Datasets:

Mahammad42
/

miami-housing-cleaned

Size int64 720 4.5k	Rooms int64 1 6	Bathrooms float64 1 5	Age int64 0 60	Zone large_stringclasses 19 values	School_Rating float64 1 10	Beach_Distance float64 0.1 50	Flood_Zone large_stringclasses 3 values	Price int64 200k 5.65M	Price_Per_SqFt float64 53.4 2.92k	Rooms_Per_SqFt float64 0.5 5.97
2,573	3	1.5	0	Urban-Inland	7	1.7	VE	1,635,740	635.73	1.17
1,155	6	3.5	29	Suburban	3	12.1	AE	1,663,472	1,440.24	5.19
2,292	6	2	47	Suburban	7	41.8	X	1,201,295	524.13	2.62
3,567	2	3	13	Rural	4	33.6	AE	2,234,671	626.48	0.56
1,486	5	3	18	Urban-Inland	9	49.7	VE	2,913,653	1,960.74	3.36
1,057	5	2.5	48	Rural	5	41.7	X	1,594,544	1,508.56	4.73
1,988	5	1.5	40	Urban-Coastal	3	14.2	AE	1,285,810	646.79	2.52
2,230	5	2	37	Suburban	9	12.5	VE	726,514	325.79	2.24
2,231	5	4	12	Urban-Coastal	10	3.9	X	2,386,464	1,069.68	2.24
2,844	3	2	40	Urban-Coastal	5	45.6	X	2,411,950	848.08	1.05
1,761	5	2	21	Urban-Coastal	2	18.7	AE	2,917,265	1,656.6	2.84
2,984	6	1.5	15	Rural	1	34.5	VE	1,170,404	392.23	2.01
2,474	6	2	16	Suburban	10	20.5	VE	1,494,325	604.01	2.43
2,097	2	2.5	15	Urban-Inland	10	3.9	X	1,561,876	744.81	0.95
3,410	6	2.5	17	Rural	9	31.4	AE	1,868,187	547.86	1.76
3,582	4	2.5	4	Urban-Coastal	10	35.4	X	2,079,493	580.54	1.12
3,256	6	1	19	Rural	5	24.8	X	504,940	155.08	1.84
3,577	3	3	11	Urban-Coastal	6	49.2	AE	355,727	99.45	0.84
1,702	6	3	40	Rural	5	25.6	AE	433,080	254.45	3.53
2,594	2	4	48	Urban-Coastal	1	2.3	X	1,164,056	448.75	0.77
1,476	5	3.5	22	Suburban	9	31.2	VE	1,847,506	1,251.7	3.39
2,338	4	2.5	35	Urban-Inland	1	3.6	X	2,724,180	1,165.18	1.71
2,316	3	2.5	24	Urban-Inland	3	19.7	AE	718,536	310.25	1.3
2,990	2	3.5	29	Urban-Inland	1	3.2	X	1,366,618	457.06	0.67
1,081	4	2.5	24	Urban-Coastal	7	40.4	VE	2,043,550	1,890.43	3.7
2,651	3	4	44	Rural	2	10.7	X	2,925,745	1,103.64	1.13
3,044	6	3.5	48	Rural	3	16.1	AE	708,211	232.66	1.97
3,979	5	4	0	Rural	3	41.3	AE	1,573,167	395.37	1.26
2,800	4	4	7	Urban-Coastal	2	6.2	X	2,590,894	925.32	1.43
2,959	6	3.5	6	Urban-Inland	1	14.7	AE	313,956	106.1	2.03
3,965	3	2	39	Urban-Coastal	2	47.5	AE	2,383,452	601.12	0.76
1,185	3	1.5	23	Urban-Coastal	3	28.2	AE	1,144,369	965.71	2.53
1,882	5	4	31	Urban-Coastal	2	24.6	VE	1,346,133	715.27	2.66
3,720	2	3.5	16	Suburban	4	48.7	AE	774,632	208.23	0.54
2,382	6	1	16	Rural	3	11.6	AE	298,773	125.43	2.52
2,420	2	2.5	37	Suburban	4	29.4	X	2,589,059	1,069.86	0.83
1,440	3	2	49	Rural	8	24.6	AE	266,719	185.22	2.08
1,863	3	2	21	Rural	9	25.4	VE	875,964	470.19	1.61
2,692	6	3	47	Suburban	10	19.3	VE	1,349,022	501.12	2.23
3,954	3	1.5	30	Urban-Coastal	2	31.5	VE	2,502,586	632.93	0.76
2,301	6	1.5	37	Rural	5	20.1	AE	2,377,027	1,033.04	2.61
1,376	5	1	18	Rural	2	18.5	AE	231,861	168.5	3.63
1,530	4	3.5	32	Urban-Coastal	9	13	VE	2,078,348	1,358.4	2.61
3,845	5	4	43	Suburban	9	49.4	AE	1,091,198	283.8	1.3
1,691	6	4	7	Suburban	7	9.9	VE	2,569,353	1,519.43	3.55
2,512	4	2.5	28	Suburban	5	46.3	VE	218,825	87.11	1.59
1,731	2	4	42	Urban-Inland	4	32	AE	1,806,669	1,043.71	1.16
