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Introduction

Titanic Survival Prediction: A Machine Learning Approach The tragic sinking of the RMS Titanic in 1912 has long been a subject of analysis and intrigue. Leveraging modern machine learning techniques, we can delve into the passenger data to predict survival outcomes based on various features. This project aims to build a robust classification model to forecast passenger survival, utilizing a comprehensive dataset that includes attributes such as age, gender, ticket class, fare, and cabin information.

Project Overview The primary objective is to develop a machine learning model that accurately predicts whether a passenger survived the Titanic disaster. The dataset encompasses a variety of features:

Pclass: Ticket class (1st, 2nd, or 3rd) Sex: Gender of the passenger Age: Age of the passenger SibSp: Number of siblings or spouses aboard Parch: Number of parents or children aboard Fare: Passenger fare Embarked: Port of embarkation (C = Cherbourg; Q = Queenstown; S = Southampton) Data Preprocessing Handling Missing Values Missing data can skew the model’s performance. For the ‘Age’ feature, missing values were imputed using the median age. The ‘Embarked’ feature’s missing values were filled with the mode, and the ‘Cabin’ feature was dropped due to a high percentage of missing data.

Encoding Categorical Variables Machine learning algorithms require numerical input. Categorical features like ‘Sex’ and ‘Embarked’ were transformed into numerical representations using label encoding.

Feature Engineering New features were derived to enhance model performance:

FamilySize: Calculated as the sum of ‘SibSp’ and ‘Parch’ plus one, representing the total number of family members aboard. Title: Extracted from the passenger’s name to capture social status and marital status. Feature Scaling Features such as ‘Age’, ‘Fare’, and ‘FamilySize’ were standardized to ensure uniformity and improve model convergence.

Model Development The dataset was partitioned into training and testing sets to evaluate model performance effectively. A RandomForestClassifier was employed due to its robustness and ability to handle feature interactions. Hyperparameter tuning was conducted using GridSearchCV to identify the optimal model parameters.

Model Evaluation The model’s performance was assessed using several metrics:

Accuracy: The proportion of correctly predicted survival statuses. Precision: The accuracy of positive survival predictions. Recall: The model’s ability to identify all actual survivors. F1 Score: The harmonic mean of precision and recall, providing a balance between the two. Cross-validation was also performed to ensure the model’s generalizability and to mitigate overfitting.

Feature Importance Analyzing feature importance revealed that certain attributes, such as Sex, Pclass, and Fare, significantly influenced survival predictions. Visualizing these importances aids in understanding the underlying patterns in the data.

Conclusion This project demonstrates the application of machine learning techniques to historical data, providing insights into the factors that influenced survival during the Titanic disaster. The developed model serves as a predictive tool and highlights the critical features that impacted survival outcomes.

Future Work Further enhancements could include:

Exploring additional feature engineering techniques to capture more intricate patterns. Implementing advanced algorithms and ensemble methods to improve prediction accuracy. Conducting deeper hyperparameter tuning and model optimization. By continually refining the model and incorporating more sophisticated techniques, the predictive accuracy can be further improved, offering deeper insights into the survival determinants of the Titanic passengers.