Human cancers display solid phenotypic contrasts that can be visualized non-invasively by clinical imaging. Radiomics alludes to the extensive evaluation of tumour aggregates by applying an enormous number of quantitative image features. A radiomics analysis of features which are extracted from CT data of patients with lung cancer quantifying tumour image intensity, shape, and texture. Many radiomics features have prognostic power. Radiogenomics analysis uncovers that a prognostic radiomics signature, catching intra-tumour heterogeneity. This information recommends that radiomics distinguishes an overall prognostic phenotype existing in lung cancer. This may have a clinical effect as imaging is regularly utilized in clinical work on, giving an exceptional chance to improve in disease treatment easily. In our project we have extracted the radiomics features from the lung cancer dataset of LIDC_IDRI. The images are labelled first based on their malignancy and then over 100 features are extracted from them. Data pre-processing and Exploratory Data Analysis is performed on the data available to get it ready for the further processes.