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

Customer Churn Prediction in the Banking sector

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

This project analyzes and predicts customer churn of a music streaming service using Spark on a large dataset.

Analyzing customer data and building machine learning model for predicting customer churn (Logistic Regression, Random Forest and XGBoost). This project is presented as final project for dibimbing's data science bootcamp batch 22 and getting 2nd best final project award in the graduation.

Performs customer churn prediction, built using logistic regression

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

This project conducts an exploratory data analysis (EDA) on a Telco customer churn dataset. It visualizes key factors influencing customer churn, including payment methods, contract types, and service usage. The insights gained aim to help businesses understand customer retention and develop strategies to reduce churn rates.

Using a Telecom's dataset, this project develops both an analysis to understand possible correlations and a model for predicting customer churn.

Creation of a dashboard in Power BI reflecting all relevant Customer Churn's Key Performance Indicators (KPIs) for Phone Now Call Centre.

Predicting Customer Retention with Machine Learning | Forked my teams original project for further customization

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.

Build a model using XGBoost algorithm to predict customer churn in banking dataset

This project predicts customer churn using machine learning. It involves data cleaning, EDA, feature engineering, and model evaluation. AdaBoostClassifier with SMOTE was optimized using GridSearchCV and validated with ROC analysis.

Customer Churn

This Excel workbook analyses the customer churn in a telecommunications company focusing on demographic factors, data consumption patterns, and contract types. The goal is to identify key drivers of churn and provide actionable insights to improve customer retention.

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

Automated Churn Prediction using Classifier Model and deploy as a Streamlit Web Application

AI-Enhanced Customer Retention System (AIECRS) is an AI-based system designed to predict customer churn and suggest retention strategies.