Page 125 - Artificial Intellegence_v2.0_Class_12
P. 125
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

