The document outlines the data science project lifecycle, encompassing six key stages: understanding the problem, gathering relevant data, data preparation, feature engineering, model building, and deployment. Each stage is crucial for breaking down the business problem and preparing data for analysis to generate desired outcomes. Ultimately, thorough evaluation is necessary to ensure the correct solution is deployed effectively.

1. Data Science Project Lifecycle
01 02
03
04
05
Gathering
Relevant
Data
Data
Preparation
& EDA
Feature
Engineering
&
Feature
Extraction
Model
Building
& Deployment
Understanding
The Problem
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2. Understanding The Problem
It is important to first
understand the client's
business problem in
order to form a
successful business
model.
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3. Gathering Relevant Data
After we've clarified the
problem statement, we'll
need to gather relevant
data in order to split the
problem down into smaller
parts.
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4. Data Preparation
We must proceed to data
preparation after acquiring
data from appropriate
sources. This stage aids in the
comprehension of the data
and prepares it for further
analysis.
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5. Data Modeling
We use the prepared data
as the input in this data
modeling procedure, and
we try to create the desired
output with it.
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6. This is the final step in the data
science project life cycle.
We need to be sure we've
chosen the right solution after
a thorough evaluation before
we deploy the model. It's then
distributed on the specified
channel and format.
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Feature Engineering &
Feature Extraction

7. www.technogeekscs.com
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