Dataset Viewer
Temperature
float64 10
35
| Humidity
float64 30
100
| Wind_Speed
float64 0.01
20
| Cloud_Cover
float64 0.02
100
| Pressure
float64 980
1.05k
| Rain
stringclasses 2
values |
---|---|---|---|---|---|
23.720338 | 89.592641 | 7.335604 | 50.501694 | 1,032.378759 | rain |
27.879734 | 46.489704 | 5.952484 | 4.990053 | 992.61419 | no rain |
25.069084 | 83.072843 | 1.371992 | 14.855784 | 1,007.23162 | no rain |
23.62208 | 74.367758 | 7.050551 | 67.255282 | 982.632013 | rain |
20.59137 | 96.858822 | 4.643921 | 47.676444 | 980.825142 | no rain |
26.147353 | 48.21726 | 15.258547 | 59.766279 | 1,049.738751 | no rain |
20.93968 | 40.799444 | 2.232566 | 45.827508 | 1,014.173766 | no rain |
32.294325 | 51.848471 | 2.873621 | 92.551497 | 1,006.041733 | no rain |
34.091569 | 48.057114 | 5.570206 | 82.524873 | 993.732047 | no rain |
19.586038 | 82.978293 | 5.760537 | 98.01445 | 1,036.503457 | rain |
29.793126 | 81.317651 | 16.926099 | 93.923294 | 1,029.40269 | no rain |
23.222373 | 76.877943 | 15.825673 | 72.86979 | 980.108934 | rain |
24.201114 | 45.146538 | 11.572713 | 5.253042 | 1,033.985867 | no rain |
33.139916 | 90.326477 | 5.771774 | 99.910178 | 987.803792 | no rain |
11.775901 | 96.960009 | 6.377567 | 47.33187 | 1,046.404288 | no rain |
12.178232 | 67.212407 | 11.844365 | 6.099704 | 1,003.291526 | no rain |
10.50546 | 75.456091 | 14.797331 | 99.142331 | 1,011.577194 | rain |
30.815496 | 65.048447 | 7.681961 | 57.037672 | 980.348663 | no rain |
29.453919 | 49.84904 | 10.191241 | 45.900402 | 1,037.707488 | no rain |
31.750304 | 50.030332 | 17.760663 | 1.978943 | 1,001.571778 | no rain |
34.465459 | 51.939437 | 12.995817 | 42.840117 | 1,018.951281 | no rain |
29.978964 | 43.3595 | 10.711003 | 18.327075 | 1,044.862056 | no rain |
21.536984 | 93.134654 | 1.424449 | 71.894899 | 990.935567 | rain |
29.513229 | 90.087869 | 3.520304 | 0.78669 | 1,040.713073 | no rain |
12.956861 | 62.314755 | 4.019832 | 53.303568 | 1,015.016725 | no rain |
25.998026 | 55.707599 | 12.462966 | 4.371622 | 1,044.711932 | no rain |
13.583832 | 95.735485 | 2.162255 | 71.483 | 1,037.564854 | rain |
33.616723 | 45.566143 | 0.579897 | 95.301949 | 1,011.008602 | no rain |
23.046208 | 68.747937 | 7.207011 | 4.332055 | 986.209824 | no rain |
20.366548 | 33.814871 | 14.377184 | 10.308416 | 982.113822 | no rain |
16.61389 | 43.384154 | 13.864987 | 51.559297 | 1,041.196157 | no rain |
29.355842 | 72.281885 | 15.853407 | 98.24621 | 1,013.199944 | no rain |
21.403758 | 76.352856 | 13.924965 | 41.145673 | 1,026.458644 | no rain |
24.210849 | 70.669707 | 12.265712 | 61.557295 | 1,041.715389 | rain |
10.469745 | 90.872099 | 9.723242 | 76.165333 | 1,001.326129 | rain |
25.440887 | 99.538365 | 4.169966 | 92.792009 | 1,042.703411 | no rain |
25.302393 | 83.470329 | 11.370961 | 17.701665 | 981.927271 | no rain |
25.42335 | 62.556425 | 12.732495 | 91.142602 | 999.594798 | no rain |
33.593702 | 88.375427 | 2.474867 | 62.642425 | 1,036.823648 | no rain |
27.045507 | 38.427118 | 11.302947 | 80.560692 | 1,039.725422 | no rain |
18.987698 | 77.828949 | 1.954974 | 29.555522 | 1,046.138478 | no rain |
20.925799 | 66.283645 | 10.941536 | 50.010096 | 1,001.210789 | no rain |
27.44078 | 42.464612 | 3.178378 | 20.895919 | 1,034.616491 | no rain |
11.505637 | 98.105353 | 2.380271 | 85.460413 | 1,048.959121 | rain |
26.669168 | 53.690619 | 2.262001 | 20.457909 | 1,016.890421 | no rain |
26.765947 | 73.079638 | 18.220506 | 54.832678 | 997.68174 | no rain |
15.259564 | 91.501597 | 11.961746 | 41.791321 | 982.727362 | no rain |
13.223157 | 65.468219 | 5.003174 | 4.958951 | 1,033.175714 | no rain |
17.885709 | 38.05965 | 1.428978 | 41.214765 | 989.719177 | no rain |
19.092769 | 87.311736 | 10.723628 | 78.439742 | 1,007.085982 | rain |
24.254919 | 54.724964 | 2.896059 | 52.893252 | 1,001.649452 | no rain |
20.965038 | 98.814681 | 15.568059 | 30.479689 | 1,009.179547 | no rain |
34.709346 | 91.867781 | 9.922193 | 44.541332 | 1,043.41993 | no rain |
12.55112 | 89.741453 | 14.528978 | 77.42656 | 1,009.816343 | rain |
15.221919 | 54.937722 | 7.914532 | 66.815376 | 1,043.892102 | no rain |
14.032738 | 82.999084 | 14.046455 | 11.090324 | 1,036.37806 | no rain |
26.327708 | 99.043034 | 13.692287 | 53.124658 | 1,000.186483 | no rain |
16.33229 | 66.842513 | 11.228324 | 19.869524 | 1,002.266231 | no rain |
