Prompt Engineer & Applied AI Builder. Directing LLMs to build agentic pipelines, automation tooling & React web apps.

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Mohammad Naved  • Peerlist

Project Title: Cryptocurrency Live Trend Analyzer (Binance API) Role: Data Analyst / Developer Project Description: Developed an automated quantitative data pipeline to extract, clean, and visualize real-time cryptocurrency market trends. Successfully integrated the live Binance REST API using Python (urllib, json) to fetch completely unstructured daily market JSON data arrays.

Cryptocurrency Live Trend Analyzer (Binance A

Project Description: Engineered an end-to-end Retail Data Analytics pipeline to process and aggregate thousands of raw transaction logs. Conducted rigorous Exploratory Data Analysis (EDA) and data cleansing operations utilizing Pandas to manage null values via median imputation and isolate duplicate/negative return records. Seamlessly integrated DuckDB directly over Pandas DataFrames to perform ultra-fast, in-memory SQL execution. Wrote complex SQL queries evaluating specific mathematical matrices like SUM(Quantity * UnitPrice) mapping GROUP BY City & Category logic variables. Generated actionable business intelligence resulting in a high-resolution Matplotlib Regional Sales bar-chart highlighting maximum revenue-generating hotspots.

E-Commerce Sales Performance & EDA Pipeline R

Project Description: Ingested and pre-processed over 5,000+ unformatted user streaming logs to extract patterns relating to user retention and viewer preferences. Re-enforced data reliability through Pandas by purging duplicated server logs and resolving missing feedback structures (NaN ratings) utilizing internal median calculations. Seamlessly orchestrated automated pipeline queries through DuckDB to synthesize rapid Dataframe outcomes without memory overheads. Queried complex metrics grouping massive segments by specific Account Subscription Tiers to discover maximum Watch-Time correlations. Measured multi-parameter user interactions, successfully ranking global streaming Content Genres based on raw view volume mapped against peak User Ratings.