Page 313 - AI Ver 3.0 Class 11
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3.   What do you mean by graphical and non-graphical data representation? Why is graphical data representation
                          more advantageous?
                     Ans.  Non-Graphical Technique for Data Representation
                          Non-graphical techniques include tabular and case forms. This is an older data representation format that is unsuitable
                          for huge datasets. Non-graphical strategies are ineffective when we want to make decisions based on a set of data.
                          Graphical Technique for Data Representation
                          Graphs are commonly used to visualise statistical data using points, lines, dots, and other geometric shapes.
                          Advantage of graphical over non-graphical data representation: The human brain is more comfortable coping
                          with complex and massive amounts of material when it is represented visually. Data visualisation refers to the
                          graphical or pictorial depiction of data using graphs, charts, and other tools.
                       4.  What is the use of autopct parameter in a pie chart?
                     Ans.  The ‘autopct’ parameter displays the percentage value of each slice. Each label will show the percentage value of
                          the corresponding slice, rounded to zero decimal place.
                       5.  Given the following conditions, identify which chart would you use?
                          i.  Suitable for comparisons, particularly when there are numerous categories or negative values.
                          ii.   Good for displaying trends, particularly small variations or data lines that cross. They are also useful with time-
                             series data.
                          iii.  Good for demonstrating the correlations and distributions of two quantitative variables. These graphs may
                             reveal positive, negative, or no associations.
                          iv.  Good for demonstrating how different numbers relate to one another, such as comparing the sizes or percentages
                             of various categories in a data set.
                     Ans.  i.  Bar charts     ii.  Line Graph
                          iii. Scatterplot    iv. Pie chart

                 B.   Long answer type questions.
                       1.  What is the transpose of a matrix? Give an example.
                     Ans.  A matrix obtained by interchanging the rows and columns of a matrix. Transpose of a matrix A is denoted by A’ or
                          AT. If order of matrix A is m X n, then order of transpose of matrix A, i.e., A’ is n × m. For example:

                                                                                 14 
                                                           12 3                   
                                                                              ‘
                                                                       T
                                                        A =        A OR A = 24     
                                                                                 
                                                            45 6  ×23           36   ×32
                                                                                 
                       2.  What are the steps taken to clean/prepare data in the data preprocessing pipeline.
                     Ans.  The following steps are taken to clean/prepare the data:
                          •   Missing Data: Missing data refers to the absence of certain values in the dataset, which can result from various
                             causes. To handle missing data, strategies include removing rows or columns with missing values, imputing
                             missing values with estimates, or utilising algorithms that can manage missing data.
                          •   Outliers: Outliers are data points that deviate significantly from the majority of the dataset, typically due to
                             errors or uncommon occurrences. Managing outliers includes detecting and excluding them, transforming the
                             data, or applying robust statistical techniques to minimise their influence.
                          •   Inconsistent Data: Inconsistent data, such as typographical errors or variations in data types, is rectified to
                             ensure uniformity and coherence across the dataset.
                          •  Duplicate Data: Duplicate data is identified and eliminated to maintain data integrity and accuracy.



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