to download project abstract of supervised and unsupervised machine learning

At DataPro, we provide final year projects with source code in python for computer science students in Hyderabad , Visakhapatnam.


We provide abstract of supervised and unsupervised machine learning.

In supervised learning, missing values usually appear in the training set. The missing
values in a dataset may generate bias, affecting the quality of the supervised learning
process or the performance of classification algorithms. These imply that a reliable
method for dealing with missing values is necessary. In this paper, we analyze the
difference between iterative imputation of missing values and single imputation in real-
world applications. We propose an EM-style iterative imputation method, in which each
missing attribute-value is iteratively filled using a predictor constructed from the known
values and predicted values of the missing attribute-values from the previous iterations.
Meanwhile, we demonstrate that it is reasonable to consider the imputation ordering for
patching up multiple missing attribute values, and therefore introduce a method for
Our experimental results demonstrate a significant performance improvement with our approach in comparison to standard machine learning methods for handling missing values in classification tasks, and highlighting its effectiveness in enhancing model accuracy and reliability.

Missing Data Imputation Using Machine Learning Algorithm - supervised and unsupervised machine learning
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