This is a documentation of one of my approaches to solving the forest cover type prediction challenge hosted by Kaggle. Feel free to use for your own reference and let me know if you have any suggestions on how I can improve the model :-)
I found this topic very engaging being a nature lover. Also the features are very friendly and don't require much domain knowledge to explore (and hopefully engineer new features).
OK let's get started.
The following is an illustration of one of my approaches to solving the Titanic Survival prediction challenge hosted by Kaggle. Below is an excerpt from the competition page.
The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships.
Support Vector Machines (SVMs) are a powerful supervised learning algorithm used for classification or for regression. SVMs are a discriminative classifier: that is, they draw a boundary between clusters of data. In this post I will demonstrate hand-written digit recognition using the SVC classifier in scikit-learn. We'll make use of the online dataset available in the UCI machine learning repository.