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SMOTE (Synthetic Minority Oversampling Technique) for Imbalanced Dataset || Class Imbalance Problem

"Are you struggling with imbalanced datasets in your machine learning projects??" What is an imbalanced class distribution?? Say, for example, you build a #machinelearning model to predict if a person has Covid. The positivity rate of Covid is 5% (say), which means 5 out of 100 people tested turn out to be positive. In such a setting, a dumb model could say that none has covid and achieve an accuracy of 95%. But is that model doing its job?? Absolutely not and this is the disadvantage of an imbalanced class distribution In this Video, we shall learn about the techniques to handle class imbalance We shall discuss * Oversampling and Undersampling * Undersampling techniques: random and near miss * Oversampling technique: random and SMOTE * Understand SMOTE (Synthetic Minority Oversampling Technique) algorithm * Disadvantages of undersampling and oversampling To learn more about 'near-miss' technique, refer here: https://analyticsindiamag.com/using-n... If you find the content USEFUL, make sure to give it a 👍 ‐----------------------------------------------------------------------- Why learn data science?? "Data Science is the sexiest job of 21st century" If you are a #btech student, #diploma in #computerscience, #graduate looking to #career #tranisition into #datascience , #machinelearning, #dataanalysis, this is a place for you Everyone of us use social media platforms such as instagram, facebook, or binge watch on OTT platforms such as #youtube , netflix, prime, hotstar or window shop on e-commerce sites such as amazon, flipkart, myntra It certainly amazes us to see the products of our choice and interest being #recommended to us, our favorite shows being filtered among millions of others However, not many get to understand how it works. Not to worry, we shall help you understand the concepts in a #simplified and #concise fashion, helping in career transition into machine learning or data science -------------------------------------------------------------------------------------------------------------------------------------- Why learn from datahat?? "Datahat is a bridge" learners --- professionals by simplified data science It provides a platform to learn, create and collaborate helping in the #career #transition to data science, machine learning and data analysis Understand the broad domain of data science and its related subdomains in machine learning, data analysis, data engineering in a self paced, #structured and #guided learning * Here are few interesting reads on our blog:   / souravagarwal54321   * Connect with us on linkedIn:   / datahat-unfoldingmystery   -------------------------------------------------------------------------------------------------------------------------------------- Additionally, here are few more useful resources for beginners and curious data enthusiasts 1. kaggle: a platform to learn, practice, compete and win cash prizes [https://www.kaggle.com/] 2. paperswithcode: website presenting the latest in machine learning and data science research and the code implementations [https://paperswithcode.com/] 3. google colab: a platform to run code, build machine learning solutions, explore the capabilities of GPU and TPU without any hassle in setting up the environment: [https://colab.research.google.com/] Remember, "the greatest investment ever made is investment in self growth and learning"