{"id":22038672,"url":"https://github.com/namratha2301/cognitiveemotion","last_synced_at":"2026-05-03T01:37:25.307Z","repository":{"id":140731394,"uuid":"556182543","full_name":"Namratha2301/CognitiveEmotion","owner":"Namratha2301","description":"Identifying Emotions from EEG Activity","archived":false,"fork":false,"pushed_at":"2024-11-06T16:27:18.000Z","size":60302,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-10-05T07:03:52.642Z","etag":null,"topics":["data-science","gru","matplotlib","python3","scikit-learn","seaborn","tensorflow","xgboost"],"latest_commit_sha":null,"homepage":"https://colab.research.google.com/drive/1kN7CFoHXXKD8FWbcWiLgs3R7y30u4Dma?usp=sharing","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/Namratha2301.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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-10-23T08:51:29.000Z","updated_at":"2024-11-06T16:27:23.000Z","dependencies_parsed_at":null,"dependency_job_id":"a97fdab6-f215-48f2-9d9b-95b52ee1120a","html_url":"https://github.com/Namratha2301/CognitiveEmotion","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Namratha2301/CognitiveEmotion","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Namratha2301%2FCognitiveEmotion","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Namratha2301%2FCognitiveEmotion/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Namratha2301%2FCognitiveEmotion/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Namratha2301%2FCognitiveEmotion/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Namratha2301","download_url":"https://codeload.github.com/Namratha2301/CognitiveEmotion/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Namratha2301%2FCognitiveEmotion/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32555839,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-03T00:31:16.350Z","status":"ssl_error","status_checked_at":"2026-05-03T00:31:15.546Z","response_time":132,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: 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":["data-science","gru","matplotlib","python3","scikit-learn","seaborn","tensorflow","xgboost"],"created_at":"2024-11-30T11:07:17.726Z","updated_at":"2026-05-03T01:37:25.288Z","avatar_url":"https://github.com/Namratha2301.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch1 align=\"center\" \u003eCognitive Emotion\u003c/h1\u003e\n\n\u003ch2 align=\"\" \u003eAbout the Project\u003c/h2\u003e\nThe project uses the EEG Brainwave Dataset from Kaggle to create \nmachine learning models that allow the user to predict the emotion of a person \ngiven his EEG Brainwave Data.The dataset was downloaded from Kaggle. \nHere is the  \u003ca href=\"https://www.kaggle.com/datasets/birdy654/eeg-brainwave-dataset-feeling-emotions\" \u003elink\u003c/a\u003e to the dataset.\n\n\u003ch2 align=\"\" \u003eSetup\u003c/h2\u003e\n\nTo run the notebook one can either prefer using Google Colab the better method or run the notebook locally. To run using Colab just use the link at the end of the ReadMe file.\n\nFor running the notebook locally, follow the steps [Windows]:\n\n1. Clone the repository using `git clone https://github.com/Namratha2301/CognitiveEmotion.git`\n2. Set directory to cloned repo `cd CognitiveEmotion`\n3. Create a python virtual environment for the project using `python -m venv env`\n4. Activate the environment using `env\\Scripts\\activate`\n5. Install the dependencies using `pip install -r requirements.txt`\n6. Open the Jupyter Notebook IDE using `jupyter notebook`\n7. The Jupyter Notebook IDE should open up allowing you to run the file\n\n\u003ch2 align=\"\" \u003eMachine Learning Models and Scores\u003c/h2\u003e\n\u003ctable align=\"center\" \u003e\n\u003cthead\u003e\n\u003cth\u003eS.No\u003c/th\u003e\n\u003cth\u003eModel\u003c/th\u003e\n\u003cth\u003ePackage\u003c/th\u003e\n\u003cth\u003eScore\u003c/th\u003e\n\u003c/thead\u003e\n\n\u003ctr\u003e\n\u003ctd\u003e\n1\n\u003c/td\u003e\n\u003ctd\u003e\nRandom Forest Classifier\n\u003c/td\u003e\n\u003ctd\u003e\nSciKit-Learn\n\u003c/td\u003e\n\u003ctd\u003e\n98.7%\n\u003c/td\u003e\n\u003c/tr\u003e\n\n\u003ctr\u003e\n\u003ctd\u003e\n2\n\u003c/td\u003e\n\u003ctd\u003e\nLogistic Regression Classifier\n\u003c/td\u003e\n\u003ctd\u003e\nSciKit-Learn\n\u003c/td\u003e\n\u003ctd\u003e\n93.2%\n\u003c/td\u003e\n\u003c/tr\u003e\n\n\u003ctr\u003e\n\u003ctd\u003e\n3\n\u003c/td\u003e\n\u003ctd\u003e\nLogistic Regression Classifier With 2 PC\n\u003c/td\u003e\n\u003ctd\u003e\nSciKit-Learn\n\u003c/td\u003e\n\u003ctd\u003e\n77.5%\n\u003c/td\u003e\n\u003c/tr\u003e\n\n\u003ctr\u003e\n\u003ctd\u003e\n4\n\u003c/td\u003e\n\u003ctd\u003e\nLogistic Regression Classifier with 10 PC\n\u003c/td\u003e\n\u003ctd\u003e\nSciKit-Learn\n\u003c/td\u003e\n\u003ctd\u003e\n86.6%\n\u003c/td\u003e\n\u003c/tr\u003e\n\n\u003ctr\u003e\n\u003ctd\u003e\n5\n\u003c/td\u003e\n\u003ctd\u003e\nLinear Support Vector Machine Classifier (SVM)\n\u003c/td\u003e\n\u003ctd\u003e\nSciKit-Learn\n\u003c/td\u003e\n\u003ctd\u003e\n96.57%\n\u003c/td\u003e\n\u003c/tr\u003e\n\n\u003ctr\u003e\n\u003ctd\u003e\n6\n\u003c/td\u003e\n\u003ctd\u003e\nExtreme Gradient Boosting Classifier\n\u003c/td\u003e\n\u003ctd\u003e\nXGBoost\n\u003c/td\u003e\n\u003ctd\u003e\n99.39%\n\u003c/td\u003e\n\u003c/tr\u003e\n\n\u003ctr\u003e\n\u003ctd\u003e\n7\n\u003c/td\u003e\n\u003ctd\u003e\nGRU\n\u003c/td\u003e\n\u003ctd\u003e\nTensorFlow\n\u003c/td\u003e\n\u003ctd\u003e\n95.46%\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/table\u003e\n\n\n\u003ch2\u003eLink to Colab File\u003c/h2\u003e\n\u003ca href=\"https://colab.research.google.com/drive/1kN7CFoHXXKD8FWbcWiLgs3R7y30u4Dma?usp=sharing\"\u003eCognitiveEmotionColab\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnamratha2301%2Fcognitiveemotion","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnamratha2301%2Fcognitiveemotion","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnamratha2301%2Fcognitiveemotion/lists"}