diff --git a/DataScienceJobSalariesModel/ds_salaries.csv b/DataScienceJobSalariesModel/ds_salaries.csv
deleted file mode 100644
index 4f56347..0000000
--- a/DataScienceJobSalariesModel/ds_salaries.csv
+++ /dev/null
@@ -1,608 +0,0 @@
-,work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size
-0,2020,MI,FT,Data Scientist,70000,EUR,79833,DE,0,DE,L
-1,2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S
-2,2020,SE,FT,Big Data Engineer,85000,GBP,109024,GB,50,GB,M
-3,2020,MI,FT,Product Data Analyst,20000,USD,20000,HN,0,HN,S
-4,2020,SE,FT,Machine Learning Engineer,150000,USD,150000,US,50,US,L
-5,2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L
-6,2020,SE,FT,Lead Data Scientist,190000,USD,190000,US,100,US,S
-7,2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
-8,2020,MI,FT,Business Data Analyst,135000,USD,135000,US,100,US,L
-9,2020,SE,FT,Lead Data Engineer,125000,USD,125000,NZ,50,NZ,S
-10,2020,EN,FT,Data Scientist,45000,EUR,51321,FR,0,FR,S
-11,2020,MI,FT,Data Scientist,3000000,INR,40481,IN,0,IN,L
-12,2020,EN,FT,Data Scientist,35000,EUR,39916,FR,0,FR,M
-13,2020,MI,FT,Lead Data Analyst,87000,USD,87000,US,100,US,L
-14,2020,MI,FT,Data Analyst,85000,USD,85000,US,100,US,L
-15,2020,MI,FT,Data Analyst,8000,USD,8000,PK,50,PK,L
-16,2020,EN,FT,Data Engineer,4450000,JPY,41689,JP,100,JP,S
-17,2020,SE,FT,Big Data Engineer,100000,EUR,114047,PL,100,GB,S
-18,2020,EN,FT,Data Science Consultant,423000,INR,5707,IN,50,IN,M
-19,2020,MI,FT,Lead Data Engineer,56000,USD,56000,PT,100,US,M
-20,2020,MI,FT,Machine Learning Engineer,299000,CNY,43331,CN,0,CN,M
-21,2020,MI,FT,Product Data Analyst,450000,INR,6072,IN,100,IN,L
-22,2020,SE,FT,Data Engineer,42000,EUR,47899,GR,50,GR,L
-23,2020,MI,FT,BI Data Analyst,98000,USD,98000,US,0,US,M
-24,2020,MI,FT,Lead Data Scientist,115000,USD,115000,AE,0,AE,L
-25,2020,EX,FT,Director of Data Science,325000,USD,325000,US,100,US,L
-26,2020,EN,FT,Research Scientist,42000,USD,42000,NL,50,NL,L
-27,2020,SE,FT,Data Engineer,720000,MXN,33511,MX,0,MX,S
-28,2020,EN,CT,Business Data Analyst,100000,USD,100000,US,100,US,L
-29,2020,SE,FT,Machine Learning Manager,157000,CAD,117104,CA,50,CA,L
-30,2020,MI,FT,Data Engineering Manager,51999,EUR,59303,DE,100,DE,S
-31,2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L
-32,2020,SE,FT,Data Scientist,60000,EUR,68428,GR,100,US,L
-33,2020,MI,FT,Research Scientist,450000,USD,450000,US,0,US,M
-34,2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L
-35,2020,MI,FT,Data Engineer,65000,EUR,74130,AT,50,AT,L
-36,2020,MI,FT,Data Science Consultant,103000,USD,103000,US,100,US,L
-37,2020,EN,FT,Machine Learning Engineer,250000,USD,250000,US,50,US,L
-38,2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S
-39,2020,EN,FT,Machine Learning Engineer,138000,USD,138000,US,100,US,S
-40,2020,MI,FT,Data Scientist,45760,USD,45760,PH,100,US,S
-41,2020,EX,FT,Data Engineering Manager,70000,EUR,79833,ES,50,ES,L
-42,2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180,PT,0,PT,M
-43,2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L
-44,2020,MI,FT,Data Engineer,88000,GBP,112872,GB,50,GB,L
-45,2020,EN,PT,ML Engineer,14000,EUR,15966,DE,100,DE,S
-46,2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S
-47,2020,SE,FT,Data Engineer,188000,USD,188000,US,100,US,L
-48,2020,MI,FT,Data Scientist,105000,USD,105000,US,100,US,L
-49,2020,MI,FT,Data Engineer,61500,EUR,70139,FR,50,FR,L
-50,2020,EN,FT,Data Analyst,450000,INR,6072,IN,0,IN,S
-51,2020,EN,FT,Data Analyst,91000,USD,91000,US,100,US,L
-52,2020,EN,FT,AI Scientist,300000,DKK,45896,DK,50,DK,S
-53,2020,EN,FT,Data Engineer,48000,EUR,54742,PK,100,DE,L
-54,2020,SE,FL,Computer Vision Engineer,60000,USD,60000,RU,100,US,S
-55,2020,SE,FT,Principal Data Scientist,130000,EUR,148261,DE,100,DE,M
-56,2020,MI,FT,Data Scientist,34000,EUR,38776,ES,100,ES,M
-57,2020,MI,FT,Data Scientist,118000,USD,118000,US,100,US,M
-58,2020,SE,FT,Data Scientist,120000,USD,120000,US,50,US,L
-59,2020,MI,FT,Data Scientist,138350,USD,138350,US,100,US,M
-60,2020,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
-61,2020,MI,FT,Data Engineer,130800,USD,130800,ES,100,US,M
-62,2020,EN,PT,Data Scientist,19000,EUR,21669,IT,50,IT,S
-63,2020,SE,FT,Data Scientist,412000,USD,412000,US,100,US,L
-64,2020,SE,FT,Machine Learning Engineer,40000,EUR,45618,HR,100,HR,S
-65,2020,EN,FT,Data Scientist,55000,EUR,62726,DE,50,DE,S
-66,2020,EN,FT,Data Scientist,43200,EUR,49268,DE,0,DE,S
-67,2020,SE,FT,Data Science Manager,190200,USD,190200,US,100,US,M
-68,2020,EN,FT,Data Scientist,105000,USD,105000,US,100,US,S
-69,2020,SE,FT,Data Scientist,80000,EUR,91237,AT,0,AT,S
-70,2020,MI,FT,Data Scientist,55000,EUR,62726,FR,50,LU,S
-71,2020,MI,FT,Data Scientist,37000,EUR,42197,FR,50,FR,S
-72,2021,EN,FT,Research Scientist,60000,GBP,82528,GB,50,GB,L
-73,2021,EX,FT,BI Data Analyst,150000,USD,150000,IN,100,US,L
-74,2021,EX,FT,Head of Data,235000,USD,235000,US,100,US,L
-75,2021,SE,FT,Data Scientist,45000,EUR,53192,FR,50,FR,L
-76,2021,MI,FT,BI Data Analyst,100000,USD,100000,US,100,US,M
-77,2021,MI,PT,3D Computer Vision Researcher,400000,INR,5409,IN,50,IN,M
-78,2021,MI,CT,ML Engineer,270000,USD,270000,US,100,US,L
-79,2021,EN,FT,Data Analyst,80000,USD,80000,US,100,US,M
-80,2021,SE,FT,Data Analytics Engineer,67000,EUR,79197,DE,100,DE,L
-81,2021,MI,FT,Data Engineer,140000,USD,140000,US,100,US,L
-82,2021,MI,FT,Applied Data Scientist,68000,CAD,54238,GB,50,CA,L
-83,2021,MI,FT,Machine Learning Engineer,40000,EUR,47282,ES,100,ES,S
-84,2021,EX,FT,Director of Data Science,130000,EUR,153667,IT,100,PL,L
-85,2021,MI,FT,Data Engineer,110000,PLN,28476,PL,100,PL,L
-86,2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M
