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Week 4/readme.md

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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**.
-