You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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.
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.
We utilize customer account data to visualize churn rate based on various factors. Additionally, we predict customer churn using a logistic regression model provided by scikit-learn.
In this project, we embark on an exciting journey to explore and analyze customer churn within the Telecom network service using the CRISP-DM (Cross-Industry Standard Process for Data Mining) framework.
This project focuses on a fictitious software company, Churn Buster, that is pitching their tool to Telecom Inc., a fictitious wireless service company. Churn Buster has built a predictive model to reduce Telecom Inc.'s customer churn
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.
Performed predictive analysis of customer churn in the banking industry and identify the factors that led customers to churn. Customer churn or customer attrition is the phenomenon where customers of a business no longer purchase or interact with the business.
This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.