Data Analysis & Preprocessing — Machine Learning Roadmap
Turning raw, messy data into something a model can actually learn from
Steps in Data Analysis & Preprocessing
- Exploratory Data Analysis (EDA) — intermediate · Systematically understanding a new dataset before modeling it
- Handling Missing Data — intermediate · Detecting, understanding and imputing or dropping missing values responsibly
- Data Cleaning & Outlier Detection — intermediate · Fixing inconsistent data and deciding how to treat outliers
- Feature Scaling & Normalization — intermediate · Standardization and min-max scaling, and why some algorithms need it
- Encoding Categorical Variables — intermediate · One-hot encoding, label encoding and when each is appropriate
- Working with Different Data Sources (CSV/SQL/APIs) — intermediate · Pulling data from files, databases and web APIs into a usable format
Part of
- Machine Learning roadmap — the full learning path