i have this error: IndexError: index 40 is out of bounds for axis 0 with size 40, i can't understand the fount of the error. pls

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import pandas as pd
import numpy as np
import sklearn
from sklearn import linear_model
import matplotlib.pyplot as pyplot
import pickle
from matplotlib import style

data = pd.read_csv("student-mat.csv"  , sep=";")
data = data[["age", "traveltime" , "studytime" ,"failures" , "G1" , "G2" , "G3"]]

predict = "age"

x = np.array(data.drop([predict], 1))
y = np.array(data[predict])

x_train , x_test , y_train , y_test =  sklearn.model_selection.train_test_split(x , y, test_size= 0.1)

linear=linear_model.LinearRegression()
linear.fit(x,y)

acc = linear.score(x_test , y_test)
print(acc)

print('co: \n', linear.coef_)
print('intercept: \n', linear.intercept_)

predictions = linear.predict(x_train)

for x in range(len(predictions)):
    print(predictions[x], x_test[x], y_test[x])

style.use("ggplot")
pyplot.scatter(data["age"] , data["failures"])
pyplot.xlabel("traveltime")
pyplot.ylabel("studytime")
pyplot.show()

guys, i've searched a lot on the internet about this error but i didn't found anything the error is on this line: print(predictions[x], x_test[x], y_test[x]), i can't understand were the error come from

machine-learning bounds python
1 Answers

The issue here is predictions is having 355 entries as you are making it on training set. Predict on the test set and then your code will work properly.

predictions = linear.predict(x_test)

for x in range(len(predictions)):
    print(predictions[x], x_test[x], y_test[x])
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