Predictive Analytics (with R)

Input File (.csv only):
Read File

Condition(s) for Subset:
None = > >= < <= != Categorical Variable
None = > >= < <= != Categorical Variable
None = > >= < <= != Categorical Variable
None = > >= < <= != Categorical Variable

Dependent Variable:
None Change to Categorical Variable

Independent Variable(s):
None

Size of Training Data:
From Top From Bottom

Size of Validation Data:
% of Training Data

Include Prediction for Validation Data with Testing Data

Regression Techniques:
Linear Regression
Decision Tree (with All 1 2 3 4 5 6 7 8 Levels)
Random Forest with trees with maximum nodes
Gradient Boosting
Extreme Gradient Boosting (XGBoost) (with maximum depth = )
Support Vector Machine (with kernel = Linear Polynomial Radial Basis Sigmoid )

Classification Techniques:
Linear Discriminant Analysis
Logistic Regression for binary classification with cut-off =
Decision Tree (with All 1 2 3 4 5 6 7 8 Levels)
Random Forest with trees with maximum nodes
Naive Bayes
Support Vector Machine (with kernel = Linear Polynomial Radial Basis Sigmoid )
Neural Network (with hidden layers)
Adaptive Boosting (AdaBoost)
Gradient Boosting for binary classification with cut-off =
Extreme Gradient Boosting (XGBoost)) for binary classification with cut-off = and maximum depth =
k-Nearest Neighbor (with k = ) (Note: numeric independent variables only)

Additional Regression Metrics:
Mean Squared Error (MSE)
Mean Absolute Error (MAE)

Additional Binary Classification Metrics:
Precision
Recall or Sensitivity
Specificity
F1 Score



Code:

​Copyright © 2019 Abhimanyu Gupta. All rights reserved.