Publications by Oruru Ndu-Nwogu
Onlinefood
Introduction The dataset contains information collected from an online food ordering platform over a period of time. It shows Demographic Information, Location Information and Order details. Attributes: Demographic Information: Age: Age of the customer. Gender: Gender of the customer. Marital Status: Marital status of the customer. Occupation: Occ...
3879 sym R (57206 sym/198 pcs) 10 img
Regression
library(readxl) AgeHeight <- read_excel("~/Downloads/CS 583/AgeHeight.xlsx") View(AgeHeight) lmHeight <- lm(height~age, data = AgeHeight) lmHeight ## ## Call: ## lm(formula = height ~ age, data = AgeHeight) ## ## Coefficients: ## (Intercept) age ## 64.928 0.635 summary(lmHeight) ## ## Call: ## lm(formula = height ~ age, da...
43 sym R (7149 sym/37 pcs) 3 img
Document
library(e1071) data(iris) set.seed(1234) rndsample<-sample(1:nrow(iris),100) tr<-iris[rndsample, ] ts<-iris[-rndsample, ] s<-svm(Species~., tr) ps<-predict(s, ts) (cm<-table(ps, ts$Species)) ## ## ps setosa versicolor virginica ## setosa 18 0 0 ## versicolor 0 17 1 ## virgin...
1561 sym R (95242 sym/49 pcs)
Titanic Analysis
Import csv file from url library(readr) T3 <- read_csv("https://goo.gl/At238b") ## Rows: 1309 Columns: 14 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## chr (8): pclass, name, ...
7559 sym R (20985 sym/87 pcs) 11 img
Decision Tree
library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(readr) library(C50) library(gmodels) set.seed(1234) credit <- read.csv("~/Downloads/CS 583/credit.csv", head...
75 sym R (63275 sym/43 pcs)
Migrants Project
Import the dataset library(readr) Migrants <- read_csv("~/Downloads/CS 583/R_Project/Migrants.csv") ## Rows: 90 Columns: 7 ## ── Column specification ──────────────────────────────────────────────────────── ## Delimiter: "," ## dbl (7...
530 sym R (15820 sym/43 pcs) 2 img
kNN Algorithm Classwork
Lazy Learning- Classification using nearest neighbors kNN Algorithm The dataset used is the Wisconsin Breast Cancer Dataset. library(readr) wisc_bc_data <- read_csv("~/Downloads/CS 583/wisc_bc_data.csv") ## Rows: 569 Columns: 32 ## ── Column specification ─────────────────────────────�...
2324 sym R (14379 sym/32 pcs)
PROJECT1
Load Your Machine Learning Datasets Load Data From CSV File #define the filename filename <- "iris.csv" #load the CSV file from the local directory dataset<-read.csv(filename, header=FALSE) #preview the first 6 rows head(dataset) ## V1 V2 V3 V4 V5 ## 1 5.1 3.5 1.4 0.2 Iris-setosa ## 2 4.9 3.0 1.4 0.2 Iris-setosa ## 3 4.7 3.2 1.3 0.2...
930 sym R (20984 sym/85 pcs) 10 img
Classwork2
library(ggplot2) library(tidyverse) library(dplyr) Diamonds Dataset ## tibble [53,940 × 10] (S3: tbl_df/tbl/data.frame) ## $ carat : num [1:53940] 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ... ## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ... ## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6...
125 sym R (114370 sym/27 pcs) 21 img
Classwork
library(ggplot2) library(tidyverse) library(dplyr) summary(mpg) ## manufacturer model displ year ## Length:234 Length:234 Min. :1.600 Min. :1999 ## Class :character Class :character 1st Qu.:2.400 1st Qu.:1999 ## Mode :character Mode :character Median :3.300 Median :2...
13 sym R (1992 sym/6 pcs) 4 img