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; 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; receiving a data record that includes an observed value of the first feature and observed values of the one or more other features in the cluster of correlated features; determining an estimated value of the first feature according to the model based on the values of the one or more other features in the cluster of correlated features; comparing the estimated value of the first feature with the observed value of the first feature; and determining whether the data record is a normal data record or an abnormal data record based on the comparison.