Logistic Regression Project: Cancer Prediction with Python

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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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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

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I know this video is about a year old, but this was an amazing walk-through. I really appreciate it!

taylorcharlesmichel

This video is highly educative. I wish he explains other ML algorithms in future videos. Thanks so much.

ayoajayi

Amazing teaching
Very much articulated...♥

ShivaNaroju-lzsz

A standard scaler 30:00 transformers your values into a range of (-3 ; +3)

Thank u for the video.

dazai

thanks, the way you tackle each part of the project helps beginners like me learn and catch up easily

linda_erose

Hello, great video
just one comment is at 22:04 the reason it's recommended to convert it into a categorical type is that python/the model will treat it inherently as an int type which indicates that one is larger or greater than the other 1 > 0 which is not what we're looking for we want the model to treat it as if 1 is a yes and 0 is a no basically otherwise great content and i hope this helps

ahmeddiaa

This is one of the best videos on data science and I have seen a lot . Thank you for this. Please keep posting

for-ever-

Man You Did Awesome.. I can't buy coffee for you for now...but hope so in Future.. please continue building models

perlovers

You're really good at explaining everything. This is really a beginner friendly project where we can learn and understand. Thankyou so much Alejandro❤

NASAverseExploration

Great explanation and real case example, thanks a lot

dataguyin

Great explainations, clear instructions and great work. I wish you could do more projects on other ML models as well. That would be really helpful. Thanks for this content man.

lasithdissanayake

this is the best tutorial i have ever watched. thanks a lot man. And
Instead of train, test. is there any benefit of using train, validation, test?

edmashokmusic

informative and useful, you should make it a video on how to deploy it using flask or any other thing

RaihanRisad

Best video i have seen.. such an amazing explaination. can you please come up with more ml projects instead of langchain?

tejaspatel

Great teaching! I am new to Python and ML and am learning a lot!

How to handle if the predictor is categorical in nature, e.g. some Yes/No or 0/1 of something, but not a number/measurement. Can the logistic regression model handle that?

arthurcwlau

Just wanted to say amazing video. Also at 9:07 when you talk about the equations, shouldn't the logistic regression equation be

1/(1+e^y) instead of e/(1+e^y)

Just noticed that but thanks for your videos, they are amazing ways to implement what im learning in projects!

AnshGupta-crqf

unbelievable I learned a lot from you!!! Thank you so much!
Cant wait to check your new tutorials, truly the best channel for beginners who wants to deep dive into AI!
Is it possible that you can make a tutorial how to build an API around it or even how how to deploy it with e.g. Flask? (as you stated it in your conclusion)
❤

mellowbeatz

best about your video is u do some eda also m0st of the yter those is explain the model and implementing them straight but u do some serious work keep up i am watching u brother

samratdutta

Isn't you had to first split the data then normalized? the way you did would cause data leakage.

shivammehra

Isn't that linear regression at 5 minutes heteroscedastic?

Orokusaki

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