?? Day 128 of 365: Handling Missing Data ??

Hey, Handlers!

Welcome to Day 128 of our #365DaysOfDataScience journey! ??

We’ll tackle a super important topic in feature engineering: Handling Missing Data. Missing values can really mess with our models, but don’t worry—we’ve got a few tricks to handle them effectively!

?? What We’ll Be Exploring Today:

- Why Handle Missing Data???

???- Understand why missing data is a problem and how it can impact our models’ performance.

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- Techniques to Handle Missing Values:??

???- Explore different approaches:

?????- Imputation (filling in the missing values)

?????- Deletion (removing rows or columns with missing values)

?????- Flagging (creating indicators for missing data)

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- Imputation Methods:??

???- Learn about various imputation techniques like:

?????- Mean/Median imputation

?????- KNN imputation

?????- Forward/Backward fill

?? Learning Resources:

- Read: Scikit-learn documentation on [`SimpleImputer`](https://scikit-learn.org/stable/modules/impute.html). This will show you how to handle missing data in Python using built-in tools.

- Watch: [Handling Missing Data in Python](https://www.youtube.com/watch?v=kv3MA_hOw2k) (YouTube) to see these techniques in action.

?? Today’s Task:

- Apply different imputation techniques to a dataset with missing values.

- Compare how each technique impacts the performance of a machine learning model (like decision trees or KNN).

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?? Tip: Take note of how each method affects the dataset and your model. Does one method work better than others for your dataset? Share your results with the group!

Let’s continue learning and refining our data handling skills! You’ve got this! ??

Happy Learning & See You Soon!

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