{"id":17893992,"url":"https://github.com/grantgasser/task2","last_synced_at":"2026-04-27T22:32:32.053Z","repository":{"id":129537671,"uuid":"126836755","full_name":"grantgasser/Task2","owner":"grantgasser","description":"Multiclass classification","archived":false,"fork":false,"pushed_at":"2018-10-04T22:11:30.000Z","size":40,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-03T04:18:09.131Z","etag":null,"topics":["keras","multiclass-classification","neural-network"],"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/grantgasser.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":"2018-03-26T13:55:39.000Z","updated_at":"2018-10-04T22:11:31.000Z","dependencies_parsed_at":"2023-05-06T09:33:16.795Z","dependency_job_id":null,"html_url":"https://github.com/grantgasser/Task2","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/grantgasser/Task2","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FTask2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FTask2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FTask2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FTask2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/grantgasser","download_url":"https://codeload.github.com/grantgasser/Task2/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/grantgasser%2FTask2/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32358509,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-27T20:07:02.737Z","status":"ssl_error","status_checked_at":"2026-04-27T20:07:00.910Z","response_time":128,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 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":["keras","multiclass-classification","neural-network"],"created_at":"2024-10-28T14:58:46.214Z","updated_at":"2026-04-27T22:32:32.035Z","avatar_url":"https://github.com/grantgasser.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Task 2: 3-Class Classification\nThis was a group project, but the code in this notebook is solely mine. \n\nThis program uses [keras](https://keras.io/). \n\n### Description\nLoading the data and pre-processing was quite similar to previous tasks as the format of the data has stayed consistent.  The only difference now is that the this is a 3-class classification task with the labels as 0, 1, and 2. We read the data from the train and test csv files into pandas dataframes representing the x training data, y training data, and x testing data. After trying One v. One and One v. Rest classification, we decided to implement a neural network in order to exceed the Hard baseline. Using keras, we defined a model, adding a 2 hidden layers, and a 'softmaxed' output layer. We used ReLU as the activation function for the other layers.  Then, calling the keras compile function, we defined the loss function as categorical cross-entropy and used the Adam optimizer. Subsequently, we converted the y labels to an 'encoded' matrix in order to use it in Keras.  We trained the model for 200 epochs and achieved an accuracy of **91.3%**. Despite that, we figured the model would perform a little worse once uploaded, since it may have over-fitted just a little. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrantgasser%2Ftask2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgrantgasser%2Ftask2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgrantgasser%2Ftask2/lists"}