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Data Model
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Classroom Contents
Predictive Analytics Using R and SQL
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- 1 Introduction
- 2 Questions
- 3 Sponsors
- 4 Past Summit
- 5 Agenda
- 6 What is Data Science
- 7 What do we want to achieve
- 8 The objective predict
- 9 The data science process
- 10 Data Profiling
- 11 Our Studio
- 12 Methodology Approach
- 13 Typical Problems
- 14 Demo
- 15 Missing or Incomplete Data
- 16 Create Data Partition
- 17 Build Support Vector Machine
- 18 Test Model
- 19 Customer Keys
- 20 Probability
- 21 Save model to disk
- 22 Text data
- 23 CSV file
- 24 Why am I showing this
- 25 Plotting the data
- 26 Ronald Reagan
- 27 Creating Data Sets
- 28 Formatting Data Sets
- 29 tfidf
- 30 Data
- 31 Training Data
- 32 Results
- 33 Recap
- 34 Preparing Data
- 35 Data Model
- 36 Data Science
- 37 Algorithms
- 38 Classification
- 39 Training and Testing
- 40 Principles of Predictive Analytics
- 41 Wheres the Data
- 42 Predict Churn
- 43 Problems Mistakes
- 44 Questions and Answers
- 45 Outro
