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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.
This is a sample code repository of the telco customer churn analysis or prediction by the classification/regression model for experiment and learning purposes.
Customer Churn Prediction in Telecom Industry where customers are choose from a variety of service provider and actively switch from one to next. with the help of Machine Learning Classification Algorithm we are going to predict the churn.
The analysis evaluates Campaigns' ROI within Regork's (imaginary company) least profitable departments, offering strategic insights to enhance marketing efforts and drive profitability.
In this project, we have worked with hundreds of anonymized features to predict if a customer is satisfied or dissatisfied with their banking experience.
Developed an end-to-end machine learning model to predict credit card customer churn. (All stages including ingestion, EDA, feature engineering, normalization, and scaling, train-validation-split & deployment)
This project aims to aims to predict the customer churn (likelihood of a customer leaving the company) for a telecom company using a variety of ML classification algorithms.
Customer churn is a significant issue for big business companies. Companies are attempting to create methods for predicting customer churn to get a direct impact on getting more revenues, particularly in telecom companies.
This project provide a template of the traditional binary classification model. Feel free to check the detailed steps of the whole process machine learning modelling.
Customer Churn Prediction is a machine learning project aimed at predicting whether a specific user will leave a service or not. The project involves extensive exploratory data analysis (EDA), model training and deployment of a Streamlit web application for user interaction.