We take a look at the titanic dataset.

What variables contributed to an individuals survival?

Did different ages or sexes have a higher rate of survival?

## Cleaning the data

We had two missing ages. We can replace that with the median.

The cabin data had very little data. We drop that.

We drop the remaining rows that have NaN's.

## Plotting the data

I started by plotting all peoples ages and their survival.

Age | Category |
---|---|

0-5 | Baby |

6-15 | Child |

16-30 | Adult |

31-40 | Mid |

41-60 | Old |

61-80 | Very Old |

I then plotted each class and their survival.

This is a a little difficult to read. Why dont we put it into a stacked bar graph to see the weight of each genre and the songs in that genre hitting Number 1.

## Data Wrangling

In an attempt to figure out which coefficients I should use, I normalized the appropriate data and ran a RFE model.

coef | |
---|---|

Pclass | 1 |

Fare | 2 |

Age | 3 |

SibSp | 4 |

Parch | 5 |

I dropped the fare data since the class data can represent similar findings but with a higher correlation.

Created dummy vriables for:

- Sex
- Age (binned with the table above)
- Embarked
- Pclass

## Regression

I ran a RFECV to figure out the optimal number of features to use to get the highest score. What I figured out was that 7 features was the best

So I ran a Logistic Regression and received these absolute coefficients.

Coefficient | Feature | |
---|---|---|

2 | 2.600351 | male |

5 | 2.032660 | Age_Baby |

10 | 1.482869 | Class_1 |

11 | 0.908808 | Class_2 |

3 | 0.585350 | Embark_C |

0 | 0.447184 | SibSp |

6 | 0.365025 | Age_Child |

9 | 0.180293 | Age_Old |

4 | 0.161709 | Embark_Q |

1 | 0.072917 | Parch |

8 | 0.049452 | Age_Mid |

7 | 0.044070 | Age_Adult |

Since 4 of the bottom 5 coefficients are dummy created variables, we can only drop the Parch variable from our model.

With the new model, we end up with this confusion matrix

Predicted Survived | Predicted did_not_survive | |
---|---|---|

Survived | 82 | 30 |

Did not Survive | 26 | 156 |

With an roc curve:

We try the model with a GridSearch, we end up with this confusion matrix

Predicted Survived | Predicted did_not_survive | |
---|---|---|

Survived | 80 | 32 |

Did not Survive | 25 | 157 |

We perform a Gridsearch for the same classification problem as above, but use KNeighborsClassifier a our estimator. The best parameters end up being

Predicted Survived | Predicted did_not_survive | |
---|---|---|

Survived | 75 | 37 |

Did not Survive | 30 | 152 |

Which turns out to be worse than our original Linear model.

Perhaps it's better to keep our model linear.

Hope you enjoy my findings :)