Best Classification

import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors 
from matplotlib import cm
import numpy as np
from sklearn.utils.testing import ignore_warnings
from sklearn.exceptions import ConvergenceWarning
from sklearn.decomposition import PCA
import seaborn as sns
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
import itertools
from string import ascii_uppercase
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

#Importing the dataset and displaying the first 5 rows
data=pd.read_csv('star.txt',sep='\s+')

---------------------------------------------------------------------------
FileNotFoundError                         Traceback (most recent call last)
<ipython-input-11-b2605bf9c20d> in <module>
      1 #Importing the dataset and displaying the first 5 rows
----> 2 data=pd.read_csv('star.txt',sep='\s+')

~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py in read_csv(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)
    686     )
    687 
--> 688     return _read(filepath_or_buffer, kwds)
    689 
    690 

~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py in _read(filepath_or_buffer, kwds)
    452 
    453     # Create the parser.
--> 454     parser = TextFileReader(fp_or_buf, **kwds)
    455 
    456     if chunksize or iterator:

~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py in __init__(self, f, engine, **kwds)
    946             self.options["has_index_names"] = kwds["has_index_names"]
    947 
--> 948         self._make_engine(self.engine)
    949 
    950     def close(self):

~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py in _make_engine(self, engine)
   1178     def _make_engine(self, engine="c"):
   1179         if engine == "c":
-> 1180             self._engine = CParserWrapper(self.f, **self.options)
   1181         else:
   1182             if engine == "python":

~/opt/anaconda3/lib/python3.8/site-packages/pandas/io/parsers.py in __init__(self, src, **kwds)
   2008         kwds["usecols"] = self.usecols
   2009 
-> 2010         self._reader = parsers.TextReader(src, **kwds)
   2011         self.unnamed_cols = self._reader.unnamed_cols
   2012 

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader.__cinit__()

pandas/_libs/parsers.pyx in pandas._libs.parsers.TextReader._setup_parser_source()

FileNotFoundError: [Errno 2] No such file or directory: 'star.txt'

data.head()

#Dropping the Sharp and the #ID from the dataset
notar=data.drop(columns=['Sharp','#ID'])