part5.Rmd

---
title: "Part5_Holdout_Prediction"
author: "Sharika Loganathan"
date: "2022-12-13"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## R Markdown

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## Load packages


```{r}
library(tidyverse)
library(caret)
```


##  Holdout set

```{r}
holdout <- readr::read_csv('fall2022_holdout_inputs.csv', col_names = TRUE)
```

```{r}
df_holdout <- holdout %>% 
  mutate(x5 = 1 - (x1 + x2 + x3 + x4),
         w = x2 / (x3 + x4),
         z = (x1 + x2) / (x5 + x4),
         t = v1 * v2) %>% 
         select(x1,x2,x3,x4,v1,v2,v3,v4,v4,v5,x5,w,z,t,m) %>%
  glimpse()
```


###  Load Best Models


```{r}
reg_best_model <- readr::read_rds('reg_best_model.rds')
cls_best_model <- readr::read_rds('cls_best_model.rds')
```




###   Important Varibles for Regression Model



```{r}
plot(varImp(reg_best_model), top=20)
```

 *Top variables for regression are z,w ,x5
 
 
 
###   Important Varibles for Classification Model


```{r}
plot(varImp(cls_best_model), top = 20)
```


*Top variables for regression are x1,x2,x3



```{r}
y <- list(predict(reg_best_model, df_holdout))
outcome <- list(predict(cls_best_model, df_holdout))
probability <- predict(cls_best_model, df_holdout, type = 'prob')%>% select(event)
my_pred <-bind_cols(list(y, outcome, probability)) %>% tibble::rowid_to_column('id')
```


```{r}
colnames(my_pred) <- c("id", "y", "outcome", "probability")
my_pred %>% head()
```

```{r}
my_pred %>% 
  readr::write_csv('pred.csv', col_names = TRUE)
```