Anomaly detection is a process of finding samples behaving abnormally compared to the majority of samples present in the dataset. Anomaly detection algorithms have important use-cases in Data Analytics and Data Science fields. For example, fraud analysts rely on anomaly detection algorithms to detect fraud in transactions.
In this article, we will learn about one of the essential topics used in scaling different attributes for machine learning: Normalization and Standardization. Normalization and Standardization are the techniques used to scale all the features in the same range. It avoids the cases of biases on higher or lower magnitude features.
In this blog, we will do hands-on on several data preprocessing techniques in machine learning like Feature Selection, Feature Quality Assessment, Feature Sampling, and Feature Reduction. We will use different datasets for demonstration and briefly discuss the intuition behind the methods.
Time series data is found everywhere, and to perform the time series analysis, we must preprocess the data first. Time Series preprocessing techniques have a significant influence on data modelling accuracy.
Text data pre-processing ensures optimal results when executed properly. Fortunately, Python has excellent support of NLP libraries such as NLTK, spaCy, and Gensim to ease our text analysis.
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