Random Variables
Data Analysis for Predictive Modeling
Data Mining Techniques for Retail Analysis
Using Decision Trees in Business Analytics
Projections
Data Mining Techniques for Financial Analytics
Modeling
Simulation Models
Predictive Models 
Classification Models Decision Trees Support Vector Machines (SVM)
Random Forests Time Series Models ARIMA Exponential Smoothing Ensemble Models Boosting Bagging
...This may include handling missing values, normalizing data, and encoding categorical
variables ...
Data Analysis for Predictive Modeling formats Normalizing or scaling data Feature Selection Feature selection is the process of identifying the most relevant
variables that contribute to the predictive model
...Tree-like model for decision making Credit scoring, customer segmentation
Random Forest Ensemble of decision trees for improved accuracy Marketing response modeling, stock price prediction Neural
...
Data Mining Techniques for Retail Analysis Segment customers based on demographics or purchasing behavior Popular algorithms for classification include Decision Trees,
Random Forests, and Support Vector Machines
...Association Rule Learning Association rule learning is used to find interesting relationships between
variables in large datasets
...
Using Decision Trees in Business Analytics The primary goal of a decision tree is to create a model that predicts the value of a target variable based on several input
variables ...Ensemble Methods Consider using ensemble methods like
Random Forests or Gradient Boosting, which combine multiple decision trees to improve accuracy and robustness against overfitting
...
Projections Regression A statistical method to model the relationship between a dependent variable and one or more independent
variables ...forecasting Monte Carlo Simulation A computational algorithm that relies on repeated
random sampling to obtain numerical results and understand the impact of risk
...
Data Mining Techniques for Financial Analytics Technique Application Decision Trees Credit risk assessment
Random Forest Fraud detection Support Vector Machines Customer segmentation 2
...Regression Regression analysis is used to predict a continuous outcome based on one or more predictor
variables ...
Modeling Use Cases Regression Analysis Estimates relationships among
variables ...Random Forests: An ensemble method that improves predictive accuracy
...
Simulation Models Stochastic Models: Unlike deterministic models, stochastic models incorporate
randomness and uncertainty, producing different outcomes even with the same initial conditions
...estimate the probability of different outcomes in a process that cannot easily be predicted due to the intervention of random
variables ...
Statistical Analysis for Sales Forecasting Regression Analysis Regression analysis explores the relationship between sales and one or more independent
variables (predictors)
...Random Forest: An ensemble method that combines multiple decision trees to improve accuracy
...
Predictive Modeling Regression Models Used to predict a continuous outcome variable based on one or more predictor
variables ...Decision Trees,
Random Forest, Support Vector Machines Time Series Models Used to analyze data points collected or recorded at specific time intervals
...
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