This project predicts whether a person survived the Titanic disaster based on various features such as age, fare, passenger class (Pclass), number of siblings/spouses aboard (SibSp), number of parents/children aboard (Parch), embarkation point (Embarked), and more. The project leverages machine learning techniques, specifically pipelines and ColumnTransformer, for efficient preprocessing and modeling.
- Handles missing values in the dataset.
- Encodes categorical variables.
- Scales numerical features for optimal model performance.
- Uses Pipeline and ColumnTransformer for seamless data preprocessing and model training.
- Trains a classification model to predict survival status.
- Saves the trained model as a .pkl (pickle) file for future use.
- Loads the serialized model to predict survival outcomes on new data.
- pandas
- numpy
- scikit-learn
- pickle (built-in Python library)