1. A computer-implemented method comprising: receiving, by operation of a computer system, a dataset of a plurality of data records, each of the plurality of data records comprising a plurality of features and a target variable associated with the plurality of features, each feature of the plurality of features representing a reading of a separate sensor coupled to a machine and measuring a condition of the machine, and the target variable representing a status of the machine; identifying a set of normal data records from the dataset based on the target variable; identifying inter-feature correlations by performing correlation analysis on the set of normal data records; and performing predictive maintenance on the machine based on a detection of an anomaly based on the inter-feature correlations for predictive maintenance, wherein the detection of the anomaly comprises: identifying a cluster of correlated features based on the inter-feature correlations, wherein a set of correlated features forms the cluster of correlated features if a correlation between each pair of correlated features of the set of correlated features exceeds a minimum threshold value; and building a model that estimates a first feature in the cluster of correlated features based on one or more other features in the cluster of correlated features.