Linear regression does not require a specific value of K, whereas KNN depends on the chosen value of K.

Linear regression can automatically handle categorical variables, whereas KNN cannot.

Linear regression is a non-parametric model, whereas KNN is parametric.

Linear regression relies on the linear relationship between variables, whereas KNN relies on similarity between data points.

To create chaos in data handling. 

To find a solution to a problem or project.

To limit the use of machine learning algorithms.

To remove data manipulation from the process.

Unsupervised Learning.

Deep Learning.

Reinforcement Learning.

Supervised Learning.

To create non-ordinal relationships

To specify the order of categories

To eliminate categories

To reduce the size of the dataset

It reduces memory usage

It aligns the dataframes properly

It avoids duplicate entries

It improves computational efficiency

There are no null values in any of the variables.

If one variable appears the other definitely does not

if one variable appears the other definitely also does

There may or may not be null values in both variables

To increase model efficiency

To distort descriptive statistics

To increase the variability in the dataset

To identify data points that deviate significantly from the rest

Identifies unique categories in the categorical variable

Creates binary vectors for each category

Applies the encoding to the data

Resets the index of the dataframe

To eliminate categorical variables

To enable mathematical computations

To enhance model interpretability

To make data more complex

To enable mathematical computations

To make data more complex

To enhance model interpretability

To reduce computational efficiency