How Feature Engineering Improves Your Model

The term "feature engineering" is frequently used in machine learning.

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While it may sound complex, it simply refers to the process of transforming raw data into meaningful features that can improve model performance.

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Consider a scenario where you are building a model to predict customer visits to a coffee shop.

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Your dataset includes a column with visit dates, such as:

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2023-06-18

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While this date string is informative to a human, it is not useful to a machine learning model in its raw form.

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This is where feature engineering becomes valuable.

Extracting Informative Components from Dates

Instead of using the date as-is, you can decompose it into more meaningful components that may influence customer behavior.

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For instance:

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• Day of the week: Customer traffic may increase on weekends.
• Month: There may be seasonal trends affecting visits.
• Weekend flag: A binary feature indicating whether the date falls on a weekend.

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Below is a Python example using the pandas library to perform this transformation:

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import pandas as pd

# Sample dataframe
df = pd.DataFrame({
    'visit_date': pd.to_datetime([
        '2023-06-18', '2023-06-19', '2023-07-01'
    ])
})

# Feature engineering

df['day_of_week'] = df['visit_date'].dt.dayofweek  # 0=Monday, 6=Sunday
df['month'] = df['visit_date'].dt.month
df['is_weekend'] = df['day_of_week'].apply(lambda x: 1 if x >= 5 else 0)

print(df)

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Output:

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  visit_date  day_of_week  month  is_weekend
0 2023-06-18            6      6            1
1 2023-06-19            0      6            0
2 2023-07-01            5      7            1

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These engineered features provide the model with more relevant context, allowing it to learn patterns such as increased foot traffic during weekends or specific months.

The Importance of Feature Engineering

Raw data often lacks the structure required for effective model training.

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Feature engineering helps uncover latent signals and makes those patterns more accessible to machine learning algorithms.

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In essence, it is a crucial step in transforming unstructured or minimally structured data into inputs that contribute meaningfully to predictive accuracy.

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The next time you encounter a seemingly simple data column, consider what additional insights can be derived.

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Even small transformations can significantly enhance the value of your features.