{"id":15132643,"url":"https://github.com/dbarty/dataanalysisexamples","last_synced_at":"2026-01-19T00:33:28.022Z","repository":{"id":257583952,"uuid":"858189337","full_name":"dbarty/DataAnalysisExamples","owner":"dbarty","description":null,"archived":false,"fork":false,"pushed_at":"2024-10-18T12:36:03.000Z","size":397,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-10-19T15:02:34.015Z","etag":null,"topics":["data-analysis","data-science","machine-learning","machinelearning","matplotlib","numpy","pandas","python","python3","seaborn"],"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/dbarty.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-09-16T13:18:59.000Z","updated_at":"2024-10-18T12:36:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"59abf84b-3c18-4fec-8350-37661659ff64","html_url":"https://github.com/dbarty/DataAnalysisExamples","commit_stats":null,"previous_names":["dbarty/dataanalysisexamples"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbarty%2FDataAnalysisExamples","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbarty%2FDataAnalysisExamples/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbarty%2FDataAnalysisExamples/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dbarty%2FDataAnalysisExamples/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dbarty","download_url":"https://codeload.github.com/dbarty/DataAnalysisExamples/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247406068,"owners_count":20933802,"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-science","machine-learning","machinelearning","matplotlib","numpy","pandas","python","python3","seaborn"],"created_at":"2024-09-26T04:22:06.409Z","updated_at":"2026-01-19T00:33:27.991Z","avatar_url":"https://github.com/dbarty.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Data Analysis Examples\n\n\u003ch2\u003eBest Practices\u003c/h2\u003e\n\u003col\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eGoal Definition and Problem Understanding\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eDefinition of the goal:\u003c/strong\u003e Clarify the question or problem to be solved. What is the analysis supposed to achieve? Examples: Prediction, pattern recognition, decision support.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eUnderstand the business context:\u003c/strong\u003e Understand the business requirements or scientific hypotheses behind the analysis.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eGather stakeholder input:\u003c/strong\u003e Clarify requirements with the stakeholders involved (departments, management, etc.).\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eData Collection\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eIdentify data sources:\u003c/strong\u003e Determine which data sources are needed for the analysis (e.g., databases, APIs, files).\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eCollect data:\u003c/strong\u003e Extract data from the identified sources. This can be done through \u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/tree/main/data-collection/database\"\u003equeries\u003c/a\u003e, \u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/tree/main/data-collection/web-scraping\"\u003eweb scraping\u003c/a\u003e, \u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/tree/main/data-collection/api\"\u003eAPIs\u003c/a\u003e, or \u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/tree/main/data-collection/files\"\u003eCSV uploads\u003c/a\u003e.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eDocument the data:\u003c/strong\u003e Record where the data came from and what features (attributes) it contains.\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003e\u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/blob/main/exploratory-data-analysis-EDA/exploratory-data-analysis-EDA-Titanic.ipynb\"\u003eExploratory Data Analysis (EDA)\u003c/a\u003e\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eUnderstand the data structure:\u003c/strong\u003e Examine the data type, dimensions (rows and columns), and data distribution.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eCalculate descriptive statistics:\u003c/strong\u003e Compute central measures such as mean, median, standard deviation, min/max, etc.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eVisualize the data:\u003c/strong\u003e \u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/tree/main/exploratory-data-analysis-EDA/visualisation\"\u003eCreate charts\u003c/a\u003e (e.g., bar charts, box plots, scatter plots) to identify patterns, distributions, or relationships between variables.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eIdentify correlations:\u003c/strong\u003e Determine correlations between variables to detect possible relationships.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003e\u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/blob/main/exploratory-data-analysis-EDA/visualisation/boxplot_outlier_detection_and_correlation_analysis.ipynb\"\u003eOutlier detection:\u003c/a\u003e\u003c/strong\u003e Identify outliers and unusual data points that could influence the analysis.\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eData Preprocessing\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eClean the data:\u003c/strong\u003e Remove or correct erroneous, incomplete, or duplicate data.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eHandle missing values:\u003c/strong\u003e Decide whether to remove, impute, or otherwise treat missing values.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eHandle outliers:\u003c/strong\u003e Decide how to handle outliers (e.g., remove, winsorize, or transform them).