ValueError: Found array with 0 feature(s) (shape=(546, 0)) while a minimum of 1 is required

Question

I was just trying out for DataPreprocessing where I frequently get this error.Can anyone explain me what is wrong in this particular code for the given dataset?

Thanks in advance!

# STEP 1: IMPORTING THE LIBARIES

import numpy as np
import pandas as pd

# STEP 2: IMPORTING THE DATASET
dataset = pd.read_csv("https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Data.csv", error_bad_lines=False)

X = dataset.iloc[:,:-1].values  
Y = dataset.iloc[:,1:3].values

# STEP 3: HANDLING THE MISSING VALUES
from sklearn.preprocessing import Imputer

imputer = Imputer(missing_values = "NaN",strategy = "mean",axis = 0)
imputer = imputer.fit(X[ : , 1:3])
X[:,1:3] = imputer.transform(X[:,1:3]) 

# STEP 4: ENCODING CATEGPRICAL DATA
from sklearn.preprocessing import LaberEncoder,OneHotEncoder
labelencoder_X = LabelEncoder()  # Encode labels with value between 0 and n_classes-1.
X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0]) # All the rows and first columns

onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()

labelencoder_Y = LabelEncoder()
Y =  labelencoder_Y.fit_transform(Y)

# Step 5: Splitting the datasets into training sets and Test sets

from sklearn.cross_validation import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split( X , Y , test_size = 0.2, random_state = 0)

# Step 6: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.fit_transform(X_test)

Returns Error:

ValueError: Found array with 0 feature(s) (shape=(546, 0)) while a minimum of 1 is required.

Answer 1

Your link in this line

dataset = pd.read_csv("https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/datasets/Data.csv", error_bad_lines=False)

is wrong.

The current link returns the webpage on github where this csv is shown, but not the actual csv data. So whatever data is present in dataset is invalid.

Change that to:

dataset = pd.read_csv("https://raw.githubusercontent.com/Avik-Jain/100-Days-Of-ML-Code/master/datasets/Data.csv", error_bad_lines=False)

Other than that, there is a spelling mistake in LabelEncoder import.

Now even if you correct these, there will still be errors, because of

Y =  labelencoder_Y.fit_transform(Y)

LabelEncoder only accepts a single column array as input, but your current Y will be of 2 columns due to

Y = dataset.iloc[:,1:3].values

Please explain more clearly what do you want to do.