elacerdajr / titanic-kaggle

Models built to predict who have survived to the tragic sinking of the RMS Titanic ship. for the Kaggle competition

titanic-kaggle

Models for predicting who have survived to the tragic sinking of the RMS Titanic ship.

• Kaggle competition: titanic;
• My Kaggle profile: elacerdajr.

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Models

• LogReg: Logistic Regression. Features: PClass, Sex, Sibsp.
• LogReg2: Logistic Regression with polynomial feartures (degree =2). Features: PClass, Sex, Sibsp.
• LogReg2+: Logistic Regression with polynomial feartures (degree =2). Features: PClass, Sex, Age, Sibsp, Parch.
• DTree: Decision Tree.

Results

A sumary of the results are presented at the table:

Model	Accurracy
	Train	Test
LogReg	0.80029	0.77033
LogReg2	0.7991	0.77511
LogReg2+	0.8305	0.78947
Decision Tree	0.8039 (avg)	0.74162

Conclusions

I noted that:

1. Looking at the coefficients at the LogReg model is a great way to infer which variables are more important to the predictions.
2. The accuracy is always lower for the test set, compered to the train set.
3. The Decision Tree model represented the higher difference between train and test sets.
4. The LogReg2+ improved somehow the results compared to the LogReg2 and LogReg models, although they are quite similar.