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Data Interview Question

Enhancing Linear Regression Techniques

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Requirements Clarification & Assessment

  1. Understand the Objective:

    • Determine the primary goals of optimizing the linear regression model. Is it to improve predictive accuracy, reduce overfitting, or enhance model interpretability?
    • Identify the business or research questions the model aims to address.
  2. Data Assessment:

    • Review the dataset to understand its structure, including the number of features, data types, and the presence of missing values or outliers.
    • Evaluate the quality and relevance of each feature in relation to the target variable.
  3. Assumption Verification:

    • Ensure that the fundamental assumptions of linear regression (linearity, independence, homoscedasticity, normality of residuals, and lack of multicollinearity) are met.
    • Identify any violations of these assumptions that may need to be addressed.
  4. Model Evaluation Metrics:

    • Define the metrics for model evaluation, such as Mean Squared Error (MSE), R-squared, or Root Mean Squared Error (RMSE).
    • Determine the acceptable thresholds for these metrics based on the context of the problem.
  5. Constraints and Resources:

    • Identify any constraints, such as computational resources, time limitations, or data availability, that may impact the model optimization process.