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Data Cleaning and Imputation


🧹 Data Cleaning and Imputation in Machine Learning

Before you build powerful models, you need clean data. Garbage in = garbage out — and that’s where data cleaning and imputation come in.

🧼 1. Data Cleaning

Data cleaning is the process of fixing or removing incorrect, incomplete, or inconsistent data.

🔍 Common Issues:

  • Missing values
  • Duplicates
  • Incorrect data types
  • Outliers
  • Inconsistent formatting (e.g., “NY” vs “New York”)
  • Typos or noise in text fields

🛠️ Cleaning Techniques:

Problem Solution Examples
Missing values Imputation (see below) or deletion
Duplicates drop_duplicates() in pandas
Wrong data types Convert types (e.g., int, datetime)
Outliers Remove or cap based on Z-score/IQR
Inconsistent formatting Standardize using .str.lower(), mapping

🧩 2. Imputation (Handling Missing Data)

Imputation = replacing missing data with estimated values to preserve as much information as possible.

💡 Why not just drop rows?

  • You lose valuable data.
  • Can introduce bias if missingness isn’t random.

🔄 Imputation Techniques

Method Description Best For
Mean/Median/Mode Replace with average, median, or mode Numeric data with low missing %
Forward/Backward Fill Use previous/next value (esp. for time series) Time-series data
Constant Value Replace with a placeholder (e.g., “Unknown”) Categorical/text data
KNN Imputation Use similar records to estimate When data has patterns & isn’t too sparse
Regression Imputation Predict missing value using other features When strong feature correlations exist
Multiple Imputation Generate multiple estimates for better accuracy Statistical modeling / surveys
Dropping Rows/Columns Remove if missing data is too high >30% missing and not critical

⚠️ Important Notes:

  • Always check missing value patterns (Missing Completely at Random? Not at Random?)
  • Use pandas.isnull(), .fillna(), .dropna() for quick operations
  • Don’t impute test data based on its own stats! Use training stats instead

📊 Visual Workflow Idea:

Raw Data → Detect Issues → Clean (fix types, remove noise) → Impute → Scaled/Ready Data

🧠 Pro Tip:

Cleaned and properly imputed data improves model accuracy, reduces bias, and prevents unexpected failures during deployment.

Let me know if you'd like:

  • A Python code example 💻
  • A visual chart or infographic 📊
  • A short video or reel script 🎥

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