About Me

About Me



I'm Palak Kakaniđź‘‹,

Passionate data-driven problem solver with one year of experience building scalable data pipelines, deploying machine learning models, and creating interactive dashboards to solve real-world problems across healthcare, education, fintech, and AI research domains. Currently pursuing my Master’s in Data Science and Analytics at Georgia State University, I bring a solid foundation in Information Technology and deep technical expertise shaped by hands-on work with organizations like Shepherd Center, Better Business Bureau, Omdena, and IIT Patna. My work has spanned time-series forecasting, generative AI, NLP, and deep learning, consistently improving model performance, operational efficiency, and user engagement. I thrive in collaborative, agile environments and enjoy bridging technical insights with business impact. Outside of work, I find balance in singing, painting, and traveling—and I'm passionate about using technology for social good. I'm always excited to collaborate on innovative projects that make a measurable difference.

Core Technical Skills:

  • Languages & Tools: Python, R, SQL, Power BI, Tableau, Git, Docker
  • Libraries/Frameworks: Scikit-learn, TensorFlow, PyTorch, Streamlit, Flask
  • Cloud Platforms: AWS, Azure, GCP
  • Concepts: NLP, Time-Series Forecasting, GenAI, CI/CD, ETL, API Integration

Projects

Projects

See Github Repository

Python

SQL

Visualization GCP (BigQuery, Looker Studio)

Data Analysis, Alpha Vantage API

Stock Market Analytics

  • Led ETL pipeline development for 6 MAANGM companies, integrating real-time and historical stock data from Alpha Vantage API into BigQuery, improving data processing efficiency by 40%.
  • Developed 20+ SQL queries for market trend analysis and volatility prediction, ensuring 99% data accuracy across 100,000+ data points. Created 10+ dashboards in Looker Studio, visualizing stock trends and trading volumes, enhancing decision-making with a 15% improvement in prediction accuracy.
  • Ensured 100% data integrity, reducing anomalies by 30% through comprehensive validation checks.
  • EduMentor AI-Powered Learning Assistant
    See Github Repository

    Python

    Flask

    Streamlit

    OpenAI GPT-4

    Firebase

    RAG

    EduMentor AI-Powered Learning Assistant

  • Developed EduMentor, an AI-powered personalized learning platform integrating Flask, Streamlit, Firebase, GPT-4 (RAG), Serper API, and BeautifulSoup to deliver adaptive quizzes, topic exploration, a knowledge graph, and chat-based assistance.
  • Enhanced learning efficiency by 50%, concept retention by 20%, and quiz relevance by 35% through real-time web integration and personalized learning paths with sub-1-minute onboarding and 99.8% uptime.
  • Engineered secure Firebase authentication, ensured 100% data privacy compliance using synthetic data and robust error handling, and led cross-functional Git integration to maintain seamless development workflows.
  • See Github Repository

    Python

    Deep Learning

    Visualization

    Data Analysis

    Medical Image Segmentation

  • Developed a U-Net model for medical image segmentation, specifically for tumor detection in class-imbalanced datasets. Applied data augmentation techniques (e.g., Horizontal Flip, Vertical Flip, Random Brightness Contrast) on the Brain Tumor, Kidney Tumor, and Retina Vessel Extraction datasets, increasing the dataset size by 50%.
  • Experimented with diverse loss functions including Dice, Cross Entropy, Combo, and Focal Loss, with Focal Loss yielding the best results. This approach improved segmentation accuracy by 12% compared to traditional models. The model achieved an IoU score of 0.825 and Dice coefficient of 0.91 on the Brain Tumor dataset, boosting tumor detection precision by 18%.
  • See Github Repository

    Python

    Machine Learning

    Visualization

    Data Analysis

    Stock Market Dashboard

  • Stock Market Data Analysis: Processed and analyzed 100% of stock data (249 rows, 17 columns) from Adani Enterprises, covering price trends, trading volume, and volatility insights.
  • Power BI Dashboard Development: Created 100% interactive Power BI dashboards, implementing 50%+ visualization components such as charts, KPIs, and trend analyses for tracking stock performance.
  • Statistical & Business Insights: Conducted 100% exploratory data analysis, identifying 15%+ stock price volatility (₹370+ standard deviation) and providing data-driven insights for risk assessment.
  • See Github Repository

