Predictive-Analytics-Female Genital Mutilation

DSC530-T301 Term Project Female Genital Mutilation (FGM) The report consists of female genital mutilation (FGM). I think this is an interesting topic with a large dataset under “Tableau Community Forums,” but I am going to limit my analysis to some areas. The data of female genital mutilation (FGM) are under UNICEF(United Nations International Children’s Fund) website. Data Source: (https://community.tableau.com/docs/DOC-10635)? ANACONDA NAVIGATOR Jupyter Notebook 6.0.1 PYTHON 3.74 Required Packages • Numpy: for basic numerical computation • SciPy: for scientific computation including statistics • StatsModels: for regression and other statistical analysis • Pandas: for representing and analyzing data
• Matplotlib: for visualization

#DSC630 Data Exploration

data <- read.csv(“C:/Users/14026/Desktop/Data Science Courses 2020/DSC53011252019/My project_DSC530/fusion_GLOBAL_DATAFLOW_UNICEF_1.0.PT_F_15-49FGM+PT_M_15-49_FGM_ELIM+PT_F_0-14_FGM+PT_F_15-49_FGM_ELIM..(1).csv”) head(data)

dim(data) #shows the dimensions of the data frame by row and column str(data) # shows the structure of the data frame #summary(data) # provides summary statistics on the columns of the data frame colnames(data) # shows the name of each column in the data frame #head(data) #shows the first 6 rows of the data frame #tail(data) #shows the last 6 rows of the data frame #View(data) #shows a spreadsheet-like display of the entire data frame #rownames(data) #nrow(data) #ncol(data) #colnames(data)

library(dplyr) glimpse(data) # Explore the data

cor.test(data$Observation.Value, data$Time.Period) #cor(data$Observation.Value, data$Time.Period, method = c(“pearson”, “kendall”, “spearman”)) data.cor= cor(data$Observation.Value, data$Time.Period, method = c(“pearson”)) data.cor

cov(data$Observation.Value, data$Time.Period) #result [1] -3.865924

library(Hmisc) data.rcorr = rcorr(as.matrix(data$Observation.Value, data$Time.Period))

#Visualizing the correlation matrix

install.packages(“corrplot”) library(corrplot) corrplot(data.rcorr)

palette = colorRampPalette(c(“green”, “white”, “red”)) (20) heatmap(x = data.rcorr, col = palette, symm = TRUE)

regression

#Run a regression analysis where Time.Period predicts Observation.Value. mod <- lm(Time.Period ~ Observation.Value, data) mod

prediction

predict(mod, Time.Period=Observation.Value)