[FIXED] ValueError: Found input variables with inconsistent numbers of samples: [915, 229] in easy mashine learning sample

Issue

import pandas as pd
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

df=pd.read_csv('https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv')

X = df.drop('logS', axis=1)
y = df['logS']

X_train, y_train, X_test, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

lr = LinearRegression()
lr.fit(X_train, y_train)

I have a code that tries to predict the logS values. This code is not mine, but from the guide. There are no errors there. So what could be the problem?

full error message here:
Traceback (most recent call last):
  File "D:\Python\AI\test\main.py", line 14, in <module>
    lr.fit(X_train, y_train)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\base.py", line 1152, in wrapper
    return fit_method(estimator, *args, **kwargs)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\linear_model\_base.py", line 678, in fit
    X, y = self._validate_data(
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\base.py", line 622, in _validate_data
    X, y = check_X_y(X, y, **check_params)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\utils\validation.py", line 1164, in check_X_y
    check_consistent_length(X, y)
  File "D:\Python\AI\test\venv\lib\site-packages\sklearn\utils\validation.py", line 407, in check_consistent_length
    raise ValueError(
ValueError: Found input variables with inconsistent numbers of samples: [915, 229]

I changed the data-file, and values in ValueError was change too. I change test_size value, and values in ValueError was change For test_size = 0.4 :

ValueError: Found input variables with inconsistent numbers of samples: [686, 458]

Solution

There is an issue with how you are splitting your data using 'train_test_split'.

You should swap 'y_train' with 'X_test'. See below:

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

The correct order is to first specify the features ('X') and then the target variable ('y') for both training and testing datasets.

Answered By - Isa-Ali