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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/)
+
+---
+