Great Work,

In this project you will apply the knowledge of Random forest which consists of an arbitrary number of simple trees, which are used to determine the final outcome. For classification problems, the ensemble of simple trees vote for the most popular class. In the regression problem, their responses are averaged to obtain an estimate of the dependent variable. Using tree ensembles can lead to significant improvement in prediction accuracy (i.e., better ability to predict new data cases).

• Shortcomming of Decision tree
• How Random Forest overcome these shortcomings
• Hyperparameters for Random Forests

• You are going to learn how to perform GridSearch on RandomForestClassifier Model.
• Also how to fit the model previously used in order to predict on test dataset.

• Learn to build and execute RandomForestClassifier on GridSearch method and interpret the results.
• Learn to use your model to predict on unseen data and derive insights by measuring accuracy, confusion matrix etc of your model performance.

• Data set used previously in logistic regression project which is named as Loan Prediction is used here.