{"id":26332648,"url":"https://github.com/bhaveshbhakta/personality-classification-using-ml","last_synced_at":"2026-06-23T19:31:22.344Z","repository":{"id":279188595,"uuid":"937984280","full_name":"BhaveshBhakta/Personality-Classification-Using-ML","owner":"BhaveshBhakta","description":"Personality Classification","archived":false,"fork":false,"pushed_at":"2025-09-08T16:54:48.000Z","size":2384,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-04T13:56:40.424Z","etag":null,"topics":["data-visualization","machine-learning","machine-learning-algorithms","personality-classification"],"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/BhaveshBhakta.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":"2025-02-24T08:33:38.000Z","updated_at":"2025-09-08T16:55:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"16ba4178-9098-41f8-9f2a-5ab89524dd43","html_url":"https://github.com/BhaveshBhakta/Personality-Classification-Using-ML","commit_stats":null,"previous_names":["bhaveshbhakta/personality-classification-using-ml"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/BhaveshBhakta/Personality-Classification-Using-ML","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BhaveshBhakta%2FPersonality-Classification-Using-ML","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BhaveshBhakta%2FPersonality-Classification-Using-ML/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BhaveshBhakta%2FPersonality-Classification-Using-ML/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BhaveshBhakta%2FPersonality-Classification-Using-ML/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/BhaveshBhakta","download_url":"https://codeload.github.com/BhaveshBhakta/Personality-Classification-Using-ML/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/BhaveshBhakta%2FPersonality-Classification-Using-ML/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34704742,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-23T02:00:07.161Z","response_time":65,"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":["data-visualization","machine-learning","machine-learning-algorithms","personality-classification"],"created_at":"2025-03-15T23:29:55.824Z","updated_at":"2026-06-23T19:31:22.325Z","avatar_url":"https://github.com/BhaveshBhakta.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Personality Classification\n\n### Project Overview\n\nThis project aims to classify **personality types** based on responses to a psychological test. Using a dataset of responses to a 60-question test (with scores ranging from -3 to 3), the goal is to develop a machine learning model that can accurately predict one of the 16 distinct personality types (MBTI types). This is a challenging multi-class classification task with applications in psychology, human resources, and self-assessment tools.\n\n-----\n\n### Technical Highlights\n\n  * **Dataset**: [Kaggle - 60k responses of 16 Personalities Test (MBTI)](https://www.kaggle.com/datasets/anshulmehtakaggl/60k-responses-of-16-personalities-test-mbt)\n  * **Size**: 59,999 entries, 62 columns.\n  * **Key Features**:\n      * 60 numerical features representing responses to a personality test.\n  * **Approach**:\n      * **Data Cleaning**: The dataset was clean with no missing values or duplicates. The `Response Id` column was dropped as it is a unique identifier.\n      * **Exploratory Data Analysis**: The code checks basic statistics, null values, duplicates, and unique values for all columns. The target variable `Personality` is well-balanced across all 16 classes.\n      * **Label Encoding**: Applied to the target `Personality` column to convert it into a numerical format for multi-class classification.\n      * **Multi-class Classification**: The target variable `Personality` has 16 distinct categories.\n      * **Models Used**:\n          * Logistic Regression, Ridge Classifier, SVC, Random Forest, XGBoost, AdaBoost, Gradient Boosting, Bagging, Decision Tree.\n  * **Best Accuracy**:\n      * **97.7%** with XGBoost Classifier.\n      * **97.4%** with Random Forest Classifier.\n      * **94.5%** with Gradient Boosting Classifier.\n      * The very high accuracies for the ensemble models suggest that the test responses provide very strong discriminative power for personality classification.\n\n-----\n\n### Purpose and Applications\n\n  * **Automated Personality Assessment**: Enables a quick and accurate classification of personality types from test responses.\n  * **Psychological Research**: Supports research in personality psychology and behavior analysis.\n  * **Human Resources**: Assists in team building, career guidance, and job-role matching.\n  * **Self-Improvement**: Provides a tool for individuals to better understand their own personality traits.\n\n-----\n\n### Installation\n\nClone the repository and extract the data from the zip file.\n\nInstall the necessary libraries:\n\n```bash\npip install pandas numpy seaborn matplotlib scikit-learn xgboost\n```\n\n-----\n\n### Collaboration\n\nWe welcome contributions to improve the project. You can help by:\n\n  * Performing comprehensive hyperparameter tuning and cross-validation for the top-performing models to ensure robustness.\n  * Investigating the impact of different preprocessing techniques.\n  * Adding explainability (e.g., SHAP or LIME) to understand which questions or groups of questions are the most critical for classifying a specific personality type.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhaveshbhakta%2Fpersonality-classification-using-ml","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbhaveshbhakta%2Fpersonality-classification-using-ml","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbhaveshbhakta%2Fpersonality-classification-using-ml/lists"}