The Random Forest is a supervised learning algorithm. The "forest" is built from a group of decision trees that are merged together to get a more accurate and stable prediction. The random forest algorithm is an easy way to measure relative importance of the features used in the prediction.
We used the Random Forest Classifier in sklearn to run this model. The Random Forest Classifier refers to the classification algorithm that uses randomness to build each individual decision tree to promote uncorrelated forests. Then it uses the forest’s predictive powers to make accurate decisions.
The datasets used for training and testing had the age nulls removed. The score for this model and data set is 81.5 percent of accurate predictions. The graph below shows that age was the most significant factor in our dataset at 29%. The number of parents/children you had accompanying you was the least significant at 4%.