Greetings,

Could someone please help with this R programming exercise? The data is below. Thank you

A- Fit a Logistic Regression Model

1. Fit a logistic regression model to the binary-classifier-data.csv dataset
2. The dataset (found inbinary-classifier-data.csv) contains three variables; label, x, and y. The label variable is either 0 or 1 and is the output we want to predict using the x and y variables.
   1. What is the accuracy of the logistic regression classifier

binary-classifier-data.csv dataset

label	x	y
0	70.88469	83.17702
0	74.97176	87.92922
0	73.78333	92.20325
0	66.40747	81.10617
0	69.07399	84.53739
0	72.23616	86.38403
0	70.92514	89.73168
0	77.57454	98.63425
0	72.75624	92.37422
0	69.0366	91.74529
0	67.20828	85.62172
0	71.10117	101.4888
0	68.01709	83.81533
0	72.37629	89.20004
0	70.7026	86.51171
0	69.2368	89.98705
0	65.37138	83.04976
0	69.16044	91.30133
0	73.8469	93.36843
0	69.52171	89.94501
0	67.95124	91.79681
0	72.7026	92.32404
0	67.47315	86.56905
0	66.26135	90.7687
0	75.57815	91.81035
0	68.41029	85.32959
0	68.09382	91.80961
0	76.10679	86.92784
0	65.16124	80.7794
0	65.77994	88.73708
0	69.54418	82.12866
0	71.99746	78.86599
0	67.13947	88.0605
0	72.5218	81.78212
0	68.49425	82.44224
0	73.62704	86.19587
0	68.60145	84.93345
0	81.06559	92.99035
0	68.15576	83.97527
0	64.3719	86.22894
0	67.90532	93.47074
0	72.37434	80.93594
0	70.7967	90.86587
0	64.99727	85.80802
0	67.24623	88.50708
0	67.52769	98.87718
0	73.17363	96.58677
0	80.03457	91.47279
0	71.40789	90.15792
0	68.69496	89.33048
0	71.2808	84.72027
0	67.54185	85.54942
0	70.98123	93.74373
0	67.01065	83.36586
0	71.41361	90.11751
0	48.48391	40.06838
0	49.05764	38.49491
0	49.03912	39.83932
0	50.17205	43.06785
0	48.87424	42.09335
0	49.14474	37.78848
0	48.40126	42.33843
0	48.67201	39.54598
0	47.14664	45.14719
0	50.02214	45.69452
0	49.25281	42.64397
0	49.09991	40.074
0	49.20397	33.66105
0	47.83637	43.82052
0	49.65752	42.55331
0	48.26329	35.64447
0	49.7379	38.84591
0	49.64305	45.91976
0	47.16221	47.57488
0	48.67494	43.56255
0	46.13059	42.06876
0	48.73462	40.56963
0	47.89646	44.92116
0	48.29925	35.40606
0	48.74338	37.1465
0	48.74028	36.52425
0	45.05579	45.21615
0	48.67989	37.875
0	47.58688	38.75329
0	49.05341	36.4743
0	48.00753	44.83579
0	48.13913	43.73264
0	46.09651	36.21656
0	49.22916	39.46246
0	47.67341	39.54826
0	47.77431	30.91093
0	47.32824	37.03312
0	47.09939	42.39616
0	49.68217	36.63031
0	50.47284	43.83972
0	48.83413	41.56444
0	49.62159	47.53151
0	49.51867	47.50066
0	42.11056	75.6791