Predict Titanic survival using data analysis and machine learning. Identify key factors like age, fare, and more for insights into passenger survival outcomes.
Welcome to the Titanic Survival Prediction repository. This project delves into the realm of data analysis and machine learning to decipher the determinants of passenger survival outcomes from the Titanic disaster. By investigating essential factors such as age, fare, and more, this project offers valuable insights into the critical elements that influenced who survived and who didn't.
This project employs a tested Titanic dataset available in the "tested.csv" file. The dataset contains passenger information including age, fare, gender, and survival status. It serves as the foundation for the analysis and prediction tasks within this project.
Utilizes Python for data analysis and machine learning tasks. Explores Titanic dataset to uncover patterns and trends. Implements classification algorithms for survival prediction. Focuses on crucial factors like age, fare, and other relevant attributes. Provides detailed visualizations to convey findings effectively. Empowers users to comprehend the intricate dynamics of survival.
Clone this repository: git clone https://github.com/yourusername/titanic-survival-prediction.git Install necessary packages: pip install -r requirements.txt Navigate through Jupyter notebooks for data analysis and model building. Experiment with alternative algorithms and feature combinations. Interpret visual outputs to understand the influence of key factors.
Contributions are welcome! Feel free to fork this repository, make improvements, and submit pull requests to enhance the project.
While the project offers insights into potential survival outcomes based on historical data, actual survival during the Titanic disaster was influenced by numerous factors. The predictions may not accurately mirror real-world scenarios.