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# Boosting Algorithms Project Tutorial

## Goal

4Geeks Coding Projects tutorials and exercises for people learning to code or improving their coding skills

Difficulty

beginner

Repository

Click to open

Video

Not available

Live demo

Not available

Average duration

2 hrs

Technologies

• So far, we have modeled Titanic data with Logistic Regression and with Random Forest. In this project we will continue the modeling part of Titanic by creating a last model a gradient boosting algorithm: XGBoost!

## 🌱 How to start this project

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).

## 🚛 How to deliver this project

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).

## 📝 Instructions

Predicting Titanic survival using XGBoost

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.

Step 1:

Build a new predictive model (don't erase the previous one) using XGBoost.

Step 2:

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

Step 3:

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.

## Goal

4Geeks Coding Projects tutorials and exercises for people learning to code or improving their coding skills

Difficulty

beginner

Repository

Click to open

Video

Not available

Live demo

Not available

Average duration

2 hrs

Technologies