Create README.md

Author: Md-Emon-Hasan

README.md

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+# Machine Learning Project: Email Spam Classifier
+
+Welcome to the **Email Spam Classifier** machine learning project repository! This project focuses on classifying emails as spam or non-spam (ham) using various machine learning techniques.
+
+## 📋 Contents
+
+- [Introduction](#introduction)
+- [Why This Project](#why-this-project)
+- [Dataset](#dataset)
+- [Features](#features)
+- [Models Implemented](#models-implemented)
+- [Evaluation Metrics](#evaluation-metrics)
+- [Setup and Installation](#setup-and-installation)
+- [Demo](#demo)
+- [Contributing](#contributing)
+- [Challenges Faced](#challenges-faced)
+- [Lessons Learned](#lessons-learned)
+- [License](#license)
+- [Contact](#contact)
+
+---
+
+## 📖 Introduction
+
+This repository contains a machine learning project focused on classifying emails as spam or non-spam using supervised learning techniques. It includes data preprocessing, model development, evaluation, and deployment aspects of the project.
+
+---
+
+## 🎯 Why This Project
+
+The primary motivation behind creating this project is to tackle the issue of email spam, which remains a significant problem affecting users' productivity and security. By accurately classifying emails, this project aims to improve email filtering systems.
+
+---
+
+## 📊 Dataset
+
+The dataset used for this project contains a collection of emails labeled as spam or ham. It includes various features extracted from the emails, such as text content, subject line, sender information, etc.
+
+---
+
+## 🌟 Features
+
+- **Data Preprocessing:** Cleaned and transformed dataset for machine learning model compatibility.
+- **Model Development:** Trained multiple machine learning models to classify emails as spam or ham.
+- **Model Evaluation:** Evaluated models using appropriate metrics to ensure accuracy and reliability.
+- **Deployment:** Implemented a simple web-based or command-line application for classifying new emails.
+
+---
+
+## 🧠 Models Implemented
+
+Several machine learning models were implemented and evaluated:
+
+- Naive Bayes Classifier
+- Support Vector Machine (SVM)
+- Random Forest Classifier
+- Logistic Regression
+- others
+
+Each model's performance was compared based on metrics such as accuracy, precision, recall, and F1-score.
+
+---
+
+## 📊 Evaluation Metrics
+
+The models were evaluated using the following metrics:
+
+- **Accuracy:** Overall correctness of the predictions.
+- **Precision:** Proportion of true positives among all positive predictions.
+- **Recall:** Proportion of true positives identified correctly.
+- **F1-score:** Harmonic mean of precision and recall, providing a balance between the two metrics.
+
+---
+
+## 🚀 Setup and Installation
+
+To run this project locally, follow these steps:
+
+1. Clone the repository:
+
+   ```bash
+   git clone https://github.com/Md-Emon-Hasan/ML-Project-Email-Spam-Classifier.git
+   ```
+
+2. Navigate to the project directory:
+
+   ```bash
+   cd ML-Project-Email-Spam-Classifier
+   ```
+
+3. Install the required dependencies:
+
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+4. Run the notebooks or scripts as per your requirements.
+
+---
+
+## 🌐 Demo
+
+Explore the live demo of the project [here](https://email-sms-classifier-by-emon.onrender.com) 
+
+---
+
+## 🤝 Contributing
+
+Contributions to enhance or expand the project are welcome! Here's how you can contribute:
+
+1. **Fork the repository.**
+2. **Create a new branch:**
+
+   ```bash
+   git checkout -b feature/new-feature
+   ```
+
+3. **Make your changes:**
+
+   - Implement new features, improve model performance, or enhance documentation.
+
+4. **Commit your changes:**
+
+   ```bash
+   git commit -am 'Add a new feature or update'
+   ```
+
+5. **Push to the branch:**
+
+   ```bash
+   git push origin feature/new-feature
+   ```
+
+6. **Submit a pull request.**
+
+---
+
+## 🛠️ Challenges Faced
+
+During the development of this project, the following challenges were encountered:
+
+- Handling text data preprocessing, including tokenization and feature extraction.
+- Dealing with class imbalance in the dataset.
+- Optimizing model performance and scalability.
+
+---
+
+## 📚 Lessons Learned
+
+Key lessons learned from this project include:
+
+- Importance of feature selection and engineering in text classification tasks.
+- Evaluation and selection of appropriate metrics based on project goals.
+- Deployment considerations for machine learning models in real-world applications.
+
+---
+
+## 📄 License
+
+This project is licensed under the Apache License 2.0. See the [LICENSE](LICENSE) file for more details.
+
+---
+
+## 📬 Contact
+
+- **Email:** [iconicemon01@gmail.com](mailto:iconicemon01@gmail.com)
+- **WhatsApp:** [+8801834363533](https://wa.me/8801834363533)
+- **GitHub:** [Md-Emon-Hasan](https://github.com/Md-Emon-Hasan)
+- **LinkedIn:** [Md Emon Hasan](https://www.linkedin.com/in/md-emon-hasan)
+- **Facebook:** [Md Emon Hasan](https://www.facebook.com/mdemon.hasan2001/)
+
+Feel free to reach out for any questions or feedback regarding the project!
+
+---
+
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