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data scientists to utilise large datasets that contain large or even all of the available data. Due to this, predictive models
            are able to better predict rare events such as disease or system failure.


            S tag e 5 : Data U nder standing
            After the initial data collection, techniques such as descriptive statistics and visualisations can be applied to datasets to
            evaluate the content, quality, and initial insights of the data. Additional data collection may be required to fill the gap.


            S tag e 6 : Data Pr epar ation
             his stage contains all the activities to build the dataset used in the subsequent modeling stage. Activities to prepare
            data include

            •   data cleansing  handling missing or invalid values, removing duplicates, applying correct formats),
            •    oining data from multiple sources  files, tables, platforms), and
            •   conversion of data to more useful variables.
            Data preparation is usually the most time consuming procedure in a data science pro ect. In many domains, some data
            preparation procedures are common for a variety of problems. . Automating certain data preparation steps in advance
            can speed up the process by minimising ad hoc preparation time.  oday's high performance, massively parallel systems
            and analytics capabilities  here data is stored allo  data scientists to prepare data more easily and quickly using very
            large datasets.


            S tag e 7 : Modelling
             he modelling stage, that begins  ith the initial version of the prepared data set, focuses on constructing predictive or
            descriptive models based on the analytic approach described in  tage  .
             et us understand these   modelling approaches.

            Modelling  A ppr oach
            Data modelling focuses on building either descriptive or predictive models.

            Descriptive model:  describe  or summarise ra  data and turn it into something that can be understood by people.
             hey are historical analytic models. Any point in time  hen an event occurred  hether it  as only a minute ago or
            a year ago is considered to be in the past.  ence, they enable us to understand ho  past behaviours may affect
            future outcomes.  ommon e amples include displaying information like a company s total inventory, average consumer
            spending, and sales gro th over time.
            Predictive model:  he ability to  predict   hat might happen is the foundation of predictive analytics.  nderstanding
            the future is the goal of these analysis.  ased on data, predictive analytics gives businesses actionable insights.  he use
            of predictive analytics is  idespread inside an organisation, from predicting consumer behaviour and purchasing trends
            to seeing trends in sales operations.  he creation of a credit score using predictive analytics is one typical application
            that most people are familiar  ith.  inancial services utilise these scores to estimate the likelihood that clients  ill pay
            their credit card bills on time in the future.
                                                                                lassification

                                                                                 egression
                                                      redictive
                                                                            ime  erious Analysis

                                                                                 rediction
                                 Data  odelling

                                                                                 lustering

                                                                               ummari ation
                                                     Descriptive
                                                                                       C apstone  P roj e ct
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