In this paper, a comprehensive approach to network intrusion detection data using Python is presented. Various techniques including XGB and DQN are discussed, necessitating a preprocessing phase at the beginning. This phase involves fixing data types, handling null and infinite values, dropping unnecessary columns, creating binary labels for threat detection, reducing memory usage, and balancing the dataset. Additionally, feature importance analysis and correlation visualization are explored to identify and drop highly correlated columns. Furthermore, machine learning processes, including data splitting, scaling, and computing class weights, are discussed, followed by the implementation of two machine learning methods: XGBoost and Deep Q-Network (DQN). For XGBoost, performance metrics such as F1-score, recall, precision and support are evaluated to assess model performance. For DQN, insights into its architecture, training objectives, and integration with Gym environment for reinforcement learning tasks are provided. The paper offers a comprehensive guide to preprocessing and machine learning approaches for network intrusion detection datasets, contributing to the advancement of cybersecurity research and applications.