These are my submissions and notebook for the Titanic Competition.
This notebook explores several models to make predictions for the Titanic Kaggle Competition test dataset. The focus of the notebook is to determine the best way to impute missing values for the most accurate competition results.
The two features being imputed are 'Fare' and 'Age'. The methods used were mean, median, and mode (only for age).
Each model shows its score during cross-validation as well as the score from the actual competition.
The maximum score documented in this notebook was 0.7791 by imputing missing age values with the mean of the feature.