-87,2021,MI,FT,Data Analytics Engineer,110000,USD,110000,US,100,US,L
-88,2021,SE,FT,Lead Data Analyst,170000,USD,170000,US,100,US,L
-89,2021,SE,FT,Data Analyst,80000,USD,80000,BG,100,US,S
-90,2021,SE,FT,Marketing Data Analyst,75000,EUR,88654,GR,100,DK,L
-91,2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,100,DE,S
-92,2021,MI,FT,Lead Data Analyst,1450000,INR,19609,IN,100,IN,L
-93,2021,SE,FT,Lead Data Engineer,276000,USD,276000,US,0,US,L
-94,2021,EN,FT,Data Scientist,2200000,INR,29751,IN,50,IN,L
-95,2021,MI,FT,Cloud Data Engineer,120000,SGD,89294,SG,50,SG,L
-96,2021,EN,PT,AI Scientist,12000,USD,12000,BR,100,US,S
-97,2021,MI,FT,Financial Data Analyst,450000,USD,450000,US,100,US,L
-98,2021,EN,FT,Computer Vision Software Engineer,70000,USD,70000,US,100,US,M
-99,2021,MI,FT,Computer Vision Software Engineer,81000,EUR,95746,DE,100,US,S
-100,2021,MI,FT,Data Analyst,75000,USD,75000,US,0,US,L
-101,2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,L
-102,2021,MI,FT,BI Data Analyst,11000000,HUF,36259,HU,50,US,L
-103,2021,MI,FT,Data Analyst,62000,USD,62000,US,0,US,L
-104,2021,MI,FT,Data Scientist,73000,USD,73000,US,0,US,L
-105,2021,MI,FT,Data Analyst,37456,GBP,51519,GB,50,GB,L
-106,2021,MI,FT,Research Scientist,235000,CAD,187442,CA,100,CA,L
-107,2021,SE,FT,Data Engineer,115000,USD,115000,US,100,US,S
-108,2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M
-109,2021,EN,FT,Data Engineer,2250000,INR,30428,IN,100,IN,L
-110,2021,SE,FT,Machine Learning Engineer,80000,EUR,94564,DE,50,DE,L
-111,2021,SE,FT,Director of Data Engineering,82500,GBP,113476,GB,100,GB,M
-112,2021,SE,FT,Lead Data Engineer,75000,GBP,103160,GB,100,GB,S
-113,2021,EN,PT,AI Scientist,12000,USD,12000,PK,100,US,M
-114,2021,MI,FT,Data Engineer,38400,EUR,45391,NL,100,NL,L
-115,2021,EN,FT,Machine Learning Scientist,225000,USD,225000,US,100,US,L
-116,2021,MI,FT,Data Scientist,50000,USD,50000,NG,100,NG,L
-117,2021,MI,FT,Data Science Engineer,34000,EUR,40189,GR,100,GR,M
-118,2021,EN,FT,Data Analyst,90000,USD,90000,US,100,US,S
-119,2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
-120,2021,MI,FT,Big Data Engineer,60000,USD,60000,ES,50,RO,M
-121,2021,SE,FT,Principal Data Engineer,200000,USD,200000,US,100,US,M
-122,2021,EN,FT,Data Analyst,50000,USD,50000,US,100,US,M
-123,2021,EN,FT,Applied Data Scientist,80000,GBP,110037,GB,0,GB,L
-124,2021,EN,PT,Data Analyst,8760,EUR,10354,ES,50,ES,M
-125,2021,MI,FT,Principal Data Scientist,151000,USD,151000,US,100,US,L
-126,2021,SE,FT,Machine Learning Scientist,120000,USD,120000,US,50,US,S
-127,2021,MI,FT,Data Scientist,700000,INR,9466,IN,0,IN,S
-128,2021,EN,FT,Machine Learning Engineer,20000,USD,20000,IN,100,IN,S
-129,2021,SE,FT,Lead Data Scientist,3000000,INR,40570,IN,50,IN,L
-130,2021,EN,FT,Machine Learning Developer,100000,USD,100000,IQ,50,IQ,S
-131,2021,EN,FT,Data Scientist,42000,EUR,49646,FR,50,FR,M
-132,2021,MI,FT,Applied Machine Learning Scientist,38400,USD,38400,VN,100,US,M
-133,2021,SE,FT,Computer Vision Engineer,24000,USD,24000,BR,100,BR,M
-134,2021,EN,FT,Data Scientist,100000,USD,100000,US,0,US,S
-135,2021,MI,FT,Data Analyst,90000,USD,90000,US,100,US,M
-136,2021,MI,FT,ML Engineer,7000000,JPY,63711,JP,50,JP,S
-137,2021,MI,FT,ML Engineer,8500000,JPY,77364,JP,50,JP,S
-138,2021,SE,FT,Principal Data Scientist,220000,USD,220000,US,0,US,L
-139,2021,EN,FT,Data Scientist,80000,USD,80000,US,100,US,M
-140,2021,MI,FT,Data Analyst,135000,USD,135000,US,100,US,L
-141,2021,SE,FT,Data Science Manager,240000,USD,240000,US,0,US,L
-142,2021,SE,FT,Data Engineering Manager,150000,USD,150000,US,0,US,L
-143,2021,MI,FT,Data Scientist,82500,USD,82500,US,100,US,S
-144,2021,MI,FT,Data Engineer,100000,USD,100000,US,100,US,L
-145,2021,SE,FT,Machine Learning Engineer,70000,EUR,82744,BE,50,BE,M
-146,2021,MI,FT,Research Scientist,53000,EUR,62649,FR,50,FR,M
-147,2021,MI,FT,Data Engineer,90000,USD,90000,US,100,US,L
-148,2021,SE,FT,Data Engineering Manager,153000,USD,153000,US,100,US,L
-149,2021,SE,FT,Cloud Data Engineer,160000,USD,160000,BR,100,US,S
-150,2021,SE,FT,Director of Data Science,168000,USD,168000,JP,0,JP,S
-151,2021,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
-152,2021,MI,FT,Data Scientist,95000,CAD,75774,CA,100,CA,L
-153,2021,EN,FT,Data Scientist,13400,USD,13400,UA,100,UA,L
-154,2021,SE,FT,Data Science Manager,144000,USD,144000,US,100,US,L
-155,2021,SE,FT,Data Science Engineer,159500,CAD,127221,CA,50,CA,L
-156,2021,MI,FT,Data Scientist,160000,SGD,119059,SG,100,IL,M
-157,2021,MI,FT,Applied Machine Learning Scientist,423000,USD,423000,US,50,US,L
-158,2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,100,US,M
-159,2021,EN,FT,Machine Learning Engineer,125000,USD,125000,US,100,US,S
-160,2021,EX,FT,Head of Data,230000,USD,230000,RU,50,RU,L
-161,2021,EX,FT,Head of Data Science,85000,USD,85000,RU,0,RU,M
-162,2021,MI,FT,Data Engineer,24000,EUR,28369,MT,50,MT,L
-163,2021,EN,FT,Data Science Consultant,54000,EUR,63831,DE,50,DE,L
-164,2021,EX,FT,Director of Data Science,110000,EUR,130026,DE,50,DE,M
-165,2021,SE,FT,Data Specialist,165000,USD,165000,US,100,US,L
-166,2021,EN,FT,Data Engineer,80000,USD,80000,US,100,US,L
-167,2021,EX,FT,Director of Data Science,250000,USD,250000,US,0,US,L
-168,2021,EN,FT,BI Data Analyst,55000,USD,55000,US,50,US,S
-169,2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L
-170,2021,MI,FT,Data Architect,170000,USD,170000,US,100,US,L
-171,2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L
-172,2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S
-173,2021,SE,FT,Principal Data Scientist,235000,USD,235000,US,100,US,L
-174,2021,SE,FT,Research Scientist,51400,EUR,60757,PT,50,PT,L
-175,2021,SE,FT,Data Engineering Manager,174000,USD,174000,US,100,US,L
