About Data Science Introduction about Data mining

About Data Science Data Mining Introduction

1. 2. Data Mining Process

2. Business Understanding Data Understanding Data Preparation Modeling Deployment Evaluation Data CRISP DM process

3. Business Understanding Data Understanding Prepare Data Building Model using Algorithms Appl y ing Model and performance evaluation Deployment Test Data Knowledge and Actions Training Data 1. Prior Knowledge 2. Preparation 3. Modeling 4. Application 5. Knowledge Process

4. Prior Knowledge Gaining information on: Objective of the problem Subject area of the problem Data

5. 2. Data Preparation Data Exploration Data quality Handling missing values Data type conversion Transformation Outliers Feature selection Sampling

6. 3. Modeling Build model Evaluation Test Data Final Model Training Data

7. 3. Modeling Spliting training and test data sets

8. 3. Modeling Spliting training and test data sets Training Data Test Data

9. 3. Modeling

10. 3. Modeling Evaluation of test dataset

11. 3. Application Product readiness Technical integration Model response time Remodeling Assimilation

12. 5. Knowledge Posterior knowledge Kotu, V., & Deshpande, B. (2014). Predictive analytics and data mining: concepts and practice with rapidminer . Morgan Kaufmann.