The accuracy exceeds the requirement of 80%Īfter several iterations of exploring and conditioning on the data, you have built a useful algorithm for predicting the survival of each passenger aboard the RMS Titanic.

I ran many graphs with different features, and the most meaningful ones came up with segregating in detail using Pclass.

On the contrary, most males did not survive, hence I labelled 'all else male did not survive' with an else statement.

The reason why I labelled 'all else female survived' with an else statement is because most of them.

Sex=Male, Age < 40, and Pclass=2 mostly survived.

Sex=Male, Age < 10, and Pclass=2 mostly survived.

Sex=Female, Age < 10, and Pclass=1 mostly did not survive.

Sex=Female, 40 < Age < 60, and Pclass=3 mostly did not survive.

We can now clearly observe where females survived and where males survived.

With these features, we arrive at 8 graphs.

Run the code cell below to remove Survived as a feature of the dataset and store it in outcomes. We will use these outcomes as our prediction targets. Since we're interested in the outcome of survival for each passenger or crew member, we can remove the Survived feature from this dataset and store it as its own separate variable outcomes.

Embarked: Port of embarkation of the passenger (C = Cherbourg Q = Queenstown S = Southampton).

Cabin Cabin number of the passenger (Some entries contain NaN).Parch: Number of parents and children of the passenger aboard.SibSp: Number of siblings and spouses of the passenger aboard.Age: Age of the passenger (Some entries contain NaN).

Pclass: Socio-economic class (1 = Upper class 2 = Middle class 3 = Lower class).

Survived: Outcome of survival (0 = No 1 = Yes).Training a Smart Cab (Reinforcement Learning)įrom a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship:.Identifying Customer Segments (Unsupervised Learning).Building a Student Intervention System (Supervised Learning).Boston House Prices Prediction and Evaluation (Model Evaluation and Prediction).Efficiently Searching Optimal Tuning Parameters.Cross-Validation for Parameter Tuning, Model Selection, and Feature Selection.Dimensionality Reduction and Feature Transformation.K-nearest Neighbors (KNN) Classification Model.Vectorization, Multinomial Naive Bayes Classifier and Evaluation.

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