Random Variables
Data Mining Techniques for Predictive Maintenance
Data Mining Techniques for Personalization
How to Interpret Machine Learning Model Results
Predictive Models
Statistical Analysis for Data Quality Improvement
Foundations
The Importance of Feature Selection
Statistical Analysis 
2 Inferential Statistics Inferential statistics use a
random sample of data to make inferences about a larger population
...Regression Analysis: A method to understand the relationship between
variables ...
Data Mining Techniques for Predictive Maintenance Common classification algorithms include: Decision Trees
Random Forest Support Vector Machines (SVM) Neural Networks Applications of Classification Application Description Failure Prediction Classifying equipment based on historical
...Regression Regression techniques are used to predict continuous outcomes based on input
variables ...
Data Mining Techniques for Personalization Common algorithms used in classification include: Decision Trees
Random Forests Support Vector Machines Naive Bayes 2
...Association Rule Mining Association rule mining uncovers relationships between
variables in large datasets
...
How to Interpret Machine Learning Model Results Feature Importance Feature importance helps in understanding which
variables are most influential in making predictions
...Tree-based Methods: For models like Decision Trees and
Random Forests, feature importance can be calculated based on the reduction in impurity
...
Predictive Models Polynomial Regression Classification Models Decision Trees
Random Forests Support Vector Machines Time Series Models ARIMA (AutoRegressive Integrated Moving Average) Exponential Smoothing
...Feature Selection: Identifying the most relevant
variables that contribute to the predictive power of the model
...
Statistical Analysis for Data Quality Improvement Scatter Plots: Helpful for identifying relationships between
variables ...Common sampling methods include:
Random Sampling: Selecting a subset of data randomly to make inferences about the population
...
Foundations Inferential Statistics Uses a
random sample of data to make inferences about a larger population
...Regression Analysis Examines the relationship between
variables to predict outcomes
...
The Importance of Feature Selection Feature selection is a crucial step in the machine learning process that involves selecting a subset of relevant features (
variables, predictors) for use in model construction
...Random Forest: Provides importance scores for features based on their contribution to the model
...
Model Training Encoding Categorical
Variables Converting categorical data into numerical format for model compatibility
...This process is often iterative and may require techniques such as: Grid Search
Random Search Bayesian Optimization Effective model tuning can significantly enhance the model's accuracy and robustness, making it better suited for real-world applications
...
Statistical Reasoning Important concepts include:
Random Variables Probability Distributions Bayes' Theorem Methodologies Statistical reasoning employs various methodologies to analyze data effectively
...
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