I will build a custom machine learning model in python
Your data's best storyteller and Bringing your game ideas to life
About this Gig
Do you have data but don't know how to build a predictive model from it? I will build a professional, accurate machine learning model tailored to your specific problem.
What I can build:
- Classification models (predict categories spam/not spam, fraud/not fraud, churn/no churn)
- Regression models (predict numbers sales, prices, demand forecasting)
- Clustering models (group your customers, products, or data segments)
- Recommendation systems (suggest products, movies, or content)
My process:
- Data cleaning and preprocessing
- Feature engineering and selection
- Model training and hyperparameter tuning
- Model evaluation (accuracy, precision, recall, F1-score, RMSE)
- Clear explanation of results
You will receive:
- Fully working Python code in Jupyter Notebook
- Model performance report with evaluation metrics
- Visualisations (confusion matrix, feature importance, ROC curve)
- Clean, commented code you can reuse
- Brief written explanation of results in plain English
Why choose me?
- Masters student in Data Science & AI at University of London
- IBM & Google Certified Data Analyst
- Built ML models achieving 98%+ accuracy on real datasets
- Experience with classification, clustering and big data
Frameworks:
Scikit-learn
•
DeepPy
•
PyTorch
•
Panda
Data type:
Text
Programming language:
Python
•
R
Tools:
Jupyter Notebook
•
TensorFlow
•
Excel
•
MLflow
APIs:
Google Cloud Vision API
•
Azure Face API
My Portfolio
FAQ
What format should my data be in?
CSV or Excel works perfectly. If your data needs heavy cleaning I'll let you know upfront
What if I don't know what kind of model I need?
No problem — just describe your problem and I'll recommend the best approach for free.
Can you deploy the model as a web app?
Yes! I can deploy using Streamlit for an additional fee. Message me to discuss.
Do you work with small datasets?
Yes, I can work with any size. I'll advise if your dataset is too small for reliable ML.