-176,2021,MI,FT,Data Scientist,58000,MXN,2859,MX,0,MX,S
-177,2021,MI,FT,Data Scientist,30400000,CLP,40038,CL,100,CL,L
-178,2021,EN,FT,Machine Learning Engineer,81000,USD,81000,US,50,US,S
-179,2021,MI,FT,Data Scientist,420000,INR,5679,IN,100,US,S
-180,2021,MI,FT,Big Data Engineer,1672000,INR,22611,IN,0,IN,L
-181,2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
-182,2021,MI,FT,Data Engineer,22000,EUR,26005,RO,0,US,L
-183,2021,SE,FT,Finance Data Analyst,45000,GBP,61896,GB,50,GB,L
-184,2021,MI,FL,Machine Learning Scientist,12000,USD,12000,PK,50,PK,M
-185,2021,MI,FT,Data Engineer,4000,USD,4000,IR,100,IR,M
-186,2021,SE,FT,Data Analytics Engineer,50000,USD,50000,VN,100,GB,M
-187,2021,EX,FT,Data Science Consultant,59000,EUR,69741,FR,100,ES,S
-188,2021,SE,FT,Data Engineer,65000,EUR,76833,RO,50,GB,S
-189,2021,MI,FT,Machine Learning Engineer,74000,USD,74000,JP,50,JP,S
-190,2021,SE,FT,Data Science Manager,152000,USD,152000,US,100,FR,L
-191,2021,EN,FT,Machine Learning Engineer,21844,USD,21844,CO,50,CO,M
-192,2021,MI,FT,Big Data Engineer,18000,USD,18000,MD,0,MD,S
-193,2021,SE,FT,Data Science Manager,174000,USD,174000,US,100,US,L
-194,2021,SE,FT,Research Scientist,120500,CAD,96113,CA,50,CA,L
-195,2021,MI,FT,Data Scientist,147000,USD,147000,US,50,US,L
-196,2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S
-197,2021,SE,FT,Machine Learning Engineer,1799997,INR,24342,IN,100,IN,L
-198,2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L
-199,2021,EN,FT,Data Science Consultant,90000,USD,90000,US,100,US,S
-200,2021,MI,FT,Data Scientist,52000,EUR,61467,DE,50,AT,M
-201,2021,SE,FT,Machine Learning Infrastructure Engineer,195000,USD,195000,US,100,US,M
-202,2021,MI,FT,Data Scientist,32000,EUR,37825,ES,100,ES,L
-203,2021,SE,FT,Research Scientist,50000,USD,50000,FR,100,US,S
-204,2021,MI,FT,Data Scientist,160000,USD,160000,US,100,US,L
-205,2021,MI,FT,Data Scientist,69600,BRL,12901,BR,0,BR,S
-206,2021,SE,FT,Machine Learning Engineer,200000,USD,200000,US,100,US,L
-207,2021,SE,FT,Data Engineer,165000,USD,165000,US,0,US,M
-208,2021,MI,FL,Data Engineer,20000,USD,20000,IT,0,US,L
-209,2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,0,US,L
-210,2021,MI,FT,Machine Learning Engineer,21000,EUR,24823,SI,50,SI,L
-211,2021,MI,FT,Research Scientist,48000,EUR,56738,FR,50,FR,S
-212,2021,MI,FT,Data Engineer,48000,GBP,66022,HK,50,GB,S
-213,2021,EN,FT,Big Data Engineer,435000,INR,5882,IN,0,CH,L
-214,2021,EN,FT,Machine Learning Engineer,21000,EUR,24823,DE,50,DE,M
-215,2021,SE,FT,Principal Data Engineer,185000,USD,185000,US,100,US,L
-216,2021,EN,PT,Computer Vision Engineer,180000,DKK,28609,DK,50,DK,S
-217,2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
-218,2021,MI,FT,Machine Learning Engineer,75000,EUR,88654,BE,100,BE,M
-219,2021,SE,FT,Data Analytics Manager,140000,USD,140000,US,100,US,L
-220,2021,MI,FT,Machine Learning Engineer,180000,PLN,46597,PL,100,PL,L
-221,2021,MI,FT,Data Scientist,85000,GBP,116914,GB,50,GB,L
-222,2021,MI,FT,Data Scientist,2500000,INR,33808,IN,0,IN,M
-223,2021,MI,FT,Data Scientist,40900,GBP,56256,GB,50,GB,L
-224,2021,SE,FT,Machine Learning Scientist,225000,USD,225000,US,100,CA,L
-225,2021,EX,CT,Principal Data Scientist,416000,USD,416000,US,100,US,S
-226,2021,SE,FT,Data Scientist,110000,CAD,87738,CA,100,CA,S
-227,2021,MI,FT,Data Scientist,75000,EUR,88654,DE,50,DE,L
-228,2021,SE,FT,Data Scientist,135000,USD,135000,US,0,US,L
-229,2021,SE,FT,Data Analyst,90000,CAD,71786,CA,100,CA,M
-230,2021,EN,FT,Big Data Engineer,1200000,INR,16228,IN,100,IN,L
-231,2021,SE,FT,ML Engineer,256000,USD,256000,US,100,US,S
-232,2021,SE,FT,Director of Data Engineering,200000,USD,200000,US,100,US,L
-233,2021,SE,FT,Data Analyst,200000,USD,200000,US,100,US,L
-234,2021,MI,FT,Data Architect,180000,USD,180000,US,100,US,L
-235,2021,MI,FT,Head of Data Science,110000,USD,110000,US,0,US,S
-236,2021,MI,FT,Research Scientist,80000,CAD,63810,CA,100,CA,M
-237,2021,MI,FT,Data Scientist,39600,EUR,46809,ES,100,ES,M
-238,2021,EN,FT,Data Scientist,4000,USD,4000,VN,0,VN,M
-239,2021,EN,FT,Data Engineer,1600000,INR,21637,IN,50,IN,M
-240,2021,SE,FT,Data Scientist,130000,CAD,103691,CA,100,CA,L
-241,2021,MI,FT,Data Analyst,80000,USD,80000,US,100,US,L
-242,2021,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
-243,2021,SE,FT,Data Scientist,165000,USD,165000,US,100,US,L
-244,2021,EN,FT,AI Scientist,1335000,INR,18053,IN,100,AS,S
-245,2021,MI,FT,Data Engineer,52500,GBP,72212,GB,50,GB,L
-246,2021,EN,FT,Data Scientist,31000,EUR,36643,FR,50,FR,L
-247,2021,MI,FT,Data Engineer,108000,TRY,12103,TR,0,TR,M
-248,2021,SE,FT,Data Engineer,70000,GBP,96282,GB,50,GB,L
-249,2021,SE,FT,Principal Data Analyst,170000,USD,170000,US,100,US,M
-250,2021,MI,FT,Data Scientist,115000,USD,115000,US,50,US,L
-251,2021,EN,FT,Data Scientist,90000,USD,90000,US,100,US,S
-252,2021,EX,FT,Principal Data Engineer,600000,USD,600000,US,100,US,L
-253,2021,EN,FT,Data Scientist,2100000,INR,28399,IN,100,IN,M
-254,2021,MI,FT,Data Analyst,93000,USD,93000,US,100,US,L
-255,2021,SE,FT,Big Data Architect,125000,CAD,99703,CA,50,CA,M
-256,2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
-257,2021,SE,FT,Principal Data Scientist,147000,EUR,173762,DE,100,DE,M
-258,2021,SE,FT,Machine Learning Engineer,185000,USD,185000,US,50,US,L
-259,2021,EX,FT,Director of Data Science,120000,EUR,141846,DE,0,DE,L
-260,2021,MI,FT,Data Scientist,130000,USD,130000,US,50,US,L
-261,2021,SE,FT,Data Analyst,54000,EUR,63831,DE,50,DE,L
-262,2021,MI,FT,Data Scientist,1250000,INR,16904,IN,100,IN,S
-263,2021,SE,FT,Machine Learning Engineer,4900000,INR,66265,IN,0,IN,L
-264,2021,MI,FT,Data Scientist,21600,EUR,25532,RS,100,DE,S
-265,2021,SE,FT,Lead Data Engineer,160000,USD,160000,PR,50,US,S
