Python Medical Recommendation Engine -- 2

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@tadinhkien

Greetings, Thank you for considering my application for this project. As an AI Engineer and Python Developer with over 8+ years of experience, I bring a wealth of knowledge and expertise in the field of Python, Deep Learning. I have carefully reviewed the project description and am eager to discuss your specific needs and requirements in more detail. My commitment is to provide dedicated support and consistent follow-up throughout the project's lifecycle. Please feel free to reach out to me to further discuss how I can contribute to the success of your project. Looking forward to the opportunity of working together. Best regards, KuroKien

@shadabkhan92

As an AI specialist, my team and I have extensive experience in implementing machine learning solutions that operate within the real world. We don't just create prototypes; we build production-ready infrastructure ensuring seamless integration into your existing workflows. From python-based ML models to creating interfaces using Flask and Streamlit, we are accustomed to end-to-end development. Our unique advantage comes from our skillset with hardware integration and the ability to deploy AI systems on edge devices, which not many can offer. Healthcare is a domain that often requires data analysis from IoT sensors and history tracking. Our proficiency in designing custom IoT hardware will be instrumental in expanding the model to incorporate medical histories or allergy data. Moreover, beyond just building your desired recommendation engine that reads symptoms and returns rated medication suggestions, our work doesn't stop there. We provide clean, well-commented code accompanied by an extensive README assisting you through system setup, usage, and even extending the model for future features. Let's combine our skillsets to produce an optimized, easy-to-use system for your patients.

@nrajuu07

I'm a machine learning developer with healthcare data experience. I'll build an end-to-end Python system — clean and normalize your patient records, engineer features, train and compare Random Forest and Logistic Regression models, and select the best performer based on accuracy, precision, and recall with full confusion matrix analysis. Deliverables include a lightweight Flask/Streamlit dashboard accepting free-text symptoms and returning ranked medication recommendations, clean well-commented code, a comprehensive README with local setup instructions, and clear extension points for medical history and allergy data integration. Ready to start immediately.

@edulov

Hello. I would like to express my keen interest in the project that you have mentioned. I understand that the requirements for this project are extensive, but I would like to highlight one particular aspect where I believe my skills and expertise can be of great value. You are aware that even the most sophisticated language models face challenges when it comes to providing accurate medical recommendations and symptom analysis. Through my research (I have had a personal interest), I have developed a stable approach that has shown its effectiveness. You can hire me directly. I look forward to discussing this further and hearing from you soon. Best regards,

@lalitdeveloper7

Hi I can build a complete Python-based medicine recommendation system that cleans and prepares your patient dataset, trains and compares models such as Random Forest and Logistic Regression, and selects the best-performing approach based on accuracy, precision, recall, and confusion matrix analysis. The solution will include a simple Flask or Streamlit interface where users enter symptoms and receive ranked medicine recommendations, along with clean, well-documented code and a README for setup, usage, and future enhancements. Please let me know further. Thanks.

@nexvative

Hi, I am a machine Learning engineer and have worked before with medical grade datasets, I can help you in achieving your desired results, If you could share the dataset with me and we can talk more about the problem further understanding the nature of the problem I believe we would be ready to start working ASAP. Thank you

@sayarsinghseo

As a highly skilled and results-driven Data Scientist and Machine Learning expert, I am confident in my ability to deliver what your project demands. I've spent years honing my talents at transforming complex data sets into streamlined, functional systems that provide valuable insights. I've also designed and implemented ranking algorithms for recommendation engines in various domains, which aligns well with your need for a medical recommendation engine. In regards to the key tasks of your project, my background and skillset are a perfect fit. My proficiency in data processing will ensure your raw patient records are transformed into clean, model-ready data. Furthermore, my experience in using and experimenting with different machine learning models including Random Forest and Logistic Regression will be valuable for choosing the most accurate model for your system. This is complemented by my expertise in evaluating models using metrics like accuracy, precision, and recall. Lastly, rest assured that not only will the end product meet all your requirements but it will be backed by concise documentation explaining how things work and how to extend the model later if needed. With me on board, you won't just get a reliable medical-recommendation engine built with Python; you'll get an asset that translates volumes of patient data into actionable insights - empowering better healthcare decisions.

