Machine Learning Projects

Feature Engineering: Scaling and Selection in python machine learning

In this project, we will learn concept of feature engineering.

Formula:

#feature scaling 
def featureScaling(arr):
    max_num = max(arr)
    min_num = min(arr)
    lst = []
    for num in arr:
        X_prime = (num - min_num) / (max_num - min_num)
        lst.append(X_prime)
    return lst
        
# tests of your feature scaler--line below is input data
data = [115, 140, 175]
print(featureScaling(data))

Output:

[0.0, 0.4166666666666667, 1.0]

Feature Scaling in Scikit-learn

Import the libraries

#importing the scikit-learn libraries
from sklearn.preprocessing import MinMaxScaler
import numpy as np

# 3 different training points for 1 feature
weights = np.array([[115], [140], [175]]).astype(float)

Now it fit into the model

# Rescale
rescaled_weights = scaler.fit_transform(weights)
rescaled_weights

Output:

array([[0. ], [0.41666667], [1. ]])

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