ahmedhghareeb

ahmedhghareeb's Stars

• ahmedhghareeb/PythonNumericalDemos

  Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

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• UAEUniversity/CIT_LSTM_TimeSeries

  LSTM Model for Electric Load Forecasting

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• ambarish03/CapacityPlanning_Spark

  In this project, we investigate the electric generation necessary to support the demand resulting from human activities. To that end, we use a feature set comprising of the ambient conditions and use them to predict the peak demand.

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• lbl-srg/bcvtb

  Building Controls Virtual Test Bed

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• jywang2016/CosimR

  Co-simulation between R(python or c/c++) and EnergyPlus through the BCVTB

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• fossasia/visdom

  A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Supports Torch and Numpy.

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• ahmedhghareeb/py4e

  Web site for www.py4e.com and source to the Python 3.0 textbook

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• ahmedhghareeb/DAT210x

  Programming with Python for Data Science Microsoft

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• ahmedhghareeb/volttron

  VOLTTRON Distributed Control System Platform

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• ahmedhghareeb/Gas-Price-Forecasting

  Forecasting Project

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• ahmedhghareeb/Basic_Statistics-

  University of Amsterdam

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• ahmedhghareeb/Ensemble-Methods-using-R

  I have done my individual project (dissertation) on ensemble methods. In which I first did the background study on different ensemble methods and then implemented Boosting, AdaBoost, Bagging and random forest techniques on underlying machine learning algorithms. I used boosting method to boost the performance of weak learner like decision stumps. Implemented bagging for decision trees (both regression and classification problems) and for KNN classifier. Used random forest for classification trees. I have implemented a special algorithm of boosting called “AdaBoost” on logistic regression algorithm using different threshold values. Then plotted the different graphs like an error rate as a function of boosting, bagging and random forest iterations. Compared results of bagging with boosting. Analysed the performance of classifier before applying ensemble methods and after applying ensemble methods. Used different model evaluation techniques like cross-validation, MSE, PRSS, ROC curves, confusion matrix, and out-of-bag error estimation to estimate the performance of ensemble techniques.

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• ahmedhghareeb/Elements-of-Statistical-Learning

  Contains LaTeX, SciPy and R code providing solutions to exercises in Elements of Statistical Learning (Hastie, Tibshirani & Friedman)

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• ahmedhghareeb/courses

  Course materials for the Data Science Specialization: https://www.coursera.org/specialization/jhudatascience/1

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• ahmedhghareeb/ensemble-of-sarima-random-forests-and-gradient-boosting-trees

  In this Project I use the Kaggle Bike sharing dataset to predict the sales of bike given a Multivariate Time series. I model the multivariate data using ensemble of Random Forests and Gradient Boosted trees. After that the residuals of the model are fit with an ARMA/ARIMA/SARIMA model and later forecasted. The residuals are later added back to the predicted values

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• ahmedhghareeb/ensemble_forecast_methods

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• ahmedhghareeb/awesome-python

  A curated list of awesome Python frameworks, libraries and software

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• eayoungs/awesome-python

  A curated list of awesome Python frameworks, libraries and software

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• ahmedhghareeb/machine-learning-nd

  Udacity's Machine Learning Nanodegree project files and notes.

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• ahmedhghareeb/IntroToDataScience

  GitHub Repository to accompany my YouTube series of videos on Introductory Data Science using R.

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• ahmedhghareeb/IntroToStatisticalLearningR-

  Work on Introduction to Statistical Learning

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• ahmedhghareeb/Tensorflow-Tutorial

  Tensorflow tutorial from basic to hard

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• ahmedhghareeb/mocap

  R interface to parsing and plotting Motion Capture ASF/AMC files

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• tylerhutcherson/CrudeOilPrediction

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• mshasan/R-programming

  Advanced Statistical Applications and Computing in R

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• ahmedhghareeb/R-programming

  Advanced Statistical Applications and Computing in R

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• ahmedhghareeb/Module1

  Teaching material for Summer Institute in Statistics for Big Data Module 1.

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• ahmedhghareeb/Module2

  Materials for Module 2 of the SISBID Workshop, Visualization of Biomedical Big Data

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• ahmedhghareeb/starlib

  My functions, code snippets and examples.

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• jestarling/bigdata

  SDS385 Big Data with Prof. James Scott | Fall 2016

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