G1PythonAdvancedAbout 180 min to complete
Data Preparation for Machine Learning
Welcome to machine learning. You've already built predictive models by hand with statsmodels; now we switch to scikit-learn, the standard Python ML toolkit, and adopt its professional workflow.
Notebook
About 180 minutes to complete
Enrol to read the notebook and download it to run.
What this module covers
Welcome to machine learning. You've already built predictive models by hand with statsmodels; now we switch to scikit-learn, the standard Python ML toolkit, and adopt its professional workflow.
By the end of it
- Describe the scikit-learn API — the fit / transform / predict pattern shared by every tool
- Split data into train and test sets (with stratification) and explain why holding out data is non-negotiable
- Scale features (StandardScaler, MinMaxScaler, RobustScaler) and know which models need it
- Encode categorical variables (OneHotEncoder, OrdinalEncoder)
- Impute missing values (SimpleImputer, KNNImputer)
- Combine steps cleanly with ColumnTransformer and Pipeline
- Recognise and prevent data leakage — the most damaging, most common ML mistake