In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.

Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

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

Bank customers churn dashboard with predictions from several machine learning models.

零售电商客户流失模型，基于tensorflow，xgboost4j-spark，spark-ml实现LR，FM，GBDT，RF，进行模型效果对比，离线/在线部署方式总结

An End to End Customer Churn Prediction solution using AWS services.

WARNING: This repository is no longer maintained :warning: This repository will not be updated. The repository will be kept available in read-only mode.

Variational deep autoencoder to predict churn customer

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This project involves predicting customer churn in a telecommunications company using machine learning techniques, exploring various features' impact, optimizing models, and identifying key factors influencing churn.

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

MLOps End-to-End: Customer Churn Prediction System

End to end projects-- Customer Churning prediction using Gradient Boost Classifier Algorithm perform pre-processing steps then fit data into the Algorithm and Hyper Parameter Tunning to reduce TN & FN value to perform our model to works with a new data. Finally deploying the model using Flask API

Developed a churn prediction model using XGBoost, with comprehensive data preprocessing and hyperparameter tuning. Applied SHAP for feature importance analysis, leading to actionable business insights for targeted customer retention.

This repository explains how to predict customer churn. An Hackathon Organized by Data Science Nigeria(DSN-AI) to help Expresso predict customer Churn. My 2nd place solution, log_loss of 0.246675. I've also added a section in the notebook to get a score of 0.246643, which could be the unofficial 1st place solution.

Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.

This repository will have all the necessary files for machine learning and deep learning based Banking Churn Prediction ANN model which will analyze tha probablity for a customer to leave the bank services in near future. Deployed on Heroku.

The Customer Churn table contains information on all 7,043 customers from a Telecommunications company in California in Q2 2022. We need to predict whether the customer will churn, stay or join the company based on the parameters of the dataset.

Infuse AI into your application. Create and deploy a customer churn prediction model with IBM Cloud Private for Data, Db2 Warehouse, Spark MLlib, and Jupyter notebooks.

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Application of K-means clustering. Prediction of customer churn using Multi-layer Perceptron ANN, Logistic Regression, SVM-RBF and Random Forest Classifier.

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This interactive web application leverages machine learning to predict whether a telecom customer is likely to churn. Users can input customer details for real-time predictions or upload a CSV file for batch analysis.

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

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A classification machine learning problem for predicting customers churn from the company based on customers who left within the last month labeled by 'yes' or 'no'

End-to-end Customer Churn Prediction system showcasing real-world ML skills. Built with XGBoost, SMOTE, and Streamlit, featuring a deployable UI, probability-based risk insights, and production-ready architecture. Demonstrates modeling judgment, deployment, and business impact.