-266,2021,MI,FT,Data Engineer,93150,USD,93150,US,0,US,M
-267,2021,MI,FT,Data Engineer,111775,USD,111775,US,0,US,M
-268,2021,MI,FT,Data Engineer,250000,TRY,28016,TR,100,TR,M
-269,2021,EN,FT,Data Engineer,55000,EUR,65013,DE,50,DE,M
-270,2021,EN,FT,Data Engineer,72500,USD,72500,US,100,US,L
-271,2021,SE,FT,Computer Vision Engineer,102000,BRL,18907,BR,0,BR,M
-272,2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,0,DE,L
-273,2021,EN,FT,Machine Learning Engineer,85000,USD,85000,NL,100,DE,S
-274,2021,SE,FT,Data Scientist,65720,EUR,77684,FR,50,FR,M
-275,2021,EN,FT,Data Scientist,100000,USD,100000,US,100,US,M
-276,2021,EN,FT,Data Scientist,58000,USD,58000,US,50,US,L
-277,2021,SE,FT,AI Scientist,55000,USD,55000,ES,100,ES,L
-278,2021,SE,FT,Data Scientist,180000,TRY,20171,TR,50,TR,L
-279,2021,EN,FT,Business Data Analyst,50000,EUR,59102,LU,100,LU,L
-280,2021,MI,FT,Data Engineer,112000,USD,112000,US,100,US,L
-281,2021,EN,FT,Research Scientist,100000,USD,100000,JE,0,CN,L
-282,2021,MI,PT,Data Engineer,59000,EUR,69741,NL,100,NL,L
-283,2021,SE,CT,Staff Data Scientist,105000,USD,105000,US,100,US,M
-284,2021,MI,FT,Research Scientist,69999,USD,69999,CZ,50,CZ,L
-285,2021,SE,FT,Data Science Manager,7000000,INR,94665,IN,50,IN,L
-286,2021,SE,FT,Head of Data,87000,EUR,102839,SI,100,SI,L
-287,2021,MI,FT,Data Scientist,109000,USD,109000,US,50,US,L
-288,2021,MI,FT,Machine Learning Engineer,43200,EUR,51064,IT,50,IT,L
-289,2022,SE,FT,Data Engineer,135000,USD,135000,US,100,US,M
-290,2022,SE,FT,Data Analyst,155000,USD,155000,US,100,US,M
-291,2022,SE,FT,Data Analyst,120600,USD,120600,US,100,US,M
-292,2022,MI,FT,Data Scientist,130000,USD,130000,US,0,US,M
-293,2022,MI,FT,Data Scientist,90000,USD,90000,US,0,US,M
-294,2022,MI,FT,Data Engineer,170000,USD,170000,US,100,US,M
-295,2022,MI,FT,Data Engineer,150000,USD,150000,US,100,US,M
-296,2022,SE,FT,Data Analyst,102100,USD,102100,US,100,US,M
-297,2022,SE,FT,Data Analyst,84900,USD,84900,US,100,US,M
-298,2022,SE,FT,Data Scientist,136620,USD,136620,US,100,US,M
-299,2022,SE,FT,Data Scientist,99360,USD,99360,US,100,US,M
-300,2022,SE,FT,Data Scientist,90000,GBP,117789,GB,0,GB,M
-301,2022,SE,FT,Data Scientist,80000,GBP,104702,GB,0,GB,M
-302,2022,SE,FT,Data Scientist,146000,USD,146000,US,100,US,M
-303,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M
-304,2022,EN,FT,Data Engineer,40000,GBP,52351,GB,100,GB,M
-305,2022,SE,FT,Data Analyst,99000,USD,99000,US,0,US,M
-306,2022,SE,FT,Data Analyst,116000,USD,116000,US,0,US,M
-307,2022,MI,FT,Data Analyst,106260,USD,106260,US,0,US,M
-308,2022,MI,FT,Data Analyst,126500,USD,126500,US,0,US,M
-309,2022,EX,FT,Data Engineer,242000,USD,242000,US,100,US,M
-310,2022,EX,FT,Data Engineer,200000,USD,200000,US,100,US,M
-311,2022,MI,FT,Data Scientist,50000,GBP,65438,GB,0,GB,M
-312,2022,MI,FT,Data Scientist,30000,GBP,39263,GB,0,GB,M
-313,2022,MI,FT,Data Engineer,60000,GBP,78526,GB,0,GB,M
-314,2022,MI,FT,Data Engineer,40000,GBP,52351,GB,0,GB,M
-315,2022,SE,FT,Data Scientist,165220,USD,165220,US,100,US,M
-316,2022,EN,FT,Data Engineer,35000,GBP,45807,GB,100,GB,M
-317,2022,SE,FT,Data Scientist,120160,USD,120160,US,100,US,M
-318,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
-319,2022,SE,FT,Data Engineer,181940,USD,181940,US,0,US,M
-320,2022,SE,FT,Data Engineer,132320,USD,132320,US,0,US,M
-321,2022,SE,FT,Data Engineer,220110,USD,220110,US,0,US,M
-322,2022,SE,FT,Data Engineer,160080,USD,160080,US,0,US,M
-323,2022,SE,FT,Data Scientist,180000,USD,180000,US,0,US,L
-324,2022,SE,FT,Data Scientist,120000,USD,120000,US,0,US,L
-325,2022,SE,FT,Data Analyst,124190,USD,124190,US,100,US,M
-326,2022,EX,FT,Data Analyst,130000,USD,130000,US,100,US,M
-327,2022,EX,FT,Data Analyst,110000,USD,110000,US,100,US,M
-328,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M
-329,2022,MI,FT,Data Analyst,115500,USD,115500,US,100,US,M
-330,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
-331,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
-332,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
-333,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
-334,2022,SE,FT,Data Engineer,165400,USD,165400,US,100,US,M
-335,2022,SE,FT,Data Engineer,132320,USD,132320,US,100,US,M
-336,2022,MI,FT,Data Analyst,167000,USD,167000,US,100,US,M
-337,2022,SE,FT,Data Engineer,243900,USD,243900,US,100,US,M
-338,2022,SE,FT,Data Analyst,136600,USD,136600,US,100,US,M
-339,2022,SE,FT,Data Analyst,109280,USD,109280,US,100,US,M
-340,2022,SE,FT,Data Engineer,128875,USD,128875,US,100,US,M
-341,2022,SE,FT,Data Engineer,93700,USD,93700,US,100,US,M
-342,2022,EX,FT,Head of Data Science,224000,USD,224000,US,100,US,M
-343,2022,EX,FT,Head of Data Science,167875,USD,167875,US,100,US,M
-344,2022,EX,FT,Analytics Engineer,175000,USD,175000,US,100,US,M
-345,2022,SE,FT,Data Engineer,156600,USD,156600,US,100,US,M
-346,2022,SE,FT,Data Engineer,108800,USD,108800,US,0,US,M
-347,2022,SE,FT,Data Scientist,95550,USD,95550,US,0,US,M
-348,2022,SE,FT,Data Engineer,113000,USD,113000,US,0,US,L
-349,2022,SE,FT,Data Analyst,135000,USD,135000,US,100,US,M
-350,2022,SE,FT,Data Science Manager,161342,USD,161342,US,100,US,M
-351,2022,SE,FT,Data Science Manager,137141,USD,137141,US,100,US,M
-352,2022,SE,FT,Data Scientist,167000,USD,167000,US,100,US,M
-353,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M
-354,2022,SE,FT,Data Engineer,60000,GBP,78526,GB,0,GB,M
-355,2022,SE,FT,Data Engineer,50000,GBP,65438,GB,0,GB,M
-356,2022,SE,FT,Data Scientist,150000,USD,150000,US,0,US,M
-357,2022,SE,FT,Data Scientist,211500,USD,211500,US,100,US,M
-358,2022,SE,FT,Data Architect,192400,USD,192400,CA,100,CA,M
-359,2022,SE,FT,Data Architect,90700,USD,90700,CA,100,CA,M
