Handling Missing Values and Noise Values (Univariate Outliers)
The lecture explains how to deal with missing values and noise values or univariate outliers in a dataset. It is part of the data cleansing or cleaning series that discusses different techniques for detecting and handling various issues in data. These tasks are performed as preprocessing the data so that only good quality data can be provided for training machine learning models. The techniques discussed are deletion strategy, replacement strategy, using statistical measures, clustering and pattern-based approaches. #datacleansing #datacleaning #machinelearning #machinelearningbasics