{"id":21485172,"url":"https://github.com/sarabdevops/pands-project","last_synced_at":"2026-05-20T09:06:47.662Z","repository":{"id":238501755,"uuid":"796692616","full_name":"sarabDevOps/pands-project","owner":"sarabDevOps","description":"Programming and Scripting project  ","archived":false,"fork":false,"pushed_at":"2024-05-20T00:17:22.000Z","size":69,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-17T09:49:49.710Z","etag":null,"topics":["anaconda","git","python","uci-machine-learning","vscode"],"latest_commit_sha":null,"homepage":"https://archive.ics.uci.edu/dataset/53/iris","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/sarabDevOps.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":"2024-05-06T12:56:59.000Z","updated_at":"2024-05-20T00:17:25.000Z","dependencies_parsed_at":"2025-03-17T10:46:59.315Z","dependency_job_id":null,"html_url":"https://github.com/sarabDevOps/pands-project","commit_stats":null,"previous_names":["sarabdevops/pands-project"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/sarabDevOps/pands-project","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarabDevOps%2Fpands-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarabDevOps%2Fpands-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarabDevOps%2Fpands-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarabDevOps%2Fpands-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/sarabDevOps","download_url":"https://codeload.github.com/sarabDevOps/pands-project/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/sarabDevOps%2Fpands-project/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":33253081,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-20T04:48:54.280Z","status":"ssl_error","status_checked_at":"2026-05-20T04:48:10.851Z","response_time":356,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["anaconda","git","python","uci-machine-learning","vscode"],"created_at":"2024-11-23T13:14:19.928Z","updated_at":"2026-05-20T09:06:47.646Z","avatar_url":"https://github.com/sarabDevOps.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# pands-project\n## The project is made for as a part of college work - Module -\u003e Programming and Scripting  \n\n### Project Overview\n\n#### Iris Dataset Exploratory Data Analysis (EDA)\nThis repository contains Python code for performing Exploratory Data Analysis (EDA) on the Iris dataset. The analysis includes generating summary statistics, creating histograms, scatter plots, and a correlation heatmap for the numeric variables.\n\n\n#### Dataset\nThe UCI Machine Learning Repository provides 150 samples of iris flowers with four features: sepal length, sepal width, petal length, and petal width. These samples make up the Iris dataset. One of the three species—Iris setosa, Iris versicolor, or Iris virginica—represents each sample.\n\n\n#### Approach\nThe approach to the analysis is as follows:\n1.\tData Fetching: The dataset is fetched using the ucimlrepo package.\n2.\tDataFrame Creation: The data is converted into a Pandas DataFrame.\n3.\tSummary Statistics: Basic summary statistics for each numeric variable are computed and saved to a text file. Provides an overview of the central tendency, dispersion, and shape of the dataset's distribution\n\n4.\tHistograms: Histograms are created for each numeric variable to visualize their distributions. Help in understanding the distribution and frequency of numeric variables.\n5.\tScatter Plots: Scatter plots for each pair of numeric variables are generated to observe relationships. Useful for identifying potential relationships and correlations between pairs of variables.\n6.\tCorrelation Heatmap: A heatmap of the correlation matrix is created to show correlations between variables.\n\n\n\n#### Directory Structure\n\n```\n ──\u003e iris_EDA_G00305450.py\n ──\u003e output_files1/\n     ──\u003e summary_statistics.txt\n     ──\u003e sepal_length_histogram.png\n     ──\u003e sepal_width_histogram.png\n     ──\u003e petal_length_histogram.png\n     ──\u003e petal_width_histogram.png\n     ──\u003e scatter_plots.png\n──\u003eREADME.md\n──\u003erequirements.txt\n```\n\n***iris_EDA_G00305450.py: Main script that performs the EDA.\n Output_files1/: Directory where the output files (summary statistics, histograms, scatter plots) are saved.***\n\n #### Results\n1. Summary Statistics: Stored in output_files1/summary_statistics.txt.\n2. Histograms: Stored as PNG files in output_files1/.\n3. Scatter Plots: Stored as scatter_plots.png in output_files1/.\n4. Correlation Heatmap: Stored as correlation_matrix.png in output_files1/.\n\n\n#### Getting Started\n\nThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. \nDownload and extract the zip folder here [pands-project](https://github.com/sarabDevOps/pands-project/archive/refs/heads/main.zip)\n\n#### Prerequisites\nVS Code . You can download here [VS Code](https://code.visualstudio.com/download)\n\n\n\n#### Installing\nOnce you have downloaded the executable file click on it and it will automatic guide you for installation.\n\nTo run the code in this repository, you need to have Python 3.x and the following Python packages installed:\n\n+ ucimlrepo\n\n+ pandas\n\n+ seaborn\n\n+ matplotlib\n\n+ You can install the required packages using pip:\n\n+ pip install ucimlrepo pandas seaborn matplotlib\n\n\n\n#### Deployment\n\nFile menu from VS Code ----\u003e Hit the open ------\u003e  select iris_EDA_G00305450 python file from file Explorer \n\nTo run -\u003e  iris_EDA_G00305450.py\n\nSometimes code shows this ERROR ` iris = fetch_ucirepo(id=53)\n           ^^^^^^^^^^^^^^^^^^^^`  has `ConnectionError: Error connecting to server` , Just run it again if you get this error.\n\n      \n\n#### Built With\n Visual Studio Code -\u003e  [VS Code](https://code.visualstudio.com/download)\n\n\n#### Versioning\n\nVersion 1\n\n\n#### Authors\n\n[SarabDevOps](https://github.com/sarabDevOps)\n\n\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](https://github.com/sarabDevOps/pands-project/blob/main/LICENSE) file for details\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarabdevops%2Fpands-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsarabdevops%2Fpands-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsarabdevops%2Fpands-project/lists"}