-360,2022,SE,FT,Data Analyst,130000,USD,130000,CA,100,CA,M
-361,2022,SE,FT,Data Analyst,61300,USD,61300,CA,100,CA,M
-362,2022,SE,FT,Data Analyst,130000,USD,130000,CA,100,CA,M
-363,2022,SE,FT,Data Analyst,61300,USD,61300,CA,100,CA,M
-364,2022,SE,FT,Data Engineer,160000,USD,160000,US,0,US,L
-365,2022,SE,FT,Data Scientist,138600,USD,138600,US,100,US,M
-366,2022,SE,FT,Data Engineer,136000,USD,136000,US,0,US,M
-367,2022,MI,FT,Data Analyst,58000,USD,58000,US,0,US,S
-368,2022,EX,FT,Analytics Engineer,135000,USD,135000,US,100,US,M
-369,2022,SE,FT,Data Scientist,170000,USD,170000,US,100,US,M
-370,2022,SE,FT,Data Scientist,123000,USD,123000,US,100,US,M
-371,2022,SE,FT,Machine Learning Engineer,189650,USD,189650,US,0,US,M
-372,2022,SE,FT,Machine Learning Engineer,164996,USD,164996,US,0,US,M
-373,2022,MI,FT,ETL Developer,50000,EUR,54957,GR,0,GR,M
-374,2022,MI,FT,ETL Developer,50000,EUR,54957,GR,0,GR,M
-375,2022,EX,FT,Lead Data Engineer,150000,CAD,118187,CA,100,CA,S
-376,2022,SE,FT,Data Analyst,132000,USD,132000,US,0,US,M
-377,2022,SE,FT,Data Engineer,165400,USD,165400,US,100,US,M
-378,2022,SE,FT,Data Architect,208775,USD,208775,US,100,US,M
-379,2022,SE,FT,Data Architect,147800,USD,147800,US,100,US,M
-380,2022,SE,FT,Data Engineer,136994,USD,136994,US,100,US,M
-381,2022,SE,FT,Data Engineer,101570,USD,101570,US,100,US,M
-382,2022,SE,FT,Data Analyst,128875,USD,128875,US,100,US,M
-383,2022,SE,FT,Data Analyst,93700,USD,93700,US,100,US,M
-384,2022,EX,FT,Head of Machine Learning,6000000,INR,79039,IN,50,IN,L
-385,2022,SE,FT,Data Engineer,132320,USD,132320,US,100,US,M
-386,2022,EN,FT,Machine Learning Engineer,28500,GBP,37300,GB,100,GB,L
-387,2022,SE,FT,Data Analyst,164000,USD,164000,US,0,US,M
-388,2022,SE,FT,Data Engineer,155000,USD,155000,US,100,US,M
-389,2022,MI,FT,Machine Learning Engineer,95000,GBP,124333,GB,0,GB,M
-390,2022,MI,FT,Machine Learning Engineer,75000,GBP,98158,GB,0,GB,M
-391,2022,MI,FT,AI Scientist,120000,USD,120000,US,0,US,M
-392,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
-393,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
-394,2022,SE,FT,Data Analytics Manager,145000,USD,145000,US,100,US,M
-395,2022,SE,FT,Data Analytics Manager,105400,USD,105400,US,100,US,M
-396,2022,MI,FT,Machine Learning Engineer,80000,EUR,87932,FR,100,DE,M
-397,2022,MI,FT,Data Engineer,90000,GBP,117789,GB,0,GB,M
-398,2022,SE,FT,Data Scientist,215300,USD,215300,US,100,US,L
-399,2022,SE,FT,Data Scientist,158200,USD,158200,US,100,US,L
-400,2022,SE,FT,Data Engineer,209100,USD,209100,US,100,US,L
-401,2022,SE,FT,Data Engineer,154600,USD,154600,US,100,US,L
-402,2022,SE,FT,Data Analyst,115934,USD,115934,US,0,US,M
-403,2022,SE,FT,Data Analyst,81666,USD,81666,US,0,US,M
-404,2022,SE,FT,Data Engineer,175000,USD,175000,US,100,US,M
-405,2022,MI,FT,Data Engineer,75000,GBP,98158,GB,0,GB,M
-406,2022,MI,FT,Data Analyst,58000,USD,58000,US,0,US,S
-407,2022,SE,FT,Data Engineer,183600,USD,183600,US,100,US,L
-408,2022,MI,FT,Data Analyst,40000,GBP,52351,GB,100,GB,M
-409,2022,SE,FT,Data Scientist,180000,USD,180000,US,100,US,M
-410,2022,MI,FT,Data Scientist,55000,GBP,71982,GB,0,GB,M
-411,2022,MI,FT,Data Scientist,35000,GBP,45807,GB,0,GB,M
-412,2022,MI,FT,Data Engineer,60000,EUR,65949,GR,100,GR,M
-413,2022,MI,FT,Data Engineer,45000,EUR,49461,GR,100,GR,M
-414,2022,MI,FT,Data Engineer,60000,GBP,78526,GB,100,GB,M
-415,2022,MI,FT,Data Engineer,45000,GBP,58894,GB,100,GB,M
-416,2022,SE,FT,Data Scientist,260000,USD,260000,US,100,US,M
-417,2022,SE,FT,Data Science Engineer,60000,USD,60000,AR,100,MX,L
-418,2022,MI,FT,Data Engineer,63900,USD,63900,US,0,US,M
-419,2022,MI,FT,Machine Learning Scientist,160000,USD,160000,US,100,US,L
-420,2022,MI,FT,Machine Learning Scientist,112300,USD,112300,US,100,US,L
-421,2022,MI,FT,Data Science Manager,241000,USD,241000,US,100,US,M
-422,2022,MI,FT,Data Science Manager,159000,USD,159000,US,100,US,M
-423,2022,SE,FT,Data Scientist,180000,USD,180000,US,0,US,M
-424,2022,SE,FT,Data Scientist,80000,USD,80000,US,0,US,M
-425,2022,MI,FT,Data Engineer,82900,USD,82900,US,0,US,M
-426,2022,SE,FT,Data Engineer,100800,USD,100800,US,100,US,L
-427,2022,MI,FT,Data Engineer,45000,EUR,49461,ES,100,ES,M
-428,2022,SE,FT,Data Scientist,140400,USD,140400,US,0,US,L
-429,2022,MI,FT,Data Analyst,30000,GBP,39263,GB,100,GB,M
-430,2022,MI,FT,Data Analyst,40000,EUR,43966,ES,100,ES,M
-431,2022,MI,FT,Data Analyst,30000,EUR,32974,ES,100,ES,M
-432,2022,MI,FT,Data Engineer,80000,EUR,87932,ES,100,ES,M
-433,2022,MI,FT,Data Engineer,70000,EUR,76940,ES,100,ES,M
-434,2022,MI,FT,Data Engineer,80000,GBP,104702,GB,100,GB,M
-435,2022,MI,FT,Data Engineer,70000,GBP,91614,GB,100,GB,M
-436,2022,MI,FT,Data Engineer,60000,EUR,65949,ES,100,ES,M
-437,2022,MI,FT,Data Engineer,80000,EUR,87932,GR,100,GR,M
-438,2022,SE,FT,Machine Learning Engineer,189650,USD,189650,US,0,US,M
-439,2022,SE,FT,Machine Learning Engineer,164996,USD,164996,US,0,US,M
-440,2022,MI,FT,Data Analyst,40000,EUR,43966,GR,100,GR,M
-441,2022,MI,FT,Data Analyst,30000,EUR,32974,GR,100,GR,M
-442,2022,MI,FT,Data Engineer,75000,GBP,98158,GB,100,GB,M
-443,2022,MI,FT,Data Engineer,60000,GBP,78526,GB,100,GB,M
-444,2022,SE,FT,Data Scientist,215300,USD,215300,US,0,US,L
-445,2022,MI,FT,Data Engineer,70000,EUR,76940,GR,100,GR,M
-446,2022,SE,FT,Data Engineer,209100,USD,209100,US,100,US,L
-447,2022,SE,FT,Data Engineer,154600,USD,154600,US,100,US,L
-448,2022,SE,FT,Data Engineer,180000,USD,180000,US,100,US,M
-449,2022,EN,FT,ML Engineer,20000,EUR,21983,PT,100,PT,L
-450,2022,SE,FT,Data Engineer,80000,USD,80000,US,100,US,M
-451,2022,MI,FT,Machine Learning Developer,100000,CAD,78791,CA,100,CA,M
-452,2022,EX,FT,Director of Data Science,250000,CAD,196979,CA,50,CA,L
