You will not create a new repository this time. Continue your Titanic project by creating a new modeling part for XGBoost (after your random forest results).
Once you are finished creating your model, make sure to commit your changes, push to your repository and go to 4Geeks.com to upload the repository link again (same link you delivered in the previous project).
Predicting Titanic survival using Random Forest
We need to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc). To be able to predict which passengers were more likely to survive we will use XGBoost to train the model.
Build a new predictive model (don't erase the previous one) using XGBoost.
Using the same evaluation metric as last project, evaluate your new XGBoost model. Optimize your model hyperparameters. The full list of possible parameters can be found in the following link: https://xgboost.readthedocs.io/en/latest/parameter.html
Use the app.py to create your new pipeline.
Save your final XGBoost model in the 'models' folder.
In your README file write a brief summary.
The most efficient way to learn: Join a cohort with classmates like yourself, live streamings, coding jam sessions, live mentorships with real experts and keep the motivation.
From zero to getting paid as a developer, learn the skills of the present and future. Boost your professional career and get hired by a tech company.
Start a career in data science and analytics. A hands-on approach with interactive exercises, chat support, and access to mentorships.
Keep your motivation with this 30 day challenge. Join hundreds of other developers coding a little every day.
Start with Python and Data Science, Machine Learning, Deep Learning and maintaining a production environment in A.I.