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I will build and deploy custom ml models with python and live streamlit demo
Kazakhstan
I speak English
ML Engineer Python Data Analysis and Predictive Modeling
ML Engineer specializing in Python automation, FastAPI backends, and NLP pipelines. I build end-to-end solutions: from raw data to trained models, REST APIs, and live dashboards your team can use with...
About this Gig
I build custom ML models, data analysis pipelines, and live Streamlit dashboards in Python end-to-end, production-ready, with clean code and GitHub repo.
What I deliver:
Classification & regression models (XGBoost, scikit-learn, RandomForest)
Customer churn prediction and risk scoring systems
Time-series forecasting (Prophet, LSTM)
Data analysis pipelines (pandas, NumPy, visualization)
Live Streamlit dashboard for your stakeholders
Stack: Python · scikit-learn · XGBoost · pandas · Streamlit · FastAPI · Matplotlib
Every project includes:
Trained model with accuracy/precision/recall metrics
Live Streamlit demo or FastAPI endpoint
GitHub repo with README and notebooks
30-day support after delivery
Need a churn predictor, demand forecaster, or data dashboard? I deliver working ML solutions not just notebooks.
Message me to discuss your dataset and goals!
Programming language:
Python
•
SQL
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
MLflow
Technology:
Python
•
TensorFlow
•
PyTorch
•
Keras
•
scikit-learn
•
Pandas
My Portfolio
FAQ
What information do you need from me to get started?
Please share your dataset (CSV, Excel, or database), a description of your goal (e.g. predict churn, classify images, analyze trends), and any specific requirements. The more context you provide, the better the results!
How fast can you deliver a working ML model?
For a standard classification or regression task: 2-4 days. I use LLM-assisted development to ship working prototypes fast. My live demos (ChurnGuard, SentimentScope, EntityRadar) were each built in under 3 days.
