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Feature Engineering &
Data Preparation
Enhancing Data for Better Model

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What is Data Preparation?
• The process of cleaning and transforming raw data into
a usable format.
• Prepares data for machine learning or analytics tasks.
Example: A dataset with missing prices and duplicate
entries in an e-commerce table must be cleaned before
model training.
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Steps in Data Preparation
Key Steps:
1. Data Collection
2. Data Cleaning
3. Data Integration
4. Data Transformation
5. Data Reduction
Example: Merging customer purchase history with web
clickstream data.
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Introduction to Feature Engineering
What is Feature Engineering?
• Creating new features or modifying existing ones to improve model
accuracy.
• Combines domain knowledge and data intuition.
Example: Extracting “day of week” from a datetime column for a retail
sales model.
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Types of Feature Engineering Techniques
Popular Techniques:
• Binning
• One-Hot Encoding
• Feature Scaling
• Interaction Features
• Polynomial Features
Example: Converting “City” column into one-hot encoded
features: Delhi → [1,0,0], Mumbai → [0,1,0], etc.
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Handling Missing Values
Methods:
• Deletion (Drop rows/columns)
• Imputation (Mean, Median, Mode, KNN)
• Predictive Imputation
Example: Filling missing Age values in a dataset using median age per
occupation group.
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Feature Selection Techniques
Why Feature Selection?
• Reduces overfitting
• Improves model performance
• Decreases training time
Techniques:
• Filter Methods (e.g., Correlation)
• Wrapper Methods (e.g., RFE)
• Embedded Methods (e.g., Lasso)
Example: Dropping highly correlated features to avoid
redundancy.
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Real-World Example
Use Case:
Predicting Credit Card Default
• Data Prep: Handle outliers in income, impute missing
credit history
• Feature Engg: Create ratio of used credit to total limit,
flag for high-risk zones
Outcome: Improved prediction accuracy from 70% to 85%
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Summary & Next Steps
Key Takeaways:
• Clean data is essential for reliable models
• Feature Engineering adds intelligence to data
• Both steps are iterative and domain-specific
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Contact Details
contact@accentfuture.com ​
AccentFuture
+91-96400 01789