{"id":25452940,"url":"https://github.com/shadowwphoenix/migraine-classification-using-svm","last_synced_at":"2025-05-16T13:10:46.269Z","repository":{"id":219948486,"uuid":"750347024","full_name":"ShadowwPhoenix/Migraine-classification-using-SVM","owner":"ShadowwPhoenix","description":"Utilizing SVM, this project predicts migraine likelihood based on user-input features, evaluating model performance with metrics, classification report, and confusion matrix, and offers interactive prediction and gratitude messages.","archived":false,"fork":false,"pushed_at":"2024-01-30T13:29:47.000Z","size":8,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-17T23:42:50.876Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"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/ShadowwPhoenix.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}},"created_at":"2024-01-30T13:27:00.000Z","updated_at":"2024-01-30T15:07:41.000Z","dependencies_parsed_at":"2024-01-30T14:53:55.561Z","dependency_job_id":null,"html_url":"https://github.com/ShadowwPhoenix/Migraine-classification-using-SVM","commit_stats":null,"previous_names":["varun5292/migraine-classification-using-svm","shadowwphoenix/migraine-classification-using-svm"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShadowwPhoenix%2FMigraine-classification-using-SVM","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShadowwPhoenix%2FMigraine-classification-using-SVM/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShadowwPhoenix%2FMigraine-classification-using-SVM/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ShadowwPhoenix%2FMigraine-classification-using-SVM/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ShadowwPhoenix","download_url":"https://codeload.github.com/ShadowwPhoenix/Migraine-classification-using-SVM/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254535822,"owners_count":22087399,"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":[],"created_at":"2025-02-17T23:41:57.347Z","updated_at":"2025-05-16T13:10:46.248Z","avatar_url":"https://github.com/ShadowwPhoenix.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"This project is a Migraine Classification system utilizing machine learning techniques, primarily Support Vector Machine (SVM). Here's a breakdown of each step:\n\nImporting Libraries: Import necessary libraries like Pandas, NumPy, Matplotlib, Seaborn, and scikit-learn modules.\n\nLoading Dataset: Read the dataset from the provided CSV file path using Pandas.\n\nData Preprocessing:\n\nSeparate features (X) and target variable (y).\nEncode categorical labels into numerical format using LabelEncoder.\nTrain-Test Split: Split the dataset into training and testing sets using train_test_split() function.\n\nBuilding SVM Model:\n\nInitialize an SVM model with a linear kernel and enable probability estimates.\nTrain the SVM model using the training data.\nModel Evaluation:\n\nPredict the target variable for the test set using the trained model.\nCalculate various evaluation metrics such as accuracy, precision, recall, and F1-score using scikit-learn metrics functions.\nPrint the classification report providing detailed metrics for each class.\nConfusion Matrix Visualization: Visualize the confusion matrix using Seaborn's heatmap.\n\nMetrics Visualization:\n\nPlot precision, recall, and F1-score for each class.\nCalculate overall metrics and plot them.\nUser Input and Prediction:\n\nPrompt the user to input values for each feature.\nCreate a DataFrame with the user input and predict the class using the trained model.\nInverse transform the predicted class to get the meaningful label (migraine or not migraine).\nThank You Message: Display a thank you message using Matplotlib with a customized message.\nThe project involves data visualization using Matplotlib and Seaborn libraries for enhanced understanding of model results and user interaction.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshadowwphoenix%2Fmigraine-classification-using-svm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshadowwphoenix%2Fmigraine-classification-using-svm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshadowwphoenix%2Fmigraine-classification-using-svm/lists"}