Predict the fate of the passengers aboard the RMS Titanic, which famously sank in the Atlantic ocean during its maiden voyage from the UK to New York City after colliding with an iceberg.

Feature engineering is so important to how your model performs, that even a simple model with great features can outperform a complicated algorithm with poor ones. In fact, feature engineering has been described as easily the most important factor in determining the success or failure of your predictive model. Feature engineering really boils down to the human element in machine learning. How much you understand the data, with your human intuition and creativity, can make the difference.

So what is feature engineering? It can mean many things to different problems, but in the Titanic competition it could mean chopping, and combining different attributes that we were given by the good folks at Kaggle to squeeze a little bit more value from them. In general, an engineered feature may be easier for a machine learning algorithm to digest and make rules from than the variables it was derived from.