The Titanic Survival Prediction Project is a machine learning endeavor aimed at developing a predictive model to determine the survival outcomes of passengers aboard the RMS Titanic. Leveraging historical passenger data, this project applies data preprocessing techniques, utilizes a Decision Tree Classifier for predictions, and employs the Local Interpretable Model-agnostic Explanations (LIME) framework to interpret the model's decisions.
- Data Preprocessing
- Model Training and Evaluation
- Interpretability with LIME
- Python 3.11.3
- Visual Studio Code
- Seaborn and Matplotlib (for data visualization)
- Pandas (for data manipulation)
- LIME (for model interpretability)
- Scikit-learn (for model training and evaluation)
- Comprehensive data preprocessing to handle missing values and convert categorical data
- Decision Tree Classifier to predict survival outcomes
- Evaluation of model performance using classification metrics and confusion matrix
- Interpretation of individual predictions with LIME