Regression With Support Vector Machine in Python - Sklearn

Подписаться 3,7 тыс.

Просмотров 2,5 тыс.

50% 1

Master the art of regression with Support Vector Machines (SVM) in Python using Scikit-Learn with this step-by-step tutorial. Designed for beginners and intermediate learners, this video guides you through the process of building and training a regression model with SVM from scratch. You'll learn how to set up your Python environment, prepare your data, and implement SVM for regression tasks. The tutorial covers key concepts like selecting the appropriate kernel, tuning hyperparameters, and evaluating your model's performance. Practical coding examples are provided to help you apply these techniques to real-world scenarios, such as predicting continuous values. By the end of this video, you’ll have a solid understanding of how SVM can be used for regression and how to leverage Scikit-Learn for your machine-learning projects. Perfect for data scientists, analysts, and anyone looking to enhance their skills in Python and machine learning. Start your journey into regression with SVM today!
Please feel free to download the data from this link and follow along:
github.com/ras...
If you haven't checked my last video on Classification with Support Vector Machine, here is the link:
• Classification With Su...
Please feel free to check out my Data Science blog where you will find a lot of data visualization, exploratory data analysis, statistical analysis, machine learning, natural language processing, and computer vision tutorials and projects:
regenerativeto...
Twitter page:
/ rashida048
Facebook Page:
regenerativeto...
#supportvectormachine #machinelearning #datascience #dataAnalytics #python #sklearn #artificialintelligence #jupyternotebook

Опубликовано:

4 окт 2024

Ссылка:

Скачать:

Готовим ссылку...

Добавить в:

Мой плейлист

Посмотреть позже

Комментарии : 3

@ahmadameri9852 Год назад

Great tutorial. Please make videos about preprocesing data and ways to make model more accurate like outlier removal. also this model tooks too long for my dataset to calculate data, is there anyway to use GPU to speed things up? and one more question: How can I make a function from this model so i can predict new data without re-training ? like in multi regression we have w1x1+w2x2+...+c=y so you can create a function like y = calc(w_list, x_list, c)

@regenerativetoday4244 Год назад

scikit learn library has model.predict() function where you can pass the new data. I can use this function in a later video as well to show you. I will definitely make videos on data preprocessing. By the time please go to my blog, you will find some tutorials there. You can use GPU in google colab when you use Keras library, not for scikit-learn. I suggest try taking a sample of the dataset to train the model for now.

@namo362 Год назад

Thank you so much when give explanations so clear, but when I want to visual (compare) the difference between y_test and y_pred, I can't understand the x_axis, could you explain it? The picture: i.imgur.com/kpUJRTN.jpg