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 # bankmarketing-modelling-in-python
-Binary classification on Bank Marketing dataset. Logistic vs Weighted Logistic Regression; improved minority recall. Notebooks + PDF. No data included.
+
+![Overview](reports/BankMarketing_lab_overview.png)
+
+## Overview
+
+This project demonstrates binary classification modeling on the **UCI Bank Marketing dataset**. The analysis compares standard **Logistic Regression** with **Weighted Logistic Regression**, showing how the weighted model improves recall for the minority class ("Yes" - customers who subscribe to a term deposit).
+
+Key findings:
+- Standard logistic regression struggles with imbalanced classes
+- Weighted logistic regression significantly improves minority class ("Yes") recall
+- Trade-offs between precision and recall are explored for business decision-making
+
+## Repository Structure
+
+```
+.
+├── data/                           # Bank marketing dataset files
+├── notebooks/
+│   └── 01_bank_marketing_model.ipynb  # Main analysis notebook
+├── reports/
+│   └── BankMarketing_lab_overview.png # Overview visualization
+├── requirements.txt                # Python dependencies
+├── LICENSE                         # MIT License
+└── README.md                       # This file
+```
+
+## Reproduce
+
+To reproduce the analysis on **Windows**:
+
+1. **Create a virtual environment:**
+   ```cmd
+   python -m venv venv
+   venv\Scripts\activate
+   ```
+
+2. **Install dependencies:**
+   ```cmd
+   pip install -r requirements.txt
+   ```
+
+3. **Open the notebook:**
+   ```cmd
+   jupyter notebook notebooks/01_bank_marketing_model.ipynb
+   ```
+
+4. Run all cells in the notebook to reproduce the analysis and results.
+
+## Attribution
+
+Dataset: [UCI Bank Marketing Data Set](https://archive.ics.uci.edu/ml/datasets/Bank+Marketing)
+
+**Citation:**
+> [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014.