# Credit-Risk-Analysis-in-R

**Repository Path**: as11221208/Credit-Risk-Analysis-in-R

## Basic Information

- **Project Name**: Credit-Risk-Analysis-in-R
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-09-08
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Credit-Risk-Analysis-in-R
Credit Risk Analysis in R using Decision Trees, LDA, Logistic Regression and Random Forests

Problem Statement
To Minimize the time consuming manual method of approving/dis-approving the loan request and follow an analytical driven method which is more automated and easier to assess the risk in approving the loan
The code is used to analyse the influence of various factors in determining whether a loan is good or bad from the past historical data.

Primary methods to be used:
1. Classification and Regression Trees
2. Linear Discriminant Analysis
3. Logistic Regression
4. Random Forest

Dataset:
The dataset is in the file data.csv. More info on this file is in Dataset_info.txt file.

Requirements: 
The code will be written in R. More on the requirements in the Requirements.txt file.

Analysis and Conclusion:
The Results will be available in the AnalysisandResults.txt file