{"id":25907926,"url":"https://github.com/capsuleismail/rt-iot2022","last_synced_at":"2026-05-12T19:47:46.788Z","repository":{"id":279607934,"uuid":"939348479","full_name":"capsuleismail/RT-IoT2022","owner":"capsuleismail","description":"RT-IoT2022 is a dataset obtained from a real-time IoT infrastructure. This project aims to compare the accuracy of three machine learning models: XGBoost and LGBMClassifier.","archived":false,"fork":false,"pushed_at":"2025-02-26T12:42:08.000Z","size":1577,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-26T13:35:36.619Z","etag":null,"topics":["datascience","jupyter-notebook","machinelearning-python","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc-by-4.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/capsuleismail.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-26T11:57:17.000Z","updated_at":"2025-02-26T12:42:11.000Z","dependencies_parsed_at":"2025-02-26T13:35:39.474Z","dependency_job_id":"b1c49cc3-69ce-4f1d-ac01-66a02929f40e","html_url":"https://github.com/capsuleismail/RT-IoT2022","commit_stats":null,"previous_names":["capsuleismail/rt-iot2022"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/capsuleismail%2FRT-IoT2022","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/capsuleismail%2FRT-IoT2022/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/capsuleismail%2FRT-IoT2022/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/capsuleismail%2FRT-IoT2022/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/capsuleismail","download_url":"https://codeload.github.com/capsuleismail/RT-IoT2022/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241622600,"owners_count":19992504,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["datascience","jupyter-notebook","machinelearning-python","scikit-learn"],"created_at":"2025-03-03T07:15:43.867Z","updated_at":"2025-11-28T19:04:34.920Z","avatar_url":"https://github.com/capsuleismail.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Introduction  \n---------------\n\nThe **RT-IoT2022** dataset is a cybersecurity dataset designed for detecting intrusions and anomalies in IoT (Internet of Things) networks. It contains a mix of numerical and categorical features extracted from network traffic, which makes it a great candidate for tree-based ensemble models.\n\nThese are the questions I've gone through on my notebooks:\n\n1. **Columns Composition via Pie-Chart.** \u003cbr/\u003e\n2. **Descriptive Statistics.** \u003cbr/\u003e\n3. **Pairplot KDE for float columns based on Target.** \u003cbr/\u003e\n4. **Pairplot KDE for numerical columns based on Target.** \u003cbr/\u003e\n5. **Correlation Plot.** \u003cbr/\u003e\n6. **Most 20 correlated columns.** \u003cbr/\u003e\n7. **Packages.** \u003cbr/\u003e\n8. **Data Preprocessing with ColumnTransformer and using Train Test Split.** \u003cbr/\u003e\n9. **Modeling with Stratified.** \u003cbr/\u003e\n\n**Citation: [RT-IoT2022](https://archive.ics.uci.edu/dataset/942/rt-iot2022). (2024). UCI Machine Learning Repository.** \u003cbr/\u003e\n--------------------------------------------------------------------------------------------------------------------\n\n\n### I. How to import the dataset via pip.\n--------------------------------------------------------------------------------------------------------------------\n```\n!pip install ucimlrepo\nImport the dataset into your code \nfrom ucimlrepo import fetch_ucirepo \n  \n# fetch dataset \nrt_iot2022 = fetch_ucirepo(id=942) \n  \n# data (as pandas dataframes) \nX = rt_iot2022.data.features \ny = rt_iot2022.data.targets\n  \n  \n# metadata \nprint(rt_iot2022.metadata) \n  \n# variable information \nprint(rt_iot2022.variables)\n\n```\n--------------------------------------------------------------------------------------------------------------------\n**XGBoost** and **LightGBM**, are the most popular gradient boosting frameworks used also by a large number of Kagglers on competition. Each has its own advantages depending on the use case:\n\n### **1. XGBoost (Extreme Gradient Boosting)**\n**Advantages:**\n- **Highly Optimized**: Implements optimized gradient boosting with parallel tree boosting.\n- **Regularization (L1 \u0026 L2)**: Reduces overfitting through built-in L1 (Lasso) and L2 (Ridge) regularization.\n- **Handling Missing Values**: Automatically learns how to deal with missing values.\n- **Tree Pruning (Depth-wise Growth)**: Prevents overfitting by pruning trees based on a threshold.\n- **Custom Objective Functions**: Supports user-defined loss functions.\n- **Distributed Computing Support**: Works with Spark, Hadoop, and Dask for large-scale training.\n\n**Best for:** \n- **General gradient boosting applications.**\n- **Tabular data with numerical \u0026 categorical features.**\n--------------------------------------------------------------------------------------------------------------------\n### **2. LightGBM (Light Gradient Boosting Machine)**\n**Advantages:**\n- **Faster Training**: Uses histogram-based learning and leaf-wise tree growth, making it faster than XGBoost.\n- **Memory Efficient**: Requires less memory due to its histogram-based approach.\n- **Good for Large Datasets**: Scales well to large datasets with high dimensionality.\n- **Handles Categorical Features Natively**: Can directly process categorical data without encoding.\n- **Efficient Parallelism**: More efficient than XGBoost when training on large datasets.\n\n**Best for:** \n- **Large-scale datasets.**\n- **low-latency applications.**\n--------------------------------------------------------------------------------------------------------------------\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcapsuleismail%2Frt-iot2022","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcapsuleismail%2Frt-iot2022","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcapsuleismail%2Frt-iot2022/lists"}