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Data Science with 1+ years in Machine Learning & Data Analytics
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MTech graduate in Information Technology (NIT Raipur, CGPA: 8.40) with hands-on expertise in machine learning, deep learning, and data analytics. Improved brain tumor classification accuracy from 92% to 97% using a novel wavelet-enhanced hybrid CNN approach - published at ISPCC 2k25. Proficient in Python, TensorFlow, Scikit-Learn, SQL, and Power BI. Built end-to-end ML pipelines and interactive dashboards with measurable business impact. Seeking a Data Science role to build scalable ML solutions and drive data-driven decisions.
National Institute of Technology, Raipur
Master of Technology – Information Technology · Information Technology
July 1, 2023 – May 1, 2025
S B Jain Institute of Technology, Nagpur
Bachelor of Engineering – Information Technology · Information Technology
July 1, 2018 – June 1, 2022
Machine Learning Intern
Machine Learning Intern
December 1, 2025 – May 1, 2026
India
MedTour Easy
Data Analytics Trainee
November 1, 2025 – November 1, 2025
India
White Horse Business Solutions
UI/UX Intern
August 1, 2021 – April 1, 2022
Nagpur, Maharashtra, India
Brain Tumor Classification using Wavelet Transform & CNNs
January 1, 2024 – December 31, 2025
Achieved a 5-point accuracy improvement (92% → 97%) by developing a novel wavelet-enhanced hybrid CNN architecture that fuses multi-resolution frequency features with deep spatial representations. Integrated multiple pretrained architectures (VGG16, ResNet50) in an ensemble pipeline for robust MRI-based brain tumor classification; validated across two independent MRI datasets to ensure cross-dataset generalization. Implemented Discrete Wavelet Transform (DWT) for multi-resolution feature extraction, capturing high- and low-frequency tumor characteristics that standard CNNs miss, directly contributing to the accuracy gain. Applied advanced data augmentation strategies (rotation, flipping, zoom, contrast normalization) to address class imbalance; evaluated model with confusion matrix, ROC-AUC, and precision-recall analysis. Research accepted and published at ISPCC 2k25, demonstrating clinical potential of the approach for computer-aided diagnosis in medical imaging.
View ProjectCustomer Churn Prediction Dashboard
January 1, 2024 – December 31, 2024
Built an end-to-end churn prediction pipeline using logistic regression and decision trees with SQL-based preprocessing, achieving 85% prediction accuracy on real customer data. Designed interactive Power BI dashboards using DAX measures to surface actionable insights on churn patterns, customer tenure distribution, and contract type influence enabling targeted retention strategies. Automated the feature engineering stage, reducing preprocessing overhead and making the pipeline reusable across future customer segments.
Traffic Speed Violation Detection System
January 1, 2021 – December 31, 2021
Developed a real-time vehicle speed violation detection system using OpenCV and YOLOv5, achieving 93% vehicle detection accuracy under varied traffic and lighting conditions. Implemented Haar Cascade classifiers for license plate localization and extraction; automated e-challan generation with structured CSV-based reporting for enforcement workflows. Research published in IJARSCT (2021), validating the system's effectiveness as a scalable, deployable traffic enforcement tool.
Machine Learning
Stanford University, Coursera
June 1, 2026 – Present
Commonwealth Bank Data Science Simulation
Forage
June 1, 2026 – Present
Deep Learning: Getting Started
LinkedIn Learning
June 1, 2026 – Present
Python Essentials 1
Cisco
June 1, 2026 – Present
British Airways Data Science Simulation
Forage
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's involvement in academic projects, research publications, and extracurricular activities (Social Work Intern, AICTA Coordinator, AdroIT Technical Coordinator) suggests a proactive, learning-oriented individual with a commitment to community and innovation. The diversity of projects, from medical imaging to customer churn and traffic violation, indicates adaptability and a broad interest in applying data science across various domains. This aligns well with a culture that values continuous learning, impact, and diverse problem-solving.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project work and research. Collaboration is evident in team-based project execution and cross-functional alignment. The UI/UX internship suggests an understanding of user-centric design, which can be beneficial in deploying user-friendly ML solutions. The coordinator roles indicate leadership and organizational capabilities.