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2.  Write down any 5 key features of machine learning.
                Ans.  Following are the key features of machine learning:
                    •   Machine learning interprets, analyses, and processes data to address real-world problems.
                    •   It learns from data and enhances its performance over time.
                    •   The technology facilitates automation and prediction based on the learned patterns.
                    •   Machine learning is the prevailing approach in contemporary AI.
                    •   It employs data analysis, training, and sometimes human review to refine its capabilities.
                  3.  List any two applications of KNN.
                Ans.  Two applications of KNN are as follows:
                    •     Image recognition: KNN may be used to categorise photographs depending on their attributes, such as pixel
                       values and colour, etc. KNN may compare the attributes of a picture with those of labelled images in the set used for
                       training and classify the majority of its K-Nearest Neighbors.
                    •     Spam detection: KNN can identify spam through the comparison of new emails to a database containing both
                       spam and non-spam emails.

                  4.  List down the real life applications of linear regression.
                Ans.  Real life applications of linear regression include:
                    •   Prediction of product demand
                    •   Sales forecasting
                    •   Analysing the effect of price change of a service
                    •   Predict the effect of fertiliser on crop yield
                    •   Prediction of revenue through advertisements
                    •   Predicting salary of a person based on the number of years of experience
                  5.  What are the four types of correlation?
                Ans.  Four Types of correlation are:
                    1.  Positive Correlation: Positive correlation is the relationship between two variables, in which both variables have a
                       linear relationship. As one variable increases/decreases, the second variable too increases/decreases. For example,
                       when fuel prices increase, prices of airline tickets also increase.
                    2.  Negative Correlation: Negative correlation is the relationship between two variables, where one variable increases
                       as the second variable decreases, and vice versa. For example, more exercising leads to a decrease in body weight.
                    3.  No Correlation: No correlation means that there is no relationship between two variables. If the value of a variable is
                       changed, another variable is not affected. For example, shirt size and monthly expense, body weight and intelligence,
                       etc.
                    4.  Non-linear Correlation: A non-linear correlation is a correlation in which all the points of a scatter plot are tend to
                       lie near a smooth curve.
                  6.  Differentiate between correlation and regression.
                Ans.  Both  Correlation  and  regression  are  statistical  measures  used  in  data  analysis,  however  they  are  not  same.  Their
                    differences can be seen:
                                 Correlation                             Regression
                      It determines the strength or degree of  It determines how one variable affects another
                      relationship between two variables.  variable.
                      It is represented by a single value.  It is represented by a regression line.
                  7.  What is the primary difference between classification and regression?             [CBSE Handbook]
                Ans.  Classification predicts discrete values, while regression predicts continuous values.





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