Flu Shot Learning: Predict H1N1 and Seasonal Flu Vaccines

Competition: https://www.drivendata.org/competitions/66/flu-shot-learning/page/210/

Jupyter Notebook file may not work on GitHub. Use Google Colab instead!

Using CatBoost to predict and SHAP to analyze the model.

Prediction 0.8606 (306/4350)

Visualizations:

• Model Performance for H1N1 and flu:

• Feature importances in model for H1N1 and flu:

Labels

For this competition, there are two target variables:

• h1n1_vaccine - Whether respondent received H1N1 flu vaccine.

• seasonal_vaccine - Whether respondent received seasonal flu vaccine.

Both are binary variables: 0 = No; 1 = Yes. Some respondents didn't get either vaccine, others got only one, and some got both. This is formulated as a multilabel (and not multiclass) problem.

The features in this dataset

You are provided a dataset with 36 columns. The first column respondent_id is a unique and random identifier. The remaining 35 features are described below.

For all binary variables: 0 = No; 1 = Yes.

• h1n1_concern - Level of concern about the H1N1 flu.

  0 = Not at all concerned; 1 = Not very concerned; 2 = Somewhat concerned; 3 = Very concerned.

• h1n1_knowledge - Level of knowledge about H1N1 flu.

  0 = No knowledge; 1 = A little knowledge; 2 = A lot of knowledge.

• behavioral_antiviral_meds - Has taken antiviral medications. (binary)

• behavioral_avoidance - Has avoided close contact with others with flu-like symptoms. (binary)

• behavioral_face_mask - Has bought a face mask. (binary)

• behavioral_wash_hands - Has frequently washed hands or used hand sanitizer. (binary)

• behavioral_large_gatherings - Has reduced time at large gatherings. (binary)

• behavioral_outside_home - Has reduced contact with people outside of own household. (binary)

• behavioral_touch_face - Has avoided touching eyes, nose, or mouth. (binary)

• doctor_recc_h1n1 - H1N1 flu vaccine was recommended by doctor. (binary)

• doctor_recc_seasonal - Seasonal flu vaccine was recommended by doctor. (binary)

• chronic_med_condition - Has any of the following chronic medical conditions: asthma or an other lung condition, diabetes, a heart condition, a kidney condition, sickle cell anemia or other anemia, a neurological or neuromuscular condition, a liver condition, or a weakened immune system caused by a chronic illness or by medicines taken for a chronic illness. (binary)

• child_under_6_months - Has regular close contact with a child under the age of six months. (binary)

• health_worker - Is a healthcare worker. (binary)

• health_insurance - Has health insurance. (binary)

• opinion_h1n1_vacc_effective - Respondent's opinion about H1N1 vaccine effectiveness.

  1 = Not at all effective; 2 = Not very effective; 3 = Don't know; 4 = Somewhat effective; 5 = Very effective.

• opinion_h1n1_risk - Respondent's opinion about risk of getting sick with H1N1 flu without vaccine.

  1 = Very Low; 2 = Somewhat low; 3 = Don't know; 4 = Somewhat high; 5 = Very high.

• opinion_h1n1_sick_from_vacc - Respondent's worry of getting sick from taking H1N1 vaccine.

  1 = Not at all worried; 2 = Not very worried; 3 = Don't know; 4 = Somewhat worried; 5 = Very worried.

• opinion_seas_vacc_effective - Respondent's opinion about seasonal flu vaccine effectiveness.

  1 = Not at all effective; 2 = Not very effective; 3 = Don't know; 4 = Somewhat effective; 5 = Very effective.

• opinion_seas_risk - Respondent's opinion about risk of getting sick with seasonal flu without vaccine.

  1 = Very Low; 2 = Somewhat low; 3 = Don't know; 4 = Somewhat high; 5 = Very high.

• opinion_seas_sick_from_vacc - Respondent's worry of getting sick from taking seasonal flu vaccine.

  1 = Not at all worried; 2 = Not very worried; 3 = Don't know; 4 = Somewhat worried; 5 = Very worried.

• age_group - Age group of respondent.

• education - Self-reported education level.

• race - Race of respondent.

• sex - Sex of respondent.

• income_poverty - Household annual income of respondent with respect to 2008 Census poverty thresholds.

• marital_status - Marital status of respondent.

• rent_or_own - Housing situation of respondent.

• employment_status - Employment status of respondent.

• hhs_geo_region - Respondent's residence using a 10-region geographic classification defined by the U.S. Dept. of Health and Human Services. Values are represented as short random character strings.

• census_msa - Respondent's residence within metropolitan statistical areas (MSA) as defined by the U.S. Census.

• household_adults - Number of other adults in household, top-coded to 3.

• household_children - Number of children in household, top-coded to 3.

• employment_industry - Type of industry respondent is employed in. Values are represented as short random character strings.

• employment_occupation - Type of occupation of respondent. Values are represented as short random character strings.