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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.
A comprehensive project predicting customer churn for a telecommunications company using Logistic Regression, Decision Trees, and Random Forest models. Includes data preprocessing, feature engineering, model evaluation, and result visualization to provide actionable insights for customer retention.
Implement a simple, pre-cancellation survery for Chargeebee merchants to collect feedback and send collected information further to destinations e.g., database, analytics or as simple as Slack.
📊 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.
"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.
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.
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