A few python scripts that perform well on Kaggle competions.

Contains various scripts using either XGBoost, PCA + SVM or Convolutional Neural Networks.

• CNNs tend to perform the best, with the VGG-like net performing the best but requiring the largest amount of time.
• SqueezeNet is fast to train but does not perform as well as VGG
• The current best script I posses is the DCCNN MNIST architecture, but since it has been trained on the entire data set it will over fit in this Kaggle dataset and give 100% accuracy. (Original accuracy = 99.77 %)

Starter dataset to teach about the basics of Machine Learning principles such as data cleaning / preprocessing and feature construction.

• Best performing model is XGBoost

A time series data set which is very useful to understand how to manipulate and train time series datasets.

• Best performing model is an ensemble of two XGBoost which learn the two different time factored outputs and then merge them into a single output.

Bank BNP Paribas contest.

• Tried various combinations of stacking and neural nets. Best was a combination of neural nets and XGBoost stacked with Logistic Regression as final layer.