DATA PREPROCESSING TECHNIQUES
DOI:
https://doie.org/10.5281/vk9fdh70Keywords:
Data preprocessing, Missing data handling, Outlier detection and treatment, Categorical variable encoding, Numerical feature scaling, Feature selection, Data normalization, Data standardization, Data cleaning, Data transformation, Data quality, Machine learning, Data analysis, Data-driven applications.,,Abstract
Data preprocessing is a critical step in the data analysis and machine learning pipeline. It involves
cleaning, transforming, and organizing raw data into a format suitable for analysis and modeling.
This paper explores various data preprocessing techniques that are essential for enhancing the
quality and usability of data. We discuss methods for handling missing values, outliers, encoding
categorical variables, scaling numerical features, and feature selection. Additionally, we delve into
the importance of data normalization and standardization. Through a comprehensive review of
these techniques, this paper aims to provide a clear understanding of data preprocessing's
significance in improving the performance and reliability of data-driven applications.
References
Acharjee, D., Mukherjee, A., Mandal, J., & Mukherjee, N. (2015). Activity recognition
system using inbuilt sensors of a smart mobile phone and minimizing feature vectors. Microsystem
Technologies.
Bayat, A., Pomplun, M., & Tran, D. A. (2014). A study on human activity recognition using
accelerometer data from smartphones. Procedia Computer Science, 34, 450–457.
Choi, S., & Yi, G. (2016). Energy consumption and efficiency issues in human activity
monitoring system. Wireless Personal Communications, 91, 1799–1815.
Dinakaran, S., & Thangaiah, P. R. J. (2013). Role of attribute selection in classification
algorithm. International Journal of Scientific and Engineering Research, 4, 67–71.
Foerster, F., Smeja, M., & Fahrenberg, J. (2019). Detection of posture and motion by
accelerometry: A validation study in ambulatory monitoring. Computers in Human Behavior, 15,
–583.
Gondalia, A., Dixit, D., Parashar, S., Raghava, V., Sengupta, A., & Sarobin, V. (2018).
IoT-based healthcare monitoring system for war soldiers using machine learning. Procedia
Computer Science, 133, 1005–1013.
Koichiro, A. (2014). Image sequence analysis of real-world human motion. Pattern
Recognition, 17(1), 73–83.SS
