Data Cleaning Project: Handling Missing Values and Outliers
Welcome back to the Gudsky AI & ML Educational Series 🚀
In this hands-on project video, we apply data cleaning techniques to fix missing values and outliers in a real dataset. This is a crucial step before training any machine learning model, ensuring our data is reliable and ready for analysis.
🔍 What you’ll learn (Practical Demo)
✅ Detecting missing values with Pandas
✅ Techniques to handle missing data:
Deletion (dropping rows/columns)
Mean, Median, Mode imputation
Advanced imputation (KNN / regression-based intro)
✅ Identifying outliers using:
Boxplot visualization
Z-score method
IQR (Interquartile Range) method
✅ Handling outliers:
Removal
Transformation (log, square root)
Capping using domain limits
✅ End-to-end cleaning workflow in Python
🧪 Why this matters
👉 Raw data is messy. Missing values and outliers can distort results and mislead ML models. By learning to clean data systematically, you ensure your models are more accurate and trustworthy.
📘 Next up
Next video: Feature Engineering: Creating New Features from Existing Data
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