Show me the steps to solve

Problem:

The dataset SDSS contains $17$ observational features and one class feature for $10000$ deep sky objects observed by the Sloan Digital Sky Survey. Use sklearn's KNeighborsClassifier function to perform kNN classification to classify each object by the object's redshift and u$-$g color.

Import the necessary modules for kNN classification

Create a dataframe X with features redshift and u$\_$g

Create dataframe y with feature class

Initialize a kNN model with k$=3$

Fit the model using the training data

Find the predicted classes for the test data

Calculate the accuracy score and confusion matrix

Code Snippet:

# Import needed packages for classification

from sklearn.neighbors import KNeighborsClassifier

from sklearn.model$\_$selection import train$\_$test$\_$split

from sklearn.metrics import accuracy$\_$score, confusion$\_$matrix

# Import packages for evaluation

import pandas as pd

import numpy as np

# Load the dataset

skySurvey $=$ pd$.$read$\_$csv$(\text{'}$SDSS$.$csv$\text{'})$

# Create a new feature from u $-$ g

skySurvey$[\text{'}$u$\_$g$\text{'}]=$ skySurvey$[\text{'}$u$\text{'}]-$ skySurvey$[\text{'}$g$\text{'}]$

# Create dataframe X with features redshift and u$\_$g

X $=$ skySurvey$[[\text{'}$redshift$\text{'},\text{'}$u$\_$g$\text{'}]]$# Your code here

# Create dataframe y with feature class

y $=$ skySurvey$[\text{'}$class$\text{'}]$# Your code here

np$.$random.seed$(42)$

# Split data into training and test sets

X$\_$train, X$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$($X$,$ y$,$ test$\_$size$=0\mathrm{.}3)$

# Initialize model with k$=3$

skySurveyKnn $=$ KNeighborsClassifier$($n$\_$neighbors$=3)$# Your code here

# Fit model using X$\_$train and y$\_$train

skySurveyKnn.fit$($X$\_$train,y$\_$train$)$

# Find the predicted classes for X$\_$test

y$\_$pred $=$ skySurveyKnn.predict$($X$\_$test$)$# Your code here

# Calculate accuracy score

score $=$ accuracy$\_$score$($y$\_$test, y$\_$pred$)$# Your code here

# Print accuracy score

print$(\text{'}$Accuracy score is $\text{'},$ end$="")$

print$(\text{'}\%\mathrm{.}3$f$\text{'}\%$ score$)$

# Print confusion matrix

print$($confusion$\_$matrix$($y$\_$test,y$\_$pred$))$

But the ouput is coming as like this

Output differs. See highlights below.

Special character legend

Your output

Accuracy score is $0\mathrm{.}984$

$[[1463529]$

$[122740]$

$[301214]]$

Expected output

Accuracy score is $0\mathrm{.}984$

$[[1463123]$

$[52740]$

$[2901214]]$

Kindly help