About this course
Data cleaning in python is really crucial from the point of view of building intelligent systems. Data cleaning is a preprocessing action that enhances the data credibility, precision, efficiency, consistency, and uniformity. It is important for developing reliable machine learning models that can produce excellent outcomes. Otherwise, no matter how excellent the model is, its outcomes cannot be relied on. Beginners with machine learning start dealing with the openly available datasets that are completely examined with such problems and are for that reason prepared to be utilized for training models and getting excellent outcomes.
But it is far from how the data is, in real life. Common issues with the data might consist of missing values, sound values or univariate outliers, multivariate outliers, data duplication, enhancing the quality of data through standardizing and stabilizing it, handling categorical functions.
The datasets that are in raw form and have all such problems cannot be benefited from, without understanding the data cleaning and preprocessing. The data retrieved from several online sources, for developing a beneficial application, is a lot more exposed to such issues. Therefore, finding out the data cleaning abilities assist users to make beneficial analyses with their organization data. Otherwise, the term ‘trash in trash out’ describes the truth that without figuring out the problems in the data, no matter how effective the design is, the outcomes would be undependable.
- Introduction to Data cleaning in python
- Detecting Missing target and Noise Values (Univariate Outliers)
- Handling Missing Noise Values (Univariate Outliers)
- Multivariate Outliers
- Anomalies in Textual data
- Structuring Textual Documents
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