Freelancers using Streamlit in LagosFreelancers using Streamlit in Lagos
ProjectsPeople
Emmanuel Ezeokeke's avatar
Emmanuel Ezeokeke
Lagos, Nigeria
AI Expert |AI Agent| AI RAG | LangChain | LangGraph | CrewAI
AI Expert |AI Agent| AI RAG | LangChain | LangGraph | CrewAI
Cover image for All My Reviews on Upwork
All My Reviews on Upwork
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Cover image for Local AI Agent Troubleshooter
Local AI Agent Troubleshooter
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4
Cover image for AI System for Doctor Appointment Management
AI System for Doctor Appointment Management
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7
Cover image for I made a tutorial for an AI agent where I built an AI-powere...
I made a tutorial for an AI agent where I built an AI-powered blockchain analytics platform (https://www.youtube.com/watch?v=ceRZWLkxEbU) built with CrewAI that analyzes crypto wallets across Ethereum, Polygon, BSC, Arbitrum, and Base. Uses 4 specialized agents - Portfolio Analyst, Transaction Specialist, Investment Strategist, and Intelligence Synthesizer - working sequentially to process on-chain data via Zapper API. Delivers good reports with portfolio composition, risk assessment, behavioral patterns, and investment recommendations through CLI and Streamlit interfaces with real-time tracking and downloadable outputs. Try it out: https://onchain-ai-agent.onrender.com/ Github code: https://github.com/Emarhnuel/Onchain-AI-agent
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Streamlit(4)
Victory Nnaji's avatar
Victory Nnaji
Lagos, Nigeria
Technical Writer & DevRel | API, AI/ML & SaaS Docs
5.0
Rating
6
Followers
Technical Writer & DevRel | API, AI/ML & SaaS Docs
Cover image for COVID-19 Dashboard for Berlin City
COVID-19 Dashboard for Berlin City
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Cover image for I just published a practical
I just published a practical guide: “How to Choose the Right AI Framework for Your Use Case.” If you're evaluating AI agents (LangChain, CrewAI, no-code tools, etc.), the real question isn’t what’s popular; it’s what fits your business. In this guide, I break down: 1. Team technical capability 2. True cost of ownership 3. Matching tools to real use cases 4. How company size impacts your decision Includes decision flowcharts, cost insights, and a roadmap from prototype to production. Key insight: The winners in AI aren’t chasing hype. They test fast, learn quickly, and scale what works. Read here: Medium: https://shorturl.at/YNyU1 Ready Tensor: https://shorturl.at/X8VgR What’s been your biggest challenge in choosing AI tools?
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Cover image for Understanding Retrieval Augmented Generation
Understanding Retrieval Augmented Generation
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Cover image for Netflix Recommender System
Netflix Recommender System
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Streamlit(1)
Damilola Akinrinmade's avatar
Damilola Akinrinmade
Lagos, Nigeria
Experienced Data Analyst, bringing your data to life
6
Followers
Experienced Data Analyst, bringing your data to life
Cover image for This project takes a fraud-detection
This project takes a fraud-detection model built in a notebook and turns it into a small, runnable system you can demo and deploy locally. In my workflow, I start with the notebook, save a single artifact, then wrap it with an API, a UI, and basic monitoring. 1 (https://1). Train and evaluate a model on creditcard.csv 2. Save a deployable artifact (.pkl) 3. Serve predictions through a FastAPI backend 4. Provide a Streamlit UI for manual testing and batch scoring 5. Log predictions and feedback labels for monitoring 6. Run basic drift checks and export feature importance for sanity checks What Problem This Solves Fraud detection is a highly imbalanced classification problem, so "accuracy" is usually misleading. What you typically want is: 1 (https://1). a risk score (fraud probability) per transaction 2. a decision policy (threshold) you can tune to match operational goals (precision/recall tradeoff) 3.  serving layer so the model works outside Jupyter 4. a monitoring loop so you can detect drift and decide when to retrain
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Cover image for Built an end-to-end e-commerce recommendation
Built an end-to-end e-commerce recommendation system using FastAPI, SQLite, and custom ML ranking models. The project supports content-based, collaborative, and hybrid recommendations, ingests a real product catalog dataset, seeds user interactions for personalization, and exposes a browsable frontend plus documented REST APIs.
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Cover image for This project was executed through
This project was executed through the use of Microsoft excel, it shows trends and metrics of Air crashes from inception till date
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Cover image for This project highlights key metrics
This project highlights key metrics in car sales and pricing, with insights into dealership distribution, sales performance, revenue, and customer preferences.
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144
Streamlit(1)
Favour Orifa's avatar
Favour Orifa
Lagos, Nigeria
Machine learning engineer building intelligent models
Machine learning engineer building intelligent models
Cover image for Detection of Gi diseases
Detection of Gi diseases
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Cover image for A RAG chatbot based on
A RAG chatbot based on the school's prospectus that assists university students with their questions
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Cover image for Thyroid recurrence prediction
Thyroid recurrence prediction
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1
Cover image for space x flight prediction
space x flight prediction
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2
Streamlit(1)