Freelancers using Streamlit in Lagos
Freelancers using Streamlit in Lagos
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Emmanuel Ezeokeke
Lagos, Nigeria
AI Expert |AI Agent| AI RAG | LangChain | LangGraph | CrewAI
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AI Expert |AI Agent| AI RAG | LangChain | LangGraph | CrewAI
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All My Reviews on Upwork
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Local AI Agent Troubleshooter
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AI System for Doctor Appointment Management
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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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62
Streamlit
(4)
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Victory Nnaji
Lagos, Nigeria
Technical Writer & Developer Relations Engineer | AI, Python
5.0
Rating
6
Followers
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Technical Writer & Developer Relations Engineer | AI, Python
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COVID-19 Dashboard for Berlin City
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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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Understanding Retrieval Augmented Generation
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5
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Netflix Recommender System
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17
Streamlit
(1)
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Damilola Akinrinmade
Lagos, Nigeria
Experienced Data Analyst, bringing your data to life
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Experienced Data Analyst, bringing your data to life
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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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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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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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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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128
Streamlit
(1)
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AMAH Daniel
Lagos, Nigeria
Finding Solutions using Intel, Orchestration and Research
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Finding Solutions using Intel, Orchestration and Research
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Creating a chatbot with contextual retrieval using Cohere comma…
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MLOps API: Sentiment Analysis with DistilBERT
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Building Automated Data Reports from Supabase with GitHub Actio…
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3
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Readability Guidelines App
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2
Streamlit
(1)
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Ayobami Akomolafe
Lagos, Nigeria
AI Engineer creating AI systems such as Chatbots & AI agents
New to Contra
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AI Engineer creating AI systems such as Chatbots & AI agents
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An Adverse Drug Effect Predictor Using AI By applying a machine learning approach to Rheumatoid Arthritis therapy, Adept-AI (Adverse Drug Effect Predictor - Artificial Intelligence) aims to improve prescription outcomes of Rheumatoid Arthritis drugs by predicting how individual patients will respond to medication based on demographic factors.
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RAG based Chatbot for Sales Assistant with streamlit.
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Web scraping and Automation with selenium
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Inventory chatbot development and integration with Whatsapp via Twilio.
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20
Streamlit
(2)
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Favour Orifa
Lagos, Nigeria
Machine learning engineer building intelligent models
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Machine learning engineer building intelligent models
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Detection of Gi diseases
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A RAG chatbot based on the school's prospectus that assists university students with their questions
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Thyroid recurrence prediction
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space x flight prediction
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Streamlit
(1)
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