This repository explores the tragic sinking of the RMS Titanic through the lens of machine learning. By analyzing passenger data, we aim to predict survival rates and gain insights into factors that influenced survival outcomes.
This project is ideal for:
Beginners in data science and machine learning Individuals interested in the historical significance of the Titanic disaster Anyone looking to practice data wrangling, analysis, and model building What you'll find here:
Python scripts for data exploration, cleaning, and feature engineering Machine learning models to predict passenger survival Visualizations to understand relationships between features and survival Code demonstrating common data science practices Getting Started:
Clone this repository. Ensure you have Python and necessary libraries installed (refer to requirements.txt). Run the Python scripts sequentially to explore data, build models, and generate visualizations. Learning Objectives:
Data wrangling techniques (handling missing values, creating new features) Exploratory data analysis (finding patterns, correlations) Building and evaluating machine learning models (classification) Understanding factors that influenced survival on the Titanic Further Exploration:
Experiment with different machine learning algorithms Fine-tune hyperparameters for improved model performance Analyze the impact of specific features on model predictions Remember: The sinking of the Titanic was a human tragedy. While machine learning offers valuable insights, it cannot fully account for the chaotic nature of the disaster.
This project is for educational purposes only. Feel free to explore, modify, and learn from the provided code!