Niyati Sharma
October 11, 2022
── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
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✔ tibble 3.1.8 ✔ dplyr 1.0.10
✔ tidyr 1.2.1 ✔ stringr 1.4.1
✔ readr 2.1.2 ✔ forcats 0.5.2
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✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()Credit risk is defined as the risk of loss resulting from the failure by a borrower to repay the principal and interest owed to the leader.So the purpose of credit analysis is to determine the creditworthiness of borrowers by measuring the risk of loss that the lender is exposed to.When calculating the credit risk of a particular borrower, lenders consider various factors like analyze different documents, such as the borrower’s income statement, balance sheet, credit reports, and other documents that reveal the financial situation of the borrower. to evaluate the characteristics of the borrower and conditions of the loan to estimate the probability of default and the subsequent risk of financial loss.
Q1. How credit risk depends on the age of the person. Q2. Dominating factor on which credit risk depends. Q3. Is credit risk depends on loan_intent?
According to research credit risk of a particular borrower, lenders consider various factors include the borrower’s capacity to repay are income, character, house ownership, and credit history. Check the relationship between the age, income with credit risk with new dataset.
This dataset contains columns simulating credit bureau data, factors on which credit risk depends. The variables of interest for me are income, age, employment length and home ownership.
Rows: 32581 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (4): person_home_ownership, loan_intent, loan_grade, cb_person_default_o...
dbl (8): person_age, person_income, person_emp_length, loan_amnt, loan_int_r...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message. person_age person_income person_home_ownership person_emp_length
Min. : 20.00 Min. : 4000 Length:32581 Min. : 0.00
1st Qu.: 23.00 1st Qu.: 38500 Class :character 1st Qu.: 2.00
Median : 26.00 Median : 55000 Mode :character Median : 4.00
Mean : 27.73 Mean : 66075 Mean : 4.79
3rd Qu.: 30.00 3rd Qu.: 79200 3rd Qu.: 7.00
Max. :144.00 Max. :6000000 Max. :123.00
NA's :895
loan_intent loan_grade loan_amnt loan_int_rate
Length:32581 Length:32581 Min. : 500 Min. : 5.42
Class :character Class :character 1st Qu.: 5000 1st Qu.: 7.90
Mode :character Mode :character Median : 8000 Median :10.99
Mean : 9589 Mean :11.01
3rd Qu.:12200 3rd Qu.:13.47
Max. :35000 Max. :23.22
NA's :3116
loan_status loan_percent_income cb_person_default_on_file
Min. :0.0000 Min. :0.0000 Length:32581
1st Qu.:0.0000 1st Qu.:0.0900 Class :character
Median :0.0000 Median :0.1500 Mode :character
Mean :0.2182 Mean :0.1702
3rd Qu.:0.0000 3rd Qu.:0.2300
Max. :1.0000 Max. :0.8300
cb_person_cred_hist_length
Min. : 2.000
1st Qu.: 3.000
Median : 4.000
Mean : 5.804
3rd Qu.: 8.000
Max. :30.000
---
title: "Final Project Proposal"
author: "Niyati Sharma"
description: Initial proposal for my final project
date: "10/11/2022"
format:
html:
toc: true
code-fold: true
code-copy: true
code-tools: true
categories:
- finalpart1
- Niyati Sharma
---
```{r}
library(tidyverse)
library(dplyr)
library(ggplot2)
knitr::opts_chunk$set(echo = TRUE)
```
## Introduction
Credit risk is defined as the risk of loss resulting from the failure by a borrower to repay the principal and interest owed to the leader.So the purpose of credit analysis is to determine the creditworthiness of borrowers by measuring the risk of loss that the lender is exposed to.When calculating the credit risk of a particular borrower, lenders consider various factors like analyze different documents, such as the borrower’s income statement, balance sheet, credit reports, and other documents that reveal the financial situation of the borrower. to evaluate the characteristics of the borrower and conditions of the loan to estimate the probability of default and the subsequent risk of financial loss.
## Research Question
Q1. How credit risk depends on the age of the person.
Q2. Dominating factor on which credit risk depends.
Q3. Is credit risk depends on loan_intent?
## Hypothesis
According to research credit risk of a particular borrower, lenders consider various factors include the borrower’s capacity to repay are income, character, house ownership, and credit history.
Check the relationship between the age, income with credit risk with new dataset.
## Dataset
This dataset contains columns simulating credit bureau data, factors on which credit risk depends.
The variables of interest for me are income, age, employment length and home ownership.
```{r}
library(readr)
df <- read_csv("C:/Users/Lenovo/Downloads/credit_risk_dataset_1.csv")
summary(df)
```