{"id":20356572,"url":"https://github.com/madhurimarawat/streamlit-programs","last_synced_at":"2025-10-09T01:36:22.966Z","repository":{"id":192218293,"uuid":"686303254","full_name":"madhurimarawat/Streamlit-Programs","owner":"madhurimarawat","description":"This repository contains programs in the Python programming language using  Module Streamlit.","archived":false,"fork":false,"pushed_at":"2023-12-05T17:30:03.000Z","size":48,"stargazers_count":2,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-04T17:43:40.846Z","etag":null,"topics":["decision-tree","if-else-statements","inbuilt-datasets-scikit-learn","kaggle-datasets","knn","linear-regression-models","logistic-regression","match-case","naive-bayes-classifier","python","random-forest","streamlit-functions","svm"],"latest_commit_sha":null,"homepage":"https://ml-model-datasets-using-apps-3gy37ndiancjo2nmu36sls.streamlit.app/","language":"Python","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/madhurimarawat.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":"2023-09-02T10:47:12.000Z","updated_at":"2025-01-25T20:01:12.000Z","dependencies_parsed_at":"2023-12-05T18:37:56.364Z","dependency_job_id":"58110073-d258-48bc-a413-c876d9936f35","html_url":"https://github.com/madhurimarawat/Streamlit-Programs","commit_stats":null,"previous_names":["madhurimarawat/streamlit-programs"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/madhurimarawat/Streamlit-Programs","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FStreamlit-Programs","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FStreamlit-Programs/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FStreamlit-Programs/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FStreamlit-Programs/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/madhurimarawat","download_url":"https://codeload.github.com/madhurimarawat/Streamlit-Programs/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/madhurimarawat%2FStreamlit-Programs/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279000718,"owners_count":26082895,"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","status":"online","status_checked_at":"2025-10-08T02:00:06.501Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"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":["decision-tree","if-else-statements","inbuilt-datasets-scikit-learn","kaggle-datasets","knn","linear-regression-models","logistic-regression","match-case","naive-bayes-classifier","python","random-forest","streamlit-functions","svm"],"created_at":"2024-11-14T23:17:04.694Z","updated_at":"2025-10-09T01:36:22.924Z","avatar_url":"https://github.com/madhurimarawat.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Streamlit-Programs\nThis repository contains programs in the Python programming language using  module Streamlit.\u003cbr\u003e\u003cbr\u003e\n\u003cimg src=\"https://analyticsindiamag.com/wp-content/uploads/2021/04/streamlit.gif\" width=\"750\" height=\"400\"\u003e\n\n---\n# Mode of Execution Used \u003ca href=\"https://github.com/madhurimarawat/Programming-in-PHP\"\u003e  \u003cimg src=\"https://code.visualstudio.com/assets/images/code-stable.png\" title=\"Visual Studio Code\" alt=\"Visual Studio Code\" width=\"40\" height=\"40\"\u003e \u003c/a\u003e \u0026nbsp;\u003cimg src=\"https://seeklogo.com/images/S/streamlit-logo-1A3B208AE4-seeklogo.com.png\" title=\"Streamlit\" alt=\"Streamlit\" width=\"40\" height=\"40\"\u003e\n\n## Visual Studio Code\n--\u003e Visit the official website:\u0026nbsp; \u003ca href=\"https://code.visualstudio.com/download\"\u003e\u003cimg src=\"https://code.visualstudio.com/assets/images/code-stable.png\" title=\"Visual Studio Code\" alt=\"Visual Studio Code\" width=\"30\" height=\"30\"\u003e\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Download according to the platform that will be used like Linux, Macos or Windows.\u003cbr\u003e\u003cbr\u003e\n--\u003e Follow the setup wizard.\u003cbr\u003e\u003cbr\u003e\n--\u003e Create a new file with the extention of .py and then this file can be executed in the server.\u003cbr\u003e\n\n## Streamlit Server\n\n--\u003e Streamlit is a python framework through which we can deploy any machine learning model and any python project with ease and without worrying about the frontend.\u003cbr\u003e\u003cbr\u003e\n--\u003e Streamlit is very user-friendly.\u003cbr\u003e\u003cbr\u003e\n--\u003e Streamlit has pre defined functions for all frontend components and we can directly use them.\u003cbr\u003e\u003cbr\u003e\n--\u003e To install streamlit in your system, just run this command-\n\n```\npip install streamlit\n```\n\n## Running Project in Streamlit Server\n\u003cp\u003eMake Sure all depencies are already satisfied before running the app.\u003c/p\u003e\n\n1. We can Directly run streamlit app  with the following command-\u003cbr\u003e\n```\nstreamlit run app.py\n```\nwhere app.py is the name of file containing streamlit code.\u003cbr\u003e\n\nBy default, streamlit will run on port 8501.\u003cbr\u003e\n\nAlso we can execute multiple files simultaneously and it will be executed in next ports like 8502 and so on.\u003cbr\u003e\n\n2. Navigate to URL http://localhost:8501\n\nYou should be able to view the homepage of your app.\n\n🌟 Project and Models will change but this process will remain the same for all Streamlit projects.\n\n---\n# About Projects\n\u003cp\u003eComplete Description about the project and resources used.\u003c/p\u003e\n\n# Linear Regression Salary Prediction\n\n--\u003e First ML model is constructed using linear regression for the dataset.\u003cbr\u003e\u003cbr\u003e\n--\u003e Then this model can be used directly.\u003cbr\u003e\u003cbr\u003e\n--\u003e The Homepage is designed for steamlit app.\u003cbr\u003e\u003cbr\u003e\n--\u003e After this the user input will be taken.\u003cbr\u003e\u003cbr\u003e\n--\u003e Finally we can run this app in the streamlit Server and get the desired output.\u003cbr\u003e\n\n## Dataset Used\n\n### Salary Dataset\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/abhishek14398/salary-dataset-simple-linear-regression\n\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Salary Dataset\" alt=\"Salary Dataset\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Contains Salary data for Regression.\u003cbr\u003e\u003cbr\u003e\n--\u003e The dataset has 2 columns-Years of Experience and Salary and 30 entries.\u003cbr\u003e\u003cbr\u003e\n--\u003e Column Years of Experience is used to find regression for Salary.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n## Algorithm Used\n\u003ch3\u003eLinear Regression\u003c/h3\u003e\n--\u003e Regression: It predicts the continuous output variables based on the independent input variable. like the prediction of house prices based on different parameters like house age, distance from the main road, location, area, etc.\u003cbr\u003e\u003cbr\u003e\n--\u003e It computes the linear relationship between a dependent variable and one or more independent features. \u003cbr\u003e\u003cbr\u003e\n--\u003e The goal of the algorithm is to find the best linear equation that can predict the value of the dependent variable based on the independent variables.\u003cbr\u003e\n\n# Naive Bayes Classifier Diabetes Prediction\n\n--\u003e First ML model is constructed using Naive Bayes Classifier for the dataset.\u003cbr\u003e\u003cbr\u003e\n--\u003e Then this model can be used directly.\u003cbr\u003e\u003cbr\u003e\n--\u003e The Homepage is designed for steamlit app.\u003cbr\u003e\u003cbr\u003e\n--\u003e After this the user input will be taken.\u003cbr\u003e\u003cbr\u003e\n--\u003e Finally we can run this app in the streamlit Server and get the desired output.\u003cbr\u003e\n\n## Dataset Used\n\n### Naive bayes classification data\n--\u003e Dataset is taken from: \u003ca href=\"https://www.kaggle.com/datasets/himanshunakrani/naive-bayes-classification-data\"\u003e\u003cimg src=\"https://cdn4.iconfinder.com/data/icons/logos-and-brands/512/189_Kaggle_logo_logos-1024.png\" height =40 width=40 title=\"Naive bayes classification data\"\u003e \u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\n--\u003e Contains diabetes data for classification.\u003cbr\u003e\u003cbr\u003e\n--\u003e The dataset has 3 columns-glucose, blood pressure and diabetes and 995 entries.\u003cbr\u003e\u003cbr\u003e\n--\u003e Column glucose and blood pressure data is to classify whether the patient has diabetes or not.\u003cbr\u003e\u003cbr\u003e\n--\u003e Dataset is already cleaned,no preprocessing required.\u003cbr\u003e\n\n## Algorithm Used\n\n\u003ch3\u003eNaive Bayes Classifiers\u003c/h3\u003e\n--\u003e Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem. \u003cbr\u003e\u003cbr\u003e\n--\u003e It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.\u003cbr\u003e\u003cbr\u003e\n--\u003e The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.\n\n# ML Model Inbuilt Datasets\n\n--\u003e In this I applied all supervised learning algorithm on inbuilt datasets in scikit-learn.\u003cbr\u003e\u003cbr\u003e\n--\u003e Then this model can be used directly.\u003cbr\u003e\u003cbr\u003e\n--\u003e The Homepage is designed for steamlit app.\u003cbr\u003e\u003cbr\u003e\n--\u003e After this the user input will be taken.\u003cbr\u003e\u003cbr\u003e\n--\u003e Finally we can run this app in the streamlit Server and get the desired output.\u003cbr\u003e\u003cbr\u003e\n--\u003e Also I made two code files for this. One files contains the implementation of this code using if else and it will run on all versions of python.\u003cbr\u003e\u003cbr\u003e\n--\u003e The second file contains code implementation using match case and will only run in python versions 3.10 and later.\n\n---\n## Libraries Used\n\u003cp\u003eShort Description about all libraries used in Project.\u003c/p\u003e\nTo install python library this command is used-\u003cbr\u003e\u003cbr\u003e\n\n```\npip install library_name\n```\n\u003cul\u003e\n  \u003cli\u003eNumPy (Numerical Python) – Enables with collection of mathematical functions\nto operate on array and matrices. \u003c/li\u003e\n  \u003cli\u003ePandas (Panel Data/ Python Data Analysis) - This library is mostly used for analyzing,\ncleaning, exploring, and manipulating data.\u003c/li\u003e\n  \u003cli\u003eMatplotlib - It is a data visualization and graphical plotting library.\u003c/li\u003e\n\u003cli\u003eScikit-learn - It is a machine learning library that enables tools for used for many other\nmachine learning algorithms such as classification, prediction, etc.\u003c/li\u003e\n\u003c/ul\u003e\n\n---\n### Additional Resources 🧮📚📓🌐\n\n1. To see more of my machine learning models, visit my repository: https://github.com/madhurimarawat/Machine-Learning-Using-Python\n\n2. I deployed my ML models that I made using streamlit:\u003cbr\u003e\u003cbr\u003e\nVisit Website from : \u003ca href=\"https://ml-model-datasets-using-apps-3gy37ndiancjo2nmu36sls.streamlit.app/\"\u003eML Algorithms on Inbuilt and Kaggle Datasets\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\nTo See codes: https://github.com/madhurimarawat/ML-Model-Datasets-Using-Streamlits\n\n3. To see my Web Scrapper project made using Streamlit:\u003cbr\u003e\u003cbr\u003e\nVisit Website from : \u003ca href=\"https://web-scrapper-functions-h6phqofpkjtaylwyn9uvzf.streamlit.app/\"\u003eWeb Scrapper\u003c/a\u003e\u003cbr\u003e\u003cbr\u003e\nTo See codes: https://github.com/madhurimarawat/Web-Scrapper-Functions\n\n---\n## Thanks for Visiting 😄\n\nDrop a 🌟 if you find this repository useful.\u003cbr\u003e\u003cbr\u003e\nIf you have any doubts or suggestions, feel free to reach me.\u003cbr\u003e\u003cbr\u003e\n📫 How to reach me:  \u0026nbsp; [![Linkedin Badge](https://img.shields.io/badge/-madhurima-blue?style=flat\u0026logo=Linkedin\u0026logoColor=white)](https://www.linkedin.com/in/madhurima-rawat/) \u0026nbsp; \u0026nbsp;\n\u003ca href =\"mailto:rawatmadhurima@gmail.com\"\u003e\u003cimg src=\"https://github.com/madhurimarawat/Machine-Learning-Using-Python/assets/105432776/b6a0873a-e961-42c0-8fbf-ab65828c961a\" height=35 width=30 title=\"Mail Illustration\" alt=\"Mail Illustration📫\" \u003e \u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadhurimarawat%2Fstreamlit-programs","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmadhurimarawat%2Fstreamlit-programs","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmadhurimarawat%2Fstreamlit-programs/lists"}