Utilizing tools such as Spark, Python (PySpark), SQL, and Databricks, performed logistic regression on customers to predict those at a higher risk of churning, then applied the model to an unseen "new customers" data set.

predict the churning rate of tele-comunication customer using svm model on Python platform

Visualization and Applying linear models on determining the churn, a hackathon winning project.

Telecom Customer segmentation and Churn Prediction

Using a Telecom's dataset, this project develops both an analysis to understand possible correlations and a model for predicting customer churn.

repo to store all assets (such as notebooks, data, etc) for Watson Studio Learning Path tutorials

Prevendo Customer Churn em Operadoras de Telecom

We utilize customer account data to visualize churn rate based on various factors. Additionally, we predict customer churn using a logistic regression model provided by scikit-learn.

Customer Churn Prediction in the Banking sector

Telecom_customer_churn

In this BI consultancy project, I advised the CMO of Maven Communications on how to reduce customer churn, using data.

My solution for DataCamp case study "Analyzing Customer Churn in Power BI".

Analyze your customer database with ease

Build a model using XGBoost algorithm to predict customer churn in banking dataset

PySpark with logistic regression predicting if customers will exit a bank service.

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

Churn prediction has become a very important part of Syriatel's company strategy. This project uses machine learning algorithms to build a model that can accurately predicts customers who are likely to churn.

This Python report is designed for a business which is worried by high customer churn.

Customer churn prediction with gradient boosted trees