Overview:
This notebook provides a comprehensive approach to solving the Titanic Machine Learning competition on Kaggle. It includes data exploration, feature engineering, model building, and evaluation strategies to predict passenger survival. The goal is to utilize machine learning techniques to achieve high accuracy in predicting survival outcomes based on historical data.
Key Features:
- Data Exploration: Initial examination and visualization of the Titanic dataset to understand patterns and relationships.
- Feature Engineering: Creation and transformation of features to improve model performance.
- Model Building: Implementation of various machine learning algorithms to predict survival, including logistic regression, decision trees, and ensemble methods.
- Evaluation: Assessment of model performance using metrics such as accuracy, precision, recall, and F1-score.
Installation and Usage:
-
Clone the repository:
git clone https://github.com/yourusername/titanic-ml-competition.git -
Navigate to the project directory:
cd titanic-ml-competition -
Install the required dependencies:
pip install -r requirements.txt -
Run the Jupyter notebook:
jupyter notebook titanic_ml_competition.ipynb
Dependencies:
- Python 3.x
- Jupyter Notebook
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn
Dataset:
The dataset consists of passenger information from the Titanic, including features such as age, sex, ticket class, and survival status. It is used to train models to predict the likelihood of survival.
Dataset Link: https://www.kaggle.com/competitions/titanic
Uploaded Date: 9/8/2024
License:
This project is licensed under the MIT License - see the LICENSE file for details.
Author:
Waqar Ali