You will get Medical cost prediction model for financial planning and risk assessment.
Rising Talent
Project details
You will get a highly accurate medical insurance cost prediction model that helps individuals, insurers, and healthcare providers make informed financial decisions. Using machine learning and data-driven insights, this model considers key factors like age, BMI, smoking status, and region to estimate medical expenses effectively.
With expertise in data science, predictive modeling, and AI deployment, this project includes exploratory data analysis (EDA), model development, evaluation, and deployment in a user-friendly Streamlit web app. This allows real-time predictions, making the tool practical and easy to use.
Expect a well-documented, high-quality solution with visual insights and reports tailored to your needs. Whether you're looking for a basic model, enhanced data insights, or a fully optimized deployment, this project delivers a reliable, efficient, and scalable solution.
With expertise in data science, predictive modeling, and AI deployment, this project includes exploratory data analysis (EDA), model development, evaluation, and deployment in a user-friendly Streamlit web app. This allows real-time predictions, making the tool practical and easy to use.
Expect a well-documented, high-quality solution with visual insights and reports tailored to your needs. Whether you're looking for a basic model, enhanced data insights, or a fully optimized deployment, this project delivers a reliable, efficient, and scalable solution.
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SQL, XGBoostWhat's included
| Service Tiers |
Starter
$20
|
Standard
$50
|
Advanced
$100
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 5 | 7 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - | - |
Frequently asked questions
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MC
Murugan C.
Feb 16, 2025
PDF and small website Data Scraping
About Eman
Data Scientist | Machine Learning | Computer Vision | AI | NLP | LLM
Giza, Egypt - 5:17 am local time
I build production-ready AI that ships fast and works reliably. From real-time voice agents to RAG chatbots and computer-vision pipelines, I turn messy data and research (e.g., RF-DETR) into products that move metrics.
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What I do
- Computer Vision: Object detection & tracking (YOLOv8/YOLO-World, RF-DETR, Detectron2), OCR, video analytics, BYTETrack/SORT, OpenCV
- LLMs & Agents: RAG over private data, tool-calling agents, NL→SQL, fine-tuning (LoRA/QLoRA, Unsloth, OpenRLHF/KTO), eval & safety (LLaMA 3, Qwen 2, Mistral, OpenAI)
- Voice & Realtime: LiveKit + Twilio assistants with Whisper/ASR and XTTS/TTS; intent routing, verification, scheduling, workflow automation
- Backend & MLOps: Python, FastAPI, LangGraph/LangChain, vLLM/Ollama, Transformers, Docker/CI, vector DBs (FAISS/Pinecone), PostgreSQL/MySQL
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Selected outcomes
- Cut call-center load by 60% with a real-time voice agent (LiveKit + Whisper + XTTS + vLLM)
- 3rd place – QTec Hackathon: CV-based supervision & anomaly detection (YOLO-World + multi-object tracking)
- Top-3 Kaggle (public comp): book-price model with MAE = 3.4
- RAG + LLM apps that boosted engagement by 30% and reduced server response by 40%
- Fine-tuned medical LLMs on OCR-generated datasets; shipped a production NL→SQL agent
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How I work
- Product-first: clear problem framing, measurable KPIs, tight feedback loops
- Production-grade: clean, documented code; tests, observability, graceful fallbacks
- Research-aware: I track and adopt the latest papers/frameworks (e.g., RF-DETR) when they deliver ROI—not hype
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Hire me for
- CV apps (detection/tracking/OCR), safety & proctoring, video analytics
- Chatbots & RAG over your docs/data; agents that call tools/APIs
- Voice bots for support, verification, scheduling, and automation
- LLM fine-tuning/alignment, evaluation, and cost/perf optimization
Let’s turn your AI idea into a reliable, high-impact product. Message me to discuss your project.
Steps for completing your project
After purchasing the project, send requirements so Eman can start the project.
Delivery time starts when Eman receives requirements from you.
Eman works on your project following the steps below.
Revisions may occur after the delivery date.
Data Collection & Preprocessing
Gather and clean the provided dataset, handle missing values, and ensure data quality for accurate predictions.
Exploratory Data Analysis (EDA)
Analyze data distributions, detect patterns, visualize relationships, and identify key factors affecting medical costs.