In this tutorial, we will walk you through a hands-on project using logistic regression for breast cancer prediction. We will be using a breast cancer dataset to build a logistic regression model that accurately predicts if a cancer is malignant or not based on certain measurements. This tutorial is perfect for beginners in machine learning and data science who want to learn how to build a logistic regression model from scratch using Python and the Scikit Learn library.

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LINKS
👉 Here is all the code in the tutorial: https://www.kaggle.com/code/alexandre...
👉 Here is the dataset: https://www.kaggle.com/datasets/uciml...

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In this video tutorial, you will learn about binary logistic regression, logistic regression models, and how to build one for a data science project. You will also get an example of a data science project that will help you understand the process of how these models work mathematically. We will be using a Jupyter Notebook for our coding exercises, and we'll provide you with all the necessary code and explanations to help you follow along.

Whether you're a data science beginner or looking for ideas for data science projects, this tutorial will give you a comprehensive overview of logistic regression and how to apply it to a real-life problem. By the end of this video, you will have a solid understanding of logistic regression and be able to apply it to your own data science projects.

Keywords: logistic regression, machine learning, python, logistic regression machine learning, logistic regression model, binary logistic regression, logistic regression example, data science project, data science project from scratch, data science project ideas, data science projects for beginners, data science, data science projects, Scikit Learn, Jupyter Notebook.

Timestamps ⏰
00:00 Intro
01:05 How the model works
02:30 Background on Linear Regression
06:50 Intuition behind Logistic Regression
10:07 Presentation of the dataset
12:25 Import dependencies and data
15:18 Clean the data
23:05 Separate predictors and target
25:33 Normalize the data
30:30 Split data into Test and Train sets
34:13 Train the model and make predictions
37:54 Evaluation of the model
41:17 Conclusion

#datascience #machinelearning #scikitlearn #python #artificialintelligence #logisticregression