Project work operational
Please find attachment to work on the discussion part .
PLease refer the document for my project work
ITS 631_ group project
Week 1 ——–
INTRODUCTION:
Problem Description
Viruses have been a treat to human life since decades. Pandemics like Spanish flu, Ebola and SARS virus have laid a drastic impact on human life. The difficult part about these viruses is that,as they change their structure frequently,it is tough to provide vaccination that can help the human immune system to fight these viruses. (RADWAN, HAFEEZ, & MAMDOUH, 2013)
After the recent COVID-19attack,it became oblivious that there might be more pandemics in near future even if the world fights against this. (Al-Turaiki, Alshahrani, & Almutairi, 2016)
The problem I chose here to find the impact of corona virus in the United States of America. Main objective is to view the total number of positive, negative cases for the critically affected states which has a death count > 1000 in order to view the mortality rate of the COVID19 virus.
Approach The database that was selected is regarding the corona cases across the globe. We want to filter the data such that we need only data from United states.
Background
Dataset Selection of database was based on the present scenario of the countys crisis related to COVID19. We get the data set which is a daily feed from John Hopkins White School of Engineering refreshed daily at 9 am Est. Our filter needs to be such that we need data only from United States.
Cleaning up of dataset Cleaning up the dataset as per the requirement was the heavy lifting that was a much-needed step in order to remove lot of noise so that the story drawn was a much closer to expected. The refined data is now reviewed in Tableau for the cluster analysis and visualize data with a bar/line graph. All the zero and null values are cleansed with some value. This extracted data is copied into a new dataset to perform further clustering and validations.
After reviewing the data set some null values under the Hospitalized column have been detected which suggests that further cleansing of data is needed.
Results
Data Analysis
The analysis we chose to perform here is the states which have death count > 100.
In order to do that we did upload the filtered data in the selected tool tableau.
Lot of irrelevant columns can be hidden and only the columns which are needed must be seen.
The picture illustrates the proper filtering of death vs state data for the purpose of data analysis.
Further Analysis
1. Sum of total no. of negative cases vs State
1. Sum of total no. positive cases vs State
1. Total no. of deaths vs State
Clustering analysis
In order to perform data mining, we chose to perform cluster analysis on the pre-selected data.
We are applying scatter plot in order to visualize the dataset in cluster method. (tableau, 2003-2020).
The graph listed below represents the total number of positive cases (county-wise) vs the total number of deaths (county-wise).
Evaluation
In order to perform model-evaluation on the data set we chose to apply linear regression.
This is performed by using trend line. (Data Crunch, 2020)
Discussion
The above graphs clearly imply:
1. Active testing is being performed in very state.
1. More than 75% of the tested cases are negative which implies that people with other type of flu diagnosed with similar symptoms have also been tested for corona.
1. Death count in NY, NJ, MI is very high. On further analysis we can see NY, NJ has a very high count of cases, thus deaths would be relatively high on comparison. But coming to MI the positive count is not very high on comparison with WA, LA which indicates either:
2. Active medication is not available in MI,
2. Patients due to lack of awareness are not reaching out to hospitals at an early stage due to various concerns.
Conclusion
Corona virus has a drastic impact on people lives altering their lifestyle.
Washington state data represents the handling of situation and proper medication can help the recovery of people.
New York data demonstrates the importance of social distancing.
Michigan data shows the awareness needed among people in order to reach out to hospitals when people undergo initial symptoms. The more the delay, the more the death count.
Week 2 ——————
The database that was selected is a daily feed from John Hopkins White School of Engineering refreshed at 9 am Est on daily basis. The data base which was downloaded as of 28th March (most recent feed) regarding the corona cases reported in each country as of 28th March.
We want to filter the data such that we need only data from United states. Hence the extracted is resulted with consists of data set from United states. Further classification is done based on data to be from only 28th and 27th of March as we may not have any data from Washington and California region regarding cases being reported on 28th.
This refined data set is obtained and now reviewed.
This selected data set has some null values under the Hospitalized column.
