For the data listed below,

Purchase	Income ($ '000)	Age	Gender
0	71.9	42	2
0	100.4	42	1
0	105.6	44	1
1	83.1	39	2
0	114.2	43	1
1	113.5	44	1
0	115.2	42	1
0	100.4	35	2
0	92.6	43	2
0	123.8	42	1
0	122.8	45	1
1	98.6	46	2
0	107.6	41	2
0	108.4	42	2
1	138.8	41	1
1	109.9	44	2
1	136.2	47	1
1	117.6	38	2
1	122.8	43	2
0	121.8	45	2
1	126.6	41	2
1	125.8	46	2
1	138.8	42	2
0	149.6	37	1
1	159.5	33	2

Code definitions: Purchase 0 - Not purchased and 1 - Purchased; Gender 1 - Male and 2 - Female

Fit a logistic regression model to predict purchase decision. Identify significant predictors and comment on classification accuracy.