Repositorio del proyecto de predicción del problema Telco Customer Churn (kaggle)

Streamlit web application for customer churn predictions.

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.

This project leverages Power BI to analyze customer churn patterns across segments, offering strategies to enhance retention.

Led exploratory data analysis for a wireless mobile network company using Python, Pandas, Numpy and data visualization - Matplotlib, Seaborn, uncovering key drivers of customer churn and implementing strategies that improved retention and boosted customer lifetime value.

This shows my complete Power BI dashboards with real world data provided by PWC Switzerland. This is a Forage virtual internship where I got to use, analyze and gain valuable insights using real world data

Marketing Analytics

Customer Churn

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 repository contains the source code and resources for a machine learning web application that predicts customer churn for a telecommunications company. The project leverages historical customer data to build predictive models, enabling the identification of customers who are likely to discontinue their service.

The core purpose of this study is to find the impact of Sentiment Analysis in predicting customer churn for the e-commerce industry by employing different predictive models. Furthermore, the study is also focused on observing which model is best in a more accurate prediction for determining the churn rate of customers.

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.

Graduation Project Repository - Bogazici University IE 492 - Spring 2024

This contains some of the data analysis projects I have worked on in excel.

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.

Perform exploratory data analysis and develop machine learning models to a telecom customer churning dataset

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. ➡️