The primary tool that I will be using for my visualization is a tableau,
tableau allows one to prepare, clean, and format and clean data of all types
ranges and then create visualizations to obtain actionable insights that can be
shared with other users. g Also, python and R will be explored for
visualization compared with tableau. g Python and SAS enterprise miner will
be used for predictive model development.
For predictive analytics, the following models will be considered
1, logistic regression algorithms are used when there is a need to predict
probabilities that categorical dependent variables will fall into two categories
of the binary response as a function of some explanatory variables. The
rationale for this model is listed below.
• Easier to inspect and less complex
• A robust algorithm as the independent variables need not have equal
variance or normal distribution.
• These algorithms do not assume a linear relationship between the
dependent and independent variables and can handle non-linear effects.
• Controls confound
The primary tool that I will be using for my visualization is a tableau,
tableau allows one to prepare, clean, and format and clean data of all
types ranges and then create visualizations to obtain actionable
insights that can be shared with other users. g Also, python and R will
be explored for visualization compared with tableau. g Python and SAS
enterprise miner will be used for predictive model development.
For predictive analytics, the following models will be considered
1, logistic regression algorithms are used when there is a need to
predict probabilities that categorical dependent variables will fall into
two categories of the binary response as a function of some
explanatory variables. The rationale for this model is listed below.
o Easier to inspect and less complex
o The robust algorithm as the independent variables need not
have equal variance or normal distribution.
o These algorithms do not assume a linear relationship between
the dependent and independent variables and can handle non-
linear effects.
o Controls confounding and tests interaction.
o It is one of the best machine learning approaches for solving
binary classification problems.
2, SVM is a supervised learning algorithm for classification or
regression problems where the dataset teaches SVM about the classes
so that SVM can classify any new data. It organizes the data into
different categories by finding a line (hyperplane) separating the
training data set into classes.
SVM offers the best classification performance (accuracy) on the
training dataset and renders more efficiency for the correct
classification of future data. The best thing about SVM is that it does
not make strong assumptions about data and does not overfit the data.
3, Naïve Bayes Classifier is amongst the most popular learning
method grouped by similarities, which works on the famous Bayes
Theorem of Probability- to build machine learning models. Naïve
Bayes Classifier is amongst the most popular learning method grouped
by similarities, which works on the famous Bayes Theorem of
Probability- to build machine learning models.
The Naive Bayes Classifier algorithm performs well when the input
variables are categorical.
A Naïve Bayes classifier converges faster, requiring relatively little
training data set than other discriminative models like logistic
regression when the Naïve Bayes conditional independence
assumption holds. With the Naive Bayes Classifier algorithm,
predicting the class of the testing data set is more effortless and a good
bet for multi-class predictions.
