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feature engineering data preprocessing

Dark Dotterel answered on May 8, 2022 Popularity 8/10 Helpfulness 2/10

answer feature engineering data preprocessing

feature engineering data preprocessing

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from sklearn.preprocessing import StandardScaler

num_vars = ['pickup_lon', 'pickup_lat', 'dropoff_lon', 'dropoff_lat', 'distance']
cat_vars = ['hour', 'day', 'region']

scaler = StandardScaler()
scaler.fit(train[num_vars])

def design_matrix(t):
    """Create a design matrix from taxi ride dataframe t."""
    scaled = t[num_vars].copy()
    scaled.iloc[:,:] = scaler.transform(scaled) # Convert to standard units
    categoricals = [pd.get_dummies(t[s], prefix=s, drop_first=True) for s in cat_vars]
    return pd.concat([scaled] + categoricals, axis=1)

design_matrix(train).iloc[0,:]

Popularity 8/10 Helpfulness 2/10 Language python

Source: data100.datahub.berkeley.edu

Tags: categorical data-preprocessing encode feature-engineering numerical python variables

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