```
usage: knn [-i N_INPUTS] [-d N_DAYS] [-j N_JUMPS] [-n N_NEIGHBORS] [-e S_END_DATE] [-t VALID_SPLIT] [-p {normalization,standardization,minmax,none}] [--no_shuffle] [-h]
```

K nearest neighbors is a simple algorithm that stores all available cases and predict the numerical target based on a similarity measure (e.g. distance functions).

```
optional arguments:
  -i N_INPUTS, --input N_INPUTS
                        number of days to use as input for prediction.
  -d N_DAYS, --days N_DAYS
                        prediction days.
  -j N_JUMPS, --jumps N_JUMPS
                        number of jumps in training data.
  -n N_NEIGHBORS, --neighbors N_NEIGHBORS
                        number of neighbors to use on the algorithm.
  -e S_END_DATE, --end S_END_DATE
                        The end date (format YYYY-MM-DD) to select for testing
  -t VALID_SPLIT, --test_size VALID_SPLIT
                        Percentage of data to validate in sample
  -p {normalization,standardization,minmax,none}, --pp {normalization,standardization,minmax,none}
                        pre-processing data.
  --no_shuffle          Specify if shuffling validation inputs.
  -h, --help            show this help message
```

![knn](https://user-images.githubusercontent.com/25267873/108604942-d169bd80-73a8-11eb-9021-6f787cbd41e3.png)