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Identifying Spam Emails
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Identifying Spam Emails

Identifying Spam Emails

IntroductionImport DataColumns AdjustmentsPreliminary AnalysisPlot the Target DistributionRemove StopwordsUsing .loc[ ] Train-Test SplitData PreprocessingModelingPutting All Together

bookPreliminary Analysis

Checking for null values and duplicates is important in the data cleaning and preparation process because this helps to ensure the quality and accuracy of the data.

  • Null values can indicate missing or incomplete data and, if not handled properly, can lead to inaccuracies in any analysis or modeling performed on the data. For example, if a null value is present in a column that is used as a predictor variable in a machine learning model, the model will not be able to predict that data point.
  • Duplicates can also lead to inaccuracies in analysis, especially if they are not identified and removed. For example, if a data point is duplicated, it will be counted twice in any analysis performed, potentially skewing the results. Additionally, duplicate data can increase the size of the dataset and slow down any analysis or modeling performed on it.

Tarefa

  1. Check for any NaN (Not a Number) values in the DataFrame df.
  2. Drop the duplicates, as they are not useful for our analysis.

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Checking for null values and duplicates is important in the data cleaning and preparation process because this helps to ensure the quality and accuracy of the data.

  • Null values can indicate missing or incomplete data and, if not handled properly, can lead to inaccuracies in any analysis or modeling performed on the data. For example, if a null value is present in a column that is used as a predictor variable in a machine learning model, the model will not be able to predict that data point.
  • Duplicates can also lead to inaccuracies in analysis, especially if they are not identified and removed. For example, if a data point is duplicated, it will be counted twice in any analysis performed, potentially skewing the results. Additionally, duplicate data can increase the size of the dataset and slow down any analysis or modeling performed on it.

Tarefa

  1. Check for any NaN (Not a Number) values in the DataFrame df.
  2. Drop the duplicates, as they are not useful for our analysis.

Mark tasks as Completed
Switch to desktopMude para o desktop para praticar no mundo realContinue de onde você está usando uma das opções abaixo
Seção 1. Capítulo 4
AVAILABLE TO ULTIMATE ONLY
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