\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eFeature engineering to improve model performance:\u003c/strong\u003e\u003c/li\u003e\n            \u003cul\u003e\n                \u003cli\u003e\u003cstrong\u003eTransformations:\u003c/strong\u003e Apply mathematical transformations (e.g., logarithmic, square root) to smooth distributions.\u003c/li\u003e\n                \u003cli\u003e\u003cstrong\u003eEncode categorical data:\u003c/strong\u003e Use \u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/tree/main/data-preprocessing/one-hot-encoding.ipynb\"\u003eone-hot encoding\u003c/a\u003e or \u003ca href=\"https://github.com/dbarty/DataAnalysisExamples/blob/main/data-preprocessing/label-encoding.ipynb\"\u003elabel encoding\u003c/a\u003e for categorical data.\u003c/li\u003e\n                \u003cli\u003e\u003cstrong\u003eCreate new features:\u003c/strong\u003e Generate new variables, e.g., by combining existing features (e.g., creating a ratio from two variables).\u003c/li\u003e\n                \u003cli\u003e\u003cstrong\u003eInteraction variables:\u003c/strong\u003e Create features that capture interactions between variables (e.g., product of two variables).\u003c/li\u003e\n            \u003c/ul\u003e\n            \u003cli\u003e\u003cstrong\u003eAdjust data formats:\u003c/strong\u003e Convert data types (e.g., string to date) or standardize and scale numerical variables, if necessary.\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eModel Selection and Development\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eSelect the analysis model:\u003c/strong\u003e Choose the appropriate model depending on the analysis goal (e.g., linear regression, decision trees, clustering, time series analysis).\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eTrain-test split:\u003c/strong\u003e Divide the data into training and test datasets to avoid overfitting.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eTrain the model:\u003c/strong\u003e Train the model with the training data.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eHyperparameter tuning:\u003c/strong\u003e Fine-tune the model to find the best parameters (e.g., using grid search or random search).\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eModel Evaluation\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eModel validation:\u003c/strong\u003e Assess the model performance using the test dataset.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eCalculate metrics:\u003c/strong\u003e Determine key metrics such as accuracy, F1-score, precision, recall, RMSE (Root Mean Squared Error) depending on the model type.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eCross-validation:\u003c/strong\u003e Perform cross-validation to verify the robustness of the model.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eCheck for bias and variance:\u003c/strong\u003e Ensure that the model does not suffer from overfitting or underfitting.\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eInterpretation of Results\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eUnderstand model results:\u003c/strong\u003e Interpret the model parameters and the relationship between features and predictions.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eIdentify important features:\u003c/strong\u003e Determine which variables contribute most to the model.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eVisualize the results:\u003c/strong\u003e Create charts or graphs to visually present the results (e.g., feature importance plots, confusion matrix).\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eConclusions and Recommendations\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eSummarize insights:\u003c/strong\u003e Summarize the key insights from the analysis in a clear and concise manner.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eDerive recommendations:\u003c/strong\u003e Provide concrete actions or decisions based on the analysis results.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eCommunicate results:\u003c/strong\u003e Present the results to stakeholders in an understandable and well-structured form, e.g., in reports or presentations.\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eModel Deployment and Automation (optional)\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eModel deployment:\u003c/strong\u003e If the model is intended for real-time use, deploy it in a system that regularly provides predictions (e.g., as a web service).\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eCreate data pipelines:\u003c/strong\u003e Implement automated processes for the regular collection, processing, and analysis of new data.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eMonitor the model:\u003c/strong\u003e Monitor the model performance to ensure it continues to function well over time and adjust it as necessary.\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n    \u003cli\u003e\n        \u003cstrong\u003eDocumentation and Maintenance\u003c/strong\u003e\n        \u003cul\u003e\n            \u003cli\u003e\u003cstrong\u003eCreate documentation:\u003c/strong\u003e Document all steps of the analysis, including data sources, data preprocessing, model selection, and results.\u003c/li\u003e\n            \u003cli\u003e\u003cstrong\u003eRegular updates:\u003c/strong\u003e Keep the analysis up to date by regularly analyzing new data and improving the model.\u003c/li\u003e\n        \u003c/ul\u003e\n    \u003c/li\u003e\n\u003c/ol\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbarty%2Fdataanalysisexamples","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdbarty%2Fdataanalysisexamples","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdbarty%2Fdataanalysisexamples/lists"}