Titanic’s Survival Prediction with TensorFlow and Keras | by Phil | Jan, 2024

Remember OceanGate’s Titan submersible that imploded on its descent to the Titanic wreck site? They never found the 5 occupants! The search and rescue team only located debris including presumed human remains.

Luckily, you and I don’t have do take such a risk to get a reliable story about the Titanic. A little exploration of the Titanic dataset is all we need. Think of it as our virtual submersible. No pressure suits, oxygen tanks, or a rickety vessel needed. Just a healthy dose of curiosity and a little know-how about TensorFlow

  1. Import Modules and Load Data

Gearing Up for the Voyage! First, we’ll grab our tools — TensorFlow’s trusty modules loaded and ready. Then, we’ll dive into the Titanic dataset, a treasure trove of data waiting to be explored. No need for submersibles or sonar — just a few lines of code to unlock the secrets of the passengers and their fateful journey.

2. Preprocess the Data

Charting the Course with preprocessing! Now, we ready the data for our journey. We’ll decode the categorical signals, like filling in missing coordinates for lost passengers and standardizing measurements to ensure a smooth voyage through our model’s computations. It’s like mapping the uncharted territories of the dataset, ensuring we can navigate its depths with confidence.

3. Define Features, Create Tensors and Build Model

Here is where we identify our key landmarks — age, sex, and fare — the factors that we suspect might have shaped the passengers’ fates. We’ll transform them into tensors, the lifeblood of our model, ready to flow through its intricate layers. Think of it as crafting a virtual ship, carefully selecting materials, and designing its structure for the task ahead.

4. Optimize, Train and Evaluate

It’s time to unleash our model upon the data! We’ll equip it with the Adam optimizer, a trusty compass guiding its learning path. Like a seasoned captain, we’ll train it through 20 voyages across the dataset, navigating twists and turns to uncover hidden patterns. Then, we’ll put it to the test, evaluating its accuracy. Will it navigate the Titanic’s secrets with precision, or will it face a storm of errors?

5. Time to Predict

With the model tuned and tested, it’s time to peer into the future (well, the past in this case!). We’ll run the test features through our virtual Titanic, letting its algorithms predict who might have made it to shore. Get ready for a dramatic reveal — will our model accurately navigate the passengers’ destinies, or will some predictions end up in icy waters?

6. Young Ladies vs Old Men!

We’ve navigated the icy depths of data, but the mysteries of survival still lurk in the shadows. Could it be that sex and age, like hidden currents, played a role in passengers’ fates? We’ll grab our magnifying glass (okay, pandas groupby) and take a closer look. Let’s see if the numbers whisper tales of chivalry, youthful resilience, or perhaps a different story altogether. Brace yourselves for some surprising twists in the Titanic’s true narrative!


The Titanic’s secrets yielded, its icy grip loosened by TensorFlow’s mighty code. We’ve charted the depths, weathered data storms, and emerged with insights. Now, let’s raise a virtual glass to our trusty vessel, for it kept us afloat through swirling algorithms and murky uncertainties. Thanks, TensorFlow, for a voyage filled with discovery, not disaster!

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