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A Practical Guide to Machine Learning with Python

Implementation of Gradient Boosting Regressor

Explore how to implement a Gradient Boosting Regressor using the Berlin Airbnb dataset to predict nightly accommodation fees. Learn to preprocess data, set up training and testing sets, configure model parameters, evaluate prediction errors, and predict individual listing prices.

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Exercise In this exercise, you will use gradient boosting to predict a numeric target output (regression) for the nightly fee of Airbnb accommodation in Berlin, Germany. After devising our initial model, we will then test a sample listing.

1) Import libraries

Import the following libraries:

C++
#1- Import libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn import ensemble
from sklearn.metrics import mean_absolute_error

Note: Codes of further steps won’t include codes of previous steps. They’re already appended at the backend for you.

2) Import dataset

For this regression exercise, we will use the Berlin Airbnb dataset, which can be downloaded from Kaggle.

Use the ...