What Is Data Mining? Data Mining and Related Terms, Knowledge based System, KDD Process Model, CRISP – DM, Terminology and Notation.
Data Preparation, Data Understanding, Data Cleaning, Missing data, Coding Systems, Discretization, Univariate Data Analysis
Types of Data, Characteristics of data, Types of Data Sets, Outliers, Find Mean , Median and Mode , Measures of Similarity.
Frequencies and the Mode, Measures of Location: Mean and Median, Measures of Spread: Range and Variance, Multivariate Summary Statistics.
Unsupervised Learning Models
Association Rules: Introduction, Discovering Association Rules in Transaction, Databases, Generating Candidate Rules, Selecting Strong Rules.
Uses of Data Visualization, Basic Charts: bar charts, line graphs, and scatterplots, boxplots and Histograms, Heat maps, Multidimensional Visualization, Specialized Visualizations, Summary of major visualizations and operations.
DBSCAN – Density Based Spatial Clustering of Applications with Noise
Clustering analysis, or simply clustering, is basically dividing data points into several specific batches or An unsupervised learning method that divides into groups. This includes various methods based on differential evolution.
- Artificial Neural Network, Convolutional Neural Network and Recurrent Neural Network
- Advantages and Disadvantages
- Hidden forms and Outputs
- What should you use for ?
Supervised Learning Models and Techniques
The Naive Bayes Classifier ,Conditional Probability, Applying the Full (Exact) Bayesian Classifier, Advantages and Shortcomings
