Making Predictions with All Three Models

End-to-End Machine Learning: Titanic Survival Prediction

2 min read

Published Nov 18 2025

KerasMachine LearningMatplotlibNumPyPandasPythonscikit-learnSciPySeabornTensorFlow

Once the models are trained and evaluated, a common final step is to use them for real predictions.

We will prepare an example passenger (or multiple passengers) and run them through:

Logistic Regression
Random Forest
Keras Neural Network

This demonstrates how to make new, real-world predictions using your pipelines and transformed data.

Create a Sample Passenger to Predict

We’ll create a small dataframe with one or more new passengers.

Here’s an example with two fictional passengers:

# Example passengers to predict

sample_passengers = pd.DataFrame([

{

"pclass": 1,

"sex": "female",

"age": 25,

"sibsp": 0,

"parch": 0,

"fare": 100,

"embarked": "S",

"class": "First",

"who": "woman",

"alone": True

{

"pclass": 3,

"sex": "male",

"age": 35,

"sibsp": 1,

"parch": 3,

"fare": 12,

"embarked": "S",

"class": "Third",

"who": "man",

"alone": False

}

])

sample_passengers

In tabular form:

pclass sex age sibsp parch fare embarked class who alone

0 1 female 25 0 0 100 S First woman True

1 3 male 35 1 3 12 S Third man False

Passenger A:

First class young woman travelling alone

Passenger B:

Third class adult man with family

We expect dramatically different survival probabilities.

Predictions with Logistic Regression

pred_lr = log_reg.predict(sample_passengers)

prob_lr = log_reg.predict_proba(sample_passengers)[:, 1]

print("Logistic Regression Predictions:", pred_lr)

print("Logistic Regression Probabilities:", prob_lr)

Output:

Logistic Regression Predictions: [1 0]

Logistic Regression Probabilities: [0.94124233 0.0275436 ]

The output gives:

0 = predicted death
1 = predicted survival
Probability = model’s confidence the passenger survives

Predictions with Random Forest

pred_rf = rf.predict(sample_passengers)

prob_rf = rf.predict_proba(sample_passengers)[:, 1]

print("Random Forest Predictions:", pred_rf)

print("Random Forest Probabilities:", prob_rf)

Output:

Random Forest Predictions: [1 0]

Random Forest Probabilities: [0.995 0.015]

Random Forest typically gives:

Stronger confidence for clear “rules”
Slightly noisier for edge cases

Predictions with the Keras Neural Network

The Keras model requires preprocessed numeric arrays, so we must transform using the fitted preprocessor:

sample_processed = preprocessor.transform(sample_passengers)

# Convert sparse matrix if needed

if hasattr(sample_processed, "toarray"):

sample_processed = sample_processed.toarray()

prob_keras = model.predict(sample_processed).ravel()

pred_keras = (prob_keras >= 0.5).astype(int)

print("Keras Predictions:", pred_keras)

print("Keras Probabilities:", prob_keras)

Output:

Keras Predictions: [1 0]

Keras Probabilities: [0.94335914 0.07789665]

Final Combined Output

results = pd.DataFrame({

"Passenger": ["A (1st class woman)", "B (3rd class man)"],

"LR_Prob": prob_lr,

"LR_Pred": pred_lr,

"RF_Prob": prob_rf,

"RF_Pred": pred_rf,

"Keras_Prob": prob_keras,

"Keras_Pred": pred_keras

})

print(results)

Passenger A (1st-class woman travelling alone)

All three models give a very high survival probability (> 94%).
Random Forest is almost certain (99.5%).
Logistic Regression and Keras also strongly agree.
This aligns with:
- “Women first” evacuation procedures
- First-class passengers having better access to lifeboats
- Historical survival rates (≈97% for 1st-class females)

All models predict survival confidently.

Passenger B (3rd-class man with family)

All three models give a very low survival probability (< 8%).
Random Forest gives near-zero chance (1.5%).
Logistic Regression agrees (≈2.7%).
Keras is slightly less certain but still low (7.8%).
This matches historical data:
- Only ~14% of 3rd-class males survived
- Men were deprioritised
- Family groups in 3rd class were often delayed or blocked from access

All models predict he does not survive.