About
Final-year B.E. Computer Science (Data Science) student with hands-on experience across Python, SQL, Power BI, and machine learning. Built through a live fintech internship, a hackathon-winning credit risk project, and applied ML/quantum-computing research. Seeking to join as data analyst to apply structured problem-solving and analytics to large-scale business decisions.
Experience
Education
Projects
Retail Sales Performance Dashboard
Built a 3-page interactive Power BI dashboard on the Superstore sales dataset covering Executive Overview, Discount-Profit Leak analysis, and Region/Category drill-down. Uncovered that 856 high-discount orders were loss-making, and flagged Central region Furniture as a systemic margin drag. Designed KPI cards (Sales $2.30M, Profit $286K, 12.5% margin), a Discount vs. Profit scatter plot by category, and a Region × Category profitability matrix.
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Hybrid Quantum-Classical Framework for Anomaly Detection in Hyperspectral Data
Led a team building a hybrid anomaly detection framework combining PCA, quantum kernel circuits, and One-Class SVM for hyperspectral imagery. Synthesized findings from 8 research papers and coordinated iterative development of an IEEE-format technical paper. Directed task allocation, technical direction, and final documentation as team lead.
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Credit Risk EDA for NBFC Client
Led a team to 1st place performing exploratory data analysis and credit risk assessment for Pragati FinCorp, an NBFC client. Identified key risk indicators and presented actionable recommendations to judges under time-constrained hackathon conditions.
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Image Classification using CNN
Built a Convolutional Neural Network achieving 90-95% validation accuracy for multi-class image classification.
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House Price Prediction (BDS602)
Linear Regression model achieving R² ~0.97; full technical report in VTU format.
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Smart Parking System (BAD601)
Owned ML prediction layer on Hadoop/Spark/Hive, achieving 91% accuracy, 88% F1-score.
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