https://github.com/nirmalyabag20/loan-status-prediction-using-machine-learning

This project focuses on predicting the loan status (approved or not approved) based on various applicant details. The goal is to develop a machine learning model that accurately classifies whether a loan should be approved, helping financial institutions make informed lending decisions.
https://github.com/nirmalyabag20/loan-status-prediction-using-machine-learning

matplotlib numpy pandas python scikit-learn seaborn support-vector-machine

Last synced: 27 days ago
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This project focuses on predicting the loan status (approved or not approved) based on various applicant details. The goal is to develop a machine learning model that accurately classifies whether a loan should be approved, helping financial institutions make informed lending decisions.

• Host: GitHub
• URL: https://github.com/nirmalyabag20/loan-status-prediction-using-machine-learning
• Owner: nirmalyabag20
• Created: 2024-09-13T06:28:11.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-13T06:52:53.000Z (4 months ago)
• Last Synced: 2024-11-07T13:42:11.448Z (2 months ago)
• Topics: matplotlib, numpy, pandas, python, scikit-learn, seaborn, support-vector-machine
• Language: Jupyter Notebook
• Homepage:
• Size: 94.7 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Project Workflow~

1.	Data Preprocessing:

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  o	Handled missing values through appropriate imputation techniques.

  

  o	Transformed categorical variables using Label Encoder.

  

  o	Scaled numerical features for better model performance.

  

2.	Exploratory Data Analysis:

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  o	Visualized the relationships between features and the loan status.

  

  o	Analyzed distribution patterns of key attributes like income, loan amount, and credit history.

  

3.	Model Building:

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  o	Implemented various machine learning algorithms including:

  

  •		Logistic Regression

  •		Random Forest

  •		Support Vector Machine (SVM)

  •		KNeighborsClassifier

  o	Performed hyperparameter tuning to optimize model performance.

  

4.	Model Evaluation:

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  o	Evaluated models using accuracy_score

  

5.	Final Model:

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  o	Selected the best-performing model based on evaluation metrics.

  

  o	Provided insights on feature importance to understand the key factors influencing loan approval.

Results~

   

  •	The final model achieved an accuracy of 78%

  

  •	The most influential features in predicting loan status included credit history, applicant’s income, and loan amount.