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The study involves predicting the attrition rate of ~72000 customers of a Telco company, and use insights from the model to develop an incentive plan for enticing would-be churners to remain with the firm. The data are available in one data file with 71,047 rows that combines the calibration and validation customers. “calibration” database consi…
Bi and Big Data Analytics, sparkR, Supervised and Unsupervised Machine Learning techniques The project's aim is of applying a supervised and an unsupervised machine learning technique on a dataset to test different models/scenario, interpret the results, perform predictions for each model and visualised the results.
Simulate one case of customer churn where we work on a data of postpaid customers with a contract. Predict whether a customer will cancel their service in the future or not.
This repository contains files and information about step 1 of Kaphta Architecture: Text classification of PubMed abstracts on anticancer activity, using the R language.
The client, a financial service institution, want to increase revenue streams and intents to target a segment of their customers who are most likely to default on the loans/Credit taken. We try to solve this problem using Logistic Regression Model.