Udacity-ML-Capstone-Kaggle-Allstate
Machine learning engineer nanodegree, capstone project.
Bryan Luke Lathrop March 6, 2017
This project encapsulates my final project for the Udacity Machine Learning Nano-degree, and is based on the Kaggle competition, Allstate Claims Severity
The project is primarily an exercise in various machine learning techniques, with a goal of demonstrating the improvement that can be acheived by their use
It is suggested to start with a brief look at either JustStacking.ipynb or JustLinear.ipynb to get a feel for the code, and then progressing to the project writeup for a full overview of the project.
I should note that input data is not included, but may be downloaded at the competition link. Intermediate cache files are available to prevent the need to re-run several calculations that may take as much as several days. The data zip includes the original data files as well as my generated data. Download each of: data / cache / output , and unzip to the top level directory of the project.
- Project Proposal
- Final Report
Project completion date: 3/14/2017
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Udacity Machine Learning Engineer Capstone Project. The project is my participation to the Kaggle competition - Jigsaw unintended bias in toxicity classification. Field: Natural Language Processing
gromag/MachineLearning-Engineer-Specialisation-Capstone-Project-Udacity
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Project from the Kaggle competition: Jigsaw unintended bias in toxicity classification
Project Overview
Natural Language Processing is a complex field which is hypothesised to be part of AI-complete set of problems, implying that the difficulty of these computational problems is equivalent to that of solving the central artificial intelligence problem making computers as intelligent as people. With over 90% of data ever generated being produced in the last 2 years and with a great proportion being human generated unstructured text there is an ever increasing need to advance the field of Natural Language Processing.
Recent UK Government proposal to have measures to regulate social media companies over harmful content, including "substantial" fines and the ability to block services that do not stick to the rules is an example of the regulamentary need to better manage the content that is being generated by users.
Other initiatives like Riot Games' work aimed to predict and reform toxic player behaviour during games is another example of this effort to understand the content being generated by users and moderate toxic content.
However, as highlighted by the Kaggle competition Jigsaw unintended bias in toxicity classification, existing models suffer from unintended bias where models might predict high likelihood of toxicity for content containing certain words (e.g. "gay") even when those comments were not actually toxic (such as "I am a gay woman"), leaving machine only classification models still sub-standard.
Having tools that are able to flag up toxic content without suffering from unintended bias is of paramount importance to preserve Internet's fairness and freedom of speech.
Project Report
Download the Project-Report.pdf
Acquiring the data
Download the data from https://www.kaggle.com/c/12500/download-all , unzip and place it in /input folder.
Python package requirements
Python entry file.
- Jupyter Notebook 86.0%
- Python 13.8%
IMAGES
VIDEO
COMMENTS
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