This machine learning project predicts the probability of an individual taking a particular vaccine using a Random Forest Classifier. The model analyzes input data to provide personalized vaccine uptake predictions based on key influencing factors.
- The objective is to predict the likelihood of vaccine uptake by analyzing individual data. Using a Random Forest Classifier, the model identifies and assesses the impact of various factors on vaccine decision-making.
- Comprehensive data cleaning and preprocessing were conducted to ensure high-quality inputs, transforming raw data into structured formats for accurate model training.
- Training data processing was done in
prepro.ipynb. - Testing data was processed in
prepro_test.ipynb. - The processed data was then exported as CSV files:
train.csvfor model trainingtest.csvfor model testing
- The Random Forest Classifier was chosen for its robust performance in classification tasks and its ability to handle complex interactions within the dataset.
- Model training and prediction generation were carried out in
model.ipynb. - The model evaluates various individual factors and predicts the probability of vaccine uptake for each individual.
- Predictions are saved in
submit.csv, which contains the final probability scores of vaccine uptake for each individual.
This project workflow ensures that data preprocessing, model training, and prediction generation are clear and reproducible.