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A classification problem on tabular data using decision trees and ensemble models like AdaBoost or XGBoost.

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.gitignore
.gitignore
 
 
Feature eng. and a simple Stacked model.ipynb
Feature eng. and a simple Stacked model.ipynb
 
 
Feature engineer, RFE and a stacked model.ipynb
Feature engineer, RFE and a stacked model.ipynb
 
 
Feature engineer, selection and a stacked model.ipynb
Feature engineer, selection and a stacked model.ipynb
 
 
Feature engineering, selection and my own Stacked model implementation.ipynb
Feature engineering, selection and my own Stacked model implementation.ipynb
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 
Roosevelt Forest - First approach to Ensemble Tree Algorithm.ipynb
Roosevelt Forest - First approach to Ensemble Tree Algorithm.ipynb
 
 
Roosevelt Forest - First intuition in a stacked Ensemble model Google.ipynb
Roosevelt Forest - First intuition in a stacked Ensemble model Google.ipynb
 
 
Roosevelt Forest - First intuition in a stacked Ensemble model.ipynb
Roosevelt Forest - First intuition in a stacked Ensemble model.ipynb
 
 
Roosevelt Forest - XGBoosting Decision Tree Experment.ipynb
Roosevelt Forest - XGBoosting Decision Tree Experment.ipynb
 
 
Roosevelt Forest Competition - Exploratory Data Analysis.ipynb
Roosevelt Forest Competition - Exploratory Data Analysis.ipynb
 
 
Roosevelt Forest Competition - Feature preprocessing and selection.ipynb
Roosevelt Forest Competition - Feature preprocessing and selection.ipynb
 
 
ensembling-with-stacknet.ipynb
ensembling-with-stacknet.ipynb
 
 
roosevelt-national-forest-classification.ipynb
roosevelt-national-forest-classification.ipynb
 
 

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Forest cover type prediction in Roosevelt National Forest of Northern Colorado

This is a Kaggle Competition

This repository contains some notebooks to analyze the problem and create a machine learning model to predict the cover type in Roosevelt National Forest.

Problem description

In this competition you are asked to predict the forest cover type (the predominant kind of tree cover) from strictly cartographic variables (as opposed to remotely sensed data). The actual forest cover type for a given 30 x 30 meter cell was determined from US Forest Service (USFS) Region 2 Resource Information System data. Independent variables were then derived from data obtained from the US Geological Survey and USFS. The data is in raw form (not scaled) and contains binary columns of data for qualitative independent variables such as wilderness areas and soil type.

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A classification problem on tabular data using decision trees and ensemble models like AdaBoost or XGBoost.

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python scikit-learn xgboost feature-engineering

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