    Python

    Machine Learning

    Visualization

    Data Analysis

    Identifying Diseases in Chest X-Rays

    The project aimed to address limited chest disease diagnostics and healthcare professional shortages in Myanmar by developing an AI-powered solution for early detection. A literature review refined goals, and a diverse dataset, including COVID-19 cases, was collected. Robust deep learning models achieved high accuracy in identifying chest diseases, with a focus on COVID-19 cases.
    See Github Repository

    Python

    Machine Learning

    Data Analysis

    Predicting Student Success

    Extensive Exploratory Data Analysis, coupled with feature engineering using Python tools, contributed to the development of a predictive model. This model utilized machine learning algorithms like logistic regression and decision trees. Derived from meticulous data analysis, the model successfully captured the intricate factors influencing student performance.
    See Github Repository

    Python

    Computer Vision

    NLP

    Data Analysis

    Image Captioning with Transformer-based Model

    Developed an advanced image captioning model, seamlessly blending computer vision and natural language processing. Meticulously preprocessed the MS-COCO dataset, tokenized captions, and integrated a Vision Transformer with a Transformer model. Through iterative fine-tuning and the implementation of custom loss functions, crafted a highly sophisticated model achieving remarkable accuracy. The outcome is an image captioning solution capable of generating coherent and contextually relevant captions for diverse images.
    See Github Repo

    Python

    Power BI

    Tableau

    Data Analysis

    Dashboard for Sales of Advertisement Campaigns

    Utilized Microsoft Power BI and Excel for comprehensive data analysis, revealing underperforming Campaigns due to low reach, high CPC, and inadequate engagement. Recommendations supported by a detailed Power BI dashboard led to strategic decision-making, enhancing GlobalShala's marketing cost-effectiveness.
    See Github Repo

    Python

    Data Analysis

    Machine Learning

    Weather Forecast

    This weather prediction project uses historical data to forecast tomorrow's temperature. It involves downloading and customizing a local weather dataset, cleaning the data, and building a robust model. The systematic approach includes implementing a testing framework for model evaluation and enhancing accuracy by adding predictors. The result is a comprehensive system for precise next-day temperature predictions, showcasing proficiency in data analysis and machine learning.

    Skills

    Skills






    Background

    Background

    My Education

    Georgia State University
    J Mack Robinson College of Business

    Aug 2024 - Dec 2025

    Masters in Data Science & Analytics

    • GPA : 4.21/4.00
    • Courses: Data Programming, Data Management, Statistical Foundations, Communicating with Data, Scalable Data Analytics, Predictive Analytics, Machine Learning for Analytics, Deep Learning & Gen AI

    Shri Govindram Seksaria Institute of
    Technology & Science

    Oct 2020 - Jun 2024

    BTech in Information Technology

    • GPA : 8.86/10.00
    • Courses: Data Structures & Algorithms (C++), Data Mining and Warehousing, Database Management Systems, Object-Oriented Programming, Mathematics, Cloud Computing, Operating Systems, Software Engineering, Computer Networks.

    Chameli Devi Public School, India

    Jul 2018 - Jun 2019

    12th Standard

    • Percentage: 90%
    • Courses: Physics, Chemistry, Mathematics

    Chameli Devi Public School, India

    Jul 2016 - Jun 2017

    10th Standard

    • CGPA: 10.00

    My Experience

    Shepherd Center, Atlanta, GA

    Jan 2025 – May 2025

    Graduate Research Assistant

    • Engineered time-series classification models on 40,000+ multimodal MS patient records (demographics, MRI, treatments), achieving 59% G-mean and 77% specificity in predicting physical outcomes (improvement, stable, worsening), supporting early tailored treatment.
    • Built scalable ML pipelines using Python, scikit-learn, and XGBoost to process 35,000+ longitudinal treatment records, incorporating 6-month resampling and outlier handling for robust data preprocessing.
    • Implemented ARIMA forecasting and SHAP explainability techniques, boosting model accuracy by 22% and enhancing rehab success prediction by 15%.

