61.4 Data Mining Process Overview
40Feature Engineering and Selection
7Chapter-2
416.1 Feature Engineering
8Data Exploration and Preprocessing
426.2 Feature Selection
92.1 Data Cleaning
436.2.1 Wrapper Methods: 6.2.2 Embedded Methods
102.2 Data Transformation
44Chapter-7
112.3 Data Reduction
45Text and Sentiment Analysis
122.4 Descriptive Statistics
467.1 Text Mining Concepts
132.5 Visualization Techniques
477.2 Natural Language Processing
14Chapter-3
487.3 Text Classification
15Supervised Learning
497.4 Sentiment Analysis
163.1 Classification Concepts
50Chapter-8
173.2 Decision Trees
51Advanced Topics
183.3 Support Vector Machines
528.1 Time Series Analysis
193.4 Naive Bayes
538.1.1 Trends and Seasonality: 8.1.2 Forecasting Models
203.5 Ensemble Methods
548.2 Social Network Analysis
213.5.1 Bagging: 3.5.2 Boosting
558.3 Predictive Modelling: 8.3.1 Regression Analysis
223.6 Model Evaluation
56Chapter-9
233.6.1 Accuracy Metrics
57Ethical Considerations in Data Mining
243.6.2 Precision, Recall, F1 Score
589.1 Privacy Concerns
253.6.3 ROC Curve
599.2 Bias and Fairness: 9.3 Transparency and Accountability
26Chapter 4
60Chapter-10
27Unsupervised Learning
61Applications of Data Mining
284.1 Clustering Concepts
6210.1 Healthcare
294.2 K-Means Clustering
6310.2 Finance
304.3 Hierarchical Clustering
6410.3 E-commerce
314.4 DBSCAN
6510.4 Social Media
324.5 Frequent Pattern Mining
66Glossary
334.6 Association Rules
67Index
344.6.1 Apriori Algorithm: 4.6.2 FP-Growth Algorithm
