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You will get Data Preprocessing and ML modeling
Project details
This project encompasses the full life cycle of a machine learning solution, starting from initial data collection to deploying the final model via a Streamlit web application.
Data Cleaning & Preprocessing:
Performed extensive data cleaning to remove duplicates, handle missing values, and correct inconsistencies.
Applied preprocessing techniques such as normalization, feature engineering, and categorical encoding to prepare the data for machine learning algorithms.
Machine Learning Modeling:
Conducted exploratory data analysis (EDA) to understand underlying patterns and correlations.
Experimented with various supervised learning models, including Random Forest, Gradient Boosting, and SVM, to find the best-performing algorithm for the given task.
Tuned hyperparameters using GridSearchCV to optimize the chosen model.
Model Validation:
Employed cross-validation techniques to gauge the model's generalization capabilities.
Utilized various metrics such as precision, recall, F1 score, and ROC AUC to evaluate model performance.
Deployment on Streamlit:
Built an interactive web application using Streamlit to showcase the model predictions.
Data Cleaning & Preprocessing:
Performed extensive data cleaning to remove duplicates, handle missing values, and correct inconsistencies.
Applied preprocessing techniques such as normalization, feature engineering, and categorical encoding to prepare the data for machine learning algorithms.
Machine Learning Modeling:
Conducted exploratory data analysis (EDA) to understand underlying patterns and correlations.
Experimented with various supervised learning models, including Random Forest, Gradient Boosting, and SVM, to find the best-performing algorithm for the given task.
Tuned hyperparameters using GridSearchCV to optimize the chosen model.
Model Validation:
Employed cross-validation techniques to gauge the model's generalization capabilities.
Utilized various metrics such as precision, recall, F1 score, and ROC AUC to evaluate model performance.
Deployment on Streamlit:
Built an interactive web application using Streamlit to showcase the model predictions.
Machine Learning Tools
NumPy, pandas, Python, Python Scikit-Learn, PyTorch, SciPy, SQLWhat's included
| Service Tiers |
Starter
$150
|
Standard
$300
|
Advanced
$750
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 7 days |
Number of Revisions | 2 | 0 | 0 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | ||
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$300 - $1,000
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JB
JAMES B.
Oct 24, 2024
Data Scientist - Financial Time Series and Xgboost
Josip is a professional with all the integrity required to perform in this field it was a pleasure working with him and will work again with him.
RE
Rachel E.
Sep 4, 2023
Data Analyst for Student Recruitment Analysis
About Josip
Senior AI Engineer (MD PhD, Msc) | RAG, LLM Agents, Fine-tuning | GCP
100%
Job Success
Split, Croatia - 12:29 am local time
MD + PhD (medical ML) and a recent MSc in AI. I work hands-on as an ML/AI engineer, mostly on healthcare AI and LLM-native apps (RAG + agents + tool/function calling), and I’m comfortable taking things from idea → prototype → production.
In the last couple of years I’ve been building and shipping:
- LLM-native SaaS for customer support (text + voice agents): RAG with LlamaIndex/LangChain, dynamic function calling, and parallel tool execution for faster inference, plus backend-heavy full-stack work.
- Cloud deployment & maintenance on AWS + GCP (Lambda/Cloud Functions, API Gateway, Postgres, etc.).
- Voice-to-voice agents using LiveKit/Twillio and LLM APIs (OpenAI / Gemini).
- Research and applied projects around clinical decision support, error checking, and safety with modern LLMs/LRMs.
Recently, I’ve also worked on research projects using supervised fine-tuning (SFT) and RLVR with medgemma27n and GPT-OSS, focused on medical error checking and improving patient outcomes.
Working freelance on:
AI in healthcare / medical AI (classical ML + deep learning)
RAG applications / RAG chatbots (LlamaIndex, LangChain, vector DBs, evaluation)
AI agents (LangChain, LlamaIndex, Autogen) + tool/function calling
Fine-tuning (SFT, preference/RLVR-style training), dataset curation, eval & guardrails
AWS Lambda + API Gateway / GCP Cloud Functions deployment
End-to-end ML pipelines (data → model → monitoring)
Data analysis, statistics, biostatistics, visualization
Academic & medical writing, literature reviews, consulting
If you message me, the fastest way to start is: what’s the goal, what data you have (and where it lives), and how the output will be used (internal tool, customer-facing app, research paper, etc.). I’m happy to jump in either as “build the thing” or “fix/upgrade what’s already there.”
Steps for completing your project
After purchasing the project, send requirements so Josip can start the project.
Delivery time starts when Josip receives requirements from you.
Josip works on your project following the steps below.
Revisions may occur after the delivery date.
EDA
Data cleaning