Python app to help you become prudent in your spendings
- Updated
Aug 7, 2020 - CSS
Python app to help you become prudent in your spendings
A look back at your Amazon shopping... parse / slice / dice Amazon purchase history CSV export
"Streamable HTTP" MCP server deployed on AWS Lambda and amazon API Gateway. The server provides a tool for Amazon Bedrock spend analysis.
The sample integration flows in this repository demonstrate how to use SAP BTP Integration Suite to pull data from the SAP Ariba Analytical Reporting API, process it into SAP Data Warehouse Cloud, and analyze it using the SAP Analytics Cloud Spend Analysis business content.
A replacement for the FPDS XML conversion utility: converts one or more FPDS data archives to a SQLite3 database.
Dashboard is prepared in Tableau to understand the spend analysis of different generations on different categories.
CREDIT CARD SPEND PREDICTION
Application for tracking grocery expenditures and predict food shortages in household pantry
SpendSmart is a cutting-edge budget suggestion app. Beyond traditional budgeting functionalities, SpendSmart empowers users to compare the cost of a single iPhone or product with other items within their budget.
Track all your daily, monthly and yearly expenses in a modern sleek UI. Looking forward for more contributors to this repository.
Heroku app that visualizes the current US Budget as a treemap
📱 A mobile-first Expense Tracker built with Angular & Tailwind CSS, featuring dashboards, calendar, budget tracking, and LocalStorage-based data management
Take Control of Your Finances
SQL analysis of 500-respondent Mumbai consumer survey examining branded vs unbranded buying behaviour across 5 income classes and 5 Mumbai regions. Covers brand affinity scoring, behaviour segmentation & purchase factor mining. Stack: Python · PostgreSQL · Power BI
NLP soft weighted voting ensemble multi-class classifier to predict spend categories using Government of California’s 2012-2015 purchase order data
Procurement spend analysis with category classification and savings ID
Predicted customer transactions using recency, frequency, spend behaviour and Social Network metrics over lifetime using MLlib
AI-driven procurement optimization platform with spend analytics, strategic sourcing algorithms, contract lifecycle management, and autonomous purchasing agents for enterprise supply chains
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