Model Development


polynomial regression, each of which has its own advantages and disadvantages depending on the dataset and the relationship between the variables.

  1. Simple Linear Regression:

    • Simple linear regression is a method to model the relationship between a single independent variable (predictor) and a dependent variable (response).
    • It assumes a linear relationship between the predictor and response variables.
    • The model equation is of the form: �=�0+�1�+�, where � is the dependent variable, � is the independent variable, �0 is the intercept, �1 is the slope, and � is the error term.
    • Simple linear regression is appropriate when there is a clear linear relationship between the variables.

  2. Multiple Linear Regression:

    • Multiple linear regression extends simple linear regression to model the relationship between multiple independent variables and a single dependent variable.
    • It assumes a linear relationship between each predictor and the response variable, holding other predictors constant.
    • The model equation is of the form: �=�0+�1�1+�2�2+...+����+�, where � is the dependent variable, �1,�2,...,�� are the independent variables, �0,�1,�2,...,�� are the coefficients, and � is the error term.

  3. Polynomial Regression:

    • Polynomial regression is a form of regression analysis in which the relationship between the independent variable and the dependent variable is modeled as an �th degree polynomial.
    • It can capture non-linear relationships between variables better than linear regression.
    • The model equation is of the form: �=�0+�1�+�2�2+...+����+�, where � is the dependent variable, � is the independent variable, �0,�1,�2,...,�� are the coefficients, and � is the error term.

These regression techniques are used to develop predictive models that can be used to estimate the price of a car based on relevant independent variables or features. Model evaluation techniques such as R-squared, mean squared error (MSE), and visualization are employed to assess the performance of the models and make informed decisions in prediction tasks.

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