Handling missing values part-2 of machine learning

Headline: Handling Missing Values Part-2: Advanced Data Cleaning Techniques in Hindi 📊
Description:
Data Science aur Machine Learning mein Data Cleaning sabse mahatvapurn step hai. Is video mein hum Machine Learning series ke Part-2 ko continue kar rahe hain, jahan hum seekhenge ki dataset mein missing values ko handle karne ke advanced methods kya hain.
Agar aapne Part-1 nahi dekha hai, toh pehle use zaroor dekhen taaki aapka foundation strong ho sake.
Topics Covered in this Video:
• Mean, Median, aur Mode Imputation kab aur kaise use karein?
• Categorical Missing Values ko handle karne ke tarike.
• SimpleImputer class ka use (Scikit-Learn).
• Real-world dataset par missing value handling ka demo.
• Kab data drop karna chahiye aur kab fill?
Data Cleaning ke bina koi bhi ML model accurate results nahi de sakta. Isliye is video ko end tak dekhein aur apne doubts comments mein puchein!

• Handling missing values in machine learning
• Data cleaning in machine learning hindi
• Missing value imputation techniques
• Scikit-Learn SimpleImputer tutorial hindi
• Mean Median Mode imputation in Python
• How to handle missing data in machine learning
• Machine Learning course in Hindi Part 2
• Feature Engineering in Hindi
• Data Science interview questions missing values
• Handling categorical missing data hindi

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