Mastering Feature Selection: With Correlation Method | Part - 1

Welcome to the first instalment of our comprehensive Feature Selection series! In this video, we dive deep into the world of data optimization through the powerful Correlation Method. Discover how selecting the right features can supercharge your data science projects.
📊 Key Takeaways:
Understand the fundamentals of the Correlation Method for feature selection.
Explore how features interact and impact each other within your dataset.
Learn techniques to unveil hidden patterns and relationships using correlation analysis.
🔍 Series Overview:
This video is part of our Feature Selection Deep Dive series, where we explore various methods to enhance your data science toolkit. Whether you're a beginner or a seasoned data scientist, each instalment equips you with practical insights to elevate your data analysis skills.
🎓 Who is This For?
Data Scientists and Analysts
Machine Learning Enthusiasts
Anyone Looking to Optimize Data for Improved Model Performance
🔗 Explore the full series and stay tuned for more in-depth insights into feature selection techniques. Don't forget to subscribe, like, and hit the notification bell to stay updated on the latest data science tutorials! 🌐🔍📈
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Resources -
Kaggle link to download the data - www.kaggle.com/datasets/uciml...
Github link to download the data - github.com/LEARNEREA/Data_Sci...
Script developed in the video - github.com/LEARNEREA/Data_Sci...
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[@himanshuworldwide6521]

You have helped me a lot, I can't tell how much I have learned from your help. Thank you so much and I request you to never delete these videos no matter what.❤🎉

[@learnerea]

Glad I could help!

[@Supernova-ji]

Thankyou so much bhaiya for teaching us(novice) in the best way possible!!👏

[@learnerea]

Glad it was helpful

[@jawadtariq7040]

i didn't find the video for heatmap correction

[@user-jh4wo6ok4s]

The video sound is small compared to other videos. I've turned on my maximum volumn :(

[@vishnuvardhanjadava4186]

li=[]
imp_features=[]
for i in df.drop(['diagnosis','id'], axis=1).columns:
for j in df.drop(['diagnosis','id'], axis=1).columns:
if i != j:
li.append(i)
if (j not in li) and (df[i].corr(df[j])>0.9 or df[i].corr(df[j])