text: Title: Dependencies too strict for numpy and scipy
  Body: **Issue**
  
  I have a request regarding the dependencies for `mljar-supervised = "0.10.4"`. In particular, my question is whether it would be possible to allow for older versions of `numpy` and `scipy` (maybe in future releases). 
  
  I am currently trying to install `mljar-supervised = "0.10.4"` along with `tensorflow = "^2.2"`. It seems to me that these two dependencies should definitely be compatible in one application. However, this is not possible due to the following reason: `because mljar-supervised (0.10.4) depends on both numpy (>=1.20.0) and scipy (1.6.1), mljar-supervised (0.10.4) is incompatible with tensorflow (>=2.2,<3.0)`
  
  **Full dependency issue**
  
  ```shell
  Because no versions of tensorflow match >2.2,<2.2.1 || >2.2.1,<2.2.2 || >2.2.2,<2.3.0 || >2.3.0,<2.3.1 || >2.3.1,<2.3.2 || >2.3.2,<2.4.0 || >2.4.0,<2.4.1 || >2.4.1,<2.5.0 || >2.5.0,<3.0
     and tensorflow (2.2.0) depends on scipy (1.4.1), tensorflow (>=2.2,<2.2.1 || >2.2.1,<2.2.2 || >2.2.2,<2.3.0 || >2.3.0,<2.3.1 || >2.3.1,<2.3.2 || >2.3.2,<2.4.0 || >2.4.0,<2.4.1 || >2.4.1,<2.5.0 || >2.5.0,<3.0) requires scipy (1.4.1).
    And because tensorflow (2.2.1) depends on numpy (>=1.16.0,<1.19.0)
     and tensorflow (2.2.2) depends on numpy (>=1.16.0,<1.19.0), tensorflow (>=2.2,<2.3.0 || >2.3.0,<2.3.1 || >2.3.1,<2.3.2 || >2.3.2,<2.4.0 || >2.4.0,<2.4.1 || >2.4.1,<2.5.0 || >2.5.0,<3.0) requires scipy (1.4.1) or numpy (>=1.16.0,<1.19.0).
    And because tensorflow (2.3.0) depends on scipy (1.4.1)
     and tensorflow (2.3.1) depends on numpy (>=1.16.0,<1.19.0), tensorflow (>=2.2,<2.3.2 || >2.3.2,<2.4.0 || >2.4.0,<2.4.1 || >2.4.1,<2.5.0 || >2.5.0,<3.0) requires scipy (1.4.1) or numpy (>=1.16.0,<1.19.0).
    And because tensorflow (2.3.2) depends on numpy (>=1.16.0,<1.19.0)
     and tensorflow (2.4.0) depends on numpy (>=1.19.2,<1.20.0), tensorflow (>=2.2,<2.4.1 || >2.4.1,<2.5.0 || >2.5.0,<3.0) requires scipy (1.4.1) or numpy (>=1.16.0,<1.19.0 || >=1.19.2,<1.20.0).
    And because tensorflow (2.4.1) depends on numpy (>=1.19.2,<1.20.0)
     and tensorflow (2.5.0) depends on numpy (>=1.19.2,<1.20.0), tensorflow (>=2.2,<3.0) requires numpy (>=1.16.0,<1.19.0 || >=1.19.2,<1.20.0) or scipy (1.4.1).
    And because mljar-supervised (0.10.4) depends on both numpy (>=1.20.0) and scipy (1.6.1), mljar-supervised (0.10.4) is incompatible with tensorflow (>=2.2,<3.0).
    So, because property-prediction-challenge depends on both tensorflow (^2.2) and mljar-supervised (0.10.4), version solving failed.
  ```
  
  **Reproducible example**
  
  - Step 1: Install poetry dependency management package with `pip install poetry==1.1.6`
  - Step 2: Create a new folder and put the following into a new file `pyproject.toml`. 
  
  ```shell
  [tool.poetry]
  name = "Dependency issues."
  version = "0.1.0"
  description = "Investigate dependency issues."
  authors = ["nobody"]
  
  [tool.poetry.dependencies]
  python = "3.8.x"
  tensorflow = "^2.2"
  mljar-supervised = "0.10.4"
  ```
  
  - Step 3: cd into the new folder and run `poetry install`