This Kaggle competition, "Titanic - Machine Learning from Disaster," challenges participants to predict whether passengers on the Titanic survived or not based on various passenger attributes such as age, gender, and ticket class. It's a great introduction to machine learning and data analysis.
The dataset for this competition contains information about passengers, including whether they survived or not. The dataset is split into a training set and a test set for model evaluation.
The primary objective is to build a machine learning model that accurately predicts the survival outcome for passengers in the test set.
train.csv: The training dataset with labeled survival outcomes.test.csv: The test dataset where you need to make predictions.gender_submission.csv: A sample submission file with the correct format.
Model performance is evaluated based on accuracy, i.e., the percentage of correctly predicted outcomes.
- Download the dataset from the Kaggle competition page.
- Use a Jupyter Notebook or any preferred data analysis tool to build and train your machine learning model.
- Make predictions on the test set and format the results according to the guidelines provided.
- Submit your predictions to Kaggle and see how well your model performs.
- Kaggle Competition Page: For the competition details, data, and leaderboards.
- Kaggle Kernels: Check out and learn from other Kaggle users' notebooks related to this competition for inspiration and guidance.
Best of luck with your machine learning journey and enjoy the Titanic competition!