Titanic_Prediction

This Titanic Prediction project aims at predicting those who survived through the catastrophe using the CatBoost algorithm. First, missing values in the data set are dealt with while second, categorical features are dealt with through creating dummy variables. Some of the features, by which the model is trained, are: Pclass, Sex (with specific attention to the female gender), Age, Fare, and Embarked. After accomplishment of the training process, the model was received the percentage of accuracy 1. 00. The feature importance feature is important and has a confusion matrix to show that the performance was perfect, as does the ROC curve. This sturdy model gives constructive evidence of CatBoost in classification errands, specifically for intervals of alternatives.