Logistic Regression Machine Learning Project.Rmd

## Logistic Regression Project


# Part 1
```{r}
adult <- read.csv("./adult_sal.csv")
head(adult)
```

```{r}
library(dplyr)
```

```{r}
adult <- select(adult,-X)
str(adult)
summary(adult)
```

```{r}
table(adult$type_employer)
```

### Data Cleaning

# Combine Employer Type
```{r}
unemp <- function(job){
  job <- as.character(job)
  if (job=='Never-worked' | job == 'Without-pay'){
    return("Unemployed")
  }else{
    return(job)
  }
}
```


#### Apply
```{r}
adult$type_employer <- sapply(adult$type_employer,unemp)
```
##
```{r}
print(table(adult$type_employer))
```


# Group Self-Employment and State and Local
```{r}
group_emp <- function(job){
  if (job=='Local-gov' | job == 'State-gov'){
    return("SL-gov")
  }else if (job=='Self-emp-inc' | job=='Self-emp-not-inc'){
    return('self-emp')
  }else{
    return(job)
  }
}
```
####
```{r}
adult$type_employer <- sapply(adult$type_employer,group_emp)
```
##
```{r}
print(table(adult$type_employer))
```

### Marital Status
```{r}
table(adult$marital)
```
##
```{r}
group_marital <- function(mar){
  mar <- as.character(mar)

# Not-Married

  if (mar=='Separated' | mar=='Divorced' | mar=='Widowed'){
    return('Not-Married')
    
    # Never-Married   
  }else if(mar=='Never-married'){
    return(mar)
    
    #Married
  }else{
    return('Married')
  }
}

```

```{r}
adult$marital <- sapply(adult$marital,group_marital)
table(adult$marital)
```

## Country
```{r}
table(adult$country)
levels(adult$country)
```


```{r}
Asia <- c('China','Hong','India','Iran','Cambodia','Japan', 'Laos' ,
          'Philippines' ,'Vietnam' ,'Taiwan', 'Thailand')

North.America <- c('Canada','United-States','Puerto-Rico' )

Europe <- c('England' ,'France', 'Germany' ,'Greece','Holand-Netherlands','Hungary',
            'Ireland','Italy','Poland','Portugal','Scotland','Yugoslavia')

Latin.and.South.America <- c('Columbia','Cuba','Dominican-Republic','Ecuador',
                             'El-Salvador','Guatemala','Haiti','Honduras',
                             'Mexico','Nicaragua','Outlying-US(Guam-USVI-etc)','Peru',
                             'Jamaica','Trinadad&Tobago')
Other <- c('South')
```

```{r}
group_country <- function(ctry){
  if (ctry %in% Asia){
    return('Asia')
  }else if (ctry %in% North.America){
    return('North.America')
  }else if (ctry %in% Europe){
    return('Europe')
  }else if (ctry %in% Latin.and.South.America){
    return('Latin.and.South.America')
  }else{
    return('Other')      
  }
}
```


```{r}
adult$country <- sapply(adult$country,group_country)
table(adult$country)
```

# Rechecking the Data
```{r}
str(adult)
```

```{r}
adult$type_employer <- sapply(adult$type_employer,factor)
adult$country <- sapply(adult$country,factor)
adult$marital <- sapply(adult$marital,factor)
```

### Part 2 (Missing Data)

```{r}
library(Amelia)
```

## Adult Transformation
```{r}
adult[adult=='?'] <- NA
table(adult$type_employer)
```

# Factor Transformation After the Code
```{r}
adult$type_employer <- sapply(adult$type_employer,factor)
adult$country <- sapply(adult$country,factor)
adult$marital <- sapply(adult$marital,factor)
```

```{r}
table(adult$type_employer)
```

```{r}
missmap(adult)
```

```{r}
missmap(adult,y.at=c(1),y.labels = c(''),col=c('yellow','black'))
```

# Dropping the NA Values/Missing Data

```{r}
adult <- na.omit(adult)
```
#str(adult)

# Missmap Afterwards

```{r}
missmap(adult,y.at=c(1),y.labels = c(''),col=c('yellow','black'))
```
```{r}
str(adult)
```
# Exploratory Data Analysis

```{r}
library(ggplot2)
library(dplyr)
```

# Histograms on EDA

# Age Based on Income
```{r}
ggplot(adult,aes(age)) + geom_histogram(aes(fill=income),
    color='black',binwidth=1) + theme_bw()
```

# Hours Per Week

```{r}
ggplot(adult,aes(hr_per_week)) + geom_histogram() + theme_bw()
```

## Rename the Country Region

```{r}
head(adult)
```

#Lots of ways to do this, could use dplyr as well
```{r}
names(adult)[names(adult)=="country"] <- "region"

str(adult)
```
# Region Based on Income
```{r}
ggplot(adult,aes(region)) + geom_bar(aes(fill=income),color='black')+theme_bw()+
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
```

#### Part 3 Logistic Regression Model
```{r}
head(adult)
```


## Train Test Data Set


# Import Library
```{r}
library(caTools)
```
# Set a random see so your "random" results are the same as this notebook
```{r}
set.seed(101) 
```
# Split up the sample, basically randomly assigns a booleans to a new column "sample"
```{r}
sample <- sample.split(adult$income, SplitRatio = 0.70) # SplitRatio = percent of sample==TRUE
```
# Training Data

```{r}
train = subset(adult, sample == TRUE)

# Testing Data
test = subset(adult, sample == FALSE)
```


```{r}
help("glm")
```

# Creating a GLM Function

```{r}
model = glm(income ~ ., family = binomial(logit), data = train)

summary(model)


help("step")
```

## Creating the Step Models

# New Step Model

```{r}
new.step.model <- step(model)

summary(new.step.model)
```

# Confusion Matrix

```{r}
test$predicted.income = predict(model, newdata=test, type="response")

table(test$income, test$predicted.income > 0.5)
```

# Accuracy

```{r}
(6372+1423)/(6372+1423+548+872)
```
# Calculate other measures of performance like, recall or precision.

#recall

```{r}
6732/(6372+548)
```

#precision

```{r}
6732/(6372+872)
```