Mutual Fund Analysis Dashboard using Python, Excel, and Power BI | Top 30 Low-Risk High-Return Schemes Identified

Detect and classify fraudulent transactions using SQL and Python. Generate behavioral features with SQLite, train a Logistic Regression model, and evaluate performance with AUC, precision, recall, and ROC analysis. A complete supervised fraud detection workflow.

Detect suspicious financial transactions using SQL and Python. Build user-level behavioral features in SQLite, apply Isolation Forest for anomaly detection, and visualize high-risk patterns. Demonstrates unsupervised fraud analytics and SQL-driven data science workflow.

AI-powered group finance assistant using MCP architecture, Gemini LLM and Streamlit.

Personal investing tracker with watchlist, portfolio analytics, and corporate events tracking. Built with Next.js 15, tRPC v11, Prisma, and InfluxDB.

This repository contains results of the completed tasks for the Quantium Data Analytics Virtual Experience Program by Forage, designed to replicate life in the Retail Analytics and Strategy team at Quantium, using Python.

Job-ready FP&A & Financial Analytics portfolio—forecasting, variance analysis, KPI dashboards, and executive reporting (Python/SQL).

🚀 AlphaCrew: Production-grade multi-agent hedge fund platform powered by CrewAI Enterprise. Features live trading via Alpaca, real-time performance monitoring with Grafana, and human oversight through Slack. Built for sophisticated algorithmic trading and portfolio management.

This repository contains all lab work and digital assessments from the Winter Semester of my M.Sc. Data Science program at VIT Vellore. Projects span across machine learning, data mining, statistical inference, time series analysis, data visualization, and Java programming—implemented using tools like Python, R, Power BI, Tableau, Excel, and Java

Demonstrates a workflow that involves fetching, processing, storing, analyzing, and reporting on financial data using machine learning techniques within a Snowflake database environment

End-to-end Credit Risk engine using Python. Achieved 93.04% Cross-Validated Recall and 0.98 ROC-AUC. Implemented advanced preprocessing (Log/Robust Scaling) and SMOTEENN to handle class imbalance. Champion model (Logistic Regression) provides full interpretability for strategic financial risk mitigation. 🏦📈

Production-style financial data engineering pipeline that standardizes NSE equity fundamentals into a query-optimized SQLite warehouse.

In this project, I analyze commercial sales data using NumPy and pandas. I visualize total revenue per product using color-coded bar charts in Matplotlib. It’s a foundational step in business data analysis and project documentation.

Interactive Power BI dashboard analyzing credit card transactions to uncover spending patterns, customer insights, and key financial KPIs for data-driven decision-making.

Portfolio Risk Simulator is an interactive web app that lets users build portfolios, analyze risk with VaR and Sharpe ratios, visualize correlations, and compare performance to benchmarks, uses real-time data.

A comprehensive MCP server for YNAB with 55+ tools covering the full API plus advanced analytics—spending trends, subscription detection, budget health scores, and savings recommendations. Works with Claude Desktop and other MCP-compatible AI assistants.

An analytical study of how Bitcoin market sentiment (Fear vs Greed Index) influences trading volume, leverage behavior, and profitability using historical data.

Automated financial reconciliation and business analytics platform for theater operations. Reduced reconciliation time by 90%.

Analyzing $33B in lending data across 2 Million+ records to identify credit risk patterns using Python, SQL, and Power BI.

Chrome extension for comprehensive expense tracking and financial analysis, empowering users with automated e-commerce price detection, OCR receipt scanning, and real-time budget monitoring across multiple currencies.