    Better Business Bureau, Atlanta, GA

    Aug 2024 – December 2024

    Graduate Research Assistant

    • Developed and fine-tuned generative AI models on 3.4M+ Better Business Bureau complaints, leveraging LLMs and Llama-based models via Hugging Face, achieving 81% cosine similarity and outperforming baseline models by 31% (previously 50%).
    • Improved AI-generated customer responses, boosting alignment with expected outcomes from 36% to 77%, significantly enhancing response consistency and customer satisfaction.
    • Integrated ChatGPT and advanced LLMs into customer service workflows, reducing response time by 40% and streamlining feedback processing, achieving 84% similarity for 5-star ratings and 83% for positive reviews, effectively decreasing human workload while maintaining high satisfaction.

    Omdena

    Jul 2023 – Sep 2023

    Machine Learning Collaborater
    Volunteer Project

    • Led a multidisciplinary team of 80 contributors in developing a Deep Learning model for chest X-ray disease detection (including COVID-19), achieving an impressive accuracy of ~88.47%.
    • Spearheaded data collection, EDA, preprocessing,model improvement, and user interface development.
    • Gained hands-on experience in medical image analysis, deep learning, and large dataset handling.

    Omdena

    Jul 2023 – Aug 2023

    Machine Learning Collaborater
    Volunteer Project

    • Collaborated with 50 team members to develop a data-driven model for predicting student success in the Turkish education system.
    • Analyzed diverse student data including demographics, socio-economic status, learning environments, and performance metrics.
    • Implemented and compared various ML algorithms (linear regression, decision trees, SVM, ANNs) achieving a 91.29% accuracy in predicting student success.
    • Sharpened skills in machine learning, data visualization, EDA, and data analysis through active project participation.

    Excelerate (Powered by
    Saint Louis University)

    Jul 2023- Aug 2023

    Data Visualisation Intern

    • Led data analysis & visualization team at Saint Louis University, driving 29% cost savings through efficient resource optimization.
    • Mastered Tableau to unlock strategic insights and deliver impactful presentations to stakeholders for 13 diverse ad campaigns.
    • Fostered a collaborative environment, ensuring successful project outcomes and earned a $500 Excelerate Scholarship.

    Excelerate (Powered by
    Illinois Institute of Technology)

    Jun 2023 – Jul 2023

    Data Analyst Intern

    • Spearheaded a 20-member team at Illinois Tech Excelerate program to analyze GlobalShala's marketing data for 11 diverse ad campaigns, driving an 80% improvement in campaign performance (measurable metrics: conversion rate, click-through rate) contributing to a $30,000-$40,000 cost saving through optimized campaign strategies.
    • Crafted captivating Power BI reports and actionable insights, optimizing future campaigns.
    • Mastered data analysis and presentation skills, leveraging Power BI to deliver impactful findings, earning a prestigious $1000 scholarship and badge.

    Indian Institute of Technology Patna

    Jun 2023 – Jul 2023

    Research Intern

    • Developed automated catalog generation system for efficient object identification & cataloging in densely packed images (e-commerce, inventory management).
    • Leveraged RoboFlow for meticulous annotation of individual products within 700-800 images featuring complex, crowded product arrangements.
    • Applied proficient PyTorch skills to construct a YOLOv7 object detection model, resulting in an accuracy of 87.93%, showcasing its effectiveness in effectively identifying objects even in densely packed scenes.
    • Gained practical skills in training and refining deep learning models, resulting in accurate detection of catalogs in images

    Muskurahat Foundation

    Feb 2023 - Mar 2023

    Summer Intern

    • Actively worked to enhance the education and well-being of underprivileged children while successfully raising Rs 1111 in support of this noble cause.
    • Developed fundraising, marketing, and communication skills while creating impactful change.

    Publications

    Publications

    1. Enhancing Medical Image Segmentation using Deep Learning: Exploring State-of-the-art Models and Loss Functions for Class Imbalanced Datasets 🔗

     Palak Kakani, Chevi Parsai and Shreya Vyas


    2. Automated Catalog Generation using Deep Learning 🔗

     Palak Kakani and Shreya Vyas

    Contact

    Get in Touch

    Connect with Me

    Email: palak11.kakani@gmail.com