CRISP - Business Understanding
Descriptive Analysis – What happened in the Past – Taking historical data Diagnostic Analytics – Why something happened
Predictive Analysis – What is likely to happen – What happens in future
Prescriptive Analysis – What needs to be done – giving suggestions to control
According to Standish Group, their chaos report 2019 shows that more than 80% of the IT project partially completed or failed across the globe.
So, to attain accuracy and for the successful completion of the projects there is a methodology called: PROJECT MANAGEMENT METHODOLOGY
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CRISP (CROSS INDUSTRY STANDARD PROCESS FOR DATA MINING)
There are 6 steps involved from end to end in a life of Data Science
- Understand the Business problem & create a project charter
- Data Collection
- Data Cleansing / Preparation / EDA / Feature Engineering
- Data Mining / Machine Learning
- Model Evaluation
- Model Deployment
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Survival Analysis mainly deals with the time to an event, including death, the onset of disease, and bankruptcy, etc. Only the occurrence of either time to event or censoring time is observed.
Step 1: Business Understanding – Once the Business problem is understood, one needs to record 2 things, one is Business Objective (Primary metric) and the other is Business Constraint (secondary metric)
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Secondary Data Source – Data collected beforehand – Publicly available data- open source as well as syndicate data. Pro: Data is available easily. Cons: Data may or may not have a variable of interest
Primary Data Source – If the data is not available, it will be collected by source by conducting surveys, design of experiment, simulation. Pro: Get data for the exact variable of interest. Con: It is very expensive and time-consuming.
Data Collection Using Survey – Understand Business Reality → Root Cause Analysis → Decision Problem (a problem on which decision to be taken) → Research Objective (should be a one-line statement with an action verb and actionable object) → Construct (Multi-dimensional object) → Aspects(Single-dimensional) → Survey Questionnaire.
Design of Experiment – When is not possible to practically capture the data
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