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.

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

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

Marketing Analytics

Customer Churn Analysis in R: Logistic, Classification Tree, XGBoost, Random Forest.

Customer Churn Analysis Report using powerbi

Build and evaluate logistic regression model using PySpark 3.0.1 library.

Hello, this is my final project with my friend when I joined Fresh Graduate Academy Program at Binar Academy in 2023.

Customer Churn Analysis with Neural Network

Telecom Customer segmentation and Churn Prediction

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

The core purpose of this study is to find the impact of Sentiment Analysis in predicting customer churn for the e-commerce industry by employing different predictive models. Furthermore, the study is also focused on observing which model is best in a more accurate prediction for determining the churn rate of customers.

Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will 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.

Prediction of whether or not a customer leaves in an specific period of time, deployed to GCP

Customer Churn Analysis

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.

Precision-driven customer churn analysis using CatBoost for accurate predictions and insightful model evaluation.