Project work operational Please find attachment to work on the discussion part . PLease refer the document for my project work ITS 631_ group project

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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20200804 IN 69255 715763 980 43535 2996 822 8714 785018 9536 16 144

20200804 KS 28876 272963 232 1196 365 0 0 301839 0 0 0

20200804 KY 32197 575658 638 8406 751 689 6873 607855 7562 7 36

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20200804 MD 91854 880492 547 5740 3530 710 10999 972346 11709 7 56

20200804 ME 3975 171786 12 3424 123 5 1611 175761 1616 -1 -1

20200804 MI 93175 1861240 694 60022 6471 801 26791 1954415 27592 8 0

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20200804 MS 62199 401592 1164 42391 1753 1074 0 463791 1074 42 44

20200804 MT 4314 176922 70 2766 64 81 1647 181236 1728 0 2

20200804 NC 128161 1725865 1166 105093 2010 1629 14987 1854026 16616 28 0

20200804 ND 6933 153537 51 5715 96 148 1571 160470 1719 1 10

20200804 NE 26956 256903 152 19677 332 254 2499 283859 2753 0 14

20200804 NH 6660 158176 23 5848 417 26 2188 164836 2214 0 1

20200804 NJ 182970 1981966 470 32697 15835 356 0 2164936 356 11 0

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20200804 NV 52179 436908 1146 1209 862 980 3241 489087 4221 15 0

20200804 NY 417589 5736178 568 73326 25175 746 70247 6153767 70993 3 0

20200804 OH 95106 1460706 1038 71338 3570 1143 19126 1555812 20269 31 127

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

20200804 VI 463 9312 364 8 24 315 9775 339 0 0

20200804 VT 1431 97370 14 1249 57 4 1559 98801 1563 0 0

20200804 WA 58715 950107 401 1600 542 0 1008822 542 4 52

20200804 WI 60171 928845 294 45368 968 770 17410 989016 18180 12 51

20200804 WV 7051 291388 111 5063 124 78 3315 298439 3393 7 0

20200804 WY 2884 52359 17 2284 27 36 327 55243 363 0 2

20200803 AK 4064 241743 38 946 25 80 2637 245807 2717 1 0

20200803 AL 92661 621390 1517 35401 1633 1217 6764 714051 7981 6 344

20200803 AR 44597 482803 513 37240 475 1424 11395 527400 12819 17 72

20200803 AS 0 1267 0 0 0 1267 0 0 0

20200803 AZ 179497 784921 2017 23702 3779 1030 6960 964418 7990 14 29

20200803 CA 514901 7669795 7629 9388 5739 142982 8184696 148721 32 0

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

20200803 DE 15055 172604 40 8267 585 106 2642 187659 2748 0 0

20200803 FL 491884 3260914 7938 7279 4752 27049 3752798 31801 73 220

20200803 GA 195435 1450966 3111 3842 2258 19503 1646401 21761 2 60

20200803 GU 368 22205 5 305 5 1 520 22573 521 0 0

20200803 HI 2242 125037 75 1294 26 45 593 127279 638 0 5

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

20200803 KS 28876 272963 232 1196 365 1064 8268 301839 9332 7 31

20200803 KY 31508 568785 612 8335 744 323 2834 600293 3157 2 33

20200803 LA 120846 1273064 1496 74246 4024 1099 13371 1393910 14470 17 0

20200803 MA 118657 1108195 375 97595 8648 199 12113 1226852 12312 10 6

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

20200803 MP 46 12255 29 2 1 536 12301 537 0 0

20200803 MS 61125 401592 1172 42391 1711 572 0 462717 572 8 66

20200803 MT 4233 175275 69 2653 64 40 5173 179508 5213 3 3

20200803 NC 126532 1710878 1057 105093 1982 1313 22587 1837410 23900 13 0

20200803 ND 6785 151966 46 5590 95 183 2383 158751 2566 0 8

20200803 NE 26702 254404 144 19575 332 311 3552 281106 3863 0 1

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

20200803 NM 21016 553237 127 8343 654 220 8147 574253 8367 3 17

20200803 NV 51199 433667 1152 1158 847 994 1753 484866 2747 15 0

20200803 NY 416843 5665931 536 73279 25172 545 51294 6082774 51839 2 0

20200803 OH 93963 1441580 1001 69501 3539 932 17065 1535543 17997 10 92

20200803 OK 38602 599461 628 31165 551 377 0 638063 377 1 34

20200803 OR 19366 397765 215 3960 328 269 3205 417131 3474 2 48

20200803 PA 114155 1142414 585 86757 7209 565 11435 1256569 12000 0 0

20200803 PR 18791 305972 533 230 380 0 324763 380 0 0

20200803 RI 19246 190820 80 1838 1010 224 3826 210066 4050 3 20

20200803 SC 92951 638724 1401 32859 1793 1163 8255 731675 9418 16 0

20200803 SD 9020 105628 39 7939 135 65 630 114648 695 0 3

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

20200803 UT 41529 498653 252 29967 314 354 3112 540182 3466 3 20

20200803 VA 93106 1062002 1205 12255 2218 1324 13758 1155108 15082 0 180

20200803 VI 439 8997 354 8 18 129 9436 147 0 0

20200803 VT 1427 95811 14 1240 57 1 556 97238 557 0 0

20200803 WA 58173 950107 401 1596 632 6120 1008280 6752 4 37

20200803 WI 59401 911435 294 44495 956 411 6769 970836 7180 1 15

20200803 WV 6973 288073 116 4918 117 119 3715 295046 3834 0 0

20200803 WY 2848 52032 19 2214 27 40 1459 54880 1499 1 0

20200802 AK 3984 239106 39 932 24 157 4300 243090 4457 0 0

20200802 AL 91444 614626 1529 35401 1627 2095 6604 706070 8699 24 0

20200802 AR 43173 471408 499 36034 458 0 0 514581 0 -2 42

20200802 AS 0 1267 0 0 0 1267 0 0 0

20200802 AZ 178467 777961 2147 23413 3765 1465 6313 956428 7778 18 25

20200802 CA 509162 7526813 7761 9356 9032 140356 8035975 149388 132 0

20200802 CO 47267 493305 335 5359 1691 458 6711 540572 7169 0 24

20200802 CT 49810 744186 69 8613 4432 0 0 793996 0 0 0

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

20200802 FL 487132 3233865 7952 7206 7104 34450 3720997 41554 62 180

20200802 GA 193177 1431463 3069 3840 3165 25390 1624640 28555 15 69

20200802 GU 367 21685 2 304 5 0 0 22052 0 0 0

20200802 HI 2197 124444 75 1269 26 86 3304 126641 3390 0 15

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

20200802 KS 27812 264695 366 1178 358 0 0 292507 0 0 0

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

20200802 MO 51840 650627 889 1253 582 5140 702467 5722 0 0

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

20200802 ND 6602 149583 45 5396 95 0 0 156185 0 0 0

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

20200802 NJ 182350 1963528 695 32475 15814 321 48859 2145878 49180 6 0

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

20200802 NY 416298 5614637 556 73222 25170 531 58430 6030935 58961 6 0

20200802 OH 93031 1424515 1049 68394 3529 944 23699 1517546 24643 14 43

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