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Features of the data:
                 • Weight: The mass of the fruit.
                 • Size: The shape or dimension (e.g., medium, long, round, small).
              The  model  is  trained  with  labelled  data  where  the  weights  and  sizes  are  associated  with  specific  fruit  types.
              The model learns to recognise patterns and relationships between the features (weight and size) and the label
              (fruit type). So, if the trained model is given new data (e.g., Weight: 120 grams, Size: Long), it predicts the fruit
              type (Banana) based on the training it received. This example demonstrates how labelled data (weight, size, and
              corresponding fruit type) helps the model learn and make predictions for new instances.

                            Labelled Data
                                                                         Prediction                Output
                                                      AI Model                                       Apple
                           Apple  Banana
                                                                                                     Grapes

                           Orange  Grapes                                                            Banana

                                                                                                     Orange


                                                     Test Data

              Unsupervised Learning

              Unsupervised learning approach works on unlabelled dataset. This means that the data which is fed to the machine
              is random and there is no knowhow available about it to the model. The machine analyses the data and identifies
              patterns, structures, or relationships on its own without any guidance. The goal is to group or organise data based
              on similarities or differences.
              In this model the major features are identified by the machine, which help the user in understanding the data.
              For example, in the data of 100 cat images, if you want to understand some pattern in the data, you would need
              to feed this data into the unsupervised learning model and train the machine. Once trained, the machine would
              identify patterns in the data. These patterns might already be known to the user, like colour or size, or different
              features of the cats.
              Unsupervised learning helps discover hidden patterns in unlabelled data. For example, imagine a photo gallery
              app that automatically organises a user's photos based on their content. The photos aren’t pre-labelled as "family,"
              "vacation," "pets," or "friends." Instead, the app uses unsupervised learning to analyse the photos and group them
              based on similarities, such as recognising the same faces, landscapes, or objects like pets or vehicles. As a result,
              the app clusters the photos into categories like "vacation," "family gatherings," or "pets," without needing any
              input or labels from the user.
              This example shows how unsupervised learning works by discovering hidden patterns in unlabelled data, just like
              the child in the swimming pool explores and learns independently.
              Let us take an example of the Unsupervised Learning - Fraud Detection:
              A bank processes a large number of transactions daily, maintaining a database with details like:
              •  Transaction amount                      • Transaction location

              •  Time of transaction                     • Account activity patterns
              The  goal  is  to  identify  potentially  fraudulent  transactions.  However,  the  transactions  are  not  pre-labelled  as
              "fraudulent" or "non-fraudulent."
              An Unsupervised learning algorithm is applied to analyse the data and group transactions based on patterns.
              The algorithm automatically identifies unusual behaviours, such as:
              •  Unusually large transactions.           • Purchases from unusual locations.
              •  Multiple transactions within a short time frame.

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