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Fiverr Pro
English
$
USD
I will build ml models, predictive analytics, and time series forecasting
About this Gig
Your data holds patterns that can predict the future, detect risk, and drive smarter decisions. I build the models that unlock them. I don't just build models, I build models that work in the real world, on real messy data, and deliver results you can act on.
WHAT I BUILD FOR YOU:
Supervised ML models classification,
regression, and ranking Predictive analytics customer churn,
price prediction, risk scoring
Time series forecasting sales,
stock prices, demand planning, sports metrics
MY TECH STACK:
- Languages: Python
- ML Libraries: Scikit-learn, XGBoost, LightGBM, CatBoost
- Deep Learning: PyTorch, TensorFlow/Keras
- Data & Viz: Pandas, NumPy, Matplotlib, Seaborn, Plotly
- Time Series: ARIMA, Prophet, LSTM networks
- Deployment-ready: Pickle/joblib model export, REST API-ready outputs
WHAT YOU RECEIVE:
- Fully trained and evaluated ML model
- Clean, well-commented Python code (.ipynb or .py)
- Model performance report (accuracy, F1, RMSE, AUC whichever applies)
- Visualizations: confusion matrix, feature importance, forecast plots
- Clear explanation of results in plain English
- Model file export ready for deployment
Programming language:
Python
•
R
Frameworks:
Scikit-learn
•
Google ML Kit
•
PyTorch
•
Panda
APIs:
Google Cloud Vision API
Tools:
Jupyter Notebook
•
OpenCV
•
Stata
•
Colab
FAQ
What data format do I need to provide?
CSV, Excel, JSON, or SQL export — any standard tabular format works. For time series projects, I need a dataset with a date/time column and the target variable. Just share what you have and I'll assess it before starting.
My dataset is small / messy / imbalanced. Can you still build a model?
Yes. Real-world data is almost never perfect. I handle missing values, outliers, class imbalance (SMOTE, class weighting), and noisy features as part of every project. A small clean dataset can often outperform a large messy one with the right approach.
What kind of time series forecasting do you do?
I build forecasting models for sales, demand, financial prices, sports performance metrics, and any sequential data with time dependency. I use classical methods (ARIMA, Prophet) for interpretability and LSTM networks for complex, long-range patterns.
Will I be able to understand and reuse the code?
Absolutely. Every notebook is structured, commented, and written to be readable by someone who isn't the original author. I include markdown explanations throughout and a summary section at the end.
I don't know what type of ML model I need. Can you advise?
That's completely normal — and it's part of what I do. Tell me your data, your goal, and the decision you're trying to make, and I'll recommend the right approach. No jargon, just clear guidance.
