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Paper 25242015Methodology of Model Creation Mgr. Peter Keys, VB a. s. ABSTRACT The goal of this session is to describe the whole process of model creation from the business request through model specification,

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How to fill out methodology of model creation:

1. Identify the problem or objective: Clearly define what you aim to achieve with your model. This could be predicting customer behavior, optimizing processes, or analyzing market trends.
2. Determine the scope and boundaries: Define the extent of your model, including the variables, data sources, and time period to be considered. This will help focus your efforts and ensure the model is realistic and achievable.
3. Select the appropriate modeling technique: Depending on your problem and data, choose a suitable modeling technique such as regression analysis, machine learning, or simulation. Consider factors like complexity, interpretability, and data requirements.
4. Define the variables and their relationships: Identify the independent and dependent variables in your model and analyze their potential relationships. This will involve conducting literature reviews, expert consultations, or exploratory analysis.
5. Design the data collection plan: Determine how you will collect the required data for your model. This may involve surveys, data scraping, experimental studies, or utilizing existing datasets. Ensure data quality and address any potential biases or limitations.
6. Preprocess and clean the data: Prepare your data for analysis by handling missing values, outliers, or inconsistencies. Apply techniques like data normalization, dimensionality reduction, or feature engineering to improve the quality and suitability of the data.
7. Develop and validate the model: Use the chosen modeling technique to build the model based on your data. Validate the model's performance by using appropriate statistical measures, cross-validation, or comparing it to existing benchmarks or expert knowledge.
8. Document the methodology: Clearly document every step of your model creation process, including decisions, assumptions, and limitations. This will help ensure transparency, reproducibility, and facilitate future model improvements or audits.

Who needs methodology of model creation?

1. Data scientists and analysts: These professionals rely on robust methodology to build accurate and reliable models that can inform business decisions, optimize processes, or solve complex problems.
2. Researchers and academics: Methodologies for model creation are essential in scientific research to test hypotheses, analyze data, or develop theories. Reproducibility and transparency are crucial in this context.
3. Organizations and businesses: Companies across various industries can benefit from methodology of model creation when making data-driven decisions, developing predictive models, or optimizing their operations.
4. Developers and engineers: Methodologies guide the systematic development and implementation of models in software systems, enabling automation and integration for practical use.
5. Policy and decision-makers: Methodologies for model creation provide insights and evidence for policy-making, strategic planning, resource allocation, or risk assessments.

Overall, anyone involved in data analysis, research, or decision-making processes can benefit from understanding and applying the methodology of model creation.

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