21.657769 | 62.951251 | 16.914793 | 97.840731 | 1,005.889096 | no rain |
16.11064 | 31.026607 | 11.649471 | 73.862537 | 1,022.395303 | no rain |
13.97424 | 79.014264 | 11.562208 | 8.100079 | 1,027.184376 | no rain |
12.759379 | 90.09343 | 6.159662 | 24.822926 | 1,048.056088 | no rain |
26.40824 | 47.322003 | 18.630125 | 7.812568 | 1,044.856124 | no rain |
13.454574 | 52.05172 | 10.342791 | 37.009636 | 1,022.83102 | no rain |
14.914559 | 65.047391 | 7.834293 | 82.268455 | 983.236715 | no rain |
19.218129 | 35.045611 | 10.832846 | 6.675555 | 1,018.171533 | no rain |
30.524831 | 36.274973 | 2.904843 | 16.53572 | 1,008.254815 | no rain |
12.427532 | 59.477753 | 5.302505 | 26.744524 | 1,018.134511 | no rain |
30.948623 | 48.285327 | 6.133637 | 62.40803 | 982.812346 | no rain |
12.40246 | 58.422611 | 10.303175 | 97.468345 | 1,049.703355 | no rain |
34.411487 | 61.807041 | 13.910217 | 82.273207 | 983.987086 | no rain |
21.71628 | 98.339138 | 5.744049 | 20.092652 | 999.087595 | no rain |
34.419027 | 96.098187 | 2.71532 | 71.006281 | 985.048682 | no rain |
25.121138 | 39.781111 | 11.184364 | 55.638775 | 1,018.332777 | no rain |
28.481589 | 91.781018 | 17.092997 | 49.162787 | 1,004.627281 | no rain |
10.979695 | 35.614466 | 8.364642 | 90.838581 | 1,006.270989 | no rain |
17.070174 | 65.534483 | 9.515458 | 58.812976 | 1,035.241726 | no rain |
13.004914 | 41.473693 | 5.78417 | 13.720967 | 1,043.281408 | no rain |
17.403505 | 55.61245 | 16.176682 | 16.496779 | 991.598789 | no rain |
12.968193 | 82.033816 | 2.413299 | 92.197341 | 999.528386 | rain |
17.949579 | 59.715384 | 3.421774 | 8.02052 | 1,013.091003 | no rain |
20.356575 | 86.48659 | 19.999132 | 13.791411 | 987.901717 | no rain |
11.603687 | 80.3171 | 13.644315 | 65.311368 | 1,007.057666 | rain |
27.311803 | 95.998761 | 10.961048 | 22.433741 | 1,001.146163 | no rain |
24.165036 | 67.390921 | 15.902159 | 82.60991 | 1,044.538344 | no rain |
16.634737 | 86.150357 | 19.937649 | 41.600648 | 993.897388 | no rain |
23.081201 | 52.603497 | 3.817486 | 48.239981 | 1,030.484798 | no rain |
12.348513 | 54.202209 | 15.827861 | 12.863816 | 995.112525 | no rain |
24.398662 | 58.035732 | 6.843242 | 2.816361 | 1,023.796087 | no rain |
33.232405 | 58.764101 | 14.624654 | 75.923623 | 1,020.739351 | no rain |
17.964224 | 37.227725 | 14.153195 | 7.39897 | 1,012.1107 | no rain |
26.685259 | 66.336926 | 19.966601 | 64.015902 | 1,037.183511 | no rain |
13.294947 | 36.776225 | 2.237441 | 53.753145 | 995.590204 | no rain |
27.90818 | 92.28335 | 0.042508 | 51.849055 | 1,013.409616 | no rain |
17.235152 | 34.329135 | 14.955689 | 29.968502 | 1,048.769232 | no rain |
14.579784 | 61.47861 | 11.375538 | 75.602864 | 992.828325 | no rain |
24.662823 | 49.928585 | 13.817483 | 40.061355 | 1,018.180272 | no rain |
10.502689 | 40.840859 | 15.070501 | 15.125457 | 1,000.517405 | no rain |
30.723501 | 84.868568 | 9.870131 | 5.604314 | 988.666424 | no rain |
10.117387 | 80.045268 | 7.130691 | 34.2548 | 1,039.611509 | no rain |
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in Data Studio
Weather Forecast Dataset
Description
This dataset contains weather-related data collected for forecasting purposes. It includes various meteorological parameters that can be used for climate analysis, weather prediction models, and machine learning applications in forecasting.
Dataset Details
Columns:
- Temperature: Measured in degrees Celsius.
- Humidity: Percentage of atmospheric humidity.
- Wind_Speed: Speed of wind in meters per second.
- Cloud_Cover: Percentage of sky covered by clouds.
- Pressure: Atmospheric pressure in hPa (hectopascal).
- Rain: Categorical label indicating whether it rained (rain) or not (no rain).
Notes:
- The dataset contains 2,500 entries.
- The Rain column is categorical, making it useful for classification models.
- This dataset can be used for time-series analysis and supervised learning tasks.
Use Cases
- Predicting rainfall using meteorological data.
- Weather forecasting using machine learning models.
- Studying correlations between temperature, humidity, and pressure.
How to Use
You can load the dataset using the datasets
library:
from datasets import load_dataset
dataset = load_dataset("Tarakeshwaran/Hackathon_Weather_Forcast")
print(dataset)
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