In this chapter, we delved into two pivotal processes: data cleaning and EDA using R and Python, with a specific focus on Excel data.

Data cleaning is a fundamental step. We learned how to address missing data, be it through imputation, removal, or interpolation. Dealing with duplicates was another key focus, as Excel data, often sourced from multiple places, can be plagued with redundancies. Ensuring the correct assignment of data types was emphasized to prevent analysis errors stemming from data type issues.

In the realm of EDA, we started with summary statistics. These metrics, such as mean, median, standard deviation, and percentiles for numerical features, grant an initial grasp of data central tendencies and variability. We then explored data distribution, understanding which is critical for subsequent analysis and modeling decisions. Lastly, we delved into the relationships between variables, employing scatter plots and correlation matrices to unearth correlations...