Data-Extensions-And-Generation

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Data-Extensions-And-Generation

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# Data Extension And Generations

Data is the most important aspect of Data Science, Data Engineering and Data Analysis. However, sometimes for projects there is insignificant and insufficient data points. In most of the cases, it is recommended to scrape and collect a purified data through a proper pipeline. But in some cases, synthesis is also an option. In this repository, I have listed some of the most common synthesis and exntension techniques of data where we can make huge datasets out of significantly small number of data points. But, the most important thing to keep in mind is, these are only to be used for educational and non-profit purposes. Commercialization or Real-life usage of synthetic data is harmful.

## Data Generations

### Extensions:

| ID | Technique | When to use |
|--------|-----------|-------------|
| 1 | Guided extension | When proportionality pattern is defined |
| 2 | Unguided extension | When proportionality pattern is undefined |
| 3 | Guided normalized extension | When proportionality pattern is defined and a particular data needs variations |
| 4 | Guided biased extension | When proportionality pattern is defined and a bias is expected |
| 5 | Unguided biased extension | When proportionality pattern is undefined  yet a bias is expected |
| 6 | Unguided normalized extension | When proportionality pattern is defined and a particular data needs variations |

### Generations

| ID | Technique | When to use |
|----|-----------|-------------|
| 1 | Blind Generation | When just filling data are needed |
| 2 | Distributed Generation | When the generated data is expected to follow a particular distribution |
| 3 | Random Generation | When the data needs a high randomness and hard for fitting |
| 4 | Phenomenal Generation | When the data generated is indicating of some phenomena |
| 5 | Natural Generation | Adding natural datasets combinations |

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