Telco Customer Churn Analysis using Python
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Updated
Jun 7, 2024 - Jupyter Notebook
Telco Customer Churn Analysis using Python
Typescript library to access Faraday's API infrastructure for B2C predictions
📊 This project focuses on customer churn analysis and prediction in the telecommunications sector. Using data analysis, modeling, and predictive techniques, it aims to understand and mitigate customer loss by developing strategies.
predictive models that can forecast the likelihood of churn for individual customers
"ChurnMaster is an advanced machine learning tool designed to predict customer churn by analyzing behavioral patterns and usage data to help businesses enhance customer retention strategies.
Focused customer retention programs
A lab about classification using the K-Nearest Neighbors approach using a customer churn dataset from the telecom industry, which includes customer data such as long-distance usage, data usage, monthly revenue, types of offerings, and other services purchased by customers.
Measure the churn/complexity ratio. Higher values mean hotspots where refactorings should happen.
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The Loblaw Data Analysis project is an initiative aimed at extracting valuable insights from large datasets collected by Loblaw Companies Limited, one of Canada's largest food and pharmacy retailers. Leveraging advanced data analysis techniques and machine learning algorithms, this project seeks to uncover trends, patterns, and correlations within
Survival Analysis with the Shifted Beta Geometric model
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The credit churn data analysis aims to investigate the factors that contribute to customer attrition in a credit card company. The dataset used in this analysis contains information on customer demographics, credit card usage, and other relevant variables.
Análise da receita dos planos pré-pagos Surf e Ultimate da Megaline. Com dados de 500 clientes, buscamos determinar qual plano contribui mais para a receita da empresa em 2018. 📊💰
Predict customer churn through supervised and unsupervised techniques, perform feature engineering, incorporate network science. The project also covers extensive data preprocessing, making informed churn assumptions and exploratory data analysis.
Machine learning model that predicts and identifies customers at risk of leaving a telecom provider, providing factors to improve model interpretability
Graph analytics for telecom customer churn prediction
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