Understanding why customers are leaving an online e-commerce company.

ChurnANNalyzer is a customer churn prediction project that utilizes Artificial Neural Network (ANN) algorithm. The project aims to analyze and predict customer churn in a given dataset.

This repository contains the implementation of Churn Prediction model on Telco dataset.

In this project , we aim to perform the data analysis on bank customers data to identify the reasons why customers leave the bank. Tools used for the analysis are Power BI and SQL.

Customer Churn project for a telecom firm. The project aims to predict the possibility of a customer to churn by using methods of Data Analysis and Machine Learning with sound accuracy and justifies its result by showing the expected cost-benefit from following their recommendations.

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Trying to predict which customers are more likely to churn

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By undertaking this project, the company aims to gain valuable insights into customer behavior, enhance service quality, and implement targeted strategies for customer retention and satisfaction. The findings will contribute to informed decision-making and the development of customer-centric business strategies.

Customer Churn Analysis

TD Bank-Real Time Churn Insights with Robust Machine Learning Models and Interactive Web Deployment

I created a Machine Learning model that can be used to predict customer churn in credit card services.

Customer Churn Analysis

analyzing customer churn in a telecom company using excel and tableau public

An Excel-based customer churn analysis using raw data, cleaned tables, pivot tables, and a visual dashboard.

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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

Customer Churn Analysis using Python and Data Visualization to identify key churn drivers, high-risk customer segments, and actionable business strategies to improve retention.

This project predicts customer churn for a telecom company using machine learning techniques such as Logistic Regression and Random Forest.

A machine learning project for predicting customer churn using Deep Learning (ANN) with TensorFlow. It integrates DVC for data versioning, MLflow for experiment tracking, and FastAPI for model deployment.

End-to-end customer churn analysis using Python and logistic regression to identify key drivers and retention stratrgies

Power BI customer churn analysis dashboard developed for Future Interns – Data Science & Analytics Internship (Task 2).

Predicting customer churn based on customer credit card data - Mini Project for Data Mining and Analytics (18CSE355T)

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E-Commerce coursework to analyze real-time customer churn and retention for a home decor retailer (Petite Escape), establishing an integrated shopping experience using an omni-channel approach and CART to improve customer conversion.

This project analyzes customer churn patterns using SQL (data preparation + feature engineering) and Power BI (visualization + insights). The goal is to identify the key factors driving churn and provide actionable recommendations for retention.

Customer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behaviour, contract details and the payment details. The data also indicates which were the customers who cancelled their service. Based on this past data, we need to build a model which can predict whether a customer will cancel their service in the future or not

Explore diverse 📊 Data Science Projects for insightful analysis and exploration! 🚀✨

Power BI dashboard analyzing customer churn drivers using interactive KPIs, segmentation, and retention insights

Machine Learning model to predict Customer Churn