https://github.com/stefagnone/bank_churn_prediction

Predicting customer churn using a binary classification model based on the Bank Churn dataset. This project involves handling missing data, training a model on key customer metrics, and assessing model accuracy, specificity, and sensitivity.
https://github.com/stefagnone/bank_churn_prediction

binary-classification customer-churn-predictio data-cleaning machine-learning model-evaluation r-programming

Last synced: about 1 month ago
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Predicting customer churn using a binary classification model based on the Bank Churn dataset. This project involves handling missing data, training a model on key customer metrics, and assessing model accuracy, specificity, and sensitivity.

• Host: GitHub
• URL: https://github.com/stefagnone/bank_churn_prediction
• Owner: stefagnone
• Created: 2024-11-14T14:04:53.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2024-11-14T14:21:00.000Z (2 months ago)
• Last Synced: 2024-11-14T15:19:42.743Z (2 months ago)
• Topics: binary-classification, customer-churn-predictio, data-cleaning, machine-learning, model-evaluation, r-programming
• Language: R
• Homepage:
• Size: 7.51 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Bank Churn Prediction Analysis

## Project Overview

In this project, I developed a predictive model to determine customer churn for a bank, utilizing Kaggle’s Bank Churn Dataset. The model identifies key features that contribute to churn and provides insights into customer retention strategies.

The analysis focused on:

- Handling missing values in the dataset.

- Building and evaluating a classification model.

- Predicting churn status for a new customer dataset.

The model helps the bank understand customer behavior and make informed decisions on retaining clients.

## Technologies Used

- **R**: Data processing, analysis, and modeling

- **Machine Learning**: Classification algorithms for predictive modeling

- **Evaluation Metrics**: Model accuracy, specificity, and sensitivity

## Repository Structure

- `Data/`: Contains datasets used in the analysis (`BankChurnDataset.csv`, `NewCustomerDataset.csv`).

- `Code/`: R script with the data preparation, modeling, and evaluation code (`classification.r`).

- `Images/`: Contains any visuals generated from the analysis.

## Key Insights

- The model achieved a high accuracy rate, with notable specificity and sensitivity, indicating effective churn prediction.

- Key predictors of customer churn include account duration, balance, and transaction frequency.

- The model provides actionable insights for improving customer retention strategies.

## Instructions

1. Clone this repository.

2. Run the R script (`classification.r`) in RStudio or another compatible R environment.

3. Ensure all necessary packages are installed.

4. Review the model output and insights from the predictions.

## Contact

Connect with me on [LinkedIn](https://www.linkedin.com/in/stefano-compagnone98/) for more information or to discuss this project further.