Detecting bugs in source code is a critical task to ensure the reliability and functionality of software systems. In this research, we delve into the synergy between data mining, data analysis and bug detection while aiming to uncover insights and methodologies that can enhance the identification process. First, we explore how data mining and data analysis techniques can guide us towards effective bug detection in source code. By leveraging publicly available data sets from the NIST SAMATE source, we conduct thorough data pre-processing for analysis. We extract meaningful features utilizing the term frequency-inverse document frequency (TFIDF) approach and investigate the impact of data analysis and data mining on the performance of bug identification techniques. We evaluate, through experimentation, the efficacy of various data analysis methodologies in improving bug detection accuracy. In order to identify the most informative variables we use feature selection techniques such as recursive feature elimination (RFE) and principal component analysis (PCA). Furthermore, we examine the impact of different hyper-parameters at various stages of data mining on the performance of classification algorithms. We implement six variants of the K-Nearest Neighbors (KNN) classifier with different settings to study and analyze the experimentation for different settings and parameter values. Empirical results show that the weighted-KNN (WKNN) variant of nearest neighbor, without cross-validation and dimensionality reduction achieves the relative optimal performance with minimum computational complexity.