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.

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.

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

🚀 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.

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

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

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

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

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

End-to-end KYC/AML compliance data analysis using mock datasets. Includes customer risk scoring, suspicious transaction flagging, and compliance reporting in Python (Pandas, Matplotlib).

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.

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. 🏦📈

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.

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.

This project documents the development of a Python-based Forex trading algorithm that integrates technical indicators and news analytics to automate trading strategies on MetaTrader 5.

modeling of pricing and analysis of stock and options

🏦🤖FinChurn is an advanced Financial Machine Learning system designed to predict customer churn and detect fraudulent activities with high accuracy. It includes a complete end-to-end ML workflow covering data preprocessing, exploratory analysis, class imbalance handling (SMOTE)