Statistics for Data Analysis | Quartiles, Quantiles and Interquartile Range
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Updated
May 20, 2024 - Jupyter Notebook
Statistics for Data Analysis | Quartiles, Quantiles and Interquartile Range
Data analysis and outliers detection of air quality data.
A outlier removal tool, removes outlier row-wise using z-score or InterQuartile Range method
Machine Learning Nano-degree Project : To identify customer segments hidden in product spending data collected for customers of a wholesale distributor
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The following data are annualized returns on a group of 15 stocks. 12.5, 13, 14.8, 11, 16.7, 9, 8.3, -1.2, 3.9, 15.5, 16.2, 18, 11.6, 10, 9.5
simple IQR & Z-score normalization script
1-Outlier detection and removal of the outlier by Using IQR The Data points consider outliers if it's below the first quartile or above the third quartile 2-Remove the Outliers by using the percentile 3-Remove the outliers by using zscore and standard deviation
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Add a description, image, and links to the iqr topic page so that developers can more easily learn about it.
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