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BABD - Coggle Diagram
BABD
Introduction
Types of models
Descriptive analytics
Predictive analytics
Prescriptive analytics
Ml = Machine Learning
Statistics
Requirements
Data
Cross-sectional data
Time series
Panel data
Algorithms
Supervised learning
Classification = categorical/discrete response
Regression = continuous/numerical response
Unsupervised learning
Applications of analytics
CRM = customer relationship management
CCP models
Response modelling
Gross lift
Net lfit
Credit risk modelling
Fraud detection
Recommender systems
Collaborative filtering
Content-based filtering
Analytical framework
KDD model
SEMMA
CRISP-DM
Analytics process model
Preprocessing
Data understanding
Table level
Data level
Variable level
Data preparation
Value transformation
Value representation
Feature engineering
RFM features
More transformations
Variable selection
Descriptive analytics
Association rules
Apriori algorithm
Derive rules
Dimensionality reduction
PCA = Principles components anaysis
How man components ?
Rotation
Clustering
Dissimilarity measures
K-means
K-medoids
Hierarchical clustering
SOM = Self-organizing maps
Predictive analytics
Sampling & evaluation
Sampling
Bias-variance trade-of
Model evaluation
Linear models
Linear regression
Logistic regression
Variable selection
Non-linear models
Transform predictors
Non-linear techniques
AI models
Neural networks
The Perceptron
MLP = Multi-layer Perceptron
Networks architecture
Deep Neural Nets
Vanishing gradient
Activation functions
Support vector machines
Maximal margin classifier
Support vector classifier
Support vector machines