Cancer is a highly fatal disease characterized by malignant growth resulting from erratic cell division and DNA alterations, stands as the leading cause of fatality among all diseases. Lung cancer, in particular, emerges as the most prevalent form, attributed mainly to factors such as smoking, passive smoking, and exposure to toxins. This malignancy poses a significant threat to both genders, with a higher mortality rate among females compared to breast, ovarian, and uterine cancers combined. Early detection of lung cancer is crucial for reducing mortality rates, with imaging techniques such as CT scans playing a pivotal role. These scans offer enhanced visibility of delicate lung tissue, facilitating the identification of infections, restricted blood flow, or tumors. Python, a versatile computational tool, is utilized in analyzing CT scan data (LIDC-IDRI), enabling swift and precise mathematical calculations, analyses, and optimizations. Its applications span various domains, including deep learning, signal processing, control systems, and computational biology, showcasing its utility in advancing research and diagnostics in lung cancer detection and management. In python, features are extracted, grayscale conversions are performed, and classification algorithms (k-mean 97.75% and KNN 98.89%) are easily customizable. For feature extraction, it extracts pixel values from the dataset. Then, it categorizes the image based on the extracted features.