Natural Language Processing (NLP), Computer Vision, Neural Networks, Random Forest Algorithm, Convolutional Neural Network (CNN).
RDBMS.
Streamlit.
ServiceNow Administration, AWS.
Problem-Solving, Critical Thinking, Teamwork, Leadership, Adaptability, Interpersonal Communication, Written Communication, Oral Communication, Lifelong Learning, Knowledge Sharing.
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Summary
Developed a web application using Streamlit framework to predict crop recommendations based on user requirements. Utilized a Random Forest Algorithm to achieve higher accuracy for the recommendation system, providing suitable crop suggestions.
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Summary
Developed a neural network architecture for high-accuracy emotion classification from the FER-2013 dataset. Implemented a CNN model to classify facial emotions (happiness, sadness, anger) and enhanced real-time detection through model optimization techniques.
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Summary
Applied Natural Language Processing (NLP) techniques, including message pre-processing, token conversion, and feature extraction, to classify documents. Utilized classifier algorithms such as Naive Bayes, SVM, and neural networks to achieve high accuracy in categorizing documents.