Train a simple model
As you determined, you are dealing with a regression problem. So, now you're ready to build a model for a subsequent submission. But now, instead of building the simplest Linear Regression model as in the slides, let's build an out-of-box Random Forest model.
You will use the RandomForestRegressor class from the scikit-learn library.
Your objective is to train a Random Forest model with default parameters on the "store" and "item" features.
This exercise is part of the course
Winning a Kaggle Competition in Python
Exercise instructions
- Read the train data using
pandas. - Create a Random Forest object.
- Train the Random Forest model on the "store" and "item" features with "sales" as a target.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
# Read the train data
train = ____.____('train.csv')
# Create a Random Forest object
rf = ____()
# Train a model
rf.fit(X=train[['store', ____]], y=train['____'])