The rest of the data is non-null and has either zero or values
Hence, this suggests that the data is cleansed and is a good candidate for the visualization purpose.
The main objective as part of data review is to:
View the total number of negative, positive cases for the most critically affected states which has death count>100 to analyze whether testing has been actively being held in the states, how bad is it contagious and the mortality rate.
The tool we selected is Tableau for public version. It is open source.
After successful download, the data set has been uploaded in an excel format.
Although it can still accept json, csv formats.
Now sort the data based on:
1. Death to be > 100 (as these states are greatly impacted),
1. On or after 27th March as we want to analyze for the current data
Once the data has been uploaded you can see the data being populated in the tableau workspace.
Now we can see lot of column such as date modified, fips codes, grade, last update, check in time, commercial state value, etc. which may not be relevant to our objective. Hence, we can hide such column.
Objective: The selected objective is to visualize the graph for the most critically affected states:
1. Sum (negative cases) vs State
1. Sum (positive cases) vs State
1. Total number of cases vs State
These number imply that active testing has been going on and more than 75% of the tested cases are negative which implies that people with other type of flu diagnosed with similar symptoms have also been tested for corona. date state positive negative hospitalizedCurrently recovered death positiveIncrease negativeIncrease totalTestResults totalTestResultsIncrease deathIncrease hospitalizedIncrease
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20200808 CA 545787 8161740 6956 10189 7371 103274 8707527 110645 178 0
20200808 CO 49893 529238 317 5490 1736 457 7311 579131 7768 7 10
20200808 CT 50320 814313 65 8721 4441 0 0 864633 0 0 0
20200808 DC 12653 200681 80 10124 590 64 4545 213334 4609 1 0
20200808 DE 15502 180392 36 8416 590 57 1053 195894 1110 2 0
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20200808 GA 213427 1584539 2878 4186 4423 40414 1797966 44837 69 274
20200808 GU 412 24117 3 321 5 1 166 24529 167 0 0
20200808 HI 3115 136131 102 1467 31 201 3761 139246 3962 2 6
20200808 IA 48283 465264 229 36882 925 418 4429 513547 4847 12 0
20200808 ID 23922 179049 201 8738 229 523 3748 202971 4271 6 14
20200808 IL 193998 2839936 1538 7840 2190 45826 3033934 48016 18 0
20200808 IN 73287 755179 960 54068 3036 1033 10329 828466 11362 13 0
20200808 KS 30638 285874 333 1293 380 0 0 316512 0 0 0
20200808 KY 34578 613857 653 8674 772 782 5659 648435 6441 8 27
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20200808 MA 120711 1182033 386 99021 8721 420 16269 1302744 16689 12 22
20200808 MD 94581 924942 515 5899 3577 775 11924 1019523 12699 12 58
20200808 ME 4026 182375 9 3504 125 12 2667 186401 2679 1 0
20200808 MI 96191 1978930 694 63636 6520 721 37805 2075121 38526 -4 0
20200808 MN 60101 861296 309 52768 1689 916 10210 921397 11126 8 48
20200808 MO 57379 695361 924 1301 0 0 752740 0 0 0
20200808 MP 48 12253 29 2 1 -1 12301 0 0 0
20200808 MS 66646 426944 1151 42391 1874 1210 5168 493590 6378 26 29
20200808 MT 4889 183957 81 3255 75 132 958 188846 1090 5 4
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20200808 NM 21965 590889 132 9166 675 192 8280 612854 8472 6 16
20200808 NV 55419 457600 976 1297 949 886 5135 513019 6021 29 0
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20200808 OH 99969 1544524 936 77429 3668 1294 20104 1644493 21398 16 69
20200808 OK 43080 650485 594 35745 603 825 6925 693565 7750 3 39
