Predictive Analytics models rely heavily on Regression, Classification and Clustering methods. When analysing the effectiveness of a predictive model, the closer the predictions are to the actual data, the better it is. This article hopes to be a one-stop reference to the major problems and their most popular/effective solutions, without diving into details for execution.A Linear Regression PlotA clustering algorithm plotPrimarily, data selection and pruning happens during the Data Preparation phase, where you take care to get rid of bad data in the first place. Then again, there are issues with the data, and their relevance to the ML model's objectives during training, troubles with usage of algorithms, and errors in the data that occur throughout. Effectively, the model is tested for bias, variance, autocorrelations, and many such errors that can occur when finalizing the model. Before finalizing the model, some defined tests are performed on the data- these are test algorithms that detect such errors.


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bias,algorithms,machine-learning,machine-learning-models,predictive-analytics