Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

Machine Learning, EDA, Classification tasks, Regression tasks for customer churn

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

Analyze your customer database with ease

Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer

Telecom Customer segmentation and Churn Prediction

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.

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.

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

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

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

Customer churn prediction with gradient boosted trees

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.

Derive insights of factors contributing to customer churn in the Telecom Industry.

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

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

End-to-End Machine Learning application to predict the customer churn. machine learning is applied to foresee if customers are likely to leave a service. 🤖💼 This involves analyzing customer data, training a model, and predicting churn probabilities. 🚀📊

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

Customer Churn Prediction in the Banking sector