20200808 OR 21010 417057 204 4111 355 374 0 438067 374 7 0
20200808 PA 118092 1214965 634 90930 7313 813 15345 1333057 16158 16 0
20200808 PR 21424 305972 399 274 738 0 327396 738 9 0
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20200808 SD 9477 110510 48 8307 146 106 1154 119987 1260 2 5
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20200808 VA 99189 1123839 1258 12843 2322 1307 12976 1223028 14283 5 4
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20200807 AL 98301 651115 1575 37923 1735 1709 7923 749416 9632 21 224
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20200807 CA 538416 8058466 7277 10011 8436 87983 8596882 96419 142 0
20200807 CO 49436 521927 317 5490 1729 448 5596 571363 6044 12 36
20200807 CT 50320 814313 65 8721 4441 75 7460 864633 7535 4 0
20200807 DC 12589 196136 90 10118 589 71 2671 208725 2742 2 0
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20200807 GA 209004 1544125 2981 4117 4109 24258 1753129 28367 91 280
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20200807 HI 2914 132370 117 1440 29 151 224 135284 375 2 11
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20200807 ID 23399 175301 242 8486 223 692 3415 198700 4107 6 23
20200807 IL 191808 2794110 1486 7822 2103 44785 2985918 46888 31 0
20200807 IN 72254 744850 990 54068 3023 1239 11520 817104 12759 10 87
20200807 KS 30638 285874 333 1293 380 921 6873 316512 7794 12 54
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20200806 CO 48988 516331 320 5471 1717 594 9303 565319 9897 7 20
20200806 CT 50245 806853 66 8721 4437 20 8969 857098 8989 0 113
20200806 DC 12518 193465 98 10094 587 75 3083 205983 3158 0 0
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20200804 ND 6933 153537 51 5715 96 148 1571 160470 1719 1 10
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20200804 OK 39463 617813 504 32319 566 861 18352 657276 19213 15 100
20200804 OR 19699 400215 219 3960 333 333 2450 419914 2783 5 33
20200804 PA 115009 1156520 656 86256 7232 854 14106 1271529 14960 23 0
20200804 PR 19324 305972 509 237 533 0 325296 533 7 0
20200804 RI 19390 192563 80 1848 1011 144 1743 211953 1887 1 11
20200804 SC 94190 646267 1458 32859 1847 1239 7543 740457 8782 54 272
20200804 SD 9079 106302 42 8008 136 59 674 115381 733 1 8
20200804 TN 112441 1480209 1433 73259 1117 1805 16312 1592650 18117 25 92
20200804 TX 451181 3433667 8674 306262 7261 9167 41095 3884848 50262 245 0
20200804 UT 41907 503049 220 30449 321 378 4396 544956 4774 7 32
20200804 VA 94251 1073787 1255 12385 2244 1145 11785 1168038 12930 26 180
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20200803 CO 47716 499496 308 5393 1691 449 6191 547212 6640 0 9
20200803 CT 50062 779120 56 8613 4437 252 34934 829182 35186 5 0
20200803 DC 12313 186502 104 9893 586 39 1584 198815 1623 0 0
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20200803 IA 45841 440634 241 33219 882 349 2008 486475 2357 6 0
20200803 ID 21344 163993 239 7370 197 230 808 185337 1038 0 18
20200803 IL 184522 2623556 1418 7723 1298 27177 2808078 28475 9 0
20200803 IN 68433 707049 904 47355 2980 576 5863 775482 6439 5 156
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20200803 MD 91144 869493 548 5740 3523 870 14981 960637 15851 8 86
20200803 ME 3970 170175 12 3396 124 12 2059 174145 2071 1 1
20200803 MI 92374 1834449 694 60022 6463 613 24369 1926823 24982 6 0
20200803 MN 56560 815444 302 49565 1656 613 13050 872004 13663 2 57
20200803 MO 52887 660061 889 1255 1047 9434 712948 10481 2 0
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20200803 MS 61125 401592 1172 42391 1711 572 0 462717 572 8 66
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20200803 NH 6634 155988 24 5820 417 21 1639 162622 1660 1 1
