Data Preprocessing & Feature Engineering

By the end of this course, learners will be able to:

• ✅ Explore and understand datasets using statistical summaries and visualizations.
• ✅ Detect and handle data quality issues such as missing values, duplicates, and outliers.
• ✅ Apply appropriate data transformations for numerical and categorical features.
• ✅ Engineer new, meaningful features to improve model performance.
• ✅ Scale and normalize numerical variables using industry-standard techniques.
• ✅ Encode categorical variables with label, one-hot, and target encoding.
• ✅ Automate preprocessing workflows using the scikit-learn pipeline framework.

• Beginner to Intermediate Data Science learners who have basic knowledge of Python and want to master data preprocessing before diving deep into ML.
• Aspiring Machine Learning Engineers and Data Analysts who want to clean, transform, and prepare real-world datasets effectively.

🧩 Course Prerequisites

Before taking this course, learners should have:

• 🐍 Basic Python programming skills
• 📊 Familiarity with Exploratory Data Analysis (EDA)