ahmedhghareeb Goto Github PK

elements-of-statistical-learning

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

emailing

How to Write the Perfect Email

energy-anomaly-dection

energy-plus

Energy Plus

energyforecasting

BFH Project 2: forecasting energy production and consumption using machine learning methods

energyr

An R package containing data published by the United States Federal Energy Regularity Commission (FERC)

energyuva

Detecting anomalies with Neural newtorks

english

ensemble-forecasting

Ensemble management experiments with sea level forecasting

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.

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

ensemble_forecast_methods

ercot-energy-load-demand

ERCOT's Energy Load Demand Forecasting

eslii_solutions

exdata_plotting1

Plotting Assignment 1 for Exploratory Data Analysis

exdata_plotting2

Peer Assessment 2 for Exploratory Data Analysis course

fault-detection-and-diagnosis

Fault Detection and Diagnosis in Air Handling Unit with using Dymola Data

fire-prediction-model

Development of fire prediction model by fusing multi-modality sensor data gathered from a Wireless Sensor Network during emergency situations such as a fire in a multi-storey building. Due to the emergency situation in a multi-story building, the collected sensor data was noisy and corrupted. Therefore, framework and algorithms were developed to analyse the data and predict its implications. Developer: Dilusha Weeraddana

flusight_ensemble

Ensemble forecast for CDC Flu Forecasting Challenge

forecasting-weight_control_product

Leveraging Ensemble Model to Forecast Monthly Sales of a Weight-Control Product Using the Information of Monthly Sales and Advertising Expenditures

forecastingelectricityprices

Thesis project on forecasting german (epex spot) electricity prices

fuzzyenergy

Forecasting and anomaly detection of gas consumption

gas-price-forecasting

Forecasting Project

geophysical_notes

Collection of geophysical notes in the form of IPython/Jupyter notebooks.

gettingandcleaningdata

Coursera Getting and Cleaning Data project

git

h2o-3

Open Source Fast Scalable Machine Learning Platform For Smarter Applications (Deep Learning, Gradient Boosting, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), ...)

h2o-tutorials

Tutorials and training material for the H2O Machine Learning Platform

hadoop-book

Example source code accompanying O'Reilly's "Hadoop: The Definitive Guide" by Tom White

hadoop_project