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How to Identify and Handle Outliers in Your Data

Hey everyone! 👋 I’m Tripathi Aditya Prakash, a full-time data analyst, and in this video, I’m diving into the essential topic of outlier detection in data analysis. Outliers can either skew your results or uncover groundbreaking insights. I’ll walk you through actionable methods and tools to identify and handle outliers effectively, along with tips from my own projects. What You’ll Learn: What Are Outliers?: Understand what outliers are, their causes, and their impact on data analysis. Why Outliers Matter: Discover how outliers can skew averages, hide trends, or indicate errors. Methods to Detect Outliers: Visualization: Use scatter plots, box plots, and histograms. Statistical Methods: Z-score, IQR (Interquartile Range). Machine Learning: Isolation Forests, DBSCAN. How to Handle Outliers: Remove, transform, or analyze outliers based on their context. Best Tools for Outlier Detection: Python libraries (Pandas, Matplotlib, Scikit-learn), Excel, Tableau, and Power BI. Take the Next Step: 📚 My Course On Topmate: Learn advanced outlier detection techniques and how to automate these processes. : https://topmate.io/tripathi_aditya_pr... 📅 1-on-1 Mentorship: Book a session with me on Topmate for personalized guidance : https://topmate.io/tripathi_aditya_pr... 🌐 Find All Socials: https://linktr.ee/tripathiadityaprakash Let’s Connect! What’s your go-to method for handling outliers? Share your tips in the comments below—I’d love to hear from you! If you found this video helpful, don’t forget to like, subscribe, and share it with someone working on data cleaning. Remember, outliers aren’t just numbers—they’re opportunities for deeper insights. Let’s master outlier detection together! 🚀