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+# 🛡️ Cyber Attack Prediction Using Random Forests 🔍
+
+Welcome to the **Cyber Attack Prediction Using Random Forests** project! This project aims to predict potential cyber attacks using machine learning techniques, specifically leveraging the power of the Random Forest algorithm. By analyzing historical network traffic data, the model identifies patterns and helps in early detection of cyber threats.
+
+## 📖 Table of Contents
+
+1. [Project Overview](#-project-overview)
+2. [Setup & Installation](#-setup--installation)
+3. [File Structure](#-file-structure)
+4. [How to Run](#-how-to-run)
+5. [Sample Run](#-sample-run)
+6. [Concepts Behind the Project](#-concepts-behind-the-project)
+7. [Technologies Used](#-technologies-used)
+8. [Parameters & Tuning](#-parameters--tuning)
+9. [License](#-license)
+10. [Contact](#-contact)
+
+## 📚 Project Overview
+
+This project utilizes the Random Forest algorithm to classify and predict cyber attacks based on various features from network data. The model is designed to enhance security measures by providing timely predictions of potential threats.
+
+### Key Features:
+- Robust classification using **Random Forest**.
+- Ability to predict multiple types of cyber attacks.
+- Utilization of historical data for model training.
+- Comprehensive evaluation metrics for performance assessment.
+
+## 🛠️ Setup & Installation
+
+To get started with this project, install the following dependencies:
+
+```bash
+pip install pandas scikit-learn matplotlib seaborn
+```
+
+## 📁 File Structure
+
+| File/Folder                                   | Description                                                              |
+|------------------------------------------------|--------------------------------------------------------------------------|
+| `Cyber_Attack_Prediction.ipynb`               | The main notebook implementing the cyber attack prediction model        |
+| `dataset/`                                    | Directory containing the dataset used for training and testing          |
+| `output/`                                     | Folder to store the prediction results and visualizations               |
+| `requirements.txt`                            | List of dependencies required for the project                          |
+
+## 🚀 How to Run
+
+1. Clone the repository and navigate to the project folder:
+    ```bash
+    git clone https://github.com/yourusername/cyber-attack-prediction.git
+    cd cyber-attack-prediction
+    ```
+
+2. Run the Jupyter Notebook to train the model and make predictions on the dataset.
+
+3. Adjust parameters such as `n_estimators` and `max_depth` in the Random Forest model for optimal results.
+
+4. View and save the prediction results in the `output/` folder.
+
+## 📸 Sample Run
+
+- **Input Data**: Features related to network traffic and system events.
+- **Prediction Result**: Classification of the event as a potential cyber attack or benign.
+
+| Input Data Example | Prediction Result               |
+|--------------------|----------------------------------|
+| Example features:  `src_ip`, `dst_ip`, `protocol`, `duration`, `bytes` | `Potential Cyber Attack` or `Benign` |
+
+## 🔬 Concepts Behind the Project
+
+- **Feature Engineering**: Selecting and transforming relevant features that contribute to predicting cyber attacks.
+- **Model Training**: Utilizing historical attack data to train the Random Forest model effectively.
+- **Evaluation Metrics**: Assessing model performance using metrics like accuracy, precision, and recall.
+
+## 🧠 Technologies Used
+
+- Python 🐍
+- Scikit-learn for machine learning
+- Jupyter Notebook for interactive coding
+- Matplotlib and Seaborn for visualizations
+
+## 📊 Parameters & Tuning
+
+You can adjust the following parameters to control the Random Forest model:
+
+| Parameter               | Default Value | Description                                          |
+|-------------------------|---------------|------------------------------------------------------|
+| `n_estimators`          | 100           | Number of trees in the forest                        |
+| `max_depth`             | None          | Maximum depth of the tree                            |
+| `min_samples_split`     | 2             | Minimum number of samples required to split an internal node |
+
+## 📄 License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## 💬 Contact
+
+If you have any questions, feel free to reach out to me at [pratikt1215@example.com] and {www.linkedin.com/in/pratikpandaofficial}.