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+# Lead Scoring with Bank Marketing Dataset
+
+[![Python](https://img.shields.io/badge/Python-3.12-blue?logo=python&logoColor=white)](https://www.python.org/)
+[![Scikit-Learn](https://img.shields.io/badge/scikit--learn-1.3.2-orange?logo=scikit-learn&logoColor=white)](https://scikit-learn.org/)
+[![Jupyter Notebook](https://img.shields.io/badge/Jupyter-Notebook-orange?logo=jupyter&logoColor=white)](https://jupyter.org/)
+
+---
+
+## Overview
+
+This notebook demonstrates building a **lead scoring model** using the Bank Marketing dataset. The goal is to predict whether a client will **convert** (sign up for a service) based on various features.
+
+We cover:
+
+1. Data preparation and handling missing values.
+2. Feature importance using ROC AUC for numerical variables.
+3. Logistic regression modeling with **one-hot encoding**.
+4. Precision, recall, and F1 score analysis to select thresholds.
+5. 5-fold cross-validation to check model stability.
+6. Hyperparameter tuning to select the best regularization parameter.
+
+---
+
+## Key Results
+
+- **Best numerical feature (ROC AUC):** `number_of_courses_viewed`  
+- **Validation AUC:** `0.794`  
+- **Threshold where precision ≈ recall:** `0.59`  
+- **Threshold with max F1:** `0.47`  
+- **Standard deviation of AUC across folds:** `0.01`  
+- **Best regularization parameter C:** `0.001`  
+
+---
+
+## Lessons Learned
+
+- ROC AUC can help identify predictive features even before modeling.
+- Logistic regression combined with one-hot encoding provides a strong baseline.
+- Threshold tuning is crucial for balancing precision and recall based on business needs.
+- Cross-validation confirms the robustness of the model and prevents overfitting.
+- Hyperparameter tuning improves model performance and reliability.
+
+---
+
+## Environment
+
+- Python 3.12  
+- Jupyter Notebook  
+- Libraries: `pandas`, `numpy`, `scikit-learn`, `matplotlib`, `seaborn`
+
+---
+
+## Dataset
+
+Bank Marketing dataset used in this project is publicly available:  
+[Bank Marketing Dataset CSV](https://raw.githubusercontent.com/alexeygrigorev/datasets/master/course_lead_scoring.csv)
+
+---
+
+## Author
+
+Created as part of **ML Zoomcamp 2025 Homework 4**.
+