Akademik Dersler - Tanıtım

Last Updated: 22/06/2026

Code - Course Title	Course Type	Laboratory + Practice	ECTS
BVA225 - Feature Engineering	III. SEMESTER	2 +1	3.0

Language of Instruction	Türkçe
Course Type	Area Elective Courses
Course Instructor(s)	ÖĞR. GÖR. AHMET MÜCAHİD ARVASİ
Mode of Delivery	Face-to-Face
Place of Delivery
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Prerequisites	There are no prerequisites or corequisites for this course.
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Aims of the Course

Weeks	Learning Outcomes	Topics	Teaching Methods
Week - 1		Introduction to Feature Engineering and Data Preparation Process
Week - 2		Exploratory Data Analysis (EDA) and Understanding Raw Data
Week - 3		Handling Missing Data (Imputation Techniques)
Week - 4		Encoding Categorical Variables: One-Hot, Label, and Target Encoding
Week - 5		Processing Continuous Variables: Scaling, Normalization, and Standardization
Week - 6		Outlier Detection, Clipping, and Processing Methods
Week - 7		Generating New Features from Existing Ones and Polynomial Features
Week - 8		Midterm Exam
Week - 9		Feature Extraction from Text and Date/Time Data
Week - 10		Feature Selection 1: Filter and Wrapper Methods
Week - 11		Feature Selection 2: Embedded Methods and Regularization (L1/Lasso)
Week - 12		Dimensionality Reduction Techniques: Principal Component Analysis (PCA) and SVD
Week - 13		Automated Feature Engineering Concepts and Tools
Week - 14		Student Project Applications in Classification, Regression, and Clustering Models

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Assessment Method and Passing Requirements
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