click here 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.
– ABSTRACT
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
imputation
ordering.
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.
