Notes

1) pandas (pd) is used for working with datasets and numpy (np) is used when working with arrays.
2) Forward fill replaces missing values with the previous non-missing value, while backward fill replaces missing values with the next non-missing value.
3) KNN is an algorithm is useful for matching a point with its closest k neighbors in a multi-dimensional space. used in numeric data . KNN is imported by {{ from sklearn.impute import KNNImputer }}
4) fit_transform: This is a method that combines two steps: fitting the imputer on the data and transforming the data using the fitted imputer. In the context of KNNImputer, the "fit" step involves learning about the distribution of non-missing values in the data, and the "transform" step involves imputing missing values using the learned information.
5) astype(int): This is a method provided by pandas that is used to convert the data type of a Series (column) to a specified data type. In this case, you're converting the data type to int , which represents integers .
6) Mapping ranges of values is a common technique used in data analysis to transform numerical data into categorical data.
7) Normalization is a scaling technique in which values are shifted and rescaled so that they end up ranging between 0 and 1.
8) Z-score is a statistical measurement that describes a value's relationship to the mean of a group of values.
9) the steps of fit.transform is the same which is :: performs two steps: fitting the scaler on the before data and then transforming the data using the fitted scaler. During the "fit" step, the scaler calculates the minimum and maximum values of each feature
10) sports_games = vg_df[vg_df['Genre'] == 'Sports'] :: this line checks the rows which are genre = sports lines.