Turning raw data into clear business insights using Python, SQL, Power BI and AI-assisted analytics. Specialized in EDA, machine learning, and interactive dashboard creation.
I am a passionate Data Analyst with strong foundations in Python, SQL, Power BI, and Exploratory Data Analysis. With hands-on experience from multiple analytics internships at companies like VOIS, Infosys, Uni Converge Technologies, and Edunet Foundation–IBM, I have worked on large-scale datasets, built interactive dashboards, and leveraged AI-assisted tools to extract actionable insights.
My focus is on delivering accurate, clear, and decision-ready reporting that drives business value. I am constantly exploring new technologies in data science, machine learning, and generative AI to stay at the forefront of the analytics field.
{ "name": "Gagan Dhanapune", "role": "AI-Driven Data Analyst", "location": "Pune, Maharashtra, IN", "status": "Open to Opportunities", "primary_skills": [ "SQL", "Python", "Power BI", "Machine Learning", "Generative AI" ], "internships": 5, "projects": 10, "certifications": 6 // and counting }
ML pricing engine trained on 113,815 Amazon India fashion orders across 37 states/UTs and 7 categories. It simulates 60 price points per run and recommends profit-maximizing prices, with model performance of R2 0.978 and average optimization uplift of +38%.
End-to-end financial analytics platform combining SQL, ML, AI, BI, and Streamlit across a 700-row, 20-column dataset. It uncovered $10.30M revenue, $2.56M profit at 24.86% margin, and 270% YoY growth from 2013 to 2014 via 7 SQL analysis modules and AI-driven recommendations.
Comprehensive EDA and ML on 8,800+ Netflix titles. Built a Random Forest classifier with interactive Power BI dashboards.
End-to-end Power BI dashboard integrating 5 datasets. Advanced DAX for RevPAR, ADR, and occupancy metrics across multiple properties.
Advanced SQL analysis of US flight delay patterns with CTEs, window functions, and aggregations. Identified weather correlations and performance gaps.
92% accuracy on CIFAR-10 using custom CNN. Integrated MobileNetV2 for real-time prediction across 1,000+ classes. Built a Streamlit app with instant image upload and prediction.
AI-powered health assistant with 10+ smart features including image-based diagnosis, voice input, and multilingual support. Integrated Gemini 1.5 Pro API for real-time medical image analysis.
Interactive Excel dashboard for hospital ER analytics. Power Query for data transformation, DAX for patient categorization, wait time analysis, and performance tracking KPIs.
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Worked on large-scale datasets with 100,000+ records using AI-assisted analytics. Comprehensive data cleaning, transformation, and EDA to extract actionable trends and business patterns.
Executed end-to-end data analysis including EDA and KPI extraction using Python, SQL, and BI tools. Built automated pipelines and interactive dashboards for stakeholder presentations.
Worked on Python-based NLP text classification — collected and prepared text datasets, explored model outputs, and assisted with documentation and testing. Collaborated with cross-functional team members and contributed to Agile workflows.
Built interactive traffic forecasting visuals using Matplotlib and Seaborn, reducing analysis time by 40%. Boosted prediction accuracy by 23% using Random Forest and XGBoost on 1M+ traffic records.
Achieved 87% accuracy predicting employee burnout using Random Forest and SVR. Analyzed a dataset of 10,000+ records — workload and job satisfaction were identified as primary burnout factors.
Selected for a 4-week internship under Shell-Edunet Skills4Future. Worked under mentor guidance on an Air Quality Index Prediction Model using Python, with project-based learning and industry-led sessions.
Worked on full-stack development project with primary focus on backend development using Node.js and Express.js. Contributed to REST API design and MongoDB integration.
Worked on a full-stack development project with primary focus on backend engineering. Contributed to API development, server-side logic, and database operations using the MERN stack.
Earned a 4-Star Gold Badge on HackerRank SQL by solving 45+ challenges — demonstrating mastery of advanced queries, joins, aggregations, and window functions.
Ranked in the Top 10 at the Codelite Pan-India Hackathon, competing against participants nationwide. Demonstrated problem-solving and technical execution under pressure.
Scored 95% among 3+ lakh students. Ranked in the top 3% nationally — a testament to strong analytical thinking and domain knowledge.
Completed 150+ hands-on labs across Google Cloud Platform including data engineering, ML, and cloud infrastructure — earning Premium Milestone status.
Regularly solving SQL and Python challenges on LeetCode to sharpen data manipulation and algorithmic thinking skills.
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