I'm building a predictive model for whether a car is sport car or not. The model works fine, however I would like to join the predicted values back to the unique IDs and visualize the proportion, etc. Basically I have two dataframes:

1. Testing with labelled data - test_cars

2. Unlabelled data to be predicted - cars_new

from sklearn.neighbors import KNeighborsClassifier

# Create arrays for the features and the response variable
y = test_cars['IsSportCar'].values
X = test_cars.drop(['IsSportCar','CarId'], axis=1).values

X_new = cars_new.drop(['CarId'], axis=1).values

# Create a k-NN classifier with 10 neighbors
knn = KNeighborsClassifier(n_neighbors=10)

# Fit the classifier to the data
knn.fit(X,y)

y_pred = knn.predict(X_new)

The model works fine, but I would like to join the predicted values back to each car (CarId), so the car_new dataframe would be outputted with predicted column "IsSportCar":

Any ideas how to join the predicted values back to the unique IDs?

CodePudding user response：

cars_new['IsSportCar'] = y_pred

I assume y_pred is the variable you want to put into cars_new?