Advance Project : Liver Disease Classification Using Machine Learning

Поделиться
HTML-код
  • Опубликовано: 20 фев 2024
  • 🚀 Welcome to the Multiverse of 100+ Data Science Project Series! 🌐 Episode 12 ventures into the realm of healthcare analytics with Liver Disease Classification using Python.
    📚 Series Overview:
    Embark on an enlightening data science journey with our Multiverse series, featuring over 100 captivating projects meticulously crafted to enhance your skills and knowledge. Whether you're a beginner or an expert, our series offers an array of projects to cater to every level.
    🩺 Episode 12 : Liver Disease Classification
    Join us as we address the crucial task of classifying liver disease using Python and machine learning algorithms. Learn how to analyze medical data, extract relevant features, and build classification models to accurately diagnose liver disease. From data preprocessing to model evaluation, this episode equips you with the tools to make informed healthcare decisions.
    🔧 Tools and Technologies:
    Python
    Jupyter Notebooks
    Pandas
    NumPy
    Scikit-learn
    📈 What You'll Learn:
    Understanding liver disease and its risk factors
    Preprocessing and analyzing medical data
    Feature selection and engineering
    Building classification models using machine learning algorithms
    Evaluating model performance and interpreting results
    🚀 Join the Multiverse Community:
    Connect with like-minded data enthusiasts, share your insights, and seek support on our vibrant community forums. The Multiverse community is your platform for collaboration, learning, and growth in the dynamic field of data science.
    🔗 Resources:
    Access the code, datasets, and additional materials on our GitHub repository. Follow along with the tutorial and uncover the secrets of Liver Disease Classification using Python.
    📸 Instagram: @knowledge_doctor.
    invitescon...
    💻 GitHub: github.com/Chando0185/Multive...
    📘 Facebook: / knowledge-doctor-progr...
    ☄️Join Discord👉
    / discord
    📌 Stay tuned for upcoming episodes in our Multiverse series! Subscribe, like, and hit the notification bell to embark on an enriching journey through the diverse landscapes of data science projects.
    🚀 Multiverse of 100+ Data Science Project Series - Where possibilities are endless, and knowledge knows no bounds. Let's explore the world of data science together! 🌌✨

Комментарии • 8

  • @dimuthuarachchi4787
    @dimuthuarachchi4787 23 дня назад

    Sir, if you don't mind, could you please share datasets links

  • @repakalokesh804
    @repakalokesh804 4 месяца назад

    Thanks brother but try to focus on covering different ML techniques like SMOTE, VIF etc in the projects bro.

    • @knowledgedoctor3849
      @knowledgedoctor3849  4 месяца назад +2

      Yes Those project in queue🌻 Right After Health care

  • @nil5896
    @nil5896 4 месяца назад

    Sir which is good to learn tensorflow or pytorch?

    • @knowledgedoctor3849
      @knowledgedoctor3849  4 месяца назад

      If you have low end pc and straight forward & easy framework you can go for tensorflow & keras.... But most of the complex code is written in pytorch like yolo🌻 As a beginners start from tensorflow

  • @user-eo2kb8ny6s
    @user-eo2kb8ny6s 4 месяца назад

    Sir can you please also explain about deployment of these data science projects ,that how can we deploy them and make live please sir explain for any one of the projects ,so we can got a overview that how to start with deployments .Sir please do this for only any of one project.
    You are doing great sir please help for this also.....please sir.

    • @knowledgedoctor3849
      @knowledgedoctor3849  4 месяца назад

      Yeah Sure Why Not, But Let Follow the Path Slowly Slowly I Will Go Deep Soon...
      Don't Worry Start Kiya tho Katam Me Hi Karunga🌻

    • @user-eo2kb8ny6s
      @user-eo2kb8ny6s 4 месяца назад

      ok sir thank you @@knowledgedoctor3849
      Sir one more question that it is possible that while building different models there will be same accuracy for more than one model.