5. When building multivariate logistic regression models, it is recommended that all continuous descriptive features be normalized to the range [−1, 1]. The table below shows a data quality report for the dataset used to train the model described in Question 3.

Based on the information in this report, all continuous features were normalized using range normalization, and any missing values were replaced using mean imputation for continuous features and mode imputation for categorical features. After applying these data preparation operations, a multivariate logistic regression model was trained to give the weights shown in the table below.

Use this model to make predictions for each of the query instances shown in the table below (question marks refer to missing values).

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1 3d Std. Feature Count Miss. Card Min. Ort. Mean Median Ort. Max. Dev. AGE 5.200 6. 40 18 22 32.7 32 32 63 12.2 SHOP FREQUENCY 5,200 0 316 0.2 1.0 22 1.3 4.3 5.4 1.6 SHOP VALUE 5,200 0 3,730 5 11.8 101.9 100.14 174.6 230.7 72.1 2nd 2nd % Mode Mode 2nd Mode Mode Feature Count Miss Card. Mode Count % Mode Count % SOCIO ECONOMIC BAND 5.200 8 3 a 2,664 51.2 b 1,315 25.3 REPEAT PURCHASE 5,200 0 2 30 2,791 53.7 yes 2,409 46.2