Welcome to the captivating "Banking Churn Analysis and Modeling" repository! Here, we dive deep into the intriguing world of customer churn within the banking sector. This project is dedicated to analyzing churn patterns, constructing predictive models, and creating dynamic Power BI reports. Within this repository, you'll find an array of valuable resources, including code snippets, datasets, comprehensive reports, interactive dashboards, and documentation. The aim is to equip you with the knowledge and tools to effectively implement churn analysis in the banking industry. Join me on this transformative journey as we unravel the secrets of customer retention and empower banks to thrive.
-
Bank churn.csv - Dataset: This carefully curated dataset provides the foundation for analyzing customer churn in the banking sector. Dive into the data to uncover patterns, trends, and key indicators of churn.
-
Banking Churn Dashboard.pbix: Immerse yourself in the power of data visualization with our interactive Power BI dashboard. Explore insightful visualizations and metrics that shed light on customer churn behavior, helping you make informed decisions.
-
Banking Churn Dashboard.pdf: For a snapshot of the Power BI dashboard's insights, access our visually engaging PDF version. This document provides a convenient way to share and review key churn analysis findings.
-
Banking Churn Modeling.ipynb: Delve into our modeling notebook to explore the underlying techniques and algorithms used for churn prediction in the banking industry. Gain a deeper understanding of the modeling process and experiment with different approaches.
-
Banking Churn Modeling.pdf: Complementing the modeling notebook, this comprehensive PDF documentation offers detailed explanations and step-by-step instructions. Follow along to replicate the churn modeling process or expand upon it with your own enhancements.
-
Data Wrangling.
- Data Cleaning.
- Handling Missing Values.
- Handling Inconsistances.
-
Exploratory Data Analysis (EDA)
- Visualizing Dependent Variable.
- Visualizing Independent Variables.
- Generating Insights.
-
Data Preprocessing.
- Variable Selection and Importance.
- Feature Transformation and Scaling.
- Splitting Data for Model Training.
-
Model Training and Evaluation.
- Selection of Classification Algorithms.
- Model Training and Tuning.
- Model Evaluation and Performance.
- Confusion Matrix Analysis.
- Accuracy, Precision, Recall, and F1 Score.
- Receiver Operating Characteristic (ROC) Curve and AUC.
- Interpretation of Evaluation Results.
- Interpretation and Insights.
- Key Findings and Patterns.
- Feature Importance and Contribution.
- 💻 Python
- 💻 HTML
- 🐼 Pandas
- 📊 Matplotlib
- 📈 Seaborn
- 📓 Jupyter Notebook
- 🔗 Git
- 🤖 Scikit-learn
- 🧠 Machine Learning Techniques
- 📊 Power BI
- Explore the data and gain insights using the Jupyter notebooks.
- Use the code and models provided to predict customer churn in your own banking datasets.
- Refer to the documentation for detailed explanations and guidelines.
Contributions are welcome! If you have any suggestions, bug fixes, or feature additions, please open an issue or submit a pull request.
For any questions or inquiries, please contact [email protected] or you can contact me on LinkedIn.
Thank you for checking out my repository! I hope you find the projects and code provided helpful and informative. If you have any questions or suggestions, please feel free to reach out.😊