@ziedb4

Hello, ​I have carefully reviewed your project description for the Python Medical Recommendation Engine. Building an end-to-end Machine Learning pipeline—from raw data cleaning to deploying an interactive web application—is a task that fits perfectly with my data science and engineering background. ​I appreciate how well-defined your scope is. Here is how I will systematically execute each phase of your project: ​?️ Step-by-Step Implementation Plan: ​1. Data Engineering Pipeline: I will clean the raw patient dataset by removing duplicates, handling missing values, and performing robust preprocessing (such as normalizing numerical values and encoding categorical data). If the symptom text requires text vectorization, I will implement TF-IDF or count vectorizers to make it fully model-ready. ​2. Model Selection & Rigorous Training: I will implement and experiment with both Logistic Regression and Random Forest classifiers. I'll split the data properly to avoid data leakage and tune the hyperparameters to optimize performance.

@southa5

Happy to take on your research data-pipeline project. I focus on solid ML engineering in Python: building data pipelines, training and tuning models in PyTorch/TensorFlow, and validating results with reproducible, readable code. Share the dataset and goals and I'll outline an approach and timeline.

@sastrya

Hi there - what you need a recommender system using a small language model which can run on any edge device/laptop without GPU. I have used these kind of models and build RAG pipeline, evaluated the same and deployed in multiple companies. Love to connect on the same. Regards, Sastry

@mubashir021

I am excited about the opportunity to develop a Python-based medical recommendation engine tailored to your requirements. With experience in machine learning and data processing, I can transform your raw patient records into a functional system that accurately suggests medications based on user-input symptoms. My approach will include creating a robust data pipeline to ensure that the dataset is clean and model-ready, along with experimenting with Random Forest and Logistic Regression to identify the most effective model based on performance metrics such as accuracy, precision, and recall. I will provide a comprehensive evaluation report with confusion matrices to support the model selection. To enhance usability, I will implement a lightweight dashboard using Flask or Streamlit, allowing users to input symptoms and receive ranked medication suggestions. You will receive well-structured, commented code along with detailed documentation for setup and future extensions. I am committed to delivering a high-quality solution that meets your needs and can ensure it runs seamlessly on your local machine. Let's discuss your project further!

@MuhammadAsad108

✔ I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Raw Patient Data ⟶⟶ Data Cleaning & Deduplication ⟶⟶ Feature Engineering ⟶⟶ Model Training (RF & LR) ⟶⟶ Performance Evaluation ⟶⟶ Medicine Recommendation Engine ⟶⟶ Streamlit/Flask Dashboard ⟶⟶ Documentation & Deployment Key Highlights ✔ End-to-end machine learning pipeline built in Python using industry-standard libraries. ✔ Data preprocessing including duplicate removal, missing value handling, normalization, and categorical encoding. ✔ Comparative model training using Random Forest and Logistic Regression with performance benchmarking. ✔ Evaluation using accuracy, precision, recall, F1-score, and confusion matrices. ✔ Ranked medicine recommendation output based on model confidence scores. ✔ NLP processing for free-text symptom input using TF-IDF, embeddings, or suitable text-vectorization techniques. ✔ Lightweight Streamlit or Flask dashboard with clean and user-friendly interface. ✔ Modular architecture allowing future integration of medical history, allergies, lab results, and risk factors. ✔ Fully commented source code, reproducible training pipeline, and model persistence. ✔ Complete README with setup instructions for virtualenv/conda environments. Best Regards, Asad Machine Learning Engineer | Data Scientist | Python & Predictive Analytics Expert

@thetechnicalhunt

You want your raw patient records turned into a working Python tool: type a symptom, get a ranked list of suitable medicines, with a clean model behind it and a simple dashboard on top. I would build exactly that, end to end and running on your machine. My approach: first a data pipeline that dedupes records and normalises the numeric and categorical fields so every row is model-ready. Then I train and compare at least Random Forest and Logistic Regression, pick the winner on accuracy, precision and recall, and hand you an evaluation report with confusion matrices and a short note on why that model won. The interface is a light Streamlit or Flask app with one symptom field and a results pane. You get clean, commented code and a README covering setup and how to add medical history or allergy data later. One question that shapes the model: is your dataset already labelled with the prescribed medicine per case, and roughly how many records is it? That decides whether this is supervised classification or needs a labelling step first. I have 15 years building production software and AI systems as the single accountable owner across data, modelling, and delivery. The deterministic side here is the cleaning, the train and eval pipeline, and a reproducible install; the model is the part that handles the messy symptom text. Keeping those separate is what keeps the results trustworthy and easy to extend. Ready to get started. Best, Kaustubh