Personal Finance Data Pipeline & Dashboard

AI‑powered personal finance app and website with Flutter and React frontend, FastAPI backend, and an MCP server pipeline for realistic financial data and AI insights. Check out my blog: https://alwaysanidhya.medium.com/how-48-hours-of-caffeine-chaos-and-code-got-us-to-the-finals-at-googles-ai-hackathon-d988690eeb7a

A data pipeline for personal finance.

Personal finance data pipeline & insights API. Ingestion, ETL, analytical modeling, and smart financial understanding.

A Python-based ETL pipeline that extracts personal finance transaction data from CSV files, transforms it using pandas, and loads it into a PostgreSQL database with star schema modeling. Demonstrates core data engineering fundamentals with proper error handling and data quality checks.

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Data Finance Pipeline – personal data engineering project

An automated ETL pipeline for personal finance data.

Personal finance database, data pipeline, tracker and dashboard.

Personal finance data pipeline using Python, PostgreSQL, and Docker.

Personal Finance ETL Pipeline with Star Schema Data Warehouse

Personal finance data analysis pipeline with validation, aggregation, and reporting

A use case for a personal finance company to create and maintain data pipelines in snowflakes.

End-to-end personal finance analytics pipeline with robust data cleaning, rule-based categorization, KPI generation, and a Streamlit dashboard.

End-to-end personal finance analytics pipeline: Python ETL, rule-based categorization, and Power BI model built using synthetic data.

An unsupervised machine learning pipeline using K-Means Clustering and Isolation Forest to automatically detect and visualize unusual spending patterns in personal finance data.

A structured personal finance data pipeline that cleans transactions, applies category logic, generates yearly metrics, and visualizes insights through a dynamic dashboard. Uses mock data to showcase ETL, modeling, and automation best practices.

An automated personal finance pipeline that extracts structured data from SMS alerts using Gemini 1.5 Flash. Infrastructure provisioned on AWS EC2 via Terraform and orchestrated with Dockerized n8n.

Personal travel data pipeline — GPS, health, finance, and activity tracking across 2–3 years of international travel. FastAPI + SQLite on Raspberry Pi, with ML analysis and an interactive public dashboard.

Advanced Excel dashboard for personal finance tracking — 7 sheets, 600+ SUMPRODUCT formulas, Power Query pipeline, VBA automation (PDF export, Outlook), KPI cards, dynamic charts & savings goal tracker. 2 years of realistic sample data included.

Built and optimized ETL pipelines for personal finance and news analytics using the Mint API and Reddit data. Leveraged Python and Selenium to extract, process, and analyze real-time financial transactions and trending news content.

An advanced, modular personal finance tracking system built with Python, Pandas, SQLite, and Power BI integration. This project enables comprehensive management of income, expenses, budgets, and investments through a clean data pipeline and interactive command-line interface

AI-Powered Personal Finance Advisor built with Go - A modern financial management system featuring intelligent investment recommendations, real-time market data integration, comprehensive transaction tracking, and advanced analytics. Built with Clean Architecture, JWT authentication, and production-ready DevOps pipeline.

A full-stack Personal Finance Management (PFM) tool built with ASP.NET Core Web API and Angular 16. Features secure user authentication, robust data management using MS SQL Server with Entity Framework, budget tracking, and data visualization. The project demonstrates Clean Architecture and an automated Azure CI/CD pipeline.

An AI-powered personal finance assistant that analyzes historical expenses, forecasts future spending using ARIMA, and provides intelligent insights through an LLM-based chat interface. The system includes a full data pipeline, trained forecasting model, and an interactive UI, making it closer to a real-world product than a standalone ML notebook.