{"id":15289074,"url":"https://github.com/dwija12903/bda-lab","last_synced_at":"2026-04-30T07:37:16.997Z","repository":{"id":256179103,"uuid":"854524911","full_name":"dwija12903/bda-lab","owner":"dwija12903","description":"This repository contains various lab files from my Big Data Analytics coursework","archived":false,"fork":false,"pushed_at":"2024-09-09T10:36:37.000Z","size":273,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-29T11:30:15.503Z","etag":null,"topics":["graphx","networkx","pysaprk-sql","pyspark","pyspark-mllib","scala"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dwija12903.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-09-09T10:32:07.000Z","updated_at":"2024-09-09T10:37:49.000Z","dependencies_parsed_at":"2024-09-09T15:37:41.476Z","dependency_job_id":null,"html_url":"https://github.com/dwija12903/bda-lab","commit_stats":null,"previous_names":["dwija12903/bda-lab"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dwija12903%2Fbda-lab","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dwija12903%2Fbda-lab/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dwija12903%2Fbda-lab/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dwija12903%2Fbda-lab/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dwija12903","download_url":"https://codeload.github.com/dwija12903/bda-lab/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245217791,"owners_count":20579297,"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":["graphx","networkx","pysaprk-sql","pyspark","pyspark-mllib","scala"],"created_at":"2024-09-30T15:59:13.434Z","updated_at":"2026-04-30T07:37:11.977Z","avatar_url":"https://github.com/dwija12903.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 📊 Big Data Analytics Lab Files\n\nThis repository contains various lab files from my **Big Data Analytics** coursework, covering topics such as **Scala**, **PySpark**, **RDD**, **SQL**, **GraphX**, **NetworkX**, **PageRank**, **Linear Regression**, and **Random Forest**.\n\n### 💻 Lab Files Included:\n\n1. **Scala Basics** 🚀\n   - Scala programming language fundamentals for big data.\n   \n2. **RDD (Resilient Distributed Dataset)** ⚙️\n   - Operations and transformations on RDDs in PySpark and Scala.\n   \n3. **SQL in PySpark** 🧮\n   - Performing SQL operations on big data using PySpark SQL.\n   \n4. **PySpark** 🔥\n   - Handling distributed data processing using PySpark.\n\n5. **GraphX** 🔗\n   - Working with graph data using GraphX in Scala.\n   \n6. **NetworkX** 🌐\n   - Graph-based data analysis using NetworkX with PySpark.\n   \n7. **PageRank Algorithm** 📈\n   - Implementation of the PageRank algorithm for ranking web pages using PySpark.\n   \n8. **Linear Regression** 📉\n   - Machine learning implementation of Linear Regression for predictive analysis using PySpark MLlib.\n   \n9. **Random Forest** 🌲\n   - Implementing the Random Forest algorithm for classification and regression tasks in PySpark MLlib.\n\n## 🚀 How to Run\n\n1. Clone the repository:\n   ```bash\n   git clone https://github.com/\u003cyour-username\u003e/Big-Data-Analytics.git\n   cd Big-Data-Analytics\n   ```\n\n2. Navigate to the respective folder and run the code using **PySpark** or **Scala**.\n\n## ⚙️ Prerequisites\n\n- **Scala** installed.\n- **PySpark** environment set up.\n- Python packages for **PySpark**, **NetworkX**, etc.\n\n## 👩‍💻 Contributing\n\nFeel free to contribute by improving the code, adding new features, or enhancing documentation. Fork the repository, create a branch, and submit a pull request.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdwija12903%2Fbda-lab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdwija12903%2Fbda-lab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdwija12903%2Fbda-lab/lists"}