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.
- R: Data processing, analysis, and modeling
- Machine Learning: Classification algorithms for predictive modeling
- Evaluation Metrics: Model accuracy, specificity, and sensitivity
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.
- 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.
- Clone this repository.
- Run the R script (
classification.r
) in RStudio or another compatible R environment. - Ensure all necessary packages are installed.
- Review the model output and insights from the predictions.
Connect with me on LinkedIn for more information or to discuss this project further.