5 Stages of Model Building in Analytics

Ivy Jul 27, 2018 No Comments

Model building is a fundamental part of analytics and data science. To make accurate predictions and generate valuable insights, it’s crucial to follow a structured approach. At Ivy Professional School, a top-ranked Analytics Institute in India, we train aspiring data scientists on the intricacies of model building through our Data Science courses. Here, we outline the five critical stages of model building in analytics to guide you through this complex yet rewarding process.

1. Analyze the Business Problem

The first step in model building is understanding the context of the business problem. This involves defining the business objective and gathering all necessary information from stakeholders. A thorough analysis helps ensure that the model is aligned with business needs and goals.

Key Focus: Identify the problem’s scope, context, and objective to establish a clear direction for model development.

2. Data Collection and Preparation

Data collection is a critical phase where relevant data is gathered according to the defined problem. The collected data is then cleaned and prepared, a process known as data wrangling. This stage ensures that the data is free of errors, duplicates, and irrelevant information, which could otherwise lead to inaccurate model predictions.

Key Steps:

• Data Cleaning: Removing inconsistencies and errors in the data.
• Data Wrangling: Structuring data in a usable format for analysis.

3. Model Building and Validation

Once the data is prepared, it’s time to build the model. This stage involves choosing the appropriate model based on the data and problem at hand. Model validation is crucial here, as it assesses the accuracy and robustness of the model. Techniques such as cross-validation are used to ensure the model’s reliability.

Key Components:

• Constructing the model: Selecting and applying algorithms that fit the data.
• Model Validation: Checking if the model performs well on unseen data.
• Assessing Fit: Ensuring the model accurately represents the underlying data patterns.

4. Model Implementation

In this stage, the validated model is implemented to address the business problem. It is essential to ensure that the model performs effectively in a real-world environment and provides actionable insights.

Objective: Deploy the model to generate results and assess its performance to ensure it meets the business objectives.

5. Strategy and Optimization

The final stage involves using the insights provided by the model to create a strategy that minimizes or eradicates the business problem. This stage is about applying the model’s outcomes to make informed business decisions and strategies.

Focus: Align insights from the model with business strategies to drive optimal solutions and value.

Why Choose Ivy Professional School for Analytics Training?

At Ivy Professional School, we provide hands-on training in data science and analytics, covering essential skills in Machine Learning, Data Wrangling, and Model Validation. As the preferred training partner for major analytics firms such as Genpact, Capgemini, eBay/PayPal, and ITC, our programs are designed to ensure our students excel in real-world applications of data science.

Ivy Pro School is the Top Ranked  Analytics Institute in India. Want to learn more about the Data Science courses at Ivy Pro School? Write to us at info@ivyproschool.com or call us at 9748441111.