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You will get Real Estate Data Science Machine Learning Project
Project details
Has Various Models that run simultaneously, and are used to get best result wither by selecting specific model or voting among themselves. Clean and self explanatory graphs using Plotly and Dash Framework, well documented and easy to understand comments for easier understanding
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, XGBoostWhat's included
| Service Tiers |
Starter
$10
|
Standard
$20
|
Advanced
$30
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Revisions | 1 | 1 | 2 |
Model Validation/Testing | - | - | - |
Model Documentation | - | - | - |
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
About Aditya
Python AI Engineer | LLM Agents | Voice AI | FastAPI |RAG & Automation
Purnia, India - 11:20 am local time
I build production-ready AI systems using Python, not experimental demos or proof-of-concepts that never see the light of day. My focus is on designing and deploying LLM-powered applications that solve real business problems with reliability, scalability, and measurable ROI.
With 2+ years of intensive hands-on experience in applied AI engineering, I've developed voice-based agents, intelligent recruitment automation systems, semantic search pipelines, and sophisticated multi-model orchestration backends, all running successfully in production environments.
𝙒𝙝𝙖𝙩 𝙨𝙚𝙩𝙨 𝙢𝙚 𝙖𝙥𝙖𝙧𝙩: I transform ambiguous business challenges into structured AI solutions with built-in guardrails, intelligent fallback logic, robust validation layers, and quantifiable performance metrics. If you need AI that delivers consistent, reliable results; I'm your engineer.
𝗖𝗢𝗥𝗘 𝗘𝗫𝗣𝗘𝗥𝗧𝗜𝗦𝗘
• Intelligent Chatbots & LLM Agents – OpenAI, Gemini, Groq, Anthropic
• Voice AI Systems – LiveKit, Twilio, real-time streaming architectures
• Document Intelligence – Resume screening, automated document processing
• RAG & Semantic Search – Vector databases, embeddings, hybrid search
• AI Workflow Automation – Complex business process automation
• Multi-Model Orchestration – Smart routing, graceful degradation, fallback logic
• Production Backend APIs – FastAPI, MongoDB, scalable microservices
• AI Safety & Reliability – Guardrails, structured outputs, validation frameworks
𝗣𝗥𝗢𝗩𝗘𝗡 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 (𝗥𝗘𝗔𝗟 𝗣𝗥𝗢𝗗𝗨𝗖𝗧𝗜𝗢𝗡 𝗠𝗘𝗧𝗥𝗜𝗖𝗦)
Voice-Based AI Booking Agent
▸ <5% human call transfer rate
▸ 90% successful booking completion
▸ 30-35% reduction in average handling time
Autonomous Recruitment Agent
▸ 35-40% improvement in candidate shortlisting relevance
▸ 50% reduction in manual recruiter effort
▸ 45% faster screening-to-interview cycles
AI Copilot MVP (Internship Project)
▸ 25-30% improvement in LLM response accuracy
▸ ~40% reduction in manual evaluation workload
▸ Reduced testing cycles from days to hours
𝗧𝗘𝗖𝗛𝗡𝗜𝗖𝗔𝗟 𝗔𝗥𝗦𝗘𝗡𝗔𝗟
Core: Python (advanced), FastAPI, RESTful architecture
AI/LLM: OpenAI API, Gemini, Groq, LangChain, Embeddings, RAG
Infrastructure: Docker, GCP, MongoDB, ChromaDB, SQL
Voice/Real-time: LiveKit, Twilio, WebRTC
Best Practices: Clean architecture, comprehensive documentation, scalable deployments, rigorous testing
𝗠𝗬 𝗘𝗡𝗚𝗜𝗡𝗘𝗘𝗥𝗜𝗡𝗚 𝗣𝗛𝗜𝗟𝗢𝗦𝗢𝗣𝗛𝗬
I believe in building AI systems that are:
🔹 Structured – Clean, modular Python architecture with intentional decision flows, not messy prompt chains
🔹 Reliable – Production-tested mechanisms including fallback models, validation layers, and graceful error handling
🔹 Scalable – Designed from day one to handle growth and evolving requirements
🔹 Documented – Clear, maintainable code that your team can understand and extend
I excel when AI is the core of your product, not just a superficial feature tacked on as an afterthought.
𝙇𝙚𝙩'𝙨 𝘽𝙪𝙞𝙡𝙙 𝙎𝙤𝙢𝙚𝙩𝙝𝙞𝙣𝙜 𝙏𝙝𝙖𝙩 𝙒𝙤𝙧𝙠𝙨
If you're developing a serious AI product or need to automate complex workflows with LLMs, I'm ready to help. I partner with clients to scope requirements, architect robust solutions, and deploy systems that deliver consistent, measurable value.
Ready to turn your AI vision into production reality? Let's talk.
Steps for completing your project
After purchasing the project, send requirements so Aditya can start the project.
Delivery time starts when Aditya receives requirements from you.
Aditya works on your project following the steps below.
Revisions may occur after the delivery date.
Data Cleaning
Looking for missing or wrong data and rectifying them
Data Wrangling
Getting the features that matters