https://github.com/gregoritsch3/ml_eda_classification_loanapprovalprediction

An EDA and Machine Learning Classification exercise on the Loan Approval dataset demonstrating EDA, feature engineering, StratifiedKFold and the use of Tensorflow NN, SVC, LinearSVC, XGBoost, Naive-Bayes, Bagging, Random Forest and Decision Tree algorithms.etc. The modela are optimized using hyperparameter tuning through GridSearchCV.
https://github.com/gregoritsch3/ml_eda_classification_loanapprovalprediction

eda feature-engineering machine-learning matplotlib numpy pandas scikit-learn scipy seaborn tensorflow

Last synced: about 1 month ago
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An EDA and Machine Learning Classification exercise on the Loan Approval dataset demonstrating EDA, feature engineering, StratifiedKFold and the use of Tensorflow NN, SVC, LinearSVC, XGBoost, Naive-Bayes, Bagging, Random Forest and Decision Tree algorithms.etc. The modela are optimized using hyperparameter tuning through GridSearchCV.

• Host: GitHub
• URL: https://github.com/gregoritsch3/ml_eda_classification_loanapprovalprediction
• Owner: Gregoritsch3
• Created: 2024-11-25T07:27:05.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-12-07T15:06:35.000Z (about 1 month ago)
• Last Synced: 2024-12-07T16:18:18.249Z (about 1 month ago)
• Topics: eda, feature-engineering, machine-learning, matplotlib, numpy, pandas, scikit-learn, scipy, seaborn, tensorflow
• Language: Jupyter Notebook
• Homepage: https://www.kaggle.com/datasets/architsharma01/loan-approval-prediction-dataset
• Size: 4.08 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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# ML_EDA_Classification_LoanApprovalPrediction

An EDA and Machine Learning Classification exercise on the Loan Approval dataset demonstrating EDA, feature engineering, StratifiedKFold and the use of Tensorflow NN, SVC, LinearSVC, XGBoost, Naive-Bayes, Bagging, Random Forest and Decision Tree algorithms, etc. The models are optimized using hyperparameter tuning through GridSearchCV on a validation set and finally evaluated on the test set.