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.

Small projects with Deep Learning magic! - Predicting Customer Churn in Banking, Predict tags on Stack Overflow, Sign Language Recognition

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.

Customer churn prediction using Neural Networks with TensorFlow.js

Data Science Projects done at Data Trained Education during PG Data Science & ML Course

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

The objective is to build a classifier for prediction of customer churn.

Telecom Customer segmentation and Churn Prediction

This is an end-to-end ML project, which aims at developing a classification model for predicting if a customer for an ecommerce business will churn or not in the following month

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

Machine-Learning-1

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

Customer Churn Prediction using PyCaret.

Customer Analytics

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.

The purpose of this assignment is to use SAS Machine Learning Platform - SAS Vyia to explore data, preform customer segmentation and predict customer churn.

In this notebook, We will evaluate the five best known machine learning algorithms to determine which one is the best fit for this given telecom churn binary classification.