In this section, we will predict Telco customer churn using machine learning algorithms.

Materi praktikum Talent Scouting Academy (TSA) Kominfo 2023

Machine learning project to predict customer churn (Telco Customer Churn) using PostgreSQL/MongoDB as data sources, with exploratory analysis, preprocessing, model training, and dashboard.

📱 Customers are likely to leave a telecom service, enabling companies to take measures for retention and create accurate churn prediction models.

This project is my personal project about Customer Churn using dataset from Telecommunication Company provided by Kaggle. I created Data Visualisation with Python to get some insight from the dataset that can used for arrange their next strategic planning.

Telco Customer Churn Analysis and Prediction using SQL, Python, and Power BI . Data Cleaning, EDA, Machine Learning, and Interactive Dashboard.

Full-stack Machine Learning application to predict telecom customer churn using FastAPI and Scikit-Learn. Features a modern Glassmorphism UI with real-time risk analysis and downloadable reports.

Telco Customer Churn Feature Engineering

Built a churn prediction model using tree-based methods (Decision Tree, Random Forest). Conducted EDA, encoded categorical variables, and visualized feature relationships. Tuned models with GridSearchCV and evaluated them using classification metrics and feature importance.

A machine learning project predicting customer churn using Logistic Regression with Class Weighting.

This repository consists of different ML and DL projects.

This is a sample code repository of the telco customer churn analysis or prediction by the classification/regression model for experiment and learning purposes.

Telco customer churn prediction using SQL analysis and machine learning (LR + SMOTE) — outputs top 100 high-risk customers for retention campaigns

Machine learning project predicting telecom customer churn using EDA, feature engineering, and classification models.

Telcom Churn Prediction - Predicting If a Customer will Churn or Not based on Telcom Dataset

How to do a simple end-to-end machine learning classification project using the telco churn dataset

This project demonstrates how to build an MCP server that bridges AI assistants with data analysis and machine learning capabilities.

Megaline Revenue Analysis: Optimización de rentabilidad e inferencia estadística para identificar ineficiencias en la segmentación de planes y mitigar riesgos de churn.

Built a machine learning model in Python to predict customer churn using the Telco dataset. Applied data cleaning, feature engineering, EDA, and trained a Random Forest classifier. Visualized insights and key churn predictors using Seaborn and Matplotlib.