20200803 NJ 182614 1981966 738 32660 15824 264 18438 2164580 18702 10 0
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20200803 PA 114155 1142414 585 86757 7209 565 11435 1256569 12000 0 0
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20200803 TN 110636 1463897 1323 70878 1092 1009 11226 1574533 12235 19 52
20200803 TX 442014 3392572 8819 297422 7016 11529 75278 3834586 86807 179 0
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20200803 VA 93106 1062002 1205 12255 2218 1324 13758 1155108 15082 0 180
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20200803 WI 59401 911435 294 44495 956 411 6769 970836 7180 1 15
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20200802 DC 12274 184918 102 9870 586 69 3342 197192 3411 1 0
20200802 DE 14949 169962 43 8235 585 72 1943 184911 2015 0 0
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20200802 GA 193177 1431463 3069 3840 3165 25390 1624640 28555 15 69
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20200802 IA 45492 438626 231 32952 876 516 4632 484118 5148 4 0
20200802 ID 21114 163185 239 7146 197 393 2033 184299 2426 8 6
20200802 IL 183224 2596379 1407 7714 1467 37478 2779603 38945 14 0
20200802 IN 67857 701186 873 47355 2975 735 9702 769043 10437 4 67
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20200802 KY 31185 565951 602 8135 742 462 0 597136 462 2 0
20200802 LA 119747 1259693 1534 74246 4007 3467 33730 1379440 37197 58 0
20200802 MA 118458 1096082 406 97595 8638 418 12959 1214540 13377 12 6
20200802 MD 90274 854512 553 5713 3515 909 14523 944786 15432 9 74
20200802 ME 3958 168116 12 3387 123 21 2307 172074 2328 0 0
20200802 MI 91761 1810080 727 60022 6457 429 24582 1901841 25011 0 0
20200802 MN 55947 802394 302 48847 1654 759 11314 858341 12073 8 33
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20200802 MP 45 11719 29 2 3 0 11764 3 0 0
20200802 MS 60553 401592 1172 35071 1703 672 0 462145 672 10 0
20200802 MT 4193 170102 72 2466 61 112 2630 174295 2742 0 3
20200802 NC 125219 1688291 1142 92302 1969 1341 25757 1813510 27098 5 0
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20200802 NE 26391 250852 142 19325 332 180 1804 277243 1984 0 7
20200802 NH 6613 154349 22 5794 416 0 0 160962 0 0 0
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20200802 NM 20796 545090 134 8286 651 196 7678 565886 7874 9 17
20200802 NV 50205 431914 1165 1127 832 1131 6768 482119 7899 0 0
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20200802 OK 38225 599461 628 30820 550 494 0 637686 494 1 13
20200802 OR 19097 394560 208 3872 326 280 3761 413657 4041 1 0
20200802 PA 113590 1130979 564 86328 7209 654 11593 1244569 12247 5 0
20200802 PR 18411 305972 506 230 539 0 324383 539 5 0
20200802 RI 19022 186994 76 1823 1007 0 0 206016 0 0 0
20200802 SC 91788 630469 1427 32859 1777 1189 9199 722257 10388 26 0
20200802 SD 8955 104998 35 7909 135 88 933 113953 1021 1 3
20200802 TN 109627 1452671 1271 68471 1073 1443 18002 1562298 19445 6 32
20200802 TX 430485 3317294 8969 282604 6837 0 0 3747779 0 0 0
20200802 UT 41175 495541 289 29389 311 473 2983 536716 3456 1 18
20200802 VA 91782 1048244 1172 12108 2218 981 11684 1140026 12665 3 17
20200802 VI 421 8868 341 8 0 0 9289 0 0 0
20200802 VT 1426 95255 15 1238 57 5 1284 96681 1289 0 0
20200802 WA 57541 943987 407 1592 1738 26136 1001528 27874 28 87
20200802 WI 58990 904666 268 43964 955 932 8721 963656 9653 1 36
20200802 WV 6854 284358 112 4897 117 119 3869 291212 3988 1 0
20200802 WY 2808 50573 18 2173 26 39 0 53381 39 0 0
20200801 AK 3827 234806 37 930 24 150 5377 238633 5527 1 0
20200801 AL 89349 608022 1488 35401 1603 1626 6278 6