Optimizing Data Preprocessing: A Guide to Effective Binning Strategies

Binning has a high significance which we often miss seeing.In real-world data, we often have continuous values, such as ages, income, or test scores. These values can vary across a wide range. For example, ages can be any number between 0 and 100, and income levels can range from very low to very high. However machine learning models, like decision trees or logistic regression, work with features (input data) that are typically categorical. Binning helps convert continuous data into categorical data by dividing it into bins or groups.

Furthermore, continuous data can be complex to work with, especially if we want to find patterns or make predictions. Binning simplifies the data by dividing it into a few meaningful categories.

Here are some types of binning with its explanation:

1.Entropy MDL Binning:

• Entropy MDL (Minimum Description Length) binning aims to find the optimal bin boundaries by minimizing the complexity of representing the data.
• It uses a measure of information entropy to determine the bin boundaries.
• The bins are created in such a way that they minimize the total number of bits needed to represent the data.
• Example: Suppose you have a dataset of customer ages, and the Entropy MDL binning algorithm may find optimal bin boundaries that group ages into bins like 0–20, 21–30, 31–40, and so on.

import numpy as np
from sklearn.preprocessing import KBinsDiscretizer

# Sample dataset of customer ages
customer_ages = np.array([18, 25, 28, 35, 38, 42, 55, 60, 70, 80])

# Specify the number of desired bins
n_bins = 4

# Create the KBinsDiscretizer with the entropy criterion
binning_model = KBinsDiscretizer(n_bins=n_bins, encode='ordinal', strategy='uniform')

# Fit the model and transform the data
binned_ages = binning_model.fit_transform(customer_ages.reshape(-1, 1)).astype(int)

# Decode bin labels to represent the bin ranges
bin_edges = binning_model.bin_edges_[0]
bin_labels = [f"{int(bin_edges[i])}-{int(bin_edges[i+1])}" for i in range(len(bin_edges) - 1)]

# Print the binned ages
for age, bin_idx in zip(customer_ages, binned_ages):
    bin_label = bin_labels[bin_idx[0]]
    print(f"Age {age} belongs to bin: {bin_label}")

2.Quantiles Binning:

• Quantiles binning divides the data into bins based on specific percentiles or quantiles.
• It ensures that each bin contains an equal number of data points.
• Common quantiles used include quartiles (four bins), quintiles (five bins), and deciles (ten bins).
• Example: If you have a dataset of exam scores, quartiles binning would create bins such as 0–25th percentile, 25th-50th percentile, 50th-75th percentile, and 75th-100th percentile.

import numpy as np
import pandas as pd

# Sample dataset of exam scores
exam_scores = np.array([75, 82, 90, 60, 68, 78, 85, 92, 95, 55])

# Calculate quartiles
q1 = np.percentile(exam_scores, 25)
q2 = np.percentile(exam_scores, 50)
q3 = np.percentile(exam_scores, 75)

# Create bin labels based on quartiles
bin_labels = [f"0-{q1}th percentile", f"{q1}-{q2}th percentile", f"{q2}-{q3}th percentile", f"{q3}-100th percentile"]

# Use pandas to bin the data
bins = pd.cut(exam_scores, [0, q1, q2, q3, 100], labels=bin_labels, include_lowest=True)

# Print the binned exam scores
for score, bin_label in zip(exam_scores, bins):
    print(f"Score {score} belongs to bin: {bin_label}")

3.Equal Width Binning:

• Equal Width binning divides the data into bins of equal width.
• It is a straightforward method but may not work well if the data distribution is skewed or has outliers.
• Example: For a dataset of income levels, equal width binning might create bins like $0-$10,000, $10,001-$20,000, and so on.

import numpy as np
import pandas as pd

# Sample dataset of income levels
income_levels = np.array([8000, 12000, 15000, 25000, 30000, 45000, 60000, 75000, 90000, 120000])

# Define the width of each bin
bin_width = 10000  # You can adjust this width as needed

# Calculate the minimum and maximum income values
min_income = min(income_levels)
max_income = max(income_levels)

# Create bin labels based on equal width
bin_labels = [f"${i}-{i + bin_width - 1}" for i in range(min_income, max_income, bin_width)]

# Use pandas to bin the data
bins = pd.cut(income_levels, bins=[i for i in range(min_income, max_income + bin_width, bin_width)], labels=bin_labels, include_lowest=True)

# Print the binned income levels
for income, bin_label in zip(income_levels, bins):
    print(f"Income ${income} belongs to bin: {bin_label}")

4.Custom Edges Binning:

• Custom Edges binning allows you to define the bin boundaries manually.
• You can specify the edges or thresholds at which data should be divided into bins.
• This method gives you fine control over the binning process, making it suitable for domain-specific requirements.
• Example: In a dataset of product prices, you might choose custom bin boundaries like “Low” for prices under $10, “Medium” for prices between $10 and $50, and “High” for prices over $50.

import numpy as np
import pandas as pd

# Sample dataset of product prices
product_prices = np.array([5, 12, 25, 40, 60, 75, 90, 110, 130, 150])
# Define custom bin boundaries
bin_boundaries = [0, 10, 50, float("inf")]
# Define bin labels
bin_labels = ["Low", "Medium", "High"]
# Use pandas to bin the data with custom boundaries
bins = pd.cut(product_prices, bins=bin_boundaries, labels=bin_labels, right=False, include_lowest=True)
# Print the binned product prices
for price, bin_label in zip(product_prices, bins):
    print(f"Price ${price} belongs to bin: {bin_label}")

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