For other aspects, please see the following files: Datasets were provided by Trilogy Education Services ( 2017). In this article, weve seen how easy it is to insert weather data into a database such as MySQL. Check all pages to see the full range of data. View . Open source, i.e. Interactive weather map allows you to watch for current temperature and weather conditions in your city or any other location on the interactive global map. Much of the automatically derived data in SWDI is from radar data that represents probable conditions for an event rather than a confirmed occurrence. We improve the accuracy of weather data by taking into account the details of the topography. Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n3 6804\n4 1966\n9 7225\n10 10142\n\n Accuracy Score\n0.9508742395837319\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.99 0.99 0.99 6766\n 4 0.74 0.79 0.77 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.92 0.96 0.94 6965\n 10 0.99 0.99 0.99 10076\n\n accuracy 0.95 26137\n macro avg 0.33 0.34 0.34 26137\nweighted avg 0.93 0.95 0.94 26137\n\nConfusion Matrix\n[[ 0 0 0 0 209 0 0 0 0 8 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 0 13 15 0 0 0 0 55 27]\n [ 0 0 0 6720 0 0 0 0 0 0 46]\n [ 0 0 0 1 1453 0 0 0 0 339 35]\n [ 0 0 0 0 2 0 0 0 0 2 2]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 2 0 0 0 0 0 0 5]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 0 285 0 0 0 0 6678 2]\n [ 0 0 0 56 0 0 0 0 0 18 10002]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n3 6771\n4 1956\n9 7247\n10 10163\n\n Accuracy Score\n0.9520602976623178\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.99 0.99 0.99 6766\n 4 0.74 0.79 0.77 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.92 0.96 0.94 6965\n 10 0.99 1.00 0.99 10076\n\n accuracy 0.95 26137\n macro avg 0.33 0.34 0.34 26137\nweighted avg 0.94 0.95 0.94 26137\n\nConfusion Matrix\n[[ 0 0 0 0 209 0 0 0 0 8 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 0 12 7 0 0 0 0 63 28]\n [ 0 0 0 6724 0 0 0 0 0 0 42]\n [ 0 0 0 1 1451 0 0 0 0 342 34]\n [ 0 0 0 0 2 0 0 0 0 2 2]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 2 0 0 0 0 0 0 5]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 0 285 0 0 0 0 6680 0]\n [ 0 0 0 20 0 0 0 0 0 27 10029]]\n", "text": "[1 1 1 0 0 0]\n 0\n0 \n0 2977\n1 19729\n3 1052\n4 9\n5 239\n6 35\n7 1862\n8 19\n10 215\n\n Accuracy Score\n0.05543865018938669\n\nClassification Report\n precision recall f1-score support\n\n 0 0.07 1.00 0.14 217\n 1 0.01 1.00 0.02 152\n 2 0.00 0.00 0.00 110\n 3 1.00 0.16 0.27 6766\n 4 0.00 0.00 0.00 1828\n 5 0.01 0.33 0.02 6\n 6 0.06 0.50 0.10 4\n 7 0.00 1.00 0.01 7\n 8 0.26 0.83 0.40 6\n 9 0.00 0.00 0.00 6965\n 10 0.06 0.00 0.00 10076\n\n accuracy 0.06 26137\n macro avg 0.13 0.44 0.09 26137\nweighted avg 0.28 0.06 0.07 26137\n\nConfusion Matrix\n[[ 217 0 0 0 0 0 0 0 0 0 0]\n [ 0 152 0 0 0 0 0 0 0 0 0]\n [ 13 86 0 0 6 1 3 1 0 0 0]\n [ 0 5499 0 1051 0 0 0 0 14 0 202]\n [1640 129 0 0 0 15 20 24 0 0 0]\n [ 4 0 0 0 0 2 0 0 0 0 0]\n [ 2 0 0 0 0 0 2 0 0 0 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [1101 5848 0 0 0 5 10 1 0 0 0]\n [ 0 8015 0 0 3 216 0 1829 0 0 13]]\n", "text": "[9 9 9 9 9 9]\n 0\n0 \n3 19\n7 661\n8 1235\n9 22770\n10 1452\n\n Accuracy Score\n0.3204269809082909\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.00 0.00 0.00 6766\n 4 0.00 0.00 0.00 