MS-Apriori is used for frequent item set mining and association rule learning over transactional data.
Simple web dashboard, which tracks transactions for one specified address on the Ethereum blockchain
A simplified tool that analyzes mock banking transaction data, identifies spending patterns, categorizes expenses, and visualizes results clearly.
A reusable Python pipeline for cleaning and standardizing transaction data using pandas. Handles type normalization, missing/invalid values, median imputation, dependent column recalculation, and consistent formatting for reliable analysis and reporting.
A Python tool for analyzing Ethereum blocks using Web3.py.
RFM-based customer segmentation analysis for an e-commerce dataset. Includes data cleaning, exploratory analysis, Recency-Frequency-Monetary scoring, segment classification, visual dashboards, and strategic business insights. Designed to identify high-value customers and guide targeted marketing actions
Pipeline for analyzing fraud in card transaction data-sets with an addition of graph features, modeled using Random Forest
Signal PrOcEssing Features for transaction/balance data - Package
Subscription tracking backend demo combining AWS Textract OCR with Plaid
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