Industry Classifier
| Industry Classifier | |
|---|---|
| Project Information | |
| Project Title | |
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| Has project status | |
| Copyright © 2016 edegan.com. All Rights Reserved. | |
Possible Tools
Python Tools
SciKit Learn SVM
http://scikit-learn.org/stable/modules/svm.html#svm
It's complexity is between O(n^2) and O(n^3). Seems easy to use. This is not a neural net; it is a support vector machine.
SciKit Learn Neural Net
http://scikit-learn.org/stable/modules/neural_networks_supervised.html
This IS a neural net using back propagation.
It's complexity is listed as: Suppose there are n training samples, m features, k hidden layers, each containing h neurons - for simplicity, and o output neurons. The time complexity of backpropagation is O(n * m * h^k * o * i), where i is the number of iterations. Since backpropagation has a high time complexity, it is advisable to start with smaller number of hidden neurons and few hidden layers for training.
WE ENDED UP USING THIS ONE
SK Neural Network Package
This is a separate package than listed above. It requires a separate installation. Documentation is provided at:
https://scikit-neuralnetwork.readthedocs.io/en/latest/index.html
We ran into deprecation warnings, and the program would not execute due to a missing g++ drive.
R Tools
R seems to have a built in package called "neuralnet".
An example is given at:
https://www.packtpub.com/books/content/training-and-visualizing-neural-network-r
Scripts
Scripts and data for this project are located in:
E:\McNair\Projects\Accelerators\Code+Final_Data\ChristyCode
Industry Classifier
This is a neural net built in python that trains on industry designation data from the SDC Platinum database. It serves as a predictive model to predict the industry allocation of given companies. The file is located in the directory listed above.
FindTrainData.py
Builds a tab-delimited text file containing 200 companies with each Industry classification (i.e. 200 biotech, 200 media etc). Hopefully if we use this as our training data, we will get more accurate classifications.
FixDescriptions.py
Deals with the problem that by output files from SDC are poorly formatted when the description goes beyond 1 line. Outputs a tab-delimited text file where the whole description is on the same line and can be read.
Addresses.txt
This text file contains investment info, name, address, city, state of Portfolio companies.
Descriptions.txt
This text file contains company, short description, major industry, minor industry of Portfolio companies.
Statistics
Stastical methods for analyzing results from a neural network.
Quick Check using excel; Finding number of correct matches between two columns:
=SUMPRODUCT(--(range1=range2))
See an example here.
Comments and Thoughts
2/17/17
Christy: No matter what parameters I change in the NN, I can't get the accuracy to go up above around 30%. Looking at the descriptions that the classifier fails on, I realized that it pretty much guesses randomly a lot of the time when the descriptions are terrible like "We provide services to our customers." I think we need to be training and classifying based on the longer description, which is why I started working on the FixDescriptions.txt script.
2/27/17
Christy: The pickle library is vital and we should remember to use it when we use black boxish libraries like the sklearn classifier.
