Реализация алгоритмов кластеризации и прогнозирования с R

3 000 руб. за проект
07 июня 2020, 23:10 • 1 отклик • 26 просмотров
1st Objective (partitioning clustering)

  • Find the ideal number of clusters – justify it by showing all necessary steps/methods,
  • K-means with the best two clusters,
  • Find the mean of each attribute for the winner cluster,
  • Check consistency of your results against 19th column,
  • Check for any pre-processing tasks (scaling, outliers)
2nd Objective (MLP)

  • Discuss the input selection problem for time series prediction and propose various input

    configurations
  • Perform any pre-processing steps (such as normalisation) before training
  • Implement a number of MLPs, using various structures (layers/nodes) / input parameters / network

    parameters and show in a table their performances comparison (based on testing data) through the provided stat. indices.
  • Provide your best results both graphically (prediction output vs desired output) and via performance indices

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Avatar r50 a6ce93fe35b158fd29ba0e8681c918c22117160e9586a56eee4ffbc20df9bda1
Заказчик
Сделал работу качественно и что не мало важно очень быстро! Всем советую этого автора!)
7 месяцев назад
R50 1da39d6c4acbd6966d1fbccb4801cadd
Фрилансер
 
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