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Python, Logistic Regression, Cross Validation, Hyperparameter Tuning, Random Forest, XGBoost

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Amolrakhunde/Credit-Card-Default-Prediction

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Credit Card Default Prediction

AlmaBetter Verfied Project - AlmaBetter School

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📋 Summary

The main objective is aimed at predicting the case of customers default payments in Taiwan. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. This would inform the issuer’s decisions on who to give a credit card to and what credit limit to provide. It would also help the issuer have a better understanding of their current and potential customers, which would inform their future strategy, including their planning of offering targeted credit products to their customers.

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💾 Project Files Description

This Project includes 1 colab notebook, 1 technical documentation as well as 1 presentation:

Executable Files:

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📖 Binary Classification Model

I have used Classification algorithms such Logisitic Regression, Decision Tree, Ensemble Techniques and SVC. Later I performed cross validation and hyperparameter tuning, in order to overcome overfitting and increase performance of model.

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📋 Execution Instruction

The order of execution of the colab notebook is as follows:

  • First, click on the open in colab button present on the top center of the notebook.
  • Downlaod the dataset from kaggle through provided link.Then, connect to the runtime and execute the cell to mount the drive or upload the data file to the current runtime.

3. File Path

  • Finally, delete the path in the dataset loading cell and replace it with the path of your current data file. Run each cell to see the output below it.

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🔗 Credits

Amol Rakhunde | Avid Learner | Data Scientist | Machine Learning Enthusiast | Deep Learning Enthusiast

Contact me for Data Science Project Collaborations

GitHub LinkedIn Medium

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🛠 Skills

Deep Learning, Machine Learning, SQL, Python, Tableau

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📚 References

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📜 Feedback

If you have any feedback, please reach out to us at amolsr92@gmail.com

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Python, Logistic Regression, Cross Validation, Hyperparameter Tuning, Random Forest, XGBoost

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