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

Comprehensive Power BI dashboards showcasing insights on Call Centre Trends, Customer Retention, and Diversity & Inclusion to drive business impact.

Customer churn prediction with Python using synthetic datasets. Includes data generation, feature engineering, and training with Logistic Regression, Random Forest, and Gradient Boosting. Improved pipeline applies hyperparameter tuning and threshold optimization to boost recall. Outputs metrics, reports, and charts.

Analysis and Prediction of the Customer Churn Using Machine Learning Models (Highest Accuracy) and Plotly Library

Machine Learning, EDA, Classification tasks, Regression tasks for customer churn

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

Analyze your customer database with ease

In this BI consultancy project, I advised the CMO of Maven Communications on how to reduce customer churn, using data.

Utilizing tools such as Spark, Python (PySpark), SQL, and Databricks, performed logistic regression on customers to predict those at a higher risk of churning, then applied the model to an unseen "new customers" data set.

An end-to-end machine learning project predicting bank customer churn with a Gradient Boosting Classifier. It features a complete pipeline for data processing, model training, and real-time predictions via a Flask API. SMOTE is used for handling imbalanced data, and MLflow is integrated for model tracking.

🎯 Production-ready ML system for customer churn prediction with SHAP explainability, intelligent retention strategies, and beautiful Flask dashboard

Proyecto de Ciencia de Datos para predecir la fuga de clientes. Implementa un enfoque avanzado con Variables Fantasma (Ghost Variables) para la selección de características y un modelo Random Forest para la clasificación.

Telecom Customer segmentation and Churn Prediction

We going to build a basic model for predicting customer churn using Telco Customer Churn dataset. We're using some classification algorithm to model customers who have left, using Python tools such as pandas for data manipulation and matplotlib for visualizations.

Churn prediction has become a very important part of Syriatel's company strategy. This project uses machine learning algorithms to build a model that can accurately predicts customers who are likely to churn.

A step-by-step customer churn prediction project using Python, including data cleaning, visualization, and logistic regression.

Predict customer churn using machine learning models with the Telco Customer Churn dataset. Includes EDA, feature engineering, and Random Forest classification.

A machine learning project for predicting customer activity decline in the "One Click" online store

📊 A machine learning project to predict customer churn using classification models like Random Forest, Decision Tree, and XGBoost. Includes data preprocessing, SMOTE for class balancing, hyperparameter tuning, and model deployment using pickle.

Moringa School: DSF-FT4 Phase 3(Project)