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

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.

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

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

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.

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

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 shows my complete Power BI dashboards with real world data provided by PWC Switzerland. This is a Forage virtual internship where I got to use, analyze and gain valuable insights using real world data

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.