Definition: Regression modeling predicts a continuous outcome or numerical value. It estimates the relationship between one dependent variable and one or more independent variables by fitting a line or curve to the data.

Example: Predicting house prices based on features like area, number of bedrooms, and location, predicting sales based on advertising expenditure, estimating the temperature based on time of day and weather conditions.


1. Select Independent Columns (X):

  • Identify and choose the independent columns in your dataset.

  • These columns, often referred to as features or predictors, are the variables that will be used to predict the dependent variable(Y).

2. Select Dependent Column (Y):

  • Identify the dependent variable or target variable (Y) that you aim to predict.

  • This column represents the output or the variable to be predicted based on the other independent variables (X).

3. Cross-Validation:

  • Determine the level or number of folds for cross-validation. Cross-validation is a resampling technique used to assess how the results of a predictive model will generalize to an independent dataset.

  • Common methods include k-fold cross-validation, where the dataset is divided into k subsets or folds. The model is trained on k-1 folds and tested on the remaining fold, repeated k times.



What-If Simulator:

Actionable Insight:

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