I will develop ai prediction model for price, demand or outcomes
Machine Learning, Deep learning, Gen AI and Agentic AI
Sobre este Serviço
Need help making data-driven decisions? I will build a custom prediction model using Python and machine learning to forecast trends, classify outcomes, or optimize your business strategy.
From data cleaning to model training and evaluation, Ill deliver clean, accurate results tailored to your needs
What This Gig Offers:
- End-to-end development of a prediction model
- Data cleaning, feature engineering & model training
- Algorithms: Linear Regression, Random Forest, XGBoost, LightGBM, etc.
- Model performance evaluation (accuracy, F1, confusion matrix)
- Easy-to-read output and result files
- Python scripts (well-commented & reusable)
Use Cases:
- Sales or demand forecasting
- Customer churn prediction model.
- Stock price or crypto trend prediction model.
- Risk assessment or classification tasks
- Custom data-based prediction model
Why Choose Me?
- Strong background in machine learning
- Clean, professional Python code
- Transparent communication and timely delivery
- Multiple revision options
Let me build a prediction model that helps you turn data into decisions!
Linguagem de programação:
Python
•
SQL
Frameworks:
Scikit-learn
•
keras
•
PyTorch
•
Panda
Ferramentas:
caderno Jupyter
•
opencv
•
fluxo tensor
•
Excel
•
Colab
Meu portfólio
Outros serviços de Ciência de dados e ML que eu ofereço
Perguntas frequentes
What do I need to provide before starting?
Please share a clean dataset (CSV/Excel) and a clear explanation of the outcome you want to predict (e.g., sales, churn, category). I’ll help guide you if you’re unsure.
What tools/libraries do you use?
I use Python with libraries like pandas, scikit-learn, XGBoost, LightGBM, TensorFlow (if deep learning is needed), and visualization tools like seaborn and matplotlib.
Can I request a custom use case?
Yes! Whether your model is related to finance, health, retail, or any custom need — I can tailor the solution to fit your domain.

