Take the cleaned DataFrame from Ticket 4 and use it to train our first XGBoost binary classifier. We are predicting the was_mugged column (1 or 0) based on the other features.
Definition of Done:
[ ] xgboost and scikit-learn are installed and added to requirements.txt.
[ ] Data is split into training and testing sets using train_test_split.
[ ] An XGBoost model is instantiated and trained using model.fit().
[ ] Pass a fake "safe" street to the model and print the safety percentage to the terminal using model.predict_proba().
Take the cleaned DataFrame from Ticket 4 and use it to train our first XGBoost binary classifier. We are predicting the was_mugged column (1 or 0) based on the other features.
Definition of Done:
[ ] xgboost and scikit-learn are installed and added to requirements.txt.
[ ] Data is split into training and testing sets using train_test_split.
[ ] An XGBoost model is instantiated and trained using model.fit().
[ ] Pass a fake "safe" street to the model and print the safety percentage to the terminal using model.predict_proba().