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-# Machine Learning Zoomcamp 2025 - Homework 3
-
-[![Python](https://img.shields.io/badge/Python-3.11-blue?logo=python&logoColor=white)](https://www.python.org/)
-[![Pandas](https://img.shields.io/badge/Pandas-1.5.3-orange?logo=pandas&logoColor=white)](https://pandas.pydata.org/)
-[![Scikit-Learn](https://img.shields.io/badge/Scikit--Learn-1.3.1-green?logo=scikit-learn&logoColor=white)](https://scikit-learn.org/stable/)
-[![Jupyter](https://img.shields.io/badge/Jupyter-Notebook-yellow?logo=jupyter&logoColor=white)](https://jupyter.org/)
-
----
-
-## Homework 3: Machine Learning for Classification
-
-This repository contains solutions for **Homework 3** of **Machine Learning Zoomcamp 2025**, focused on **classification tasks** using the Bank Marketing dataset.
-
----
-
-## 📂 Project Overview
-
-- **Dataset:** [Bank Marketing Dataset](https://raw.githubusercontent.com/alexeygrigorev/datasets/master/course_lead_scoring.csv)  
-- **Target variable:** `converted` (whether the client signed up)  
-- **Objective:** Data preprocessing, exploratory analysis, feature selection, and training logistic regression models (regularized and unregularized).  
-
-**Tech Stack:**  
-- **Python 3.11** – core programming language  
-- **Pandas** – data manipulation  
-- **NumPy** – numerical operations  
-- **Scikit-Learn** – machine learning models, feature selection, evaluation  
-- **Jupyter Notebook** – interactive coding and documentation  
-
----
-
-## 🔹 Questions & Answers
-
-| Question | Task | Answer |
-|----------|------|--------|
-| 1 | Mode of `industry` | `retail` |
-| 2 | Biggest correlation (numerical features) | `annual_income` and `interaction_count` |
-| 3 | Biggest mutual information (categorical features) | `lead_source` |
-| 4 | Logistic regression validation accuracy | 0.74 |
-| 5 | Least useful feature (feature elimination) | `lead_score` |
-| 6 | Best `C` value for regularized logistic regression | 1 |
-
----
-
-## 📌 Approach / Key Steps
-
-1. **Data Cleaning & Preparation**  
-   - Filled missing values: categorical → `'NA'`, numerical → `0.0`  
-   - Verified feature types and correlations  
-
-2. **Exploratory Analysis**  
-   - Mode of categorical variables  
-   - Correlation matrix for numerical features  
-
-3. **Feature Selection**  
-   - Calculated mutual information for categorical variables using `mutual_info_score`  
-   - Identified least useful features via feature elimination  
-
-4. **Model Training**  
-   - Logistic Regression with one-hot encoded categorical variables  
-   - Regularized logistic regression with hyperparameter tuning (`C` values)  
-
----
-
-## 📈 Results
-
-- Baseline logistic regression accuracy: **0.74**  
-- Least useful feature: **`lead_score`**  
-- Best regularization parameter `C`: **1**  
-
----
-
-## ⚙ How to Run
-
-1. Clone the repository:  
-   ```bash
-   git clone https://github.com/yourusername/ml-zoomcamp-hw3.git
-   ```
-
-2. Install requirements:  
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-3. Open the Jupyter Notebook and run cells sequentially:  
-   ```bash
-   jupyter notebook
-   ```
-
----
-
-## 📚 References
-
-- [Bank Marketing Dataset](https://raw.githubusercontent.com/alexeygrigorev/datasets/master/course_lead_scoring.csv)  
-- [Scikit-Learn Documentation](https://scikit-learn.org/stable/)  
-- [Pandas Documentation](https://pandas.pydata.org/)  
-- [NumPy Documentation](https://numpy.org/)  
-- [Jupyter Notebook Documentation](https://jupyter.org/)
-
----
-