Understanding why customers are leaving an online e-commerce company.
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Updated
Jun 7, 2023
Understanding why customers are leaving an online e-commerce company.
ChurnANNalyzer is a customer churn prediction project that utilizes Artificial Neural Network (ANN) algorithm. The project aims to analyze and predict customer churn in a given dataset.
Explainable customer churn prediction system using XGBoost, SHAP, and FastAPI with calibrated thresholds and ROI-driven retention insights.
This repository contains the implementation of Churn Prediction model on Telco dataset.
In this project , we aim to perform the data analysis on bank customers data to identify the reasons why customers leave the bank. Tools used for the analysis are Power BI and SQL.
Customer Churn project for a telecom firm. The project aims to predict the possibility of a customer to churn by using methods of Data Analysis and Machine Learning with sound accuracy and justifies its result by showing the expected cost-benefit from following their recommendations.
I created a Machine Learning model that can be used to predict customer churn in credit card services.
Customer churn analysis project using Python, SQL, and Power BI to identify churn drivers, revenue impact, and retention opportunities.
Trying to predict which customers are more likely to churn
By undertaking this project, the company aims to gain valuable insights into customer behavior, enhance service quality, and implement targeted strategies for customer retention and satisfaction. The findings will contribute to informed decision-making and the development of customer-centric business strategies.
Machine learning application that predicts customer churn using the Telco Customer Churn dataset with a Random Forest model and interactive Streamlit dashboard.
Machine learning project for customer churn analysis and predictive classification using Python and scikit-learn.
Designed and implemented an end-to-end Customer Churn Prediction solution using Python, Machine Learning, and EDA. Cleaned and analyzed customer data, engineered features, trained classification models, evaluated performance using industry-standard metrics, and delivered business insights to improve customer retention.
End-to-end Customer Churn Prediction & Analytics using SQL, XGBoost, and Streamlit with an interactive dashboard for business insights.
ChurnLens — Explainable AI platform for customer churn prediction, risk scoring, SHAP insights, retention actions & batch intelligence. Built with XGBoost, FastAPI, Gradio, MLflow, Docker & AWS ECS/Fargate. 🚀
Telecom Customer Churn Analysis using Python, Pandas, Matplotlib and Seaborn. This project explores customer churn patterns and visualizes key factors affecting customer retention in telecom companies.
ML project to predict telecom customer churn using Logistic Regression and XGBoost models .
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