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. ➡️

A Python-based project for analyzing customer churn using data visualization and machine learning models to predict churn probability. Employs libraries like Pandas, Scikit-learn, and Matplotlib for data preprocessing, model training, and insightful visualizations.

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

Customer Churn Analysis Report using powerbi

Marketing Analytics

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

Build and evaluate logistic regression model using PySpark 3.0.1 library.

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

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.

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

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.

Derive insights of factors contributing to customer churn in the Telecom Industry.

This project leverages Power BI to analyze customer churn patterns across segments, offering strategies to enhance retention.

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

An end-to-end machine learning project predicting bank customer churn with a Gradient Boosting Classifier. It features a complete pipeline for data processing, model training, and real-time predictions via a Flask API. SMOTE is used for handling imbalanced data, and MLflow is integrated for model tracking.