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# Regression
**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.
**Steps:**
**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.
## **Reports:**
**Summary:**
**What-If Simulator:**
**Actionable Insight:**