Random Variables
Supervised Learning
Utilizing Machine Learning for Predictions
Machine Learning Techniques for Business Insights
Strategies for Text Mining
Classification
The Science Behind Predictive Analytics
Supervised Learning 
Features: The input
variables used to make predictions
...Random Forest: An ensemble method that combines multiple decision trees for improved accuracy
...
Utilizing Machine Learning for Predictions Regression A statistical method for modeling the relationship between a dependent variable and one or more independent
variables ...Customer segmentation, risk assessment
Random Forest An ensemble method that constructs multiple decision trees and merges them to improve accuracy and control overfitting
...
Machine Learning Techniques for Business Insights Linear Regression Predicts a continuous value based on the linear relationship between input
variables ...Customer segmentation, loan approval
Random Forest An ensemble method that combines multiple decision trees to improve accuracy
...
Strategies for Text Mining Feature Extraction: Identifying relevant features or
variables from the text that can be used for analysis
...Random Forest: An ensemble learning method that uses multiple decision trees to improve classification accuracy
...
Classification Logistic Regression A statistical model that uses a logistic function to model binary dependent
variables ...Random Forests An ensemble method that constructs multiple decision trees and merges them to improve accuracy and control overfitting
...
The Science Behind Predictive Analytics algorithms include: Linear Regression: Used for predicting a continuous outcome based on the linear relationship between
variables ...Random Forest: An ensemble method that combines multiple decision trees to improve accuracy
...
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