Prediction
We are provided with the testing dataset on which we have to perform the prediction. To predict, we will pass the test dataset into our trained model and save it into a CSV file containing the information, passengerid and survival. PassengerId will be the passengerid of the passengers in the test data and the survival will column will be either 0 or 1.
Python3
ids = test['PassengerId'] predictions = randomforest.predict(test.drop('PassengerId', axis=1)) # set the output as a dataframe and convert # to csv file named resultfile.csv output = pd.DataFrame({'PassengerId': ids, 'Survived': predictions}) output.to_csv('resultfile.csv', index=False) |
This will create a resultfile.csv which looks like this
Titanic Survival Prediction Using Machine Learning
In this article, we will learn to predict the survival chances of the Titanic passengers using the given information about their sex, age, etc. As this is a classification task we will be using random forest.
There will be three main steps in this experiment:
- Feature Engineering
- Imputation
- Training and Prediction