https://github.com/patilni3/loan_approval
Loan Approval using Random Forest Algorithm
https://github.com/patilni3/loan_approval
decision-tree loan-approval machine-learning-algorithms matplotlib pandas random-forest seaborn sklearn
Last synced: 22 days ago
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Loan Approval using Random Forest Algorithm
- Host: GitHub
- URL: https://github.com/patilni3/loan_approval
- Owner: PatilNi3
- Created: 2022-11-26T17:11:46.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-11-26T17:41:13.000Z (over 2 years ago)
- Last Synced: 2025-04-30T05:09:37.396Z (22 days ago)
- Topics: decision-tree, loan-approval, machine-learning-algorithms, matplotlib, pandas, random-forest, seaborn, sklearn
- Language: Jupyter Notebook
- Homepage:
- Size: 44.9 KB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# LOAN_APPROVAL
Loan Approval using Random Forest Algorithm
## About Project:
In this project we will check whether customer is applicable for loan or not. First it checks that if the our customer has a good credit history or not and based on that, it classifies the customer into two groups, Again it checks that the income of the customer and again classifies into two groups. And Finally, it checks the loan amount requested by the customer. Based on the outcomes from these three features, the decision tree and random forest decides if the customer’s loan should be approved or not.
## Libraries Used:
1. Pandas
2. Matplotlib
3. Seaborn
4. SKlearn
## Algorithms Used:
1. Decision Tree
2. Random Forest
## Dataset Used:
• loan_data.csv (https://drive.google.com/file/d/157J3bT1torNBJaO9iwt_yKhdphPEZDTE/view)
## Accuracy:
1. Accuracy: 84
2. P5recision: 84 %
3. Recall: 100 %
4. F1-Score: 91 %
# Thank You.☻