If you have further questions or comments about the GHCN data or the Climate Data Online interface, you can send an email to NOAA NCEI at ncei.orders@noaa.gov or call them at 1-828-271-4800. For help on getting started with the Weather Data Services page, see Getting Started With Weather Data Service. Use the basic weather parameters for your project now and create a simple weather report for your location, for example. A mainly sunny sky. If you dont have an account, you can simply sign up for a free trial for Weather Data Services to obtain access. The top-level includes locations and columns information. We apologize for any inconvenience. Weather has a major impact on daily life (consumption, behaviour, etc.) Partly cloudy skies early followed by mostly cloudy skies and a few showers later at night. Use the search bar to enter a location of interest (name, address, zip code, etc.). with our weather API. Note that in this example we simply empty any existing data within the weather data table. GitHub - StephGerron/Weather_Database.ipynb StephGerron / Weather_Database.ipynb Public Notifications Star master 1 branch 0 tags Code StephGerron Final challenge e4161b4 on Nov 15, 2019 2 commits Failed to load latest commit information. +49 (0) 30 200 74 280, Meteomatics Ltd. Winds light and variable. Of those, the station with the highest observation count is USC00519281 WAIHEE 837.5 with a total of 2,772 observation across the dataset. The URL Winter Storm To Move Across Country This Week, WWII Structure Slides Off Cliff In San Francisco, Mother Nature Magic Trick In Yosemite National Park, Damaging Winds, Isolated Tornadoes Possible, How Were Cats Domesticated? Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. APIs relevant to your problem quickly and easily. Our Python script is split into two parts. A variety of subscriptions with various limits on calls/min, data availability, and service. Winds light and variable. Low 47F. Custom Business API packages are delivered within one business day. THE WMS/WFS capability of our data enables integration into visualizations. Select the desired station from the list or from the map to view . "text/html": "

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". The data sources in SWDI will not provide complete severe weather coverage of a geographic region or time period due to a number of factors (e.g., reports for a location or time period not provided to NOAA). Use `zero_division` parameter to control this behavior.\n _warn_prf(average, modifier, msg_start, len(result))\nC:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1245: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Winds light and variable. Open source, i.e. Mars Facts. Below each location is an array of values. Explore the Benefits of Using a Weather API. Partly cloudy. This free .ipynb template is designed for any Data Scientist, Data Analyst, Business Analyst, Data Engineer, or Data Architect dealing with a DataFrame Object. There are a total of 9 weather stations in Hawaii, according to the dataset. A station whose period of record includes the currently selected date is represented with a blue disc. Winds NE at 5 to 10 mph. Some clouds this evening will give way to mainly clear skies overnight. Winds light and variable. Our first step is to create a MySQL database and create an empty table within the new database where the weather data will reside. Meteomatics' science and engineering teams are continuously working on comprehensive documentation that ensures users from all industries can leverage the value provided by the API for their purposes. Global Historical Climatology Network daily (GHCNd), Science & information for a climate-smart nation, Daily summaries of past weather by location, GHCN (Global Historical Climatology Network) Daily Documentation, From giant hailstones to the most snow in a day, state extremes show us just how BIG the weather can get, Global warming increased risk, intensity of Louisiana's extreme rain event, Daily Temperature and Precipitation Reports - Data Tables. Start your project, trial or comparison in a few seconds: the free trial access is available immediately after registration and you can get started straight away. High around 65F. Low 46F. Mostly sunny skies. Much of the automatically derived data in SWDI is from radar data that represents probable conditions for an event rather than a confirmed occurrence. Usually, just a few minutes later, you'll receive an email stating that your order has been processed. Hourly Local Weather Forecast, weather conditions, precipitation, dew point, humidity, wind from Weather.com and The Weather Channel It includes DNI, DHI and GHI indices for the Clear Sky and Cloudy Sky models. To review, open the file in an editor that reveals hidden Unicode characters. Let us advise you to find the right solution for you! business forward. They need shelter, food, and water. In complex use cases, we manage to deliver highly up-to-date and precise weather data and weather forecasts: individually tailored with the data that is really needed. Learn more about bidirectional Unicode characters, "text": " ID Name Date Time Event Status Latitude Longitude Maximum Wind Minimum Pressure Low Wind SW Low Wind NW Moderate Wind NE Moderate Wind SE Moderate Wind SW \\\n0 EP011949 UNNAMED 19490611 0 TS 20.2N 106.3W 45 -999 -999 -999 -999 -999 -999 \n1 EP011949 UNNAMED 19490611 600 TS 20.2N 106.4W 45 -999 -999 -999 -999 -999 -999 \n2 EP011949 UNNAMED 19490611 1200 TS 20.2N 106.7W 45 -999 -999 -999 -999 -999 -999 \n3 EP011949 UNNAMED 19490611 1800 TS 20.3N 107.7W 45 -999 -999 -999 -999 -999 -999 \n4 EP011949 UNNAMED 19490612 0 TS 20.4N 108.6W 45 -999 -999 -999 -999 -999 -999 \n\n Moderate Wind NW High Wind NE High Wind SE High Wind SW High Wind NW \n0 -999 -999 -999 -999 -999 \n1 -999 -999 -999 -999 -999 \n2 -999 -999 -999 -999 -999 \n3 -999 -999 -999 -999 -999 \n4 -999 -999 -999 -999 -999 \n\n[5 rows x 22 columns]\n". Get the weather data that is relevant to your problem quickly and easily. This product provides users with current, forecast and historical solar radiation data for any coordinates on the globe. The weather API can be used in many ways and must deliver different values depending on the use case. In this example we are using MySQL version 8. Schweiz The meteocache matches the data in time and space and ensures that the weather API can return the requested data query efficiently and very quickly despite the size and format of the original data set. Our different ways of requesting weather data make it possible to obtain the most accurate information on the weather situation of the respective location or route. The most recent setup file that can be downloaded is 156.1 MB in size. Data are currently available in Shapefile (for GIS), KMZ (for Google Earth), CSV (comma-separated), and XML formats. Our built-in antivirus checked this download and rated it as virus free. A set of stations from that location will be displayed (if any exist). Your search results show up in the left column with a map ofyour ZIP code on the right. integration into visualizations. You can choose from the following list: In this package, up to 500 queries per day are available to you free of charge. in real time. Weather Data Analysis (Part I).ipynb_ Rename notebook Rename notebook. Stations whose period of record does not include the currently selected date are plotted as white. "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Maximum WindMinimum PressureLow Wind NELow Wind SELow Wind SWLow Wind NWModerate Wind NEModerate Wind SEModerate Wind SWModerate Wind NWHigh Wind NEHigh Wind SEHigh Wind SWHigh Wind NW
14735100960-999-999-999-999-999-999-999-999-999-999-999-999
23277251010000000000000
556335-999-999-999-999-999-999-999-999-999-999-999-999-999
26134301008000000000000
607325-999-999-999-999-999-999-999-999-999-999-999-999-999
2303865987707050703030253015151015
24599105958807060704040404020201520
22508251007000000000000
18047251006-999-999-999-999-999-999-999-999-999-999-999-999
1067245-999-999-999-999-999-999-999-999-999-999-999-999-999
\n

