• Machine learning project involved drawing inferences from 2 cases namely bank marketing & insurances .The concept of clustering, CART, Random forest, Artificial neural networks are used to draw inferences from these case studies. Various performance metrics have been used to validate the performance of predictions on test and train sets.

Predictive modeling project was to investigate the parameters contributing for customer churn (attrition) in the Telecom Industry. A Logistic Regression Model was developed and validated with test data to predict customer churn

involved drawing inferences from 2 cases namely bank marketing & insurances .The concept of clustering, CART, Random forest, Artificial neural networks are used to draw inferences from these case studies. Various performance metrics have been used to validate the performance of predictions on test and train sets.

was to investigate the parameters contributing for customer churn (attrition) in the Telecom Industry. A Logistic Regression Model was developed and validated with test data to predict customer churn.

is to use the dataset to build regression model to predict satisfaction. Approached with EDA, co linearity check ,Linear regression, factor analysis and labeling of arrived factors and model performance measures

computed the mean cold storage and used hypothesis testing to evaluate if any correction action is required from plants side or procurement side