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.29 0.01 7\n 8 0.00 1.00 0.01 6\n 9 0.31 1.00 0.47 6965\n 10 0.97 0.14 0.24 10076\n\n accuracy 0.32 26137\n macro avg 0.12 0.22 0.07 26137\nweighted avg 0.45 0.32 0.22 26137\n\nConfusion Matrix\n[[ 0 0 0 0 0 0 0 0 0 217 0]\n [ 0 0 0 0 0 0 0 0 0 152 0]\n [ 0 0 0 0 0 0 0 2 0 103 5]\n [ 0 0 0 0 0 0 0 18 1228 5520 0]\n [ 0 0 0 0 0 0 0 1 0 1791 36]\n [ 0 0 0 0 0 0 0 0 0 4 2]\n [ 0 0 0 0 0 0 0 0 0 4 0]\n [ 0 0 0 0 0 0 0 2 0 0 5]\n [ 0 0 0 0 0 0 0 0 6 0 0]\n [ 0 0 0 0 0 0 0 0 0 6964 1]\n [ 0 0 0 19 0 0 0 638 1 8015 1403]]\n", "text": " precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 1.00 0.12 0.21 6766\n 4 0.00 0.00 0.00 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.00 0.00 0.00 6965\n 10 0.40 1.00 0.57 10076\n\n accuracy 0.42 26137\n macro avg 0.13 0.10 0.07 26137\nweighted avg 0.41 0.42 0.27 26137\n\nConfusion Matrix\n[[ 0 0 0 0 0 0 0 0 0 0 217]\n [ 0 0 0 0 0 0 0 0 0 0 152]\n [ 0 0 0 0 0 0 0 0 0 0 110]\n [ 0 0 0 801 0 0 0 0 0 0 5965]\n [ 0 0 0 0 0 0 0 0 0 0 1828]\n [ 0 0 0 0 0 0 0 0 0 0 6]\n [ 0 0 0 0 0 0 0 0 0 0 4]\n [ 0 0 0 0 0 0 0 0 0 0 7]\n [ 0 0 0 0 0 0 0 0 0 0 6]\n [ 0 0 0 0 0 0 0 0 0 0 6965]\n [ 0 0 0 0 0 0 0 0 0 0 10076]]\n", "text": "[10 10 10 3 3 3]\n 0\n0 \n3 4350\n4 2037\n9 6946\n10 12804\n\n Accuracy Score\n0.2898190304931706\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.00 0.00 0.00 6766\n 4 0.00 0.00 0.00 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.31 0.31 0.31 6965\n 10 0.42 0.54 0.47 10076\n\n accuracy 0.29 26137\n macro avg 0.07 0.08 0.07 26137\nweighted avg 0.25 0.29 0.27 26137\n\nConfusion Matrix\n[[ 0 0 0 217 0 0 0 0 0 0 0]\n [ 0 0 0 0 0 0 0 0 0 2 150]\n [ 0 0 0 21 3 0 0 0 0 38 48]\n [ 0 0 0 0 1246 0 0 0 0 1989 3531]\n [ 0 0 0 1696 3 0 0 0 0 112 17]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 4 0 0 0 0 0 0 0]\n [ 0 0 0 3 4 0 0 0 0 0 0]\n [ 0 0 0 0 6 0 0 0 0 0 0]\n [ 0 0 0 1117 0 0 0 0 0 2181 3667]\n [ 0 0 0 1286 775 0 0 0 0 2624 5391]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n3 6783\n4 2141\n8 7\n9 7033\n10 10173\n\n Accuracy Score\n0.9541645942533573\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 1.00 1.00 1.00 6766\n 4 0.71 0.83 0.77 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.71 0.83 0.77 6\n 9 0.93 0.94 0.94 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.95 26137\n macro avg 0.40 0.42 0.41 26137\nweighted avg 0.94 0.95 0.95 26137\n\nConfusion Matrix\n[[ 0 0 0 0 209 0 0 0 0 8 0]\n [ 0 0 0 6 1 0 0 0 0 122 23]\n [ 0 0 0 11 14 0 0 0 0 57 28]\n [ 0 0 0 6763 0 0 0 0 2 0 1]\n [ 0 0 0 0 1523 0 0 0 0 268 37]\n [ 0 0 0 0 2 0 0 0 0 2 2]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 0 0 0 7]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 0 0 390 0 0 0 0 6574 1]\n [ 0 0 0 2 0 0 0 0 0 0 10074]]\n", "text": "[10 10 10 4 4 4]\n 0\n0 \n3 5219\n4 3259\n10 17659\n\n Accuracy Score\n0.6367984083865784\n\nClassification Report\n precision recall f1-score support\n\n 0 0.00 0.00 0.00 217\n 1 0.00 0.00 0.00 152\n 2 0.00 0.00 0.00 110\n 3 0.99 0.76 0.86 6766\n 4 0.51 0.92 0.66 1828\n 5 0.00 0.00 0.00 6\n 6 0.00 0.00 0.00 4\n 7 0.00 0.00 0.00 7\n 8 0.00 0.00 0.00 6\n 9 0.00 0.00 0.00 6965\n 10 0.56 0.97 0.71 10076\n\n accuracy 0.64 26137\n macro avg 0.19 0.24 0.20 26137\nweighted avg 0.51 0.64 0.54 26137\n\nConfusion Matrix\n[[ 0 0 0 0 217 0 0 0 0 0 0]\n [ 0 0 0 3 0 0 0 0 0 0 149]\n [ 0 0 0 13 17 0 0 0 0 0 80]\n [ 0 0 0 5153 0 0 0 0 