I am a mechanical engineer with training in data science and experience in data analysis, machine learning, and performance metric optimization.
Currently, I work as an account manager at Amazon Ads marketing agency, where I use data analysis to increase sales and improve the efficiency of advertising campaigns for multiple companies.
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The K-means algorithm is a popular method of clustering data and segmenting large data sets into groups with similar characteristics. It is used in fields such as marketing, pattern recognition, and image analysis. However, it has several limitations. For example, it requires the user to predefine the number of clusters. It
is also sensitive to initialization and has difficulty handling complex shapes and outliers. To overcome these challenges, we employ genetic algorithms in Python to enhance centroid selection and more effectively identify intricate structures within the data.
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