https://github.com/animesh-chourey/loan-classifier

Trained machine learning algorithms (Logistic Regression, KNN, SVM, Decision Tree) specifically, after performing visualization and pre-preocessing tasks on a loan dataset. Executed the evaluation metrics such as F1-score, Log loss and jaccard-similarity score to assess the algorithms performance.
https://github.com/animesh-chourey/loan-classifier

decision-tree f1-score jaccard-similarity knn logistic-regression logloss matplotlib numpy pandas preprocessing-data svm

Last synced: 3 months ago
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Trained machine learning algorithms (Logistic Regression, KNN, SVM, Decision Tree) specifically, after performing visualization and pre-preocessing tasks on a loan dataset. Executed the evaluation metrics such as F1-score, Log loss and jaccard-similarity score to assess the algorithms performance.

• Host: GitHub
• URL: https://github.com/animesh-chourey/loan-classifier
• Owner: Animesh-Chourey
• Created: 2022-07-17T15:56:15.000Z (almost 3 years ago)
• Default Branch: master
• Last Pushed: 2022-08-26T11:41:28.000Z (over 2 years ago)
• Last Synced: 2025-01-11T19:45:29.756Z (4 months ago)
• Topics: decision-tree, f1-score, jaccard-similarity, knn, logistic-regression, logloss, matplotlib, numpy, pandas, preprocessing-data, svm
• Language: Jupyter Notebook
• Homepage:
• Size: 29.3 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Trained various machine learning classification algorithms and found the best one for this specific dataset by accuracy evaluation 

methods to predict whether the loan cases will be paid off or not.

| **Algorithm**           | **F1-score** | **Jaccard** | **LogLoss** |

|-------------------------|--------------|-------------|-------------|

| **KNN**                 | 0.632        | 0.653       | -           |

| **Decision Tree**       | 0.626        | 0.679       | -           |

| **SVM**                 | 0.621        | 0.722       | -           |

| **Logistic Regression** | 0.63         | 0.74        | 0.532       |