7842 rows 14 columns

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", "text/plain": " Maximum Wind Minimum Pressure Low Wind NE Low Wind SE Low Wind SW Low Wind NW Moderate Wind NE Moderate Wind SE Moderate Wind SW Moderate Wind NW High Wind NE High Wind SE \\\n14735 100 960 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 \n23277 25 1010 0 0 0 0 0 0 0 0 0 0 \n5563 35 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 \n26134 30 1008 0 0 0 0 0 0 0 0 0 0 \n6073 25 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 \n \n23038 65 987 70 70 50 70 30 30 25 30 15 15 \n24599 105 958 80 70 60 70 40 40 40 40 20 20 \n22508 25 1007 0 0 0 0 0 0 0 0 0 0 \n18047 25 1006 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 \n10672 45 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 \n\n High Wind SW High Wind NW \n14735 -999 -999 \n23277 0 0 \n5563 -999 -999 \n26134 0 0 \n6073 -999 -999 \n \n23038 10 15 \n24599 15 20 \n22508 0 0 \n18047 -999 -999 \n10672 -999 -999 \n\n[7842 rows x 14 columns]", "text": "[10 10 10 4 4 4]\n 0\n0 \n3 6813\n4 2985\n9 5619\n10 10720\n\n Accuracy Score\n0.7632092436010254\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 0.86 0.86 0.86 6766\n 4 0.55 0.91 0.69 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 0.76 0.62 0.68 6965\n 10 0.76 0.81 0.78 10076\n\n accuracy 0.76 26137\n macro avg 0.27 0.29 0.27 26137\nweighted avg 0.76 0.76 0.76 26137\n\nConfusion Matrix\n[[ 0 0 0 0 217 0 0 0 0 0 0]\n [ 0 0 0 6 0 0 0 0 0 111 35]\n [ 0 0 0 12 16 0 0 0 0 51 31]\n [ 0 0 0 5847 0 0 0 0 0 0 919]\n [ 0 0 0 22 1655 0 0 0 0 129 22]\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 0 0 0 0 0 0 0 7]\n [ 0 0 0 6 0 0 0 0 0 0 0]\n [ 0 0 0 28 1089 0 0 0 0 4295 1553]\n [ 0 0 0 892 0 0 0 0 0 1033 8151]]\n", "text": "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\nSTOP: TOTAL NO. High 66F. The first part of the sets up some variables to customize the weather data that is entered. Choosing a college major: one girls journey into math and data science, Internal Content Indexing NLU Service Now Available on the CityFALCON APICityFALCON Blog, 5 Reasons to Outsource Your Annotation & Labeling Projects, When you have excluded the impossible, whatever remains, however improbable must be the truth, # This is the core of our weather query URL, #Locations for the weather data. A few clouds from time to time. Weather Underground provides local & long-range weather forecasts, weather reports, maps & tropical weather conditions for locations worldwide 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".

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