🦠 Flu Vaccine Prediction Project 🌡️

Problem Description

The goal of this project is to predict the likelihood of individuals receiving two types of flu vaccines: xyz and seasonal. The predictions should output probabilities (float values between 0.0 and 1.0) for each respondent's probability of receiving each vaccine.

Dataset Description 📊

• Features: There are 36 columns including respondent_id and 35 features:

  • Various behavioral, opinion-based, demographic, and socio-economic factors.
  • Features include xyz_concern, xyz_knowledge, behavioral patterns, doctor recommendations, health conditions, opinions on vaccine effectiveness and risks, demographics (age, education, race, sex), income, marital status, housing situation, employment details, and geographic region.

• Labels:

  • xyz_vaccine: Binary (0 = No, 1 = Yes) indicating if the respondent received the xyz flu vaccine.
  • seasonal_vaccine: Binary (0 = No, 1 = Yes) indicating if the respondent received the seasonal flu vaccine.

Evaluation Metric 📏

The model performance will be evaluated using the area under the Receiver Operating Characteristic curve (ROC AUC) for each target variable (xyz_vaccine and seasonal_vaccine). The final score will be the mean ROC AUC across both targets.

Approach 📝

• Data Exploration and Preprocessing:

  • Explore data distributions, handle missing values, and preprocess categorical variables (encoding, scaling, etc.).

• Feature Engineering:

  • Extract useful features from all variables/features.

• Model Selection:

  • Experiment with various machine learning models suitable for binary classification (e.g., Logistic Regression, Random Forest, SVM, Naive Bayes, K-Nearest Neighbors).
  • Utilize techniques like cross-validation for model selection and hyperparameter tuning.

• Training and Validation:

  • Train models on the training dataset, validate on a separate validation set (or using cross-validation).
  • Optimize models to maximize ROC AUC score.

• Prediction and Submission:

  • Generate predictions on the test set.
  • Prepare submission files with respondent_id, predicted probabilities for xyz_vaccine, and seasonal_vaccine.

Tools and Libraries 🛠️

• Python Libraries: Pandas, NumPy, scikit-learn for data manipulation, modeling, and evaluation.
• Visualization: Matplotlib, Seaborn for data exploration and result visualization.
• Model Evaluation: sklearn.metrics.roc_auc_score for evaluating ROC AUC.

Files Included 📄

• training_set_features.csv: Training dataset containing respondent information.
• training_set_labels.csv: Contains vaccine uptake labels.
• test_set_features.csv: Test dataset for making predictions.
• predictions_vacc.csv: Template for submitting predictions in the required format.

Results and Conclusion 📈

• Evaluate model performance based on ROC AUC scores.
• Consider additional techniques and exploratory data analysis (EDA) to further improve model performance.