1,625	6	4	44	Urban-Coastal	2	30	AE	1,386,764	853.39	3.69
3,744	6	2	0	Urban-Inland	2	10.6	VE	1,947,776	520.24	1.6
1,974	6	2	36	Urban-Coastal	10	39.4	X	2,681,020	1,358.17	3.04
1,699	3	2.5	31	Rural	2	46	VE	2,538,350	1,494.03	1.77
2,937	5	3	16	Rural	4	38.4	AE	2,068,032	704.13	1.7
1,355	2	4	31	Urban-Coastal	9	13.7	AE	332,069	245.07	1.48
2,034	3	4	12	Urban-Coastal	2	18.6	AE	2,401,516	1,180.69	1.47
3,399	3	1.5	37	Suburban	7	31.3	VE	1,059,704	311.77	0.88
1,881	4	1.5	2	Suburban	7	29.3	X	1,950,058	1,036.71	2.13
3,125	5	2	42	Rural	9	12.5	VE	662,371	211.96	1.6
3,123	6	2	40	Rural	3	4	AE	976,653	312.73	1.92
2,590	2	3	38	Urban-Inland	8	11.9	VE	1,359,149	524.77	0.77
3,328	3	1.5	11	Urban-Inland	3	39.5	AE	2,353,010	707.03	0.9
3,021	5	3.5	36	Suburban	6	5.7	VE	1,303,355	431.43	1.66
2,859	3	3.5	27	Rural	7	20.7	AE	515,879	180.44	1.05
1,723	5	2.5	32	Rural	1	44.8	VE	1,851,004	1,074.29	2.9
1,855	2	3.5	12	Rural	10	17.4	VE	1,744,251	940.3	1.08
1,012	2	1.5	37	Suburban	8	6	X	759,145	750.14	1.98
1,333	5	3	45	Urban-Inland	8	24.3	AE	460,528	345.48	3.75
3,040	3	3.5	37	Urban-Coastal	4	39.3	VE	2,966,918	975.96	0.99
2,129	4	2.5	14	Urban-Inland	5	40.4	VE	1,200,427	563.85	1.88
1,628	2	3.5	47	Urban-Inland	10	37	AE	1,293,817	794.73	1.23
2,505	2	3.5	23	Urban-Coastal	1	5.9	X	1,454,843	580.78	0.8
1,275	6	2.5	0	Suburban	7	47.5	VE	1,165,928	914.45	4.71
2,332	2	2.5	2	Urban-Inland	7	12.2	X	663,547	284.54	0.86
1,089	6	2.5	28	Urban-Coastal	1	43.9	X	1,003,525	921.51	5.51
2,188	6	2.5	35	Suburban	8	13.9	AE	550,926	251.79	2.74
2,730	4	2.5	26	Urban-Coastal	8	47.2	VE	835,127	305.91	1.47
2,002	3	1	44	Suburban	8	2.4	X	2,389,709	1,193.66	1.5
2,415	2	3.5	33	Suburban	6	27.1	AE	1,847,193	764.88	0.83
1,501	5	1.5	11	Urban-Coastal	10	12.6	VE	2,967,726	1,977.17	3.33
2,892	2	3	33	Urban-Coastal	2	41.5	X	569,833	197.04	0.69
2,824	4	4	15	Urban-Inland	8	22.9	AE	2,566,604	908.85	1.42
2,119	3	1	21	Urban-Coastal	9	39	X	357,946	168.92	1.42
2,387	6	2.5	24	Suburban	3	25.7	AE	1,828,782	766.14	2.51
3,559	5	2	49	Urban-Inland	8	21.5	VE	547,578	153.86	1.4
3,206	3	1.5	15	Rural	4	10.4	X	1,625,172	506.92	0.94
2,997	6	4	39	Rural	1	13.5	VE	1,837,869	613.24	2
1,748	5	3	24	Urban-Inland	5	8.3	X	1,140,141	652.25	2.86
1,160	6	2.5	49	Suburban	7	30.5	X	2,622,382	2,260.67	5.17
1,355	4	3	15	Rural	2	28.4	X	2,105,076	1,553.56	2.95
2,419	3	3	0	Urban-Inland	7	38.7	X	2,529,834	1,045.82	1.24
1,623	3	2.5	29	Rural	1	16	X	2,273,865	1,401.03	1.85
2,404	3	2.5	12	Urban-Inland	3	18.4	X	646,989	269.13	1.25
3,455	2	1	9	Urban-Coastal	1	49.1	AE	412,846	119.49	0.58
3,716	2	2.5	9	Urban-Inland	6	24.5	VE	970,447	261.15	0.54
3,797	6	3	11	Urban-Inland	6	24.3	VE	607,893	160.1	1.58
1,252	5	3.5	18	Suburban	7	25.5	VE	742,946	593.41	3.99
1,986	6	3	25	Urban-Inland	3	39.5	X	2,781,850	1,400.73	3.02
3,479	6	1.5	8	Urban-Inland	7	33.3	VE	1,883,726	541.46	1.72
2,098	5	1	17	Suburban	9	40.3	X	2,439,545	1,162.8	2.38
3,547	3	2.5	30	Suburban	6	32.8	X	1,903,081	536.53	0.85
1,742	4	4	37	Urban-Coastal	10	31.1	AE	1,091,206	626.41	2.3

