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Understanding the problem statement
Importing the dataset from AWS
Importing important libraries and understanding its use
Understanding Confusion Matrix and Statistics
Understanding relation between variables using Uni-varoate and Bi-variate analysis
Using summary function in R and interpreting the result
Using box-plot for finding outliers and fixing them
Using barplot for visualiztaion
Converting categorical into factor vectors
Defining evaluation matrics and understanding "Kappa"
Splitting the Dataset into Train and test for cross validation
Applying Logistic Regression for training
Using the ensembling method Decision Tree and C5.0 models
Applying boosting model GBM
Selecting the best model for hyperparameter tuning
Use Grid Search and Cross Folds Validation method for optimizing the model and preventing over-fitting
Plotting the results of the model for visualizition
Making final predictions using the model and saving the result
Customer churn refers to a decision made by the customer about ending the business relationship. It is also referred to the loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue. So, it is very important to predict the users likely to churn from the business relationship and the factors affecting the customer decisions. Here we are going to show how logistic regression model using R can be used to identify the customer churn in the telecom dataset.