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Model
                                                                Clustering Model Training


                               Unlabelled Data                                              Clustering on the Basis of Type

                 Clustering – Example 1

                 Rohan is  the owner  of daily  needs departmental  store. He wants to improve his  marketing strategies by
                 grouping  customers  based  on  their  purchasing  behaviour.  The  dataset  contains  different  details  about  the
                 customers like age, purchasing power, and their annual expenditure (Money spent to buy from the store).
                 Your aim is to group the customers into clusters like:

                 a.  High income high spending patterns
                 b.  Young and low income customers with average spending patterns
                 c.  Middle aged customers with moderate spending patterns.

                 In this example clustering is used where the algorithm will analyse the data and group customers into clusters
                 based on similarities in their spending behaviour and income.
                 The outcome will be clusters under the following categories:
                    • Cluster 1: High-income customers with high spending.

                    • Cluster 2: Young customers with low spending.
                    • Cluster 3: Middle-aged customers with moderate spending.
                 This is how clustering technique works. The clustering model will be able to identify clusters based on some
                 similarities or patterns which are not defined in the input. For example, age, purchasing power and the annual
                 amount spent on the purchases (in store) are the only features known, but clusters based on age and purchasing
                 power  have  been  grouped  together  and  given  as  output.  Once  the  clusters  are  made,  the  store  can  offer
                 personalised discounts or loyalty programs to high-value customers and target low-spending customers with
                 promotional offers to increase their spending.

                 Difference between Classification and Clustering
                 Classification: It's a supervised learning technique that assigns data points to predefined categories or labels.
                 Labelled data is fed to the model. For example, classifying emails as "Spam" or "Not Spam."
                 Clustering: It is an unsupervised learning technique used to group similar data points into clusters. The input data is
                 unlabelled data. For example, grouping customers based on shopping behaviour to target personalised marketing.

                 Association

                 Association is an unsupervised learning method that is used to find interesting relationships or patterns among
                 variables in a dataset. It is widely employed to identify relationships between items, sets frequently purchased
                 items together in large databases, helping to analyse how items or events are related to each other.




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