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Visualize the data in 2-D scatter plot and write the inferences, Make a boxplot for each feature and highlight the outlier, if any, then remove the outlier, make again box plot to show the outlier effect and write the inferences.

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sabyasachidatta/Breast-Cancer-Wisconsin-Dataset

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Breast-Cancer-Wisconsin-Dataset

  1. You need to download ‘breast cancer wisconsin’ data using the library Scikit learn; ref is given below.
  2. Remove the missing/infinite values using the mean strategy if required.
  3. Visualize the data in 2-D scatter plot and write the inferences, How the data look like.
  4. Make a boxplot for each feature and highlight the outlier, if any, then remove the outlier, make again box plot to show the outlier effect and write the inferences.
  5. Normalized the data if required, and write a note for what, why and how you performed normalization.

Ref: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html#sklearn .datasets.load_breast_cancer

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Visualize the data in 2-D scatter plot and write the inferences, Make a boxplot for each feature and highlight the outlier, if any, then remove the outlier, make again box plot to show the outlier effect and write the inferences.

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