Titanic_Pipeline

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.

Project Features

1. Data Preprocessing

• Handles missing values in the dataset.
• Encodes categorical variables.
• Scales numerical features for optimal model performance.

2. Pipeline Integration

• Uses Pipeline and ColumnTransformer for seamless data preprocessing and model training.

3. Model Training

• Trains a classification model to predict survival status.

4. Serialization

• Saves the trained model as a .pkl (pickle) file for future use.

5. Prediction

• Loads the serialized model to predict survival outcomes on new data.

Libraries

• pandas
• numpy
• scikit-learn
• pickle (built-in Python library)