Bodo is a platform that enables machine learning practitioners to rapidly explore data and build complex pipelines. It allows developers to scale their codes from laptops to the Bodo platform, making it easy to distribute data across processes for parallel execution. The platform is used in this example to detect potential cases of credit card fraud using classification techniques from machine learning. The dataset consists of transactions made by European cardholders in September 2013 and has a strong imbalance between fraudulent and non-fraudulent records. To mitigate this, the dataset is sub-sampled to achieve a balanced distribution. Features are analyzed to identify those that correlate meaningfully with the output, and weakly-correlated columns are dropped. A Random Forest Classifier is trained on the rebalanced data, and its performance is evaluated using metrics such as the Receiver Operating Characteristic (ROC) curve and confusion matrices. The model's performance appears to be good, but oversampling the dataset can further improve it. Bodo provides a platform for building and deploying machine learning models, making it easier to scale and distribute code across processes.