naives-Bayes.Rmd

---
title: "R Notebook"
output: html_notebook
---

```{r}
#Load Libraries
library(naivebayes)
library(caret)
```

```{r}
#Getting the dataset
df <- read.csv("amirsecretfile.csv")
head(df)
```

```{r}
#Setting outcome variables as categorical
#Setting outcome variables as categorical
df$gender <- ifelse(df$gender=="F",0,1)
df$City <- as.factor(df$City)
df$gender <- as.factor(df$gender)
```

```{r}
#Studying the structure of the data
 str(df)
```
```{r}
#checking for missing values
sum(is.na(df))
```
```{r}
#train and test data sets
set.seed(1234)
ind <- sample(2, nrow(df), replace = T, prob = c(0.8, 0.2))
train <- df[ind == 1,]
test <- df[ind == 2,]

```

```{r}
#Model Building
model <- naive_bayes(gender ~ FamilyIncome + EdYears + Grocery + Cosmatics + MF + 
                       BoughtCosmatics + Response + City, data = train, usekernel = T) 
summary(model) 
```
```{r}
#Prediction
Predict <- predict(model,newdata = test )
#create confusion matrix
confusionMatrix(test$gender, Predict)
```
The final output shows that I have built a Naive Bayes classifier that can predict whether a person is male or female, with an accuracy of approximately 99%.