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. j Also, python and R will be explored for visualization compared
with tableau. j 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. Also, python and R will be explored
for visualization compared with tableau. j 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.
