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

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

Telecom Customer segmentation and Churn Prediction

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.

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. 🚀📊

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.

This project leverages ML algorithms to predict and tackle customer churn effectively.

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

The project predicts bank customer churn using an Artificial Neural Network (ANN). It includes data preprocessing, model training with TensorFlow and Keras, and deployment via a Streamlit app. The model's performance is visualized using TensorBoard, showcasing effective machine learning techniques for customer retention.

📱 Customers are likely to leave a telecom service, enabling companies to take measures for retention and create accurate churn prediction models.

Predict customer churn using machine learning models with the Telco Customer Churn dataset. Includes EDA, feature engineering, and Random Forest classification.

Customer churn prediction with gradient boosted trees

The repository presented steps for building a model that predicted whether a customer would switch telecommunication service providers.

An end-to-end ML application that predicts bank customer churn using 9 different models and provides AI-generated retention strategies with Groq LLM. Built with Streamlit for interactive predictions and visualizations.