{"id":25167337,"url":"https://github.com/nick-peter-marcus/chocolate-bar-analysis","last_synced_at":"2026-05-10T06:50:26.751Z","repository":{"id":175711353,"uuid":"654363575","full_name":"nick-peter-marcus/chocolate-bar-analysis","owner":"nick-peter-marcus","description":"Analyzing Chocolate Bar Features and Ratings - Data Visualization, Decision Trees, Random Forest","archived":false,"fork":false,"pushed_at":"2023-11-01T18:56:38.000Z","size":2198,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-09T06:33:27.666Z","etag":null,"topics":["data-analysis","data-visualization","decision-trees","python","random-forest","seaborn","sklearn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/nick-peter-marcus.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2023-06-16T01:13:09.000Z","updated_at":"2024-03-08T15:31:11.000Z","dependencies_parsed_at":null,"dependency_job_id":"ac31045d-224b-42d0-b75f-0edc825011a2","html_url":"https://github.com/nick-peter-marcus/chocolate-bar-analysis","commit_stats":null,"previous_names":["nick-peter-marcus/chocolate-bar-analysis"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nick-peter-marcus%2Fchocolate-bar-analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nick-peter-marcus%2Fchocolate-bar-analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nick-peter-marcus%2Fchocolate-bar-analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nick-peter-marcus%2Fchocolate-bar-analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nick-peter-marcus","download_url":"https://codeload.github.com/nick-peter-marcus/chocolate-bar-analysis/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247052618,"owners_count":20875685,"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":["data-analysis","data-visualization","decision-trees","python","random-forest","seaborn","sklearn"],"created_at":"2025-02-09T06:33:28.475Z","updated_at":"2026-05-10T06:50:21.724Z","avatar_url":"https://github.com/nick-peter-marcus.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Chocolate Bar Analysis\nIn this project, I am analyzing a dataset taken from kaggle containing ratings and characteristics of over 2500 chocolate bars. The dataset can be found here:\nhttps://www.kaggle.com/datasets/nyagami/chocolate-bar-ratings-2022\n\n## 1. Exploratory Analyses and Data Visualization\nIn the first step, I perform some explorative analyses, observing frequencies and distributions of the variables both statistically and graphically. For this, NumPy, pandas, Matplotlib, and Seaborn are utilized.\n\nIn this process, some variables were recoded and a copy of the dataset has been saved for the next step of the analysis.\n\n## 2. Using Random Forest and Decision Tree to predict Chocolate Bar Ratings\nIn the next step, I build models to predict the ratings of the chocolate bars given their characteristics. Here, Random Forest and Decision Tree Regressors where used. Both methods were observed with default parameters, as well as optimized parameters derived from Hyper Parameter Tuning.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnick-peter-marcus%2Fchocolate-bar-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnick-peter-marcus%2Fchocolate-bar-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnick-peter-marcus%2Fchocolate-bar-analysis/lists"}