Question: The Banknote Dataset involves predicting whether a given banknote is authentic given a number of measures taken from a photograph. It is a binary (

The Banknote Dataset involves predicting whether a given banknote is authentic given a number of measures taken from a photograph.
It is a binary (2-class) classification problem. The number of observations for each class is not balanced. There are 1,372 observations with 4 input variables and 1 output variable. The variable names are as follows:
1. Variance of Wavelet Transformed image (continuous).
2. Skewness of Wavelet Transformed image (continuous).
3. Kurtosis of Wavelet Transformed image (continuous).
4. Entropy of image (continuous).
Class (0 for authentic, 1 for inauthentic).
The dataset is given in data_banknote_authentication.txt file.
Use 80% of the set as train data and 20% as test data (Do not forget to shuffle dataset). Create a classifier. You can use whatever method (algorithm/approach) you want.
Compute the accuracy on test dataset.
sample of the dataset (try accuracy on the sample as it should read from data_banknote_authentication.txt file ) :
3.4566,9.5228,-4.0112,-3.5944,0
0.32924,-4.4552,4.5718,-0.9888,0
4.3684,9.6718,-3.9606,-3.1625,0
3.5912,3.0129,0.72888,0.56421,0
2.0922,-6.81,8.4636,-0.60216,0
3.2032,5.7588,-0.75345,-0.61251,0
1.5356,9.1772,-2.2718,-0.73535,0
1.2247,8.7779,-2.2135,-0.80647,0
3.9899,-2.7066,2.3946,0.86291,0
1.8993,7.6625,0.15394,-3.1108,0
-1.5768,10.843,2.5462,-2.9362,0
3.404,8.7261,-2.9915,-0.57242,0
4.6765,-3.3895,3.4896,1.4771,0
2.6719,3.0646,0.37158,0.58619,0
0.80355,2.8473,4.3439,0.6017,0
1.4479,-4.8794,8.3428,-2.1086,0
5.2423,11.0272,-4.353,-4.1013,0
5.7867,7.8902,-2.6196,-0.48708,0
0.3292,-4.4552,4.5718,-0.9888,0
3.9362,10.1622,-3.8235,-4.0172,0
0.93584,8.8855,-1.6831,-1.6599,0
4.4338,9.887,-4.6795,-3.7483,0
0.7057,-5.4981,8.3368,-2.8715,0
1.1432,-3.7413,5.5777,-0.63578,0
-0.38214,8.3909,2.1624,-3.7405,0
6.5633,9.8187,-4.4113,-3.2258,0
4.8906,-3.3584,3.4202,1.0905,0
-0.24811,-0.17797,4.9068,0.15429,0
1.4884,3.6274,3.308,0.48921,0
4.2969,7.617,-2.3874,-0.96164,0
-0.96511,9.4111,1.7305,-4.8629,0
-1.6162,0.80908,8.1628,0.60817,0
2.4391,6.4417,-0.80743,-0.69139,0
2.6881,6.0195,-0.46641,-0.69268,0
3.6289,0.81322,1.6277,0.77627,0
4.5679,3.1929,-2.1055,0.29653,0
3.4805,9.7008,-3.7541,-3.4379,0
4.1711,8.722,-3.0224,-0.59699,0
-0.2062,9.2207,-3.7044,-6.8103,0
-0.0068919,9.2931,-0.41243,-1.9638,0
0.96441,5.8395,2.3235,0.066365,0
2.8561,6.9176,-0.79372,0.48403,0
-0.7869,9.5663,-3.7867,-7.5034,0
2.0843,6.6258,0.48382,-2.2134,0
-0.7869,9.5663,-3.7867,-7.5034,0
3.9102,6.065,-2.4534,-0.68

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