Titanic survival predictor
In this tutorial, we will learn to use TFLearn and TensorFlow to model the survival chance of titanic passengers using their personal information (such as gender, age, and so on). To tackle this classic machine learning task, we are going to build a DNN classifier.
Let's take a look at the dataset (TFLearn will automatically download it for you).
For each passenger, the following information is provided:
survivedSurvived (0 = No; 1 = Yes) pclass Passenger Class (1 = st; 2 = nd; 3 = rd) name Name sex Sex age Age sibsp Number of Siblings/Spouses Aboard parch Number of Parents/Children Aboard ticket Ticket Number fare Passenger Fare
Here are some samples extracted from the dataset:
survived | pclass | name | sex | age | sibsp | parch | ticket | fare |
1 | 1 | Aubart, Mme. Leontine Pauline | Female | 24 | 0 | 0 | PC 17477 | 69.3000 |
0 | 2 | Bowenur, Mr. Solomon | Male | 42 | 0 | 0 | 211535 | 13.0000 |
1 | 3 | Baclini, Miss. Marie Catherine | Female | 5 | 2 | 1 | 2666 | 19.2583 |
0 | 3 | Youseff, Mr. Gerious | Male | 45.5 | 0 | 0 | 2628 | 7.2250 |
There are two classes in...
