Difference between revisions of "Data Preprocessing"
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[http://www.google.com/search?q=Data+Preprocessing+machine+learning+ML ...Google search] | [http://www.google.com/search?q=Data+Preprocessing+machine+learning+ML ...Google search] | ||
| − | * [[Data | + | * [[AI Governance]] |
| − | * [[ | + | ** [[Data Science]] / [[Data Governance]] |
| − | * [[ | + | *** [[Benchmarks]] |
| − | * [[Data Encoding]] | + | *** [[Data Preprocessing]] |
| − | + | **** [[Feature Exploration/Learning]] ...inspection, data profiling, selection | |
| − | * [[Feature | + | **** [[Data Quality]] ...[[AI Verification and Validation|validity]], accuracy, [[Data Quality#Data Cleaning|cleaning]], completeness, consistency, uniformity, [[Data Quality#Data Encoding|encoding]], [[Data Quality#Zero Padding|padding]], [[Data Quality#Data Augmentation, Data Labeling, and Auto-Tagging|augmentation, labeling, auto-tagging]], [[Data Quality#Batch Norm(alization) & Standardization| normalization, standardization]], and [[Data Quality#Imbalanced Data|imbalanced data]] |
| + | *** [[Bias and Variances]] | ||
| + | *** [[Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog]] | ||
| + | **** [[Natural Language Processing (NLP)#Managed Vocabularies |Managed Vocabularies]] | ||
| + | **** [[Datasets]] | ||
| + | *** [[Privacy in Data Science]] | ||
| + | *** [[Data Interoperability]] | ||
| + | *** [[Excel - Data Analysis]] | ||
| + | * [[Visualization]] | ||
* [[Hyperparameter]]s | * [[Hyperparameter]]s | ||
| − | * [[ | + | * [[Evaluation]] |
| − | * [[ | + | ** [[Evaluation - Measures]] |
| − | * [[ | + | * [[Train, Validate, and Test]] |
* [[Python]] | * [[Python]] | ||
* [http://scikit-learn.org/stable/modules/preprocessing.html Sklearn.preprocessing] | * [http://scikit-learn.org/stable/modules/preprocessing.html Sklearn.preprocessing] | ||
Revision as of 15:24, 19 September 2020
YouTube search... ...Google search
- AI Governance
- Data Science / Data Governance
- Benchmarks
- Data Preprocessing
- Feature Exploration/Learning ...inspection, data profiling, selection
- Data Quality ...validity, accuracy, cleaning, completeness, consistency, uniformity, encoding, padding, augmentation, labeling, auto-tagging, normalization, standardization, and imbalanced data
- Bias and Variances
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
- Privacy in Data Science
- Data Interoperability
- Excel - Data Analysis
- Data Science / Data Governance
- Visualization
- Hyperparameters
- Evaluation
- Train, Validate, and Test
- Python
- Sklearn.preprocessing
- The Passenger Screening Kaggle challenge 1st place solution was won in part due to data preparation/generation.
- Data Pre Processing Techniques You Should Know | Maneesha Rajaratne - Towards Data Science
- Machine Learning(ML) — Data Preprocessing | Raji Adam Bifola
- Most Influential Data Preprocessing Algorithms | S. García, J. Luengo, F. Herrera
- How to fix an Unbalanced Dataset | Will Badr - Amazon Web Services
- Creating and Using Datasources | Amazon Web Services
- Jon Tupitza Famous Jupyter Notebooks:
- The COVID Tracking Project - software used
Contents
Splitting Data - training and testing sets
Time-Series Data
- Time-based Algorithms
- A Comparison of Time Series Databases and Netsil’s Use of Druid | Netsil
- Microsoft announces the general availability of Azure Time Series Insights | Ryan Waite - Microsoft
- Top 10 Time Series Databases | Outlyer
Categorical Variables
Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. There are a variety of coding systems that can be used when recoding categorical variables. Coding Systems for Categorical Variables In Regression Analysis | UCLA institute for Digital Research & Education Statistical Consulting
SQL Database Optimization