0 0 1613]\n [ 0 0 0 1 1678 0 0 0 0 0 149]\n [ 0 0 0 0 4 0 0 0 0 0 2]\n [ 0 0 0 0 4 0 0 0 0 0 0]\n [ 0 0 0 2 0 0 0 0 0 0 5]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 0 1117 0 0 0 0 0 5848]\n [ 0 0 0 41 222 0 0 0 0 0 9813]]\n", "text": " precision recall f1-score support\n\n 0 0.50 0.01 0.03 217\n 1 0.00 0.00 0.00 152\n 2 0.91 0.19 0.32 110\n 3 1.00 1.00 1.00 6766\n 4 0.74 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.78 1.00 0.88 7\n 8 0.83 0.83 0.83 6\n 9 0.94 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.70 0.56 0.57 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 3 0 0 0 205 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 21 11 7 0 0 0 0 48 23]\n [ 0 0 0 6765 0 0 0 0 1 0 0]\n [ 3 0 0 0 1534 0 0 0 0 279 12]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 1 0 301 0 0 0 0 6662 1]\n [ 0 0 1 1 11 0 0 2 0 0 10061]]\n", "text": " 0\n0 \n0 6\n2 26\n3 6784\n4 2062\n5 2\n7 11\n8 6\n9 7125\n10 10115\n\n Accuracy Score\n0.9586027470635498\n\nClassification Report\n precision recall f1-score support\n\n 0 0.50 0.01 0.03 217\n 1 0.00 0.00 0.00 152\n 2 0.81 0.19 0.31 110\n 3 1.00 1.00 1.00 6766\n 4 0.74 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.64 1.00 0.78 7\n 8 0.83 0.83 0.83 6\n 9 0.94 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.68 0.56 0.56 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 3 0 0 0 205 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 21 11 7 0 0 0 0 48 23]\n [ 0 0 0 6765 0 0 0 0 1 0 0]\n [ 3 0 0 0 1534 0 0 0 0 279 12]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 1 0 301 0 0 0 0 6662 1]\n [ 0 0 4 1 11 0 0 4 0 0 10056]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n0 4\n2 21\n3 6785\n4 2043\n5 2\n7 8\n8 5\n9 7139\n10 10130\n\n Accuracy Score\n0.9591383861958144\n\nClassification Report\n precision recall f1-score support\n\n 0 0.50 0.01 0.02 217\n 1 0.00 0.00 0.00 152\n 2 0.95 0.18 0.31 110\n 3 1.00 1.00 1.00 6766\n 4 0.75 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.88 1.00 0.93 7\n 8 1.00 0.83 0.91 6\n 9 0.93 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.73 0.56 0.58 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 2 0 0 0 206 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 20 11 7 0 0 0 0 49 23]\n [ 0 0 0 6766 0 0 0 0 0 0 0]\n [ 2 0 0 0 1528 0 0 0 0 285 13]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 0 0 295 0 0 0 0 6669 1]\n [ 0 0 1 1 3 0 0 1 0 0 10070]]\n", "text": "[10 10 10 9 4 4]\n 0\n0 \n0 6\n2 21\n3 6785\n4 2042\n5 2\n7 8\n8 5\n9 7139\n10 10129\n\n Accuracy Score\n0.9591001262577955\n\nClassification Report\n precision recall f1-score support\n\n 0 0.50 0.01 0.03 217\n 1 0.00 0.00 0.00 152\n 2 0.95 0.18 0.31 110\n 3 1.00 1.00 1.00 6766\n 4 0.75 0.84 0.79 1828\n 5 1.00 0.33 0.50 6\n 6 0.00 0.00 0.00 4\n 7 0.88 1.00 0.93 7\n 8 1.00 0.83 0.91 6\n 9 0.93 0.96 0.95 6965\n 10 0.99 1.00 1.00 10076\n\n accuracy 0.96 26137\n macro avg 0.73 0.56 0.58 26137\nweighted avg 0.95 0.96 0.95 26137\n\nConfusion Matrix\n[[ 3 0 0 0 205 0 0 0 0 9 0]\n [ 0 0 0 6 0 0 0 0 0 123 23]\n [ 0 0 20 11 7 0 0 0 0 49 23]\n [ 0 0 0 6766 0 0 0 0 0 0 0]\n [ 3 0 0 0 1527 0 0 0 0 285 13]\n [ 0 0 0 0 2 2 0 0 0 2 0]\n [ 0 0 0 0 2 0 0 0 0 2 0]\n [ 0 0 0 0 0 0 0 7 0 0 0]\n [ 0 0 0 1 0 0 0 0 5 0 0]\n [ 0 0 0 0 295 0 0 0 0 6669 1]\n [ 0 0 1 1 4 0 0 1 0 0 10069]]\n".
Samsung Promotions Claims 2022,
Depop Refresh Summary Is Not Purchasable,
Articles W