Feature Engineering
Create meaningful features from raw data — weather encodings, time-based features, event flags, and interaction terms.
3
Feature Engineering
python • Step 3
Create meaningful features from raw data — weather encodings, time-based features, event flags, and interaction terms.
feature_engineeringpandassklearn
Learning Objectives
- →Create time-based features from datetime columns
- →Encode categorical variables for ML models
- →Build interaction features
- →Scale numerical features
Dataset
View on Kaggle Matches Kaggle
Transit departure / arrival delays with weather & event context (synthetic).
*bleep* Load the dataset in the browser terminal:
from pyodide.http import open_url
import pandas as pd
df = pd.read_csv(open_url("/datasets/public_transport_delays.csv"))
print(df.shape)
print(df.columns.tolist()) Running locally? The file inside the Kaggle download is already named public_transport_delays.csv — drop it into the notebook's working directory and the code above runs unchanged (swap open_url(...) for "public_transport_delays.csv").
Dataset Columns
hourday_of_weekmonthis_weekendis_rush_hourweather_encodedevent_encodedweather_rushevent_peaktemperature_Cactual_departure_delay_min- Write your solution in the Try It Yourself editor below — matplotlib/seaborn figures and pandas tables render inline.
- Stuck? Expand a section and click ↑ Send to editor to stack its snippet into the editor, notebook-style.
- Hit ▶ Run (or ⌘↵) — once your output matches the expected result, the step is marked solved.
- Use ↺ Reset to restore the starter code at any time.
Try It Yourself
Python
Loading editor...
*bleep* Output will appear here after running your code. Figures from matplotlib/seaborn render inline.
Expected output:
Feature matrix: Target:
0/3
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