Please fill missing values in balance column with group mean. There are two different groups of columns in the dataset. To check whether to use group mean of a column with categorical data, study their group means of balance; if there are big differences between group means, the column is a good candidate. To check whether to use group mean of a column with numerical data, exam the density plots we have created, based on their patterns, select the column having similar distribution as the balance column. If a column eventually selected has numerical data, create bins first, then use bin mean to fill the missing value. Please make this decision by yourself after checking all candidates, and explain your decision.

region	age	income	long_Month	longten	internet	balance	class
2	44	64	3.7	37.45	0	2.014903021	1
3	33	136	4.4	42	0	2.724579503	1
3	52	116	18.15	1300.6	0	3.409496184	0
2	39	78	11.8	487.4	0	2.602689685	0
3	22	19	10.9	504.5	1	2.1690537	1
2	35	76	6.05	239.55	1	3.146305132	0
3	59	166	9.75	449.05	0	2.48490665	0
1	41	72	24.15	1659.7	0	2.803360381	0
2	33	125	4.85	17.25	1	NaN	1
3	35	80	7.1	47.45	0	3.16758253	0
1	38	37	8.55	308.7	0	3.731699451	0
1	38	75	5.1	146.25	1	2.420368129	0
3	57	162	16.15	946.9	0	3.401197382	0
1	29	77	6.7	140.95	1	3.188416617	1
3	30	16	3.75	25.65	1	NaN	1
1	52	120	20.7	1391.05	0	3.091042453	0
3	33	101	5.3	253.35	1	3.286534473	0
3	48	67	15.05	810.45	0	3.305053521	0
3	43	36	12.5	153.75	0	2.890371758	0
2	21	33	2.2	2.2	0	3.701301974	0
2	40	37	8.25	399.15	1	3.33220451	0
1	37	36	10.6	582.6	1	2.90416508	0
1	53	155	21	1519.2	1	3.526360525	0
1	50	140	6.5	247.55	0	3.555348061	0
1	27	55	4.8	54.1	1	NaN	0
2	46	163	33.9	1947.95	1	2.621038824	0
3	35	52	4.25	82.7	1	NaN	1
2	60	211	21.15	1228.7	1	3.993602992	0
1	57	186	9.8	428.25	0	3.583518938	0
1	41	39	6.55	67.8	0	2.983153491	1
2	57	22	41.75	3043.05	0	2.931193752	0
3	41	30	2.5	31.25	0	3.984343667	0
2	28	29	4.25	78	0	2.876385516	0
1	43	76	14.7	897.05	0	2.397895273	0
1	41	74	14.5	963.3	1	2.833213344	0
1	51	63	12.85	585.6	1	2.656756907	0
3	41	36	7.75	361	1	2.917770732	1
3	34	33	2.95	18.9	0	NaN	1
1	36	29	3.25	16.8	1	2.740840024	1
2	34	27	6.3	150.9	1	NaN	1
1	52	49	24.75	1349.05	0	3.102342009	0
3	22	24	7.8	63	1	3.113515309	0
1	26	26	4.85	33.7	0	NaN	0
1	27	47	6.25	330.4	0	2.656756907	0
1	62	27	15.5	967.1	0	3.540959324	0
2	52	30	10.4	447.85	0	2.944438979	0
2	40	127	19.7	909.9	1	2.691243083	0
2	39	137	12.7	676.9	0	2.442347035	1
2	50	80	28.8	1558.1	0	3.056356895	0
3	55	30	10.25	395.85	0	2.756840365	0
2	51	438	29	1815.4	1	3.208825489	0
2	39	79	15.25	653.7	1	3.305053521	1
3	47	63	9.05	373.65	0	1.909542505	0
1	67	51	57.05	4168.25	0	2.957511061	0
3	43	61	3.15	7.9	1	2.788092909	1
1	57	22	8.55	381.5	0	2.983153491	0
2	48	91	24.5	1531.9	0	2.525728644	0
1	68	244	30.25	2186.2	0	3.425889994	0
1	42	80	7.85	196.65	1	2.351375257	0
3	34	83	8.9	379.55	1	1.749199855	0
2	31	21	1.65	3.2	0	NaN	1
3	48	24	11.85	230.7	0	3.555348061	1
2	53	351	5.5	185.3	0	2.957511061	0
2	52	169	14.65	618.15	1	2.656756907	0
2	54	50	17.25	1067.8	1	4.471638793	1
2	35	161	3.4	10.35	1	NaN	1
