The project involves analyzing certain issues of customer churn faced by telecom companies. It requires simulating a case of customer churn using techniques such as Logistic Regression, KNN, Naive Bayes. Models are required to be build so as to predict whether a customer will cancel their service in the future or not and then model comparison measures are made for taking interpretation and recommendations from the best model. *Skills and Tools* KNN, Naive Bayes, Logistic Regression, Model Comparison Measures

Heavy plotting with some good insights . Practicing meaningful EDAs

The project computed the mean cold storage temperature in different seasons and used hypothesis testing to evaluate if any corrective action is required from the plant's side or the procurement's side.

The objective of the project is to use the dataset Factor-Hair-Revised.csv to build a regression model to predict satisfaction. Project Approach:  Data Exploration  Collinearity of the variables  Initial Regression analysis  Factor Analysis  Labelling and interpreting of the factors  Regression analysis using the factors as independent variable  Model performance measures

Exercise was done for a commercial bank whose target was to increase its income by disbursing loans through Net interest margin. It wanted to segregate (classify ) target customers based on past experience. Decision trees and clustering was used extensively to come to conclusion keeping the threshold probability very high as defaulting customers would mean a huge loss