Outliers are the anomalies in the dataset that disturb the machine learning and give biased results.

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- Outliers are unexpected or random values - Outliers leads to causes wrong prediction like missing values - Outliers are anomalies in data - Outliers are mistakes during data collection - Outliers are will cause to wrong insights from data

Outliers are the anomalies of the data that will disturb the whole analysis of the data. 2. So we have to find them and reduce or impute them using different methods. 3. There are also different types of outliers so we have to consider them while detecting them. 4. We can use statistical methods like Inter Quartile Range or Visualization methods to detect outliers and to deal with them we can remove them, transform them, or impute them. 5. So we must deal with these because if not then it will create issues in the future.

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waar jata h...................

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1. Outliers are anomiles of data that disturb our insight about data set. 2. to get accurate insights from data we must need to remove outliers. 3. there are differnet statistical or visualization ways to remove outhliers like histogram, box plot that is also called Inter Quartile Range and Z-score method. 4. We can remove outliers, transform them and in many cases we can impute them by their mean, median and mode. 5. Inshort, if we don't detect and remove outliers, it will squid over dataset and we don't get our desierd results from data.

program to war gya

1. Outliers are the anomalies of the data that will disturb the whole analysis of the data. 2. So we have to find them and reduce or impute them using different methods. 3. There are also different types of outliers so we have to consider them while detecting them. 4. We can use statistical methods like Inter Quartile Range or Visualization methods to detect outliers and to deal with them we can remove them, transform them, or impute them. 5. So we must deal with these because if not then it will create issues in the future.