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Note, Continuous data includes values that can be measured and take any value within a range (e.g., height,
                 temperature). It is analysed using regression and probability distributions.
                 Discrete data consists of countable values with no in-between (e.g., number of students, dice rolls). It is analysed
                 using frequency counts and probability tables.


                              Task                                                          21 st  Century   #Critical Thinking
                                                                                                Skills


                   Identify the Model: Classification or Regression?
                   1.  Estimating the price of a house


                   2.  Determining if an email is spam or not


                   3.  Predicting a student's test score out of 100


                   4.  Identifying the species of a flower

                   5.  Predicting whether a customer is eligible for a bank loan or not?


                   6.  Predicting weather for next 24 hours





                 Sub Categories of Unsupervised Learning

                 Unsupervised Learning can further be divided into: Clustering and Association. Let us discuss these in detail.

                 Clustering

                 Clustering is a machine learning approach where the machine partitions the dataset into different clusters or
                 categories based on machine generated algorithms. The data fed to such a model is usually unlabelled or random
                 and thus the developer feeds in the data directly into the
                 machine and instructs it to build its own algorithm. The                         short hair people
                 machine then forms a pattern or cluster based on training                        long hair people
                 data and groups those that follow the same pattern. Like,
                 Model  segregates  people  with  long  and  short  hair  and
                 forms two clusters based on it as shown in the graph.
                 The  best clustering  is the  one  that  minimises  the  error.
                 Clustering works on discrete dataset. For example, if you
                 have random data of insects  and reptiles, since  you are
                 unable to find any meaningful pattern amongst them, you
                 would  feed  their  data  into the  clustering  algorithm.  The
                 algorithm would then analyse the data and divide them into
                 clusters according to their similarities based on the trends noticed. The clusters are then given as the output.


                                                                          Advanced Concepts of Modeling in AI   195
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