-453,2022,MI,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,S
-454,2022,EN,FT,Computer Vision Engineer,125000,USD,125000,US,0,US,M
-455,2022,MI,FT,NLP Engineer,240000,CNY,37236,US,50,US,L
-456,2022,SE,FT,Data Engineer,105000,USD,105000,US,100,US,M
-457,2022,SE,FT,Lead Machine Learning Engineer,80000,EUR,87932,DE,0,DE,M
-458,2022,MI,FT,Business Data Analyst,1400000,INR,18442,IN,100,IN,M
-459,2022,MI,FT,Data Scientist,2400000,INR,31615,IN,100,IN,L
-460,2022,MI,FT,Machine Learning Infrastructure Engineer,53000,EUR,58255,PT,50,PT,L
-461,2022,EN,FT,Financial Data Analyst,100000,USD,100000,US,50,US,L
-462,2022,MI,PT,Data Engineer,50000,EUR,54957,DE,50,DE,L
-463,2022,EN,FT,Data Scientist,1400000,INR,18442,IN,100,IN,M
-464,2022,SE,FT,Principal Data Scientist,148000,EUR,162674,DE,100,DE,M
-465,2022,EN,FT,Data Engineer,120000,USD,120000,US,100,US,M
-466,2022,SE,FT,Research Scientist,144000,USD,144000,US,50,US,L
-467,2022,SE,FT,Data Scientist,104890,USD,104890,US,100,US,M
-468,2022,SE,FT,Data Engineer,100000,USD,100000,US,100,US,M
-469,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
-470,2022,MI,FT,Data Analyst,135000,USD,135000,US,100,US,M
-471,2022,MI,FT,Data Analyst,50000,USD,50000,US,100,US,M
-472,2022,SE,FT,Data Scientist,220000,USD,220000,US,100,US,M
-473,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
-474,2022,MI,FT,Data Scientist,140000,GBP,183228,GB,0,GB,M
-475,2022,MI,FT,Data Scientist,70000,GBP,91614,GB,0,GB,M
-476,2022,SE,FT,Data Scientist,185100,USD,185100,US,100,US,M
-477,2022,SE,FT,Machine Learning Engineer,220000,USD,220000,US,100,US,M
-478,2022,MI,FT,Data Scientist,200000,USD,200000,US,100,US,M
-479,2022,MI,FT,Data Scientist,120000,USD,120000,US,100,US,M
-480,2022,SE,FT,Machine Learning Engineer,120000,USD,120000,AE,100,AE,S
-481,2022,SE,FT,Machine Learning Engineer,65000,USD,65000,AE,100,AE,S
-482,2022,EX,FT,Data Engineer,324000,USD,324000,US,100,US,M
-483,2022,EX,FT,Data Engineer,216000,USD,216000,US,100,US,M
-484,2022,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
-485,2022,SE,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,M
-486,2022,SE,FT,Data Scientist,230000,USD,230000,US,100,US,M
-487,2022,EN,PT,Data Scientist,100000,USD,100000,DZ,50,DZ,M
-488,2022,MI,FL,Data Scientist,100000,USD,100000,CA,100,US,M
-489,2022,EN,CT,Applied Machine Learning Scientist,29000,EUR,31875,TN,100,CZ,M
-490,2022,SE,FT,Head of Data,200000,USD,200000,MY,100,US,M
-491,2022,MI,FT,Principal Data Analyst,75000,USD,75000,CA,100,CA,S
-492,2022,MI,FT,Data Scientist,150000,PLN,35590,PL,100,PL,L
-493,2022,SE,FT,Machine Learning Developer,100000,CAD,78791,CA,100,CA,M
-494,2022,SE,FT,Data Scientist,100000,USD,100000,BR,100,US,M
-495,2022,MI,FT,Machine Learning Scientist,153000,USD,153000,US,50,US,M
-496,2022,EN,FT,Data Engineer,52800,EUR,58035,PK,100,DE,M
-497,2022,SE,FT,Data Scientist,165000,USD,165000,US,100,US,M
-498,2022,SE,FT,Research Scientist,85000,EUR,93427,FR,50,FR,L
-499,2022,EN,FT,Data Scientist,66500,CAD,52396,CA,100,CA,L
-500,2022,SE,FT,Machine Learning Engineer,57000,EUR,62651,NL,100,NL,L
-501,2022,MI,FT,Head of Data,30000,EUR,32974,EE,100,EE,S
-502,2022,EN,FT,Data Scientist,40000,USD,40000,JP,100,MY,L
-503,2022,MI,FT,Machine Learning Engineer,121000,AUD,87425,AU,100,AU,L
-504,2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
-505,2022,EN,FT,Data Scientist,120000,AUD,86703,AU,50,AU,M
-506,2022,MI,FT,Applied Machine Learning Scientist,75000,USD,75000,BO,100,US,L
-507,2022,MI,FT,Research Scientist,59000,EUR,64849,AT,0,AT,L
-508,2022,EN,FT,Research Scientist,120000,USD,120000,US,100,US,L
-509,2022,MI,FT,Applied Data Scientist,157000,USD,157000,US,100,US,L
-510,2022,EN,FT,Computer Vision Software Engineer,150000,USD,150000,AU,100,AU,S
-511,2022,MI,FT,Business Data Analyst,90000,CAD,70912,CA,50,CA,L
-512,2022,EN,FT,Data Engineer,65000,USD,65000,US,100,US,S
-513,2022,SE,FT,Machine Learning Engineer,65000,EUR,71444,IE,100,IE,S
-514,2022,EN,FT,Data Analytics Engineer,20000,USD,20000,PK,0,PK,M
-515,2022,MI,FT,Data Scientist,48000,USD,48000,RU,100,US,S
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diff --git a/README.MD b/README.MD
index ca398ce..ea7d3bd 100644
--- a/README.MD
+++ b/README.MD
@@ -10,4 +10,4 @@ Below is a table of all the Machine Learning models created:
 |----------|----------|-----------------|
 |1. | Test Model | [Click to open](https://github.com/psavarmattas/Machine-Learning-Models/tree/master/TestModel) |
 |2. | Stroke Prediction Model | [Click to open](https://github.com/psavarmattas/Machine-Learning-Models/tree/master/StrokePredictionModel) |
-|3. |[UNDER-DEVELOPMENT] Data Science Job Salaries Model | [Click to open](https://github.com/psavarmattas/Machine-Learning-Models/tree/master/DataScienceJobSalariesModel) |
+|3. |[UNDER-DEVELOPMENT] Pneumonia Classification Model | [Click to open](https://github.com/psavarmattas/Machine-Learning-Models/tree/master/PneumoniaClassificationModel) |
diff --git a/StrokePredictionModel/main.ipynb b/StrokePredictionModel/main.ipynb
index 7701090..b53a95b 100644
--- a/StrokePredictionModel/main.ipynb
+++ b/StrokePredictionModel/main.ipynb
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -272,7 +272,7 @@
        "9          Rural             120.46  36.8           smokes       1  "
       ]
      },
-     "execution_count": 2,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -325,7 +325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -456,7 +456,7 @@
        "max      48.900000     1.000000  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -467,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -499,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -543,7 +543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
