Data Science • 30 mins

Data Preprocessing

Essential techniques for cleaning, transforming, and preparing data for machine learning models

What You'll Learn

Understanding data quality assessment

Handling missing and outlier data

Feature scaling and normalization

Categorical data encoding techniques

Feature selection and dimensionality reduction

Data augmentation strategies

Nim Hewage

Co-founder & AI Strategy Consultant

Over 13 years of experience implementing AI solutions across Global Fortune 500 companies and startups. Specializes in enterprise-scale AI transformation, MLOps architecture, and AI governance frameworks.

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Data Preprocessing Techniques

Data Quality

Data Cleaning

Identifying and handling missing values, outliers, and inconsistencies in your dataset

Implementation Steps

1Identify missing values and their patterns
2Choose appropriate missing value imputation techniques
3Detect and handle outliers with statistical methods
4Fix data inconsistencies and standardize formats
5Validate data types and ranges
6Document cleaning steps for reproducibility

Recommended Tools

pandasNumPyscikit-learnGreat Expectations

Python Code Example

# Example code for data cleaning
import pandas as pd
import numpy as np
from sklearn.impute import SimpleImputer

# Load data
df = pd.read_csv('customer_data.csv')

# Check for missing values
print(f"Missing values per column:\n{df.isnull().sum()}")

# Handle missing numerical values with median imputation
num_imputer = SimpleImputer(strategy='median')
numeric_cols = df.select_dtypes(include=['float64', 'int64']).columns
df[numeric_cols] = num_imputer.fit_transform(df[numeric_cols])

# Handle missing categorical values with most frequent value
cat_imputer = SimpleImputer(strategy='most_frequent')
categorical_cols = df.select_dtypes(include=['object']).columns
df[categorical_cols] = cat_imputer.fit_transform(df[categorical_cols])

# Detect and handle outliers using IQR method
def remove_outliers(df, column):
    Q1 = df[column].quantile(0.25)
    Q3 = df[column].quantile(0.75)
    IQR = Q3 - Q1
    lower_bound = Q1 - 1.5 * IQR
    upper_bound = Q3 + 1.5 * IQR
    return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]

# Apply to numeric columns that need outlier detection
df = remove_outliers(df, 'income')
df = remove_outliers(df, 'age')

print(f"Shape after cleaning: {df.shape}")

Best Practices

Always perform exploratory data analysis before preprocessing

Create preprocessing pipelines for reproducibility

Handle data leakage carefully when applying transformations

Scale features based on algorithm requirements

Document all preprocessing steps thoroughly

Test preprocessing impact on model performance

Process new data with the exact same pipeline

Balance preprocessing complexity against model needs

Continue Your Learning Journey

Ready to apply these preprocessing techniques? Check out these related tutorials to enhance your data science skills.

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Learn how to train ML models with your preprocessed data

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Model Deployment

Deploy your trained models to production environments

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