Problem_26: Implement a Custom Scaler Class using min-max scaling #ai #coding #ml #google

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ABOUT THIS PROBLEM:

Problem: Create a custom class to scale features using min-max scaling. Apply this scaler to a dataset and train a simple model on the scaled data.

Hint: Min-max scaling transforms features by scaling each feature to a given range, typically 0 to 1.

SOLUTION:
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Problem_14:
Implement a Gradient Boosting Classifier using Scikit-learn #pythoncode :

class MinMaxScaler:
def fit(self, X):

def transform(self, X):

def fit_transform(self, X):

# Example usage:
# scaler = MinMaxScaler()

#SAMPLE_INPUT
import numpy as np

# Sample dataset
[2, 3],
[3, 4],
[4, 5]])

# Using the MinMaxScaler
scaler = MinMaxScaler()

print("Original Data:\n", X)
print("Scaled Data:\n", X_scaled)

#Sample_output
Original Data:
[[1 2]
[2 3]
[3 4]
[4 5]]
Scaled Data:
[[0. 0. ]
[0.33 0.33]
[0.67 0.67]
[1. 1. ]]

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