It is most useful for series that exhibit strong trends.

It uses lagged values of the series to predict future values

It models the relationship between the current value and past values.

It captures the autocorrelation in the time series data

Integration process to achieve stationarity by differencing the time series

Increment for adjusting the series to account for seasonality

Indicator for the moving average component

Independence of residuals in the error term

Using association to analyze correlation rules

Same as frequent itemset mining

Finding of strong association rules using frequent itemsets

Finding Itemsets for future trends

Support(A U B) / Support (A)

Support(A ∩ B) / Support (B)

Support(A U B) / Support (B)

Support(A ∩ B) / Support (A)

Semantic segmentation assigns a class to each pixel, instance segmentation also distinguishes object instances

Instance segmentation detects only one class, while semantic handles all

Instance segmentation does not use CNNs

Semantic segmentation is only used for medical imaging

Replace missing values with the mean of the column.

Impute missing values using linear regression

Replace missing values with the mode (most frequent value) of the column

Replace missing values with random noise

(y - y`)2 / 2N

(y - y`) / N

(y - y`) / 2N

(y - y`)

Model evaluation

Model deployment

Data cleaning

Feature engineering

Data visualization

Data aggregation

Data normalization

Data storage

Pie charts fail to represent proportions when categories are not mutually exclusive.

 Pie charts are less visually appealing than bar charts.

 Pie charts provide limited precision when comparing the sizes of slices.

Pie charts are not as effective as line charts for representing trends over time.