Data sanity refers to the correctness of the data showing absence of discrepancies such as duplicates, single value columns, drifts, etc and its conformance to Machine learning requirements. This solution checks the input dataset for discrepancies like Data Duplicates, Single value column, Drifts, Variable mix, and gives an early warning to the user to perform data preparation before model training. The early identification of the data discrepancies avoids percolation of error to classification models and prediction

The solution accepts tabular dataset containing at least one valid Label variable to be used in Classification modeling. The acceptable data types for variables include Numerical, Categorical and Strings. The solution gives the proportion of integer and strings mix within train test splits of the given dataset. The correctness of the dataset can be reported for various Label assignments (if more than one Label exist). The solution expects the user to define explicitly for Label, Index and Categorical variables in a dataset for every run.

PACE - ML is Mphasis Framework and Methodology for end-to-end machine learning development and deployment. PACE-ML enables organizations to improve the quality & reliability of the machine learning solutions in production and helps automate, scale, and monitor them. Need customized Machine Learning and Deep Learning solutions? Get in touch!