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Fiverr Pro
English
$
USD
I will build a custom machine learning model for your dataset
AI Automation and Data Analytics
I help businesses save time and increase revenue through automation, data analysis, and AI workflows.
BSc Mathematics (Hons), University of Birmingham. Google ML Certificate.
I ran a fitness franchi...
About this Gig
Need a machine learning model built for your data? I'll design, train, evaluate, and deliver a production-ready ML pipeline so you can make smarter predictions without hiring a full-time data scientist.
I build models for:
Classification (fraud detection, churn prediction, sentiment analysis, image tagging)
Regression (price prediction, demand forecasting, sales estimation)
Clustering (customer segmentation, anomaly detection)
NLP (text classification, topic modelling, named entity recognition)
Time series forecasting (ARIMA, Prophet, LSTM)
My ML tech stack:
scikit-learn · XGBoost · LightGBM · TensorFlow / Keras · PyTorch · Hugging Face · NLTK · spaCy · statsmodels
What you get:
Problem framing and model selection
Data preprocessing and feature engineering
Model training + hyperparameter tuning
Full evaluation report (accuracy, F1, RMSE, confusion matrix, etc.)
Clean, documented Python code (Jupyter notebook or .py scripts)
Deployment-ready model file (.pkl / .h5 / ONNX) on Premium
FAQ
Do I need a large dataset?
Not necessarily. I work with datasets from a few hundred rows upward. I'll tell you honestly if the data is too small to produce a reliable model.
Will the model work on new data?
Yes — you receive a saved model file you can load and run predictions on anytime, plus code showing how to do so.
Can you deploy the model as an API?
Yes — included in the Premium package (FastAPI). I can also advise on hosting options (Heroku, AWS, Render, etc.).
What if my data is confidential?
I sign NDAs on request. Your data is never shared with third parties.
Can you explain how the model works to non-technical stakeholders?
Absolutely - I include a plain-English interpretation of model results in every package.
