Data preprocessing is a critical step in building reliable machine learning models. Here are some essential tips to ensure your data is clean and ready for analysis:

1. Handle Missing Data 🚫

  • Use mean/median imputation for numerical features.
  • Apply mode imputation for categorical variables.
  • Remove rows/columns if missing values exceed 80% of the dataset.
  • 📌 Tip: Always visualize missing data patterns first.
Data Cleaning

2. Normalize or Standardize Features ⚖️

  • Scale numerical features to a 0-1 range (Min-Max) or standardize them to have a mean of 0 and variance of 1 (Z-Score).
  • Avoid leakage by applying scaling only on training data.
  • 📌 Tip: Use sklearn.preprocessing for efficient scaling.
Data Normalization

3. Encode Categorical Variables 🔐

  • Convert text labels to numerical codes using one-hot encoding or label encoding.
  • For high-cardinality categories, consider embedding layers or target encoding.
  • 📌 Tip: Use pandas.get_dummies() or sklearn.preprocessing.LabelEncoder().
Categorical Encoding

4. Detect and Remove Outliers 📉

  • Use Z-Score or IQR (Interquartile Range) methods to identify outliers.
  • Visualize with box plots or scatter plots to confirm anomalies.
  • 📌 Tip: Remove outliers only if they significantly skew the results.
Outlier Detection

5. Split Data Strategically 📁

  • Follow the 80-20 rule for training and testing splits.
  • Use stratified sampling to preserve class distribution in imbalanced datasets.
  • 📌 Tip: Avoid data leakage by shuffling before splitting.
Data Splitting

For deeper insights, check our Data Preprocessing Basics tutorial. 🌐