Day 66 of 100 days learning Data science #dataanalysis #motivation #dataconsistency #datamanagement

Day 66 of 100 days learning Data science challenge,

This weekend, I dedicated my time to revisiting essential topics, ensuring a strong foundation. I then delved into Video 40 from the CampusX 100 days ML playlist. Here's a summary:

1. **What are outliers?**
Outliers, data points significantly differing from the majority, can distort analyses. Identifying them is crucial in maintaining data accuracy.

2. **Effect on ML algorithms:**
Outliers impact ML models, potentially leading to biased results. Managing outliers is essential for accurate decision boundaries and predictions.

3. **How to treat outliers?**
Outliers can be handled through removal, transformation, or using robust models, depending on data characteristics and ML algorithm requirements.

4. **How to detect outliers?**
Detecting outliers involves statistical methods (Z-score), visualizations (box plots), and mathematical approaches (Interquartile Range), offering a comprehensive view of data distribution.

5. **Techniques for outlier detection and removal:**
Methods like Z-score and Interquartile Range identify and handle outliers, ensuring robustness in statistical analysis.

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