####Supervised learning #####Linear Regression

• Linear Regression
• Assumptions
• Variance Inflation Factor
• T Test
• F Test
• Adj. R Squared vs R Squared
• Mape
• Interpreting Actual vs Predicted Plot
• Interpreting Residual vs Predicted Plot

File:LinearRegression.R

Required packages: car, lmtest, ggplot2

Input parameters:

 c_path_in - path pointing to the input .csv file
c_path_out - output folder path
c_var_in_independent - one ore more independent variable(s)
c_var_in_dependent - one dependent variable

Outputs:

 parameterEstimates (Data Frame) - Contains estimates of all the inpendent variables used for building the model &
is exported as Estimates.csv to the location "c_path_out"
modelStatistic (Data Frame) - Contains various model statistics & is exported as ModelStatistic.csv to the location "c_path_out"
durbinWatsonTest (List) - Contains statistic used for testing autocorrelation
goldfledQuantdtTest (List) - Contains statistic used for testing homoscedasticity
ActualPredicted.png file is exported to the location "c_path_out"
ResidualPredicted.png file is exported to the location "c_path_out"

#####Logistic Regression

• Logistic Regression
• Chi-Square Test
• Model Evaluation Metrics(Gains Chart, Lift Chart, K-S Test, Confusion Matrix, ROC)
• Hosmer-Lemeshow Test

File: LogisticRegression.R

Required packages: car, ResourceSelection, ggplot2

Input parameters:

c_path_in - path pointing to the input .csv file
c_path_out - output folder path
c_var_in_independent - one or more independent variables
c_var_in_dependent - one binary dependent variable
x_val_event - level of dependent variable

Outputs:

parameterEstimate(Data Frame) - Contains estimates of all the independent varaible used for building the model & is exported as Estimate.csv file to the location "c_path_out"
modelStatistic(Data Frame) - Contains various model ststistics & is exported as ModelStatistic.csv to the location "c_path_out"
hosmerLemeshowTest(List) - Contains statistic for testing goodness of fit
ks_out(Data Frame) - For measuring the performance of the classification model
GainsChart.png is exported to the location "c_path_out"
LiftChart.png is exported to the location "c_path_out"

File: LogisticModelAnalysis.R

Input parameters:

c_path_out - output folder path
modelObj - Logistic Model Object

Description:

Contains function for generating Confusion Matrix for all the cut points between 0.01 & 0.99. ROC curve & Sensitivity - Specificity curve is plotted & exported as ROC.png & Sensitivity-Specificity.png respectively to the location "c_path_out"

#####Bayesian Belief Network

Understanding Bayesian Network using bnlearn - R Package, 1Scoring

#####Naive Bayes

• Simple Explanation
• Bayesian Network vs Bayes Classifier

####Classification #####EM Clustering

• Expectation Maximization

####Ensemble #####Random Forest

• Random Forest
• Confidence Interval

####Recommender System Introduction

####Factor Analysis #####Mutual Information

• Mutual Information

#####Linear Discriminant Analysis

• Linear Discriminant Analysis
• LDA & QDA
• PCA vs LDA

File: Distance.R

Description:

Contains various method for calculating distance between two vectors