You will get Real-Time Spam Detection App | Full-Stack ML Solution
Project details
Build your own AI-Powered Email Spam Classifier with a full-stack production-ready solution! This project includes a highly accurate ML model (98.02%) using TF-IDF and Naive Bayes, wrapped in a modern FastAPI backend and a clean, responsive Next.js frontend. Get features like real-time spam detection, batch email analysis, JWT + OAuth login, API key management, and a live analytics dashboard. The system is fully containerized with Docker, secured with HTTPS, and ready for deployment with Nginx reverse proxy. Whether you're looking to detect spam at scale or want a complete ML pipeline in production — this project delivers it all.
Machine Learning Tools
GitHub Copilot, Microsoft Excel, MLflow, NLTK, NumPy, pandas, Python, Python Scikit-Learn, SciPy, SQL, TextBlob, Word2vec, XGBoostWhat's included $1,000
These options are included with the project scope.
$1,000
- Delivery Time 7 days
- Number of Revisions 2
- Number of Model Variations 2
- Number of Scenarios 2
- Number of Graphs/Charts 0
- Model Validation/Testing
- Model Documentation
- Data Source Connectivity
Optional add-ons
You can add these on the next page.
Fast 5 Days Delivery
+$300About Qadeer
AI Engineer | Multi-Agent Systems, AI Automation & LLM Apps
Khushab, Pakistan - 10:52 am local time
I'm an AI/ML Engineer, AI automation specialist, and backend developer. I don't hand over notebook demos — I ship systems that run in production and keep working.
What I build:
• Multi-agent AI systems that automate complex, multi-step business workflows
• AI voice agents for appointment booking, outbound calling, and customer automation
• Custom LLM applications — with commercial and open-source LLMs — RAG assistants, chatbots, and voice agents
• Machine learning across NLP, computer vision, and time-series forecasting
• Production ML with full MLOps and CI/CD across AWS, Azure, and GCP
• Robust backends, APIs, and integrations in FastAPI and Django
Recent projects:
• Real-estate AI platform — data scraping → ML valuation modeling → MLOps → cloud deployment + CI/CD
• Agentic AI voice agents for appointment booking and autonomous phone conversations
• AI recruitment system that screens and ranks candidates
• AI lead-generation & outreach system that finds and engages prospects at scale
My stack:
• Agentic AI & LLMs: LangChain, LangGraph, CrewAI, LlamaIndex, RAG, MCP, OpenAI, Hugging Face, open-source LLMs
• ML & Data Science: Scikit-learn, TensorFlow, Computer Vision, NLP, time-series forecasting, predictive modeling
• Voice & automation: Twilio, LiveKit, n8n, Zapier
• Backend: FastAPI, Django, REST APIs
• MLOps & DevOps: Python, AWS, Azure, GCP, Docker, CI/CD
• Databases: PostgreSQL, MySQL, MongoDB
• Vector databases: Pinecone, Qdrant, Weaviate, Milvus, ChromaDB, FAISS, pgvector
Tell me the business problem you want solved and I'll map out exactly how I'd build it.
Steps for completing your project
After purchasing the project, send requirements so Qadeer can start the project.
Delivery time starts when Qadeer receives requirements from you.
Qadeer works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Analysis & Planning
Conduct comprehensive project scoping session with client to understand specific requirements, target accuracy metrics, deployment preferences, and integration needs. Define technical specifications, performance benchmarks, and delivery timeline.
Data Collection & Preprocessing
Gather and curate high-quality email datasets from multiple sources. Implement robust data cleaning pipeline including text normalization, HTML stripping, encoding standardization, and duplicate removal. Create balanced training/validation/test.