Publications by Gamal Gabr
Predicting House Prices
#suppress warnings globally options(warn=-1) options(scipen = 999) #suppress the appearance of scientific notation throughout the analysis rm(list=ls()) 0.0.1 BACKGROUND In order to put my newly acquired data science skills to the test, I decided to the explore the well-known Ames Housing dataset. This particular dataset documents...
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#suppress warnings globally options(warn=-1) options(scipen = 999) #suppress the appearance of scientific notation throughout the analysis # load necessary libraries library(tidytext) library(tidyverse) library(rvest) library(ggplot2) library(sentimentr) library(gridExtra) library(kableExtra) library(lubridate) 0.0.1 PROJE...
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R Markdown #clear current workspace rm(list=ls()) #MISSION I wanted to set myself the challenge of building a neural network from scratch, depending solely on base R. I think the best way to fully get to grips with an algorithm is to code it from the ground up - one is left with little wiggle room for winging it. Moreover, I wanted to tackle the...
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#call libraries #visual theme #call data prep data #recipe_obj <- recipe(Churn ~ ., data = train_data) %>% step_naomit(all_predictors(), all_outcomes())%>% #step_zv(all_predictors()) %>% # check any zero variance features #step_log(‘TotalCharges’) %>% # log transform TotalCharges feature # convert Senior citizen to factor #step_discretize(�...
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#call libraries #visual theme #call data prep data #train conrol metrics and model building #XGBUP PREDICT #RF UPSAMPLE BUILD MODEL AND PREDICT #RF_UP highchartie<-hchart(newRFUP, “bar”, hcaes(x = metricsRPUP, y = Percentage)) #rf model ...
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Telephone: 07482993818 Email: gamal.gabr444@outlook.com Dear reader, It is my sincere hope that on this day, you are armoured with much patience - a true ramble is set to unfold! Here goes…in 2019, whilst juggling a minimum 30 hour/week post to pay my mortgage, I graduated with a BSc(Hons) in Psychology - for the vast majority of my ...
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MISSION rm(list=ls()) #clear workspace I wanted to set myself the challenge of building a neural network from scratch, depending solely on base R. I think the best way to fully get to grips with an algorithm is to code it from the ground up - one is left with little wiggle room for winging it. Moreover, I wanted to tackle the well-known MN...
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R Markdown #clear current workspace rm(list=ls()) Y<-c(3,4,2,3,4) Y<-as.matrix(Y) Experimental encoding function: classes<-unique(Y) hot_encode<-matrix(0, ncol=length(unique(classes)), byrow = T, nrow= nrow(Y), dimnames = list(NULL, c(unique(classes)))) works<-for(i in 1:nrow(Y)){ for(j in 1:length(classes)){ if(Y[i] == classes[j]){hot_encode[i,j...
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