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The aim of this project is to use the logistic regression mode as a binary classifier to analyse credit card risk. The recommended model helps to predict the high-risk cases. The accuracy, precision, and recall metrics are used to evaluate this model performance.

License: MIT License

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creditcardfrauddetection fraud-prevention logistic-regression python supervised-machine-learning

credit-risk-classification's Introduction

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Hello and welcome to my GitHub page! ๐Ÿ‘‹

My name is Shiva, and I'm delighted to share my journey with you. After exploring various fields, I have found my true passion in the world of data. Recently, I have been deeply immersed in this exciting field, expanding my knowledge by learning new tools and techniques such as Python, SQL, and Machine Learning. I was enrolled in University of Western Australia's Data Analytics Bootcamp, where I was gaining hands-on experience and building projects that showcase my skills. I'm continuously improving my knowledge and skills in this field, and I cannot wait to see where this journey takes me. I'm excited to share my progress with you, and thank you for stopping by my page.

  • ๐Ÿ”ญ Iโ€™m currently working to improve my visualisation skills using Power BI.
  • ๐ŸŒฑ Iโ€™m currently learning more about data analytics and data visualisation.
  • ๐Ÿ“ซ How to reach me: You can shoot me an email at [email protected].
  • ๐Ÿ˜„ Pronouns: Her/Hers.
  • โšก Fun fact: Aside from my passion for data, I find joy camping and traveling. There's something truly magical about immersing oneself in nature's beauty, whether it's pitching a tent under the stars or embarking on an adventure to explore new landscapes.

๐Ÿš€ Some Tools I Have Used and Learned

Here are some of the tools and technologies that I have used and learned:

  • Python
  • SQL
  • PostgreSQL
  • MongoDB
  • Power BI
  • Tableau
  • Supervised machine learning
  • Unsupervised machine learning
  • Java Script
  • HTML/CSS
  • Excel
  • Visual Basic

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