# Classification
**Definition:** Classification is a type of predictive modeling that aims to categorize or assign observations or instances to a predefined set of classes or categories. It's used to predict the category or class of a new dataset, based on training from historical data.
**Example:** Email spam detection, sentiment analysis, disease diagnosis (e.g., classifying a patient as having a particular disease or not based on symptoms).
**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 Page**
\
\
**Simulator Overview:**
\
\
**Actionable Insights:**