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Miami Housing Dataset (Cleaned)

A cleaned version of the Denisijcu/miami_real_estate_data.csv Miami real estate dataset.

Dataset Summary

Rows: 5,105
Columns: 11 (9 original + 2 derived features)
Target variable: Price (house sale price in USD)

Schema

Column	Type	Description
Size	int64	House size in square feet (720–4,500)
Rooms	int64	Number of rooms (1–6)
Bathrooms	float64	Number of bathrooms, standardized to .0/.5 increments
Age	int64	Age of the property in years (0–60)
Zone	string	Neighborhood zone (19 categories)
School_Rating	float64	Nearby school rating (1.0–10.0)
Beach_Distance	float64	Distance to the nearest beach in miles (0.1–50.0)
Flood_Zone	string	FEMA flood zone designation (AE, X, VE)
Price	int64	Sale price in USD ($200K–$5.65M)
Price_Per_SqFt	float64	Derived: Price / Size
Rooms_Per_SqFt	float64	Derived: Rooms per 1,000 sqft

Cleaning Steps Applied

Bathroom standardization: Rounded 3,968 non-standard fractional bathroom values (e.g., 2.2, 3.7) to nearest 0.5 increment (standard half-bath convention)
Zone name standardization: Stripped whitespace, applied title case
Flood zone standardization: Stripped whitespace, uppercased
Derived features added: Price_Per_SqFt and Rooms_Per_SqFt for easier analysis

Data Quality Notes

No missing values in the original dataset
No exact duplicate rows
2 price outliers detected via IQR (>$4.4M) — retained as they represent legitimate luxury properties
19 zone categories — 4 dominant zones (~25% each: Urban-Inland, Rural, Suburban, Urban-Coastal) plus 15 specific Miami neighborhoods (5–10 properties each)
Very low correlations between features and Price — this is a challenging regression target
Flood zones are evenly distributed: AE (33.7%), X (33.5%), VE (32.8%)

Usage

from datasets import load_dataset

ds = load_dataset("Mahammad42/miami-housing-cleaned", split="train")
df = ds.to_pandas()

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