1	47	212	7.45	320.9	0	3.208825489	0
3	61	53	12.25	631.7	1	2.1690537	0
3	33	73	5.85	216.6	1	3.102342009	0
3	20	17	3.75	12.3	0	NaN	1
2	33	23	10.2	196.3	1	2.674148649	1
3	36	107	11.55	491.55	0	3.068052935	1
2	25	21	8.65	426.6	0	2.197224577	1
3	58	83	19.3	1323.2	0	3.124565145	0
1	20	17	3.05	40.3	1	2.63905733	0
3	25	76	24.05	1536.55	0	3.056356895	0
2	24	19	4	46	0	3.305053521	1
1	61	41	9.6	353.55	0	2.251291799	0
3	39	105	6.6	159.7	0	3.617651945	0
1	54	31	5.85	97	1	NaN	0
2	40	41	25.25	1725.1	0	3.496507561	0
3	50	102	12.6	760.3	1	2.525728644	0
2	22	25	12.05	666	1	1.871802177	0
1	42	68	17.3	997.85	0	2.957511061	0
1	55	79	13.8	668.65	0	3.238678452	0
3	31	28	3.45	13.3	1	3.157000421	1
2	48	64	14.8	708.45	0	2.833213344	0
2	36	38	10.15	538.65	0	2.876385516	0
3	23	37	5.35	144.05	1	2.197224577	0
2	64	98	26.15	1805.1	0	2.420368129	0
1	52	195	23.15	1420.95	0	3.583518938	1
2	35	47	5.2	46.9	0	NaN	1
1	47	65	12.4	744.45	0	3.597312261	0
1	50	150	19.3	930.8	1	3.926911618	1
2	39	106	9.05	568.15	0	2.505525937	0
2	43	33	4.25	30	0	2.788092909	0
3	25	38	13.9	255	0	1.32175584	0
3	32	125	3.8	31.75	1	2.944438979	1
1	37	145	8.6	241.45	1	2.876385516	0
1	44	99	22.05	841.55	0	2.277267285	0
1	34	22	4.85	75.45	1	3.548179572	0
2	60	31	32.25	2349.25	0	3.511545439	0
1	36	25	1.9	3.6	0	1.609437912	0
3	25	57	20.25	923.95	1	2.277267285	0
2	51	41	12.1	492.8	1	2.277267285	0
3	25	20	8.15	209.35	1	NaN	1
3	43	101	13.65	817.65	0	3.032546247	0
1	37	56	10.25	681.95	0	2.374905755	0
2	27	22	17.2	893.3	0	2.583997552	0
1	37	108	21.8	1292	1	2.079441542	0
3	48	115	12.8	785.7	1	2.847812143	0
3	54	53	8.1	225.4	1	4.034240638	0
3	53	242	8.95	396.35	0	3.349904087	0
1	24	20	3.35	7.55	1	2.224623552	0
2	47	123	2.5	9.25	1	2.621038824	0
3	44	47	7.05	182.3	0	2.674148649	0
3	37	48	12.9	669.75	0	2.1690537	0
2	64	13	14.6	921.7	0	2.756840365	0
2	52	78	18.6	1179.05	0		0
3	26	34	6.5	198.7	0	2.772588722	0
1	46	81	17.45	1067	1	2.327277706	0
1	45	86	45.05	3235.9	1	3.555348061	0
3	27	26	12.2	477.5	1	2.224623552	1
3	34	51	4.7	61.5	0	2.803360381	0
2	56	57	6.2	135.2	1	2.351375257	0
1	48	41	4.35	59.15	0	2.079441542	0
2	46	168	14.2	928.8	1	3.657130756	0
2	33	29	4.65	35.45	1	NaN	1
3	58	167	24.3	1642.35	0	2.847812143	0
3	24	29	4.45	43.7	1	NaN	1
1	55	104	15	960.95	0	3.669951444	0
3	22	46	9.35	311.85	1	1.791759469	1
3	26	46	11	350.05	1	1.791759469	0
1	33	51	2.9	12.05	0	NaN	1
2	42	65	14.2	1007.5	1	3.449987546	0
2	33	48	3.15	39.75	0	3.417726684	0
3	34	46	7.1	125.05	1	NaN	0
2	63	43	14.85	808.95	1	2.397895273	0
3	53	110	17.45	1249.8	0	2.691243083	0
1	39	26	14.45	349.9	1	3.135494216	1
3	28	34	15	934.05	0	2.862200881	0
2	54	21	10.6	565.55	0	2.833213344	0
3	33	38	5.05	48.6	1	2.525728644	0
1	21	19	7.9	98.35	1	1.609437912	1
1	39	40	36.25	2553.7	0	3.650658241	0
3	69	58	6.35	118.05	1	2.463853241	0
2	65	128	21.2	1325.05	0	2.674148649	0
3	66	460	89.4	6353.9	1	2.931193752	0

region, category, and class are categorical data and the rest are numerical data

I am not too sure how to approach this question. Can someone explain this in detail.

Needs to be done using python