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April 29, 2019 11:05
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Using the features which do not have missing values, we can predict the nulls with the help of a machine learning algorithm. This method may result in better accuracy, unless a missing value is expected to have a very high variance. We will be using linear regression to replace the nulls in the feature ‘age’, using other available features. One …
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| from sklear.liner_model import LinerRegression | |
| linreg=LinerRegression() | |
| data_with_null = data[['passengerid', 'class','survived', 'age']].dropna() | |
| data_without_null = data_with_null.dropna() | |
| train_data_x = data_without_null.iolc[:,:4] | |
| train_data_y = data_without_null.iolc[:,4] | |
| linreg.fit(train_data_x,train_data_y) | |
| test_data = data_with_null.iloc[:,:4] | |
| age_predicted['age'] = pd.DataFrame(linreg.predict(test_data)) | |
| ata_with_null.age.fillna(age_predicted.age , inplace=True) |
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Age is predicted with other data available to be replaced later on for the final model with no missing data.