suresh/dionis
Dionis predictors blender
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Discovered Oct 19, 2026
suresh / archive-blog
Suresh's archive blog on data science
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suresh/kaggle-titanic
A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstartes basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.
suresh/BlueBook
This IPython Notebook contains a quantitative pricing model created for Fast Iron in the Kaggle competition 'Blue Book for Bulldozers'. The model predicts the sale price of a particular piece of heavy equipment so that Fast Iron can create a 'Blue Book' to enable customers to valuate their heavy equipment fleet at auction. Here python is used as a medium to apply supervised and unsupervised machine learning techniques to explain 88.90% of the variance observed in the training set and score an RMSLE of 0.745 when predicting values on the test set. In this competition 590 data scientists created predictive models based on a 'training dataset', provided by Fast Iron, and then used those models to predict sale prices on a 'test set' to compete for a $10,000 dollar award for the team or individual with the most accurate model. The model and methods used for my entry, which scored in the upper 20%, is shown in BlueBook.ipynb.
suresh/DataPython
IPython notebooks with analysis