l4n14ke4/titanic-survival-prediction-using-python

Implementing Logistic Regression in Python for classification- Titanic survival or deceased.

Titanic-Survival-Prediction-using-Python

Dataset Overview:

The data has been split into two groups: training set (train.csv) test set (test.csv)

The training set should be used to build our machine learning models. Our model will be based on “features” like passengers’ gender and class. we can also use feature engineering to create new features.

The test set should be used to see how well our model performs on unseen data.

Data Dictionary

VariableDefinitionKey survival Survival 0 = No, 1 = Yes pclass Ticket class 1 = 1st, 2 = 2nd, 3 = 3rd sex Sex Age Age in years sibsp # of siblings / spouses aboard the Titanic parch # of parents / children aboard the Titanic ticket Ticket number fare Passenger fare cabin Cabin number embarked Port of Embarkation C = Cherbourg, Q = Queenstown, S = Southampton Variable Notes pclass: A proxy for socio-economic status (SES) 1st = Upper 2nd = Middle 3rd = Lower

age: Age is fractional if less than 1. If the age is estimated, is it in the form of xx.5

sibsp: The dataset defines family relations in this way... Sibling = brother, sister, stepbrother, stepsister Spouse = husband, wife (mistresses and fiancés were ignored)

parch: The dataset defines family relations in this way... Parent = mother, father Child = daughter, son, stepdaughter, stepson Some children travelled only with a nanny, therefore parch=0 for them.

What to predict:

For each passenger in the test set,Our model will be trained to predict whether or not they survived the sinking of the Titanic.