The Kaggle Titanic project is a beginner-friendly binary classification task: use labeled passenger records in train.csv to predict whether passengers in the unlabeled test.csv survived. Kaggle evaluates submissions by accuracy and requires one 0-or-1 prediction for each of the test set’s 418 passenger IDs. It is a historical prediction exercise—not a way to explain the sinking or establish what caused an individual passenger to survive.
What the Titanic machine-learning project asks you to predict
Kaggle describes the competition as a way to “Predict survival on the Titanic and get familiar with ML basics.” The competition page dates to 2012. Its task is to predict the binary Survived outcome: 1 means survived and 0 means deceased. The labeled training file supplies outcomes for model development; the test file has passenger information but withholds the outcomes you must predict. Kaggle’s competition overview and evaluation details describe the task and submission.
The historical context and the machine-learning data are different things. Kaggle’s overview says 1,502 of 2,224 passengers and crew died. Separately, it identifies 418 rows in the competition’s unlabeled test set. Those dataset counts do not establish that the competition files are a complete or representative passenger manifest.
What is in the Titanic dataset?
Kaggle provides train.csv, test.csv, and gender_submission.csv. Training data includes the target; test data has similar passenger fields without the target. The sample submission demonstrates the required output shape and uses a simple rule: predict survival for female passengers and death for male passengers. The Kaggle data page and data dictionary describe the files and fields.
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| Field | What it represents | Interpretation notes |
|---|---|---|
Survived |
Whether the passenger survived; the prediction target. | Present as a label in the training data, withheld for test rows. |
Pclass |
Ticket class. | Kaggle describes it as a proxy for socioeconomic status: first class as upper, second as middle, and third as lower. |
Sex |
Passenger sex. | A categorical field; algorithms may require categorical values to be encoded. |
Age |
Passenger age. | Children under one year may have fractional ages; estimated ages are represented with a half-year value. |
SibSp |
Number of siblings and spouses aboard. | Kaggle’s definition of siblings includes step-siblings; spouses means husband or wife. |
Parch |
Number of parents and children aboard. | Some children travelled with a nanny, so a zero does not necessarily mean a child travelled alone. |
Ticket |
Ticket number. | A recorded ticket identifier, not itself an outcome label. |
Fare |
Ticket fare. | Inspect the data and choose a representation appropriate to the model. |
Cabin |
Cabin information. | Inspect for missingness before deciding how to use it. |
Embarked |
Port of embarkation. | A categorical field that may need encoding for the selected algorithm. |
PassengerId |
Passenger identifier. | Keep it to match predictions to test passengers and build the submission; do not treat it as a meaningful passenger trait without justification. |
Before fitting a model, check column types and missingness. Categorical fields need an encoding strategy for many algorithms, and missing values need an explicit treatment. Learn imputation values and encoding rules from the training portion only, then apply the fitted transformations to validation or test rows. That prevents information from held-out rows influencing model preparation.
Build a baseline and validate it fairly
A baseline provides a reference point, not proof that a model is useful. Kaggle’s supplied gender_submission.csv predicts that all female passengers survived and all male passengers did not. It is a simple rule rather than a sophisticated model, and the official material does not establish a particular score for it.
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- Inspect the files. Load the training and test CSVs. Review column names, data types, missing values, and the distribution of
Survivedin the training data. - Separate target and predictors. Remove
Survivedfrom the training predictors. RetainPassengerIdseparately for matching predictions to test passengers. - Create a held-out validation split. Set aside labeled training rows before fitting. Use only the fitting portion to learn imputation, encoding, feature construction, and model parameters; evaluate predictions against the held-out labels.
- Compare approaches on the same split. Record the split and calculate accuracy consistently. You can also inspect a confusion matrix or class-specific measures as supplementary diagnostics, but distinguish them from Kaggle’s official metric.
- Choose a workflow based on evidence. Consider accuracy alongside interpretability, how the approach handles missing and categorical values, and complexity. These are useful project considerations, not additional leaderboard criteria.
- Refit and predict. After settling on a workflow, fit it using the labeled training data, generate predictions for the test rows, and keep each prediction paired with its corresponding
PassengerId.
Kaggle’s official metric is accuracy: the percentage of predictions that are correct. The official pages do not establish a best algorithm, model performance result, or feature-importance finding, so treat those as questions to answer with your own clearly described validation rather than assumed outcomes. See Kaggle’s evaluation page for the competition metric.
Create the required Kaggle submission CSV
Your submission must have a header and exactly two columns, PassengerId and Survived, with 418 prediction rows. The passenger IDs may appear in any order, but every prediction must correspond to the right test passenger. Use binary survival predictions: 1 for survived and 0 for deceased. The example header is PassengerId,Survived.
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- Check that the file contains both required column names and no extra columns.
- Check that it has 418 data rows, plus the header.
- Check that every
PassengerIdmatches a test passenger and appears once. - Check that every
Survivedvalue is 0 or 1.
Save the result as a CSV and submit it through the Titanic competition page on Kaggle. Kaggle scores it using accuracy against the withheld outcomes; the official overview specifies the format and scoring metric.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What this project can—and cannot—tell you
The exercise teaches a supervised classification workflow: start with labeled examples, prepare passenger fields, validate predictions without fitting on the evaluation rows, then produce a correctly formatted prediction file. A model can identify patterns associated with survival in these records, but predictive association is not causation. The competition task does not establish why the sinking unfolded as it did or prove that changing any one recorded passenger characteristic would have changed an outcome.
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