Hands-On Machine Learning: Logistic Regression with Python and Scikit-Learn

Whether you're a beginner or looking to refresh your knowledge, this machine learning tutorial has got you covered. We'll take you step by step through the concept of Logistic Regression, its mathematical foundation, and its practical applications.
After we take a look at how you can use scikit-learn to code the logistic regression and evaluate the model.
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As a full-time data analyst/scientist at a fintech company specializing in combating fraud within underwriting and risk, I've transitioned from my background in Electrical Engineering to pursue my true passion: data. In this dynamic field, I've discovered a profound interest in leveraging data analytics to address complex challenges in the financial sector.
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[@RyanAndMattDataScience]

Hey guys I hope you enjoyed the video! If you did please subscribe to the channel! Join our Data Science Discord Here: discord.com/invite/F7dxbvHUhg If you want to watch a full course on Machine Learning check out Datacamp: datacamp.pxf.io/XYD7Qg Want to solve Python data interview questions: stratascratch.com/?via=ryan I'm also open to freelance data projects. Hit me up at [e-mail] *Both Datacamp and Stratascratch are affiliate links.

[@crystalford7692]

Hey, I just discovered you this week and want to tell you what a huge help you have been! Thanks!

[@gabrielseverus8821]

Hey Ryan! I just found your videos while doing some pre-learning on Machine Learning concepts before I take a class this Spring semester. I wonder if you could go more in-depth on how to improve the model score. Often, I find myself with a model that has an F1 score between 60 - 80, and I'm not sure how to improve it. Thanks for all the videos; I've been learning so much from you. Keep up the good work!

[@RyanAndMattDataScience]

I can definitely add to the backlog. Once I finish my current set of vids I’ll be prepping for March

[@RyanAndMattDataScience]

d = {'miles_per_week': [37,39,46,51,88,17,18,20,21,22,23,24,25,27,28,29,30,31,32,33,34,38,40,42,57,68,35,36,41,43,45,47,49,50,52,53,54,55,56,58,59,60,61,63,64,65,66,69,70,72,73,75,76,77,78,80,81,82,83,84,85,86,87,89,91,92,93,95,96,97,98,99,100,101,102,103,104,105,106,107,109,110,111,113,114,115,116,116,118,119,120,121,123,124,126,62,67,74,79,90,112], 'completed_50m_ultra': ['no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','no','yes','yes','yes','yes','no','yes','yes','yes','no','yes','yes','yes','yes','yes','yes','yes','yes','no','yes','yes','yes','yes','yes','yes','yes','no','yes','yes','yes','yes','yes','yes','yes','no','yes','yes','yes','yes','yes','yes','yes','no','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes','yes',]}

[@kamranzamanni6826]

Thanks👍🏽

[@ayseljafarzade1076]

thanks for explanation!

[@RyanAndMattDataScience]

No problem

[@mehdismaeili3743]

Excellent .

[@averysnyder4794]

Great stuff! keep it up

[@RyanAndMattDataScience]

Thanks! Video every other day

[@Eric-f2n4w]

Well explained.

[@RyanAndMattDataScience]

Thanks

[@TRZMac]

Why not add an extra minute to the video and actually show the plot? I realized you have done the same thing with the Decision tree video. I as a beginner would really appreciate adding that extra minute or two, just to get the full idea of the process. And thanks for the effort and the great videos you make.

[@RyanAndMattDataScience]

I’ve been doing that on the newer videos. This is when I had just started the KZbin channel. Now I make a slide deck and show Python code for all vids

[@TRZMac]

@@RyanAndMattDataScience Will check those out! Thanks for the quick response!

[@mervlot_tech]

please where is the data

[@eyamestiri2236]

Thank you.

[@RyanAndMattDataScience]

No problem

[@baoxuan2279]

That's what i need

[@wuqianyi1021]

j

[@wuqianyi1021]

I'm glad I found this gem, great content

[@RyanAndMattDataScience]

Thanks for checking it out!