Great question! The minmaxscaler in python do is basically a way to normalize your data between a range (usually 0 to 1).
It works by subtracting the min value from each point and dividing by the max-min. Super simple but super useful for algorithms like neural nets or k-means where scale matters.
Here's a tiny example:
```python
from sklearn.preprocessing import MinMaxScaler
data = [[10], [20], [30]]
scaler = MinMaxScaler()
print(scaler.fit_transform(data)) # Outputs [[0.], [0.5], [1.]]
```
Check out sklearn's docs for more!
It works by subtracting the min value from each point and dividing by the max-min. Super simple but super useful for algorithms like neural nets or k-means where scale matters.
Here's a tiny example:
```python
from sklearn.preprocessing import MinMaxScaler
data = [[10], [20], [30]]
scaler = MinMaxScaler()
print(scaler.fit_transform(data)) # Outputs [[0.], [0.5], [1.]]
```
Check out sklearn's docs for more!