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Machine Learning with a Random Forest Classifer and Cross Validation Function

An introduction to implementing a machine learning framework to predict the accuracy os predictions.

A Data Science Framework

This python notebook will go through the process of importing, cleaning, and processing your data before implementing your model. 90% of a data scientist's work is understanding the data -- including data types, attributes/variables, and values within each variable. Once the data has been processed, it's pretty easy to run it through any given machine learning algorithm. Better yet, we can run the algorithm as many times as we want to yield the most accurate results.

This notebook gives the user an understanding on how to clean, process, and implement a machine learning algorithm end-to-end. You can pick any machine learning algorithm you'd like with the same functions and methods found in sklearn. Just make sure that they also have the same inputs. The cross validation function is pretty general so there shouldn't be much tweaking if you want to use it for other purposes.

What's included

This repo includes a ipython notebook with notations and the dataset in csv format. The guide is also offered as an HTML file.

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Machine learning with a Random Forest classifier and Cross Validation function

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