Mostafizur RahmanServices / Predictive Modeling & Forecasting
Predictive Modeling & Forecasting
Predictions you can evaluate, explain and use.
- Predictive Analytics
- Time-Series Forecasting
- Classification
- Regression
- Machine Learning
- Model Evaluation
- Python
- Scikit-learn
Final pricing depends on data volume, complexity, and agreed deliverables.
Discuss your projectService overview
The problem, and what you gain
The problem it solves
Decisions about churn, demand or risk rely on instinct because the available history is not being used systematically.
The value to you
You receive a tested, explained model that shows which factors matter, how reliable the predictions are, and how to put them to use.
Scope of work
What this service covers
- Historical data preparation and feature engineering
- Baseline models and model comparison
- Classification for predicting categories or outcomes
- Regression for predicting numerical values
- Time-series forecasting for future trends and demand
- Evaluation using metrics suited to the task
- Threshold analysis for classification where appropriate
- Feature importance and model interpretation
- Error analysis, assumptions, and limitations
My process
How the work runs
Define
The target, success metric and constraints are agreed.
Prepare
Features are engineered and a simple baseline is set.
Model
Candidate models are compared and tuned on held-out data.
Evaluate
Metrics, thresholds and error are examined and reported honestly.
Explain
Drivers are explained and the results are presented with next steps.
What I need from you
Inputs to get started
- A historical dataset that includes the outcome to predict
- A clear definition of the target
- Business context, such as the cost of a missed case compared with a false alarm
- The time range and any seasonality to account for
- Any constraints, such as the need for an interpretable model
What you will get
Your deliverables
- Documented notebook with code, methodology, and results
- Baseline and candidate-model performance using suitable metrics
- Performance results, error analysis, assumptions, and limitations
- Feature importance or other suitable explanations where applicable
- Plain-language explanation of the results and recommended next steps
FAQ
Frequently asked questions
How accurate will the model be?
That depends on the data. I report metrics measured on held-out data and explain what they mean for your use case.
How much data do I need?
It varies by problem. We review your data in the first step and agree what is realistic.
Can you explain why the model predicts what it does?
Yes. Explanations are part of every delivery.
Does this include deployment?
The service delivers the model, code and results. Deployment can be scoped separately.
Which models do you use?
The choice depends on the problem. Options may include linear and logistic regression, tree-based models, random forests, gradient boosting, and statistical forecasting methods such as SARIMA. I select models based on the data, evaluation results, and business requirements.
Get started
Ready to talk about Predictive Modeling & Forecasting?
Send a short description of your data and what you need to achieve. I'll reply with a scope and a quote. Starting at $300.