Predict and submit to Kaggle

To send a submission to Kaggle you need to predict the survival rates for the observations in the test set. In the last exercise of the previous chapter, we created simple predictions based on a single subset. Luckily, with our decision tree, we can make use of some simple functions to "generate" our answer without having to manually perform subsetting.

First, you make use of the .predict() method. You provide it the model (my_tree_one), the values of features from the dataset for which predictions need to be made (test). To extract the features we will need to create a numpy array in the same way as we did when training the model. However, we need to take care of a small but important problem first. There is a missing value in the Fare feature that needs to be imputed.

Next, you need to make sure your output is in line with the submission requirements of Kaggle: a csv file with exactly 418 entries and two columns: PassengerId and Survived. Then use the code provided to make a new data frame using DataFrame(), and create a csv file using to_csv() method from Pandas.

Instructions

Impute the missing value for Fare in row 153 with the median of the column.
Make a prediction on the test set using the .predict() method and my_tree_one. Assign the result to my_prediction.
Create a data frame my_solution containing the solution and the passenger ids from the test set. Make sure the solution is in line with the standards set forth by Kaggle by naming the column appropriately.

Take Hint (-30xp)