Random Forest Algorithm & Supervised Learning using Python

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.
hrough this training we are going to learn and apply how the random forest algorithm works and several other important things about it.
The course includes the following;
1) Extract the Data to the platform.
2) Apply data Transformation.
3) Bifurcate DatTa into Training and Testing Data set.

4) Built Random Forest Model on Training Data set.
5) Predict using Testing Data set.
6) Validate the Model Performance.
7) Improve the model Performance using Random Forest.
8) Predict and Validate Performance of Model.

Random forest in Python offers an accurate method of predicting results using subsets of data, split from global data set, using multi-various conditions, flowing through numerous decision trees using the available data on hand and provides a perfect unsupervised data model platform for both Classification or Regression cases as applicable; It handles high dimensional data without the need any pre-processing or transformation of the initial data and allows parallel processing for quicker results.

The unique feature of Random forest is supervised learning. What it means is that data is segregated into multiple units based on conditions and formed as multiple decision trees. These decision trees have minimal randomness (low Entropy), neatly classified and labeled for structured data searches and validations. Little training is needed to make the data models active in various decision trees.