{"id":28197622,"url":"https://github.com/datkanber/advanced-eda","last_synced_at":"2025-05-16T17:15:16.025Z","repository":{"id":267182315,"uuid":"899106578","full_name":"datkanber/advanced-eda","owner":"datkanber","description":"Step-by-step guide for advanced Exploratory Data Analysis (EDA) to uncover patterns and prepare data.","archived":false,"fork":false,"pushed_at":"2024-12-08T21:35:59.000Z","size":739,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-08T22:27:32.981Z","etag":null,"topics":["data-science","exploratory"],"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/datkanber.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":"2024-12-05T16:24:12.000Z","updated_at":"2024-12-08T21:36:30.000Z","dependencies_parsed_at":"2024-12-08T22:37:36.084Z","dependency_job_id":null,"html_url":"https://github.com/datkanber/advanced-eda","commit_stats":null,"previous_names":["datkanber/advanced-eda"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datkanber%2Fadvanced-eda","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datkanber%2Fadvanced-eda/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datkanber%2Fadvanced-eda/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datkanber%2Fadvanced-eda/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/datkanber","download_url":"https://codeload.github.com/datkanber/advanced-eda/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254573582,"owners_count":22093732,"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-science","exploratory"],"created_at":"2025-05-16T17:14:57.601Z","updated_at":"2025-05-16T17:15:16.013Z","avatar_url":"https://github.com/datkanber.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Advanced Functional Exploratory Data Analysis\n\nThis repository focuses on **Advanced Functionalized Exploratory Data Analysis (EDA)**, providing a step-by-step guide to uncover patterns, identify relationships, and prepare datasets for further analysis.\n\n## 📖 What is EDA?\nExploratory Data Analysis (EDA) is a critical step in data science that helps to:\n- Summarize the main characteristics of datasets.\n- Visualize relationships between variables.\n- Detect anomalies and patterns.\n- Check assumptions and validate statistical techniques.\n\n### 🔍 Key Analysis Areas:\n1. **Categorical Variables**: Distribution and frequency analysis.\n2. **Numerical Variables**: Summary statistics and visualizations (histograms, boxplots).\n3. **Target Variable**: Correlation and relationships with other variables.\n4. **Correlation Analysis**: Identifying highly correlated features.\n\n---\n\n## 🚀 Features\n- **Data Cleaning**: Handle missing values, remove outliers.\n- **Descriptive Statistics**: Quick summaries of numerical and categorical data.\n- **Correlation Heatmaps**: Visualize feature relationships.\n- **Automated Functions**: Tools for summarizing data and identifying insights.\n\n---\n\n## 📊 Dataset Examples\n- **Titanic Dataset**: Survival analysis based on passenger data.\n- **NBA Dataset**: Performance metrics for NBA players.\n- **Fraud Detection Dataset**: Identifying fraudulent transactions.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatkanber%2Fadvanced-eda","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatkanber%2Fadvanced-eda","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatkanber%2Fadvanced-eda/lists"}