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@@ -590,10 +590,10 @@
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        "                            \n",
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@@ -2506,7 +2506,7 @@
   },
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-   "execution_count": 13,
+   "execution_count": 15,
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    "outputs": [
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@@ -2634,7 +2634,7 @@
        "4          Urban             186.21  29.0  formerly smoked       1  "
       ]
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-     "execution_count": 13,
+     "execution_count": 15,
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@@ -2647,7 +2647,7 @@
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-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
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@@ -2693,14 +2693,77 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2733</th>\n",
+       "      <th>3378</th>\n",
+       "      <td>1</td>\n",
+       "      <td>40.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>81.59</td>\n",
+       "      <td>27.2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3225</th>\n",
+       "      <td>1</td>\n",
+       "      <td>34.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>91.02</td>\n",
+       "      <td>25.8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1713</th>\n",
+       "      <td>1</td>\n",
+       "      <td>45.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>115.23</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3067</th>\n",
        "      <td>1</td>\n",
        "      <td>25.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>181.30</td>\n",
-       "      <td>35.8</td>\n",
+       "      <td>76.44</td>\n",
+       "      <td>48.3</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
@@ -2708,93 +2771,30 @@
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1027</th>\n",
-       "      <td>1</td>\n",
-       "      <td>36.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>76.93</td>\n",
-       "      <td>21.6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
+       "      <th>3451</th>\n",
        "      <td>1</td>\n",
+       "      <td>8.0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1522</th>\n",
-       "      <td>1</td>\n",
-       "      <td>60.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>208.05</td>\n",
-       "      <td>35.3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4898</th>\n",
-       "      <td>0</td>\n",
-       "      <td>78.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>243.73</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2925</th>\n",
-       "      <td>1</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>112.62</td>\n",
-       "      <td>24.8</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
+       "      <td>83.55</td>\n",
+       "      <td>22.4</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>0</td>\n",
        "      <td>1</td>\n",
        "      <td>0</td>\n",
-       "      <td>0</td>\n",
        "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
        "      <td>0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -2803,49 +2803,49 @@
       ],
       "text/plain": [
        "      gender   age  hypertension  heart_disease  ever_married  \\\n",
-       "2733       1  25.0             0              0             0   \n",
-       "1027       1  36.0             0              0             1   \n",
-       "1522       1  60.0             0              1             1   \n",
-       "4898       0  78.0             0              1             1   \n",
-       "2925       1  56.0             0              0             1   \n",
+       "3378       1  40.0             1              0             1   \n",
+       "3225       1  34.0             0              0             1   \n",
+       "1713       1  45.0             0              0             1   \n",
+       "3067       1  25.0             0              0             0   \n",
+       "3451       1   8.0             0              0             0   \n",
        "\n",
        "      avg_glucose_level   bmi  stroke  work_type_Govt_job  work_type_Private  \\\n",
-       "2733             181.30  35.8       0                   0                  1   \n",
-       "1027              76.93  21.6       0                   0                  1   \n",
-       "1522             208.05  35.3       0                   0                  1   \n",
-       "4898             243.73  28.0       0                   0                  0   \n",
-       "2925             112.62  24.8       0                   1                  0   \n",
+       "3378              81.59  27.2       0                   0                  1   \n",
+       "3225              91.02  25.8       0                   0                  1   \n",
+       "1713             115.23  28.0       0                   0                  1   \n",
+       "3067              76.44  48.3       0                   0                  1   \n",
+       "3451              83.55  22.4       0                   0                  0   \n",
        "\n",
        "      work_type_Self-employed  work_type_children  Residence_type_Rural  \\\n",
-       "2733                        0                   0                     0   \n",
-       "1027                        0                   0                     0   \n",
-       "1522                        0                   0                     0   \n",
