Using ML tools learned recently in courses on the Kaggle Titanic challenge

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First

Tools learned in the Andrew Ng Coursera Machine Learning course offered by Stanford. (Octave Gnu)

Algorithm	Score
Support Vector Machine	0.78947
Logistic Regression	0.76315
Neural Network:
- 7x7 lambda 0.005	0.75358
- 7x2 lambda 0.3	0.77990

Second

Tools from Machine Learning A-Zª: Hands-On Python & R In Data Science (Python, Jupyter Notebooks)

• Kernel Support Vector Machine - scores:
  • linear: 0.76555
  • rbf: 0.78229
  • poly: 0.76794
  • sigmoid: 0.63875
• Decision tree - scores:
  • entropy: 0.72488
  • gini: 0.70574