In this chapter, we explored data preprocessing and feature engineering with Python. This had helped you gain important skills for data analysis. The main focus of this chapter was on cleaning and filtering out dirty data. We started with EDA and discussed data filtering, handling missing values, and outliers. After this, we focused on feature engineering tasks such as transformation, feature encoding, feature scaling, and feature splitting. We then explored various methods and techniques we can use when it comes to feature engineering.
In the next chapter, Chapter 8, Signal Processing and Time Series, we will focus on the importance of signal processing and time series data in Python. We'll start this chapter by analyzing time series data and discussing moving averages, autocorrelations, autoregressive models, and ARMA models. Then, we will look at signal processing and discuss Fourier transform, spectral transform, and filtering on signals.
