Using Machine Learning to Identify Fraud in the Enron Corpus
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Updated
Nov 30, 2016 - Python
Using Machine Learning to Identify Fraud in the Enron Corpus
📧 💰 Python + Machine Learning / I analyse the Enron Scandal data and try to predict those who were involved in the Enron fraud based on their financial data
My Machine Learning First Project on Github
Gender Classification based on height, weight and shoe size using different Machine Learning Algorithms
Text Classification
a decision tree based on ID3 algorithm
Enron fraud detect classifier using Decision tree algorithm
My implementation of homework 3 for the Introduction to Machine Learning class in NCTU (course number 1181).
different NN models for classify images
Implementation of Decision tree learning algorithm with chi-square pruning
Analysis and Prediction of Poker Hand Strength
Demonstration of vectorization of movies, recommendation using collaborative filtering and classification.
Various Classification models used are Logistic regression, K-NN, Support Vector Machine, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification using Python
Decision Tree Classifier model implemented in a python program.
Applying Machine Learning Algorithms using R, Python, and RapidMiner
Implementing decision tree classifier from scratch (Machine Learning)
A model to identify unique persons and hence reduce deduplication of records using Decision Trees and Classification.
A Decision Tree Classifier built from scartch in python 3 using the supervised learning methodology. The example here uses the iris data set, but you can load any dataset and it will run for that, just need to change the loading code. To run the program you just have to run the python file. ( Python DecisionTree_fromScratch.py )
Yet another project in CSC 869 Data Mining for partial completion of the class in San Francisco State University.
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