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Popular pages
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Learn by Marketing – Data Mining + Marketing in Plain English
Data Science Reading List A curated list of academic, popular science, and managerial books on Data Mining, Marketing, and Machine Learning.Read more » Predictive Analytics Methods & Tools Explore d...
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Simple Linear Regression Explained – Learn by Marketing
M is the slope or the “weight” given to the variable X. X is the input you provide based on what you know. b is the intercept. Essentially given 0 for your input, how much of Y do we start off with. ...
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Explaining the lm() Summary in R – Learn by Marketing
Explaining the lm() Summary in R Summary: Residual Standard Error: Essentially standard deviation of residuals / errors of your regression model. Multiple R-Squared: Percent of the variance of Y intac...
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Neural Network Basics and Concepts – Learn by Marketing
Neural Network Basics and Concepts Summary: Artificial Neural Networks takes input variables and multiplies them by learned weights. The product of these numbers are then used as inputs to a “hidden”...
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Logistic Regression Explained – Learn by Marketing
Logistic Regression Explained Summary: Logistic Regression is a tool for classifying and making predictions between zero and one. Coefficients are long odds. Odds are relative so when interpreting c...
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Naive Bayes in R Tutorial – Learn by Marketing
Naive Bayes in R Tutorial Summary: The e1071 package contains the naiveBayes function. It allows numeric and factor variables to be used in the naive bayes model. Laplace smoothing allows unrepresent...