1 Chapter 1 Introduction to data science
857.6.3 Fitting Logistic Regression to the Training set:
2Conclusion
867.6.4 Output:
3Questions: References
877.6.5 Predicting the Test Result
4Chapter 2 What is data analysis?
887.6.6 Test Accuracy of the result
52.1 Considerations/issues in data analysis
897.6.7 Output:
62.2 Data analysis process
907.6.8 Visualizing the training set result
72.3 Why Data Analysis?
917.6.9 Output:
82.4 Data Analysis Tools
927.7 The goal of the classifier:
92.5 Types of Data Analysis: Techniques and Methods
937.8 Linear Classifier:
102.6 Data Analysis Process
947.8.1 Visualizing the test set result:
112.7 What Is the Importance of Data Analysis in Research?
957.8.2 Output:
122.8 Data Analysis Methods
967.9 Conclusion
132.9 Conclusion: 2.10 Questions
977.10 Questions: 7.11 References
142.11 References
98Chapter 8 KNN classifier
15Chapter 3 What is data mining?
998.1 K-Nearest Neighbor(KNN) Algorithm for Machine Learning
163.1 What is data mining?
1008.2 Why do we need a K-NN Algorithm?
173.2 Why is data mining important?
1018.3 How does K-NN work?
183.3 Types of data mining techniques
1028.4 How to select the value of K in the K-NN Algorithm?
193.4 Data mining software and tools
1038.5 Advantages of KNN Algorithm:
203.5 Benefits of data mining
1048.6 Disadvantages of KNN Algorithm:
213.6 Industry examples of data mining
1058.7 Python implementation of the KNN algorithm
223.7 Data mining history and origins
1068.7.1 Steps to implement the K-NN algorithm:
233.8 The Data Mining Process
1078.7.2 Data Pre-Processing Step:
243.9 Limitations of Data Mining
1088.7.3 Output:
253.10 Conclusion
1098.7.4 Output: By executing the above code, we will get the below graph:
263.11 Questions
1108.7.5 Visualizing the Test set result:
273.12 References
1118.7.6 Output:
28Chapter 4 Data Warehousing
1128.8 Conclusion
294.1 Basic Concepts
1138.9 Questions: 8.10 References
304.2 What Is a Data Warehouse?
114Chapter 9 SVM Algorithm for Regression
314.3 Differences between Operational Database Systems and Data Warehouses
1159.1 Data Preparation and Settings Choice for Support Vector Machines
324.4 Data Warehousing: A Multitiered Architecture
1169.2 Types of SVM
334.5 Data Warehouse Architecture: Basic
1179.3 Hyperplane and Support Vectors in the SVM algorithm:
344.6 Properties of Data Warehouse Architectures
1189.4 How does SVM works?
354.7 Types of Data Warehouse Architectures
1199.4.1 Linear SVM:
364.8 Data Warehouse Models: Enterprise Warehouse, Data Mart, and Virtual Warehouse
1209.4.2 Non-Linear SVM:
374.9 Metadata Repository
1219.5 Python Implementation of Support Vector Machine
384.10 Data Cube: A Multidimensional Data Model
1229.5.1 Data Pre-processing step
394.11 Data Warehouse Design Process
1239.5.2 Fitting the SVM classifier to the training set:
404.12 Data Warehouse Usage for Information Processing
1249.5.3 Output:
414.13 From Online Analytical Processing to Multi-dimensional Data Mining
1259.5.4 Creating the confusion matrix:
424.14 Data Warehouse Implementation
1269.5.5 Output:
434.15 Conclusions
1279.5.6 Output:
444.16 Questions
1289.5.7 Output:
454.17 References
1299.6 Minimum Descriptor Length
46Chapter 5 Introduction to various data mining models
1309.7 ODM Model Seeker (Java Interface Only)
475.1 Data Mining Models
1319.8 Conclusion
485.2 Predictive data mining models
1329.9 Questions: 9.10 References
495.3 Top 5 Types of Predictive Models
133 Chapter 10 Descriptive models of data mining
505.4 Common Predictive Algorithms
13410.1 Clustering in Data Mining
515.5 Benefits of Predictive Modeling
13510.1.1 Different types of Clustering
525.6 Challenges of Predictive Modelling
13610.1.2 What is Cluster Analysis?
535.7 Limitations of Predictive Modeling
13710.1.3 Different types of Clustering
545.8 The Future of Predictive Modeling
13810.1.4 Different types of Clusters
555.9 Predictive Modeling in Platforms
13910.1.5 Enhanced k-Means Algorithm
565.10 Conclusion
14010.1.6 Data for k-Means
575.11 Questions: 5.12 References
14110.1.6.1 Scalability through Summarization
58Chapter 6 Predictive Models
14210.1.6.2 Scoring (Applying Models)
596.1 Classification:
14310.1.7 Orthogonal Partitioning Clustering (O-Cluster)
606.1.1 What is the Classification Algorithm?
14410.1.7.1 O-Cluster Data Use
616.1.2 Costs
14510.1.7.2 Binning for O-Cluster
626.1.3 Learners in Classification Problems:
14610.1.7.3 O-Cluster Attribute Type
636.2 Types of ML Classification Algorithms:
14710.1.7.4 O-Cluster Scoring
646.3 Evaluating a Classification model:: 6.3.1 Use cases of Classification Algorithms
14810.1.8 K-Means and O-Cluster Comparison
656.4 Priors
14910.2 Association Models in Data Mining
666.5 Naive Bayes Algorithm
15010 .2.1 Finding Associations Involving Rare Events
676.6 Adaptive Bayes Network Algorithm
15110.2.2 Finding Associations in Dense Data Sets
686.7 ABN Model Types
15210.2.3 Data for Association Models
696.8 ABN Rules
15310.2.4 Apriori Algorithm
706.8.1 ABN Build Parameters
15410.3 Feature Extraction in Data Mining
716.8.2 ABN Model States
15510.3.1 Non-Negative Matrix Factorization
726.9 Comparison of NB and ABN Models
15610.3.2 NMF for Text Mining
736.10 Attribute Importance
15710.4 Conclusion
746.11 Conclusion
15810.5 Questions
756.12 Questions: 6.13 References
15910.6 References
76Chapter 7 Regression
160Chapter 11 Predictive models vs Descriptive models
777.1 Regression
16111.1 The pros and cons of descriptive and predictive models of data mining on basis of some points are discussed below:
787.2 Logistic Function (Sigmoid Function):
16211.2 Conclusion:
797.3 Assumptions for Logistic Regression:
16311.3 Questions: 11.4 References
807.4 Logistic Regression Equation:
164Chapter 12 Applications of data mining
817.5 Type of Logistic Regression:
16512.1 Applications in various sectors ad industries:
827.6 Python Implementation of Logistic Regression (Binomial)
16612.2 Conclusion
837.6.1 Steps in Logistic Regression:
16712.3 Questions: 12.4 References
847.6.2 Data Pre-processing step:
168Glossary
