How to Solve Multi Class Imbalance Problem using SMOTE in Machine Learning ?? || PYTHON
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"🎥 Solving Multiclass Imbalance Problem
"🎥 Solving Multiclass Imbalance Problem in Machine Learning: Oversampling with SMOTE 🔄📊
Are you struggling with imbalanced classes in your machine learning projects?
In this video, we dive deep into solving the multiclass imbalance problem using the powerful SMOTE (Synthetic Minority Over-sampling Technique) oversampling technique.
Imbalanced datasets can hinder the performance of your models and lead to biased results. By generating synthetic samples for the minority classes, we can effectively balance the dataset and improve the accuracy of our models.
By the end of this video, you'll have a solid understanding of how to tackle class imbalance and enhance the performance of your machine learning models.
To learn about the concept of SMOTE in detail, check the link here: SMOTE (Synthetic Minority Oversamplin...
If you find the content USEFUL, make sure to give a THUMBS UP :)
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Why learn #datascience ??
"Data Science is the sexiest job of 21st century"
If you are a #btech student, #diploma in #computerscience, #graduate looking to #career #transition 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: Medium: souravagarwal54321
* Connect with us on linkedIn: LinkedIn: datahat-unfoldingmystery
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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"
Are you struggling with imbalanced classes in your machine learning projects?
In this video, we dive deep into solving the multiclass imbalance problem using the powerful SMOTE (Synthetic Minority Over-sampling Technique) oversampling technique.
Imbalanced datasets can hinder the performance of your models and lead to biased results. By generating synthetic samples for the minority classes, we can effectively balance the dataset and improve the accuracy of our models.
By the end of this video, you'll have a solid understanding of how to tackle class imbalance and enhance the performance of your machine learning models.
To learn about the concept of SMOTE in detail, check the link here: SMOTE (Synthetic Minority Oversamplin...
If you find the content USEFUL, make sure to give a THUMBS UP :)
---------------------------------------------------------------------------------------------------------
Why learn #datascience ??
"Data Science is the sexiest job of 21st century"
If you are a #btech student, #diploma in #computerscience, #graduate looking to #career #transition 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: Medium: souravagarwal54321
* Connect with us on linkedIn: 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"
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