6 Step Data Science Methodology

In this video, we'll explore the widely-used CRISP-DM process model, which outlines the six stages involved in a data mining project. You'll learn about the importance of understanding the business problem and data before diving into data preparation and modeling. We'll discuss importance of evaluating the effectiveness of your predictive model and ultimately deploying it into production. Whether you're a beginner or experienced data analyst, this video will provide an overview of the CRISP-DM process and its applications. Join us as we dive deep into the world of data mining and uncover the key insights and strategies you need to succeed. 1. Business Understanding: In this stage, the focus is on understanding the business problem that needs to be solved. It involves defining the problem, identifying the goals and objectives, and understanding the project requirements. 2. Data Understanding: In this stage, the focus is on understanding the data that is available for the project. This involves gathering and exploring the data, identifying any quality issues, and determining the variables that are relevant to the project. 3. Data Preparation: In this stage, the focus is on preparing the data for analysis. This involves cleaning and transforming the data, creating new variables, and integrating data from multiple sources. 4. Modeling: In this stage, the focus is on building and validating a predictive model. This involves selecting an appropriate algorithm, building the model, and evaluating its performance. 5. Evaluation: In this stage, the focus is on evaluating the effectiveness of the model. This involves assessing its accuracy, reliability, and generalizability, and determining whether it meets the project requirements. 6. Deployment: In this stage, the focus is on deploying the model into production. This involves integrating the model into the business process, creating user interfaces and reports, and monitoring its performance over time.