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em_bonds_diversification

Analysing the diversification benefit of EM bonds in a global portfolio, as published in Towards Data Science on Medium.com

emotion-classification

Feature selection via dependence maximization for EEG based emotion classification.

emotion_analysis

emotion_analysis_on_twitter

Trained and evaluated performance of various classifiers (SVM, Naïve Bayes, Logistic Regression, Random Forest) for Natural Language Processing (NLP) in order to mine real-time data from Twitter and rate each tweet on its primary emotional content. Applied Synthetic Minority Oversampling Technique (SMOTE) on unbalanced dataset, achieved classification accuracy of 95%.

empire

Empire is a PowerShell and Python post-exploitation agent.

employee-attrition-ml

Predict employee attrition using a neural network in python/tensorflow

employee-resignation-model

Using Kaggle Human Resources data to predict employee resignation.

empyrial

AI and data-driven quantitative portfolio management for risk and performance analytics 投资组合管理

empythy

Automated NLP sentiment predictions- batteries included, or use your own data

encoder_decoder

Four styles of encoder decoder model by Python, Theano, Keras and Seq2Seq

encog-financial

This repo holds external indicators, strategies and other software used to link Encog to platforms such as Ninjatrader, MT4, MT5 and others.

end_to_end_supply_chain_network_design

endtoend---predictive-modeling-using-python

energy-weather-modelling

Use of time series modelling tools including ARIMA, LSTM, and Monte Carlo simulation to model electricity consumption, rainfall and temperature data.

english-lexicalized-text-substituion

SemEval Task 2007.

english-words

:memo: A text file containing 479k English words for all your dictionary/word-based projects e.g: auto-completion / autosuggestion

engsoccerdata

English and European soccer results 1871-2014

enpls

R package for ensemble partial least squares regression, a unified framework for feature selection, outlier detection, and ensemble learning

ensemble-predictive-model-forecasting-amgen-stock-price-at-year-end-31s

The basis of this project involves analyzing Amgen future profitability based on its current business environment and financial performance. Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market. The dataset used for this analysis was downloaded from Yahoo finance for year 2009 to 2019. There are multiple variables in the dataset – date, open, high, low, volume. Adjusted close. The columns Open and Close represent the starting and final price at which the stock is traded on a day. High and Low represent the maximum, minimum price of the share for the day. The profit or loss calculation is usually determined by the closing price of a stock for the day, I used the adjusted closing price as the target variable. I downloaded data on the inflation rate, unemployment rate, Industrial Production Index, Consumer Price Index for All Urban Consumers: All Items and Real Gross Domestic Product as independent variables, Quarterly Financial Report: U.S. Corporations: Cash Dividends Charged to Retained Earnings All Manufacturing: All Nondurable Manufacturing: Chemicals: Pharmaceuticals and Medicines Industry, Producer Price Index by Industry: Pharmaceutical Preparation Manufacturing, 30-Year Treasury Constant Maturity Rate, and Producer Price Index by Industry: Pharmaceutical and Medicine Manufacturing Index. The independent variables are economic parameters which was obtained from Federal Reserve Economic Data (FRED) website. Methodology 1. Linear Regression: The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable. I used linear regression tool in Alteryx with ARIMA tool to forecast the stock prices for the year. The algorithm was trained with the historical data to see how the variables impact on the dependent variable. The test data was used to predict the adjusted closing price for the year and predicted a stock price of $193.38. 2. Support Vector Machines (SVM): Support Vector Networks (SVN), are a popular set of supervised learning algorithms originally developed for classification (categorical target) problems and can be used for regression (numerical target) problems. SVMs are memory efficient and can address many predictor variables. This model finds the best equation of one predictor, a plane (two predictors) or a hyperplane (three or more predictors) that maximally separates the groups of records, based on a measure of distance into different groups based on the target variable. A kernel function provides the measure of distance that causes to records to be placed in the same or different groups and involves taking a function of the predictor variables to define the distance metric. I used the SVM tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $189.44. 3. Spline Model: The Spline Model tool was used because it provides the multivariate adaptive regression splines (or MARS) algorithm of Friedman. This statistical learning model self-determines which subset of fields best predict a target field of interest and can capture highly nonlinear relationships and interactions between fields. I used the Spline tool in Alteryx with ARIMA tool to forecast the stock prices for the year and predicted a stock price of $201.84. The results from the models was weighted by comparing the RMSE of each model. A lower RMSE indicates that the model’s predictions were closer to the actual values. However, a simpler model with the same RMSE as a more complex model is generally better, as simpler models are less likely to be overfit. Though the Spline model had a lower RMSE, the Linear Regression model had fewer variables. Thus, we combined the 3 models with the ARIMA forecast in a model ensemble, which allows us to use the results of multiple models. The forecasted stock price is $197.99 with 1.5% increase for 31st December 2019. Apart from economic parameters, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors which can often be impossible to predict beforehand hence the model predicts that the stock price of Amgen will continue to rise except there is a drastic downturn of the company.

ensfm

This is our implementation of ENSFM: Efficient Non-Sampling Factorization Machines (WWW 2020)

entity_embeddings_categorical

Discover relevant information about categorical data with entity embeddings using Neural Networks (powered by Keras)

envlp

Automatically exported from code.google.com/p/envlp

envscaclassifier

Classification of Categorical Time Series Using the Spectral Envelope and Optimal Scalings

eo-bow

Python implementation of EO-BoW method

epsilon

A general convex solver based on functions with efficient proximal operators.

epydoc

eqnfree

Equation-free modeling of multiscale systems

equipment_failure

era-ratio

Ninjatrader special target calculation

erlang-poker-ml

Knowledge management system and decision-tree classifier in Erlang for Texas Hold'em outcome prediction