Min-Max Scaler

easy· Machine Learningruns: 0

Implement a feature scaler that maps each feature into a given range (default [0, 1]) using the formula (X - min) / (max - min). Provide fit, transform, and fit_transform.

wpm 0acc 100%time 0:000 / 612

import numpy as np

class MinMaxScaler:
    def __init__(self, feature_range: tuple[float, float] = (0.0, 1.0)):
        self.low, self.high = feature_range
        self.min_ = None
        self.max_ = None

    def fit(self, X):
        self.min_ = X.min(axis=0)
        self.max_ = X.max(axis=0)
        return self

    def transform(self, X):
        scale = self.max_ - self.min_
        scale = np.where(scale == 0, 1, scale)
        normalized = (X - self.min_) / scale
        return normalized * (self.high - self.low) + self.low

    def fit_transform(self, X):
        return self.fit(X).transform(X)

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