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Sai Ganesh Devi Prasad Robbi

Visakhapatnam • r**************************@gmail.com • 990****976 • linkedin.com/••••• • drivetube.ai/•••••

Career Objective

Recent B.Tech graduate specializing in Artificial Intelligence and Machine Learning with hands-on experience in Python, Machine Learning, Deep Learning, Computer Vision, and AWS Cloud Computing. Completed internships at BSNL, APSSDC (AWS Cloud & DevOps), and the Centre of Excellence in Maritime & Shipbuilding, gaining practical experience in cloud technologies, data analytics, and AI application development. Built end-to-end machine learning projects including CardioPredict (cardiovascular disease prediction) and Spotify Genre Segmentation, with strong skills in Python, Scikit-learn, Flask, Git, Docker, and AWS services. Passionate about developing intelligent, scalable, and impactful software solutions. This aligns with the experience and skills already reflected in your resume.

Education

Gitam Deemed to be University
B.Tech ECE (AIML) • Rishikonda • AUG 2022 – APR 2026
Sri Chaitanya Junior College
MPC • MAR 2020 – APR 2022
Sri Chaitanya EM School
SSC • MAR 2019 – MAR 2020

Technical Skills

Programming Languages: Python,Java,C
Frameworks and Libraries: PyTorch,FastViT,Flask,scikit-learn,NumPy,OpenCV,Pandas,Matplotlib
Data and Analytics: Machine learning,Tableau
Tools and Methodologies: Git,GitHub,VSCode,Reproducible experiment tracking
Computer Vision & Imaging: Image preprocessing,Data augmentation,Intensity windowing,Slice selection
ML Modeling & Evaluation: Deep learning,Hyperparameter tuning,Cross-validation,Stratified sampling,Model evaluation Accuracy, Precision, Recall, F1-score
AWS Cloud Computing: Amazon EC2,Amazon S3,AWS IAM,Amazon VPC,Amazon RDS,Amazon CloudWatch,AWS CLI,AWS CloudFormation (Basics)
DevOps: GitHub,Linux,Shell Scripting (Bash),Version Control,Deployment Automation
Networking & Security: TCP/IP Fundamentals,DNS,HTTP/HTTPS,SSH,Security Groups,IAM Roles & Policies
Monitoring & Infrastructure: Cloud Resource Monitoring,Log Management,Infrastructure Provisioning,Cloud Deployment,Virtual Machines,Storage Management

Projects

MedScanXR – Lung X-ray Disease Detection
Tools Used: Python, FastViT, PyTorch, Flask, OpenCV, NumPy, Data augmentation, Stratified sampling, Model evaluation
  • Designed and trained a FastViT-based deep learning model to detect lung diseases from chest X-ray images, focusing on maximizing recall and F1-score.
  • Implemented advanced image preprocessing including resizing, normalization and augmentation pipelines to improve model generalization.
  • Used stratified data splitting to maintain class balance and prevent biased model training during evaluation.
  • Conducted hyperparameter tuning and evaluated models using Accuracy, Precision, Recall and F1-score to select the best-performing checkpoint.
  • Deployed the trained model as a Flask web application enabling real-time image upload and disease prediction through REST endpoints.
MedScanCT – Lung CT Scan Analysis
Tools Used: Python, FastViT, PyTorch, Flask, Intensity windowing, Slice selection, Cross-validation, Model evaluation
  • Built a FastViT-based model tailored for lung disease detection from CT scan slices, optimizing for robust feature extraction across slices.
  • Applied domain-specific preprocessing such as intensity windowing, normalization and slice selection to enhance relevant feature contrast.
  • Employed patient-wise and stratified splitting strategies to prevent data leakage and ensure robust evaluation.
  • Validated model performance through cross-validation and tracked metrics to compare model variants.
  • Developed a Flask-based web interface for real-time CT scan analysis and visualization of model predictions.
Spotify Genre Segmentation | Jul 2026 – Jul 2026
Tools Used: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, VS Code, Git, GitHub, Colab Notebook
  • Performed data preprocessing and exploratory data analysis (EDA) on Spotify song features.
  • Built a K-Means clustering model to group songs based on audio characteristics and genres.
  • Visualized feature relationships using correlation matrices, PCA, and multiple statistical plots.
  • Analyzed playlist genres and song clusters to identify listening patterns and music trends.
  • Developed a basic music recommendation system using clustered song similarities.
CardioPredict | Aug 2026 – Aug 2026
Tools Used: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn, Flask, VS Code, Git, GitHub, Colab Notebook
  • Developed a machine learning model for early cardiovascular disease prediction using clinical datasets.
  • Performed data preprocessing, feature engineering, and correlation analysis to improve model performance
  • Compared Logistic Regression, Decision Tree, Random Forest, SVM, and KNN models for prediction accuracy.
  • Evaluated models using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Confusion Matrix.
  • Deployed the best-performing model as a Flask web application for real-time heart disease prediction.

Certifications

AWS Cloud Computing -Devops — APSSDC • Jul 2025
IOT & Embedded Systems — CEMS • Jun 2025
BSNL — BSNL • Oct 2024

Achievements

  • Solved 260+ DSA problems across LeetCode and GeeksforGeeks

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