Bank Churning Analysis

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This project analyzes customer churn for a banking institution. Using a dataset of customer demographics and account information, the goal is to uncover trends, visualize customer behavior, and support data-driven decision-making to reduce churn rates.

📁 Project Structure

• app.py — Streamlit dashboard application for interactive data exploration.
• bank_churning_analysis.ipynb — Jupyter Notebook with detailed exploratory data analysis and statistical insights.
• summary_report.pdf — Executive summary report outlining key findings and recommendations.
• requirements.txt — Python package dependencies.
• .gitignore — Git configuration to ignore unnecessary files.

✨ Key Features

• Exploratory Data Analysis (EDA):
  Comprehensive analysis of customer demographics, account activity, and churn distribution.

• Visualization:

  • Customer distribution by country.
  • Demographics (age, gender, income) visualizations.
  • Churn patterns and risk segmentation.

• Dashboard Application:
  A user-friendly Streamlit dashboard to interactively explore insights and monitor churn risk indicators.

• Prediction and Customer Classification:

  • Sklearn library was used to label-encode, standardize, split the dataset into train and test sets.
  • Classifying and assessing the classification report.
  • Clustering visualization using seaborn and matplotlib.

🚀 Live Dashboard

Click here to view the live Streamlit app

If you haven't deployed the app yet, you can still run it locally by following the instructions below.

🛠️ Technologies Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Streamlit
• Scikit-learn
• Jupyter Notebook

⚙️ Setup Instructions

1. Clone this repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run app.py

📊 Project Highlights

• Data cleaning and preprocessing for accurate analysis.
• Visual insights on customer demographics and account behavior.
• Business-driven summary report with actionable insights.
• Interactive web app for real-time data exploration.

📜 License

This project is open-source and available under the MIT License.

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✍️ Author

Fehintolu Samuel