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Requirements Clarification & Assessment
Understanding the Dataset:
Number of Instances: Evaluate if the number of instances is significantly larger than the number of features (n >> p). If not, consider collecting more data to ensure the robustness of the model.
Feature Relevance: Assess if all 500 features are relevant to the problem at hand. This involves domain knowledge to determine which features are likely to be informative.
Problem Context:
Objective: Clearly define the objective of the analysis or model. Is it prediction, classification, or exploratory analysis?
Computational Resources: Consider the computational resources available, as high-dimensional datasets can be resource-intensive.
Data Quality:
Missing Values: Check for missing values and decide on imputation methods.
Data Types: Verify that data types are correctly assigned and consistent across the dataset.
Feature Relationships:
Correlation: Identify highly correlated features that may lead to multicollinearity issues.
Redundancy: Look for redundant features that do not add value to the model.