Upon careful scrutiny of the dataset, it becomes evident that a prevalent issue pertains to missing data, presenting a significant challenge to our analytical endeavors. These gaps manifest diversely, with some cells conspicuously devoid of information while others bear the “not_available” label, complicating our analysis. To confront this issue, we’ve discerned a range of approaches and methodologies for managing these data gaps. These include the deletion of rows or columns when the missing values are minimal and randomly distributed, the practice of imputation, encompassing techniques such as mean, median, or mode imputation, linear regression imputation, interpolation, K-Nearest Neighbors (KNN), and the advanced Multiple Imputation by Chained Equations (MICE). For categorical data, we consider the creation of a distinct category for missing values labeled as “Unknown” or “N/A.” In select cases, the omission of imputation and the treatment of missing data as a unique category within our analysis may prove insightful. Furthermore, for intricate analyses, the employment of advanced statistical techniques like Expectation-Maximization (EM) algorithms or structural equation modeling may become indispensable for effectively handling missing data. To prevent the recurrence of missing or erroneous data in future entries, the establishment of data validation rules in tools such as Excel serves as a proactive measure to maintain data quality and integrity. By integrating these strategies, we can not only address the immediate issues related to missing data but also enhance the overall reliability and robustness of our data analysis efforts.
