Titanic Survival Prediction with Logistic Regression (still under works)

This project uses a logistic regression model to predict the survival probability of passengers on the Titanic. By analyzing the Titanic dataset, we aim to understand which factors influenced survival and use this information to build a predictive model.

Dataset

The dataset, provided by Kaggle, includes various features such as:

• Passenger demographics (Age, Gender, etc.)
• Ticket information (Fare, Class, etc.)
• Family relationships (Siblings/Spouses, Parents/Children aboard)

Dependencies

Ensure you have the following libraries installed:

• pandas - for data manipulation
• numpy - for numerical operations
• scikit-learn - for building and evaluating the logistic regression model
• matplotlib or seaborn (optional) - for visualizing data trends

titanic-survival-prediction/
├── data/                # Contains the Titanic dataset
├── notebooks/           # Jupyter notebooks for data exploration and model training
├── models/              # Trained models and serialized files
└── README.md            # Project documentation