This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.

This project aims to Predict customer churn in subscription business using ML. Dataset has usage behavior, demographics, churn status. Trained Logistic Regression, Random Forest, Gradient Boosting. Best model deployed for future churn prediction.

This project aims to aims to predict the customer churn (likelihood of a customer leaving the company) for a telecom company using a variety of ML classification algorithms.

The analysis evaluates Campaigns' ROI within Regork's (imaginary company) least profitable departments, offering strategic insights to enhance marketing efforts and drive profitability.

Customer Churn Prediction is a machine learning project aimed at predicting whether a specific user will leave a service or not. The project involves extensive exploratory data analysis (EDA), model training and deployment of a Streamlit web application for user interaction.

Tree methods for customer churn prediction. Creating a model to predict whether or not a customer will Churn .

Unlock actionable insights and boost customer retention with this Power BI project. Analyze and visualize risk factors to proactively prevent churn. ➡️

Predict and prevent customer churn in the telecom industry with this project. Leverage advanced analytics and machine learning on a diverse dataset to build a robust classification model. Clone the repository, explore insights, and enhance customer retention strategies.

The repository presented steps for building a model that predicted whether a customer would switch telecommunication service providers.

Precision-driven customer churn analysis using CatBoost for accurate predictions and insightful model evaluation.

Customer Analytics

End-to-End Machine Learning application to predict the customer churn. machine learning is applied to foresee if customers are likely to leave a service. 🤖💼 This involves analyzing customer data, training a model, and predicting churn probabilities. 🚀📊

Telco Churn Analysis and Modeling is a comprehensive project focused on understanding and predicting customer churn in the telecommunications industry. Utilizing advanced data analysis and machine learning techniques, this project aims to provide insights into customer behavior and help develop effective strategies for customer

Predict customer churn using machine learning. This project employs a RandomForestClassifier to analyze customer data and determine the likelihood of churn. Explore the Jupyter Notebook for insights into the data and model, and contribute to the project's development.

The Customer Churn table contains information on all 7,043 customers from a Telecommunications company in California in Q2 2022. We need to predict whether the customer will churn, stay or join the company based on the parameters of the dataset.

Customer Churn Prediction

Employ Artificial Neural Networks for Customer Churn Prediction, empowering businesses to proactively retain customer loyalty and optimize strategies. Utilize advanced analytics to foresee and address potential attrition seamlessly.

Welcome to the Customer Churn Prediction repository, which is a Customer Churn Prediction Flask app repository! This app is designed to predict customer churn a trained model with 90% accuracy.

Customer-Churn-ANN