Greetings, Could someone please help with this R programming exercise The data is below Thank you A Fit a Logistic Regression Model Fit a logistic regression model to the binary classifier data csv dataset The dataset (found in binary 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 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

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Question: Greetings, Could someone please help with this R programming exercise? The data is below. Thank you A- Fit a Logistic Regression Model Fit a logistic

Greetings,

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

A- Fit a Logistic Regression Model

Fit a logistic regression model to the binary-classifier-data.csv dataset
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

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