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Analytic Approach: It represents a problem in the context of statistical techniques and machine
                      learning so that the organisation can determine the most appropriate for the desired outcome.


                        Capstone Project:  A  capstone  pro ect  is  a  comprehensive,  independent,  and  final  pro ect
                      undertaken as part of the curriculum designed to assess the skills, knowledge, and expertise a
                      student has acquired.

                             Cross-validation: It is a resampling technique for evaluating machine learning models on a small
                       sample of data.


                            Data Collection: It is a process in which  the data  scientists  identify available data  sources
                      (structured, unstructured, and semi-structured) relevant to the problem area.

                          Data Storytelling:  It  is  a  means  of  delivering  facts   ith  a  compelling  narrative  to  a  specific
                      audience.


                          Data Understanding: It is  a process  in which the techniques such as descriptive statistics
                      and visualisations can be applied to datasets to evaluate the content, quality, and initial insights
                      of the data.


                          Deployment: It is the process in which a model is deployed into the production environment or
                      an equivalent test environment once it has been built and authorised by the business sponsors.



                          Design Thinking: It is a methodology that provides a solution-based approach for solving
                      problems.


                          Hyperparameters: These are parameters whose values govern the learning process.


                          Loss Function: It is a way of determining how well a certain algorithm models the data.

                            Mean Squared Error: It is the most basic and widely-used loss function, and it is frequently
                      taught in Machine Learning courses.


                          Modelling: It is the process through  which  several  models based on graphical  data  can  be
                      constructed and even tested for advantages and disadvantages.


                            Root Mean Square Error: It is a metric for determining ho   ell a regression line fits the data
                      points.


                          Training Dataset: It is used to fine tune the machine learning model and train the algorithm.

                            Train Test Split Evaluation: It is a procedure that measures the performance of machine learning
                      algorithms when they need to make predictions on data that were not used to train the model.



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