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I analyzed a dataset of telco customers' activity and how it relates to churn. This repository is designed for data cleaning and exploratory data analysis (EDA), which will be expanded into feature engineering and modelling.
The analysis delves into the factors that have the biggest impact on employee churn and need to be addressed immediately. It then suggests several changes the XYZ company should make in the workplace to retain more employees.
Neste projeto será realizado o processo de EDA (Exploratory Data Analysis) com foco na análise de Churn a partir do datas ser Bank Customer Churn Dataset, que pode ser encontrado no Kaggle e disponibilizado por Gaurav Topre.
Developed a desktop application, that uses ML algorithms to accurately predict customer churn based on customer details. The application helps to identify customers at risk of churning using regression models.
This project develops a machine learning model to predict customer churn for a California-based telecom company using data from 7043 customers. Our goal is to enhance customer retention strategies through detailed data analysis and feature engineering.
This project aims to conduct an analysis of costumers behavior and perception of the brand, by implementing different marketing analytics techniques and methods: RFM (recency, frequency, monetary) model, churn classification, MBA (market basket analysis) and sentiment analysis.
Utilizing machine learning tools to forecast customer churn. Explore visualizations highlighting unique profiles of churning and non churn customers, providing valuable insights for retention strategies.
✨ The current project is a basic approach of data analysis and machine learning modeling to get business insights and propose strategies of customer segmentation and churn prediction to mitigate the high churn rate.
Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️