-       "4898                        1                   0                     0   \n",
-       "2925                        0                   0                     0   \n",
+       "3378                        0                   0                     1   \n",
+       "3225                        0                   0                     0   \n",
+       "1713                        0                   0                     0   \n",
+       "3067                        0                   0                     0   \n",
+       "3451                        0                   1                     0   \n",
        "\n",
        "      Residence_type_Urban  smoking_status_Unknown  \\\n",
-       "2733                     1                       0   \n",
-       "1027                     1                       0   \n",
-       "1522                     1                       0   \n",
-       "4898                     1                       0   \n",
-       "2925                     1                       0   \n",
+       "3378                     0                       0   \n",
+       "3225                     1                       0   \n",
+       "1713                     1                       0   \n",
+       "3067                     1                       1   \n",
+       "3451                     1                       1   \n",
        "\n",
        "      smoking_status_formerly smoked  smoking_status_never smoked  \\\n",
-       "2733                               0                            1   \n",
-       "1027                               0                            1   \n",
-       "1522                               0                            0   \n",
-       "4898                               0                            0   \n",
-       "2925                               0                            1   \n",
+       "3378                               0                            1   \n",
+       "3225                               0                            1   \n",
+       "1713                               0                            1   \n",
+       "3067                               0                            0   \n",
+       "3451                               0                            0   \n",
        "\n",
        "      smoking_status_smokes  \n",
-       "2733                      0  \n",
-       "1027                      0  \n",
-       "1522                      1  \n",
-       "4898                      1  \n",
-       "2925                      0  "
+       "3378                      0  \n",
+       "3225                      0  \n",
+       "1713                      0  \n",
+       "3067                      0  \n",
+       "3451                      0  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2857,7 +2857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -2884,7 +2884,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2902,7 +2902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2912,7 +2912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -2921,7 +2921,7 @@
        "((3337, 17), (1644, 17))"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2947,7 +2947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -2959,7 +2959,7 @@
        "DecisionTreeClassifier(max_depth=5, random_state=0)"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2985,7 +2985,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3003,7 +3003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3026,7 +3026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -3044,7 +3044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -3053,7 +3053,7 @@
        "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3073,7 +3073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -3090,7 +3090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -3117,7 +3117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3144,7 +3144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
@@ -3175,7 +3175,7 @@
    "source": [
     "print(\"Gender = Male \\nAge = 40 \\nHypertention = 0 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
     "Avg Glucose Level = 100 \\nbmi = 35 \\nWork Type Govt Job = 0 \\nWork Type Private = 1 \\nWork Type Self-Employed = 0 \\n\\\n",
-    "Work Type Children = 0 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
+    "Work Type Children = 0 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0 \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
     "Smoking Status Never Smoked = 1 \\nSmoking Status Smoked = 0\")\n",
     "\n",
     "predictionOutcome = model.predict([[1,40,0,0,0,100,35,0,1,0,0,0,1,0,1,0,0]])\n",
@@ -3195,7 +3195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
@@ -3226,7 +3226,7 @@
    "source": [
     "print(\"Gender = Female \\nAge = 35 \\nHypertention = 1 \\nHeart Disease = 0 \\nEver Married = 1 \\n\\\n",
     "Avg Glucose Level = 80 \\nbmi = 45 \\nWork Type Govt Job = 1 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
-    "Work Type Children = 0 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Formerly Smoked = 1 \\n\\\n",
+    "Work Type Children = 0 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 1 \\n\\\n",
     "Smoking Status Never Smoked = 0 \\nSmoking Status Smoked = 0\")\n",
     "\n",
     "predictionOutcome = model.predict([[1,35,1,0,1,80,45,1,0,0,0,0,1,1,0,0,0]])\n",
@@ -3236,6 +3236,230 @@
     "else:\n",
     "    print(\"\\nModel predicts STROKE\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Case 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gender = Male \n",
+      