Lift Charts & Gain Charts: These are widely used in campaign targeting problems, to determine which decile can we target customers for a specific campaign. Also, it tells you how much response you can expect from the new target base. ROC Curve: The ROC curve is the plot between false positive rate and True Positive rate. Gini coefficient: This is the ratio of area between the ROC curve and the diagonal line & the area of the above triangle Cross Validation: splitting the data into two parts, where one part is used for "training" your model, and the second part is used to make predictions. By this you can test the model on the data that was "not seen" by it previously, and check how it could possibly behave with external data.

In today's Digital age, insights received from data science are extremely important to deliver the best customer experience. Data Scientists use various techniques such as Regression, SVM, Neural network, Nearest neighbor, Naive Bayes, Decision Tree and Ensemble models. These algorithms help to identify previously unrecognized patterns and trends hidden within vast amounts of structured and unstructured information. These patterns are used to create predictive models that try to forecast future behavior. These models have many practical business applications: predicting patients at risk, they help banks decide which customers to approve for loans, and marketers use them to determine which leads to target with campaigns. But how to determine if the predictive models you create are accurate, meaningful representations that will prove valuable to your organization?