Build a classification model for reducing the churn rate for a telecom company

Two differrent approach to predict Churn customers and finding out important variables that drives churn

Telecom churn prediction based webapp

Supervised learning algorithm was used to build churn prediction model to help solve a telecoms company's customer churn problem.

Analysing customer-level data of a leading telecom firm, building predictive models to identify customers at high risk of churn and identifying the main indicators of churn.

In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

This is a Streamlit web application for predicting Telecom Churn. The app uses a trained machine learning model to predict whether a customer is likely to churn or not based on certain input features.

Analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn (usage-based churn) and identify the main indicators of churn.

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

This project involved analyzing 10,000 customer records, applying data preparation techniques, and training supervised machine learning models, achieving 94% accuracy. Model efficiency was further refined using cross-validation and hyperparameter tuning, ensuring reliability and performance

Designing strategies to pull back potential churn customers of a telecom operator by building a model which can generalize well and can explain the variance in the behavior of different churn customer. Analysis being done on large dataset which has lot of scope for cleaning and choosing the right model for prediction.

A delinquency model which can predict in terms of a probability for each loan transaction, whether the customer will be paying back the loaned amount within 5 days of insurance of loan.

Analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

Telecom Churn analysis using various tree based classification models

Telecom-Churn-Case-Study

upGrad's Telecom Churn Case Study hosted on Kaggle platform

This repository hosts a logistic regression model for telecom customer churn prediction. Trained on historical data, it analyzes customer attributes like account weeks, contract renewal status, and data plan usage to forecast churn likelihood. Its insights aid telecom companies in proactively retaining customers and mitigating churn rates.

To explore and analyze the Telecom Churn dataset to understand factors contributing to customer churn and to develop a predictive model that can forecast customer churn with high accuracy

This repository showcases machine learning projects covering diverse topics such as book recommendations, New York Airbnb analysis, and telecom churn prediction. Each project utilizes various techniques and algorithms to tackle specific challenges and extract meaningful insights from the data.

Build a classification model for reducing the churn rate for a telecom company