Publications by Ostinato Rigore
Exploratory Data Analysis
AIM The goal here is to establish some understanding of the dataset, the basic data structure, the correlation relations - pairwise and with the Outcome as well as show mastery of visualisations. Flow Load Dependencies Data Cleaning Outcome Variables Top-Distributions Correlation Loading Dependencies vgDf <- read.csv("C:/Users/PC/Documents/R_4...
2771 sym R (11811 sym/36 pcs) 9 img
Insurance Exploratory Data Analysis
library(tidyverse) library(ggplot2) library(corrplot) insurance <- read.csv("C:/Users/PC/Documents/R_4DS/Insurance/insurance.csv") head(insurance) ## age sex bmi children smoker region charges ## 1 19 female 27.900 0 yes southwest 16884.924 ## 2 18 male 33.770 1 no southeast 1725.552 ## 3 28 male 3...
1511 sym R (7709 sym/38 pcs) 10 img 1 tbl
Student_Educational_EDA
edu_Df <- read.csv("C:/Users/PC/Documents/R_4DS/Education/xAPI-Edu-Data.csv") Data Attributes 1 Gender - student’s gender (nominal: ‘Male’ or ‘Female’) 2 Nationality- student’s nationality (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Moroc...
4117 sym R (19348 sym/72 pcs) 25 img 3 tbl
Psedo Facebook EDA
facebook_Df <- read.csv("C:/Users/PC/Documents/R_4DS/Facebook/pseudo_facebook.csv") str(facebook_Df) ## 'data.frame': 99003 obs. of 15 variables: ## $ userid : int 2094382 1192601 2083884 1203168 1733186 1524765 1136133 1680361 1365174 1712567 ... ## $ age : int 14 14 14 14 14 14 13 13 13 13 ... ## $ dob...
1555 sym R (7868 sym/34 pcs) 8 img 1 tbl
Books EDA
books_Df <- read.csv("C:/Users/PC/Documents/R_4DS/Books/bestsellers with categories.csv") str(books_Df) ## 'data.frame': 550 obs. of 7 variables: ## $ Name : Factor w/ 351 levels "10-Day Green Smoothie Cleanse",..: 1 2 3 4 5 6 7 8 9 10 ... ## $ Author : Factor w/ 248 levels "Abraham Verghese",..: 125 220 135 96 175 97 97 13 115 ...
2000 sym R (9692 sym/32 pcs) 9 img
(Inadequate) Predictive Modelling of Loan Status.
Load Libraries Data Inspection and Cleaning loan_Df <- read.csv("C:/Users/PC/Documents/R_4DS/bank_loan.csv", na.strings = c("", " ", NA)) glimpse(loan_Df) Rows: 614 Columns: 13 $ Loan_ID [3m[38;5;246m<fct>[39m[23m LP001002, LP001003, LP001005, LP001006, LP001008, LP001011, LP001013, LP001014, LP001018, LP001020, LP0010~ $ Gender ...
7243 sym R (8805 sym/30 pcs) 1 img
(Gentle) Cluster Analysis of the World by Happiness
wHappiness_Df <- read.csv("C:/Users/PC/Documents/R_4DS/WorldHappinessReport/world-happiness-report-2021.csv") glimpse(wHappiness_Df) Rows: 149 Columns: 20 $ ï..Country.name [3m[38;5;246m<fct>[39m[23m Finland, Denmark, Switzerland, Iceland, Netherlands, Norway, Sweden, Luxembourg, ~ $ Regional.indicator ...
7686 sym R (10038 sym/24 pcs) 8 img
Logistic Regression of Bank Loans
Load Library Load Dataset loan_Df <- read.csv("C:/Users/PC/Documents/R_4DS/bank_loan.csv", stringsAsFactors = FALSE) Exploratory Dataset ##drop cases where there is no Loan Amount df <- loan_Df %>% filter_at(vars(LoanAmount, Loan_Amount_Term), all_vars(!is.na(.))) %>% mutate(Loan_Status = if_else(Loan_Status == "Y", 1,0)) %>% mutat...
703 sym R (15588 sym/58 pcs) 8 img