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

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

Analyze your customer database with ease

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.

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

We going to build a basic model for predicting customer churn using Telco Customer Churn dataset. We're using some classification algorithm to model customers who have left, using Python tools such as pandas for data manipulation and matplotlib for visualizations.

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".

Customer churn prediction with gradient boosted trees

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. 🚀📊

The core purpose of this study is to find the impact of Sentiment Analysis in predicting customer churn for the e-commerce industry by employing different predictive models. Furthermore, the study is also focused on observing which model is best in a more accurate prediction for determining the churn rate of customers.

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.

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

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