{"id":15136127,"url":"https://github.com/j0fin/iris-says","last_synced_at":"2025-04-06T02:28:10.165Z","repository":{"id":105868280,"uuid":"287579557","full_name":"j0fiN/Iris-Says","owner":"j0fiN","description":"A minimalist platform for learning, understanding and realising Iris Flower Classification.:cherry_blossom:","archived":false,"fork":false,"pushed_at":"2021-06-09T03:05:40.000Z","size":8337,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-12T08:34:23.406Z","etag":null,"topics":["ai","education","educational-project","educational-tool","flask","flask-application","machine-learning","plotly","plotly-express","pycharm-ide","sklearn-library","visualization","website"],"latest_commit_sha":null,"homepage":"","language":"HTML","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/j0fiN.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","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":"2020-08-14T16:40:34.000Z","updated_at":"2022-05-24T00:42:44.000Z","dependencies_parsed_at":null,"dependency_job_id":"a85952d1-d8db-46f4-9b10-499ad2415d83","html_url":"https://github.com/j0fiN/Iris-Says","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/j0fiN%2FIris-Says","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/j0fiN%2FIris-Says/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/j0fiN%2FIris-Says/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/j0fiN%2FIris-Says/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/j0fiN","download_url":"https://codeload.github.com/j0fiN/Iris-Says/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247425449,"owners_count":20936951,"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":["ai","education","educational-project","educational-tool","flask","flask-application","machine-learning","plotly","plotly-express","pycharm-ide","sklearn-library","visualization","website"],"created_at":"2024-09-26T06:04:14.439Z","updated_at":"2025-04-06T02:28:10.147Z","avatar_url":"https://github.com/j0fiN.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Iris says!:cherry_blossom:  \n\n### A minimalist platform for learning, understanding and realising Iris Flower Classification.\nThis is an **educational tool to encourage learning Iris Flower Classification** with inbuilt *graphical visualisations* and *on-spot prediction system*. We have loaded the platform with 7 highly optimized, pretrained models of different algorithms namely,  \n- Decision Tree Classifier\n- Gaussian Naive Bayes\n- K-Neighbors Classifier\n- Logistic Regression\n- Random Forest Classifier\n- Support Vector Machine\n- Multinomial Naive Bayes.  \n\u003e The architecture of the model are saved and are reused for **faster prediction.**  \n\u003chr\u003e  \n\n### Algorithm :cherry_blossom:  \nDue to continuous prediction calls, we devised a simple algorithm for **isolated prediction**.  \n- Each time a user wants to predict with a particular model, the measurands along with the model key is sent to the server.\n- The model is searched and when found, loaded as an object file with all the architecture expanded and ready for prediction.\n- The expanded object takes in the measurands via the predict function. (The functions are stored within the object's architecture).\n- Values are predicted and then return to the DOM.\n- Then a javascript function call deletes all the prior data, to avoid unexpected object expansion errors during the process.\n \n## To Get Started:cherry_blossom: \n### Production\n```bash\ngit clone https://github.com/j0fiN/Iris-Says.git\ncd Iris-Says\npip install -r requirements.txt\npython run.py\n```  \n### Testing (algorithm test)\n```bash\npython -m unittest discover tests\n```  \n\u003chr\u003e\n\n## Learn it!:cherry_blossom:  \n\u003e A full description available about the **dataset and the models.**\n\u003cimg src=\"https://github.com/j0fiN/Iris-Says/blob/master/iris/static/images/home_page_snap.PNG\" alt=\"Home page\" width=\"800\" height=\"400\"\u003e\n\u003chr\u003e  \n\n## Interact with it!:cherry_blossom:  \n\u003e Loaded with major graphs which are useful and not very complex to grasp. **Simplicity had been maintained!**\n\u003cimg src=\"https://github.com/j0fiN/Iris-Says/blob/master/iris/static/gif/graph_page_gif.gif\" alt=\"GIF\" width=\"800\" height=\"400\"\u003e\n\u003chr\u003e  \n\n## Realise how it works!:cherry_blossom:  \n\u003e **With robust, yet flexible configurations**, users can select his own settings and get wonderful predictions.\n\u003cimg src=\"https://github.com/j0fiN/Iris-Says/blob/master/iris/static/images/predict_page_snap.PNG\" alt=\"Predict page\" width=\"800\" height=\"400\"\u003e\n\u003chr\u003e  \n\n## Major Reach:cherry_blossom:\nThis platform is majorily developed for **beginners in Data Analytics/Machine Learning**. Giving a strong foundation in these topics enhances them to move forward faster in this ever-growing field. They will understand how to approach any data and analyze them and then use it to build powerful machine learning models.  \nThe platform can play a major part in **showcasing AI and machine learning for students in high schools and other bootcamps**.  \n\n## Higher Optimizations:cherry_blossom:\n- The tool can **grow in size** to explore various other famous datasets and the usage of machine learning in each of them and not only iris(A good example would be Boston says!).  \n- The tool can become a platform for users to **develop their own models on that dataset and upload them**. They can also write content about the database.\n- The Graphical visualisations can be **enhanced** using various tools of javascript.\n\n## Tools used to develop the project:cherry_blossom:\n- Flask (Python)\n- Scikit-Learn (Python)\n- Plotly-Express (Python)\n- Basic webtools(HTML, CSS, JS(some JQuery too!))\n\n## Contribution:cherry_blossom: \nDo contribute if you have ideas, **:star: the repo** if you find it impressive!  \n \n\u003e Made with :heart: =\u003e **PYTHON**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fj0fin%2Firis-says","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fj0fin%2Firis-says","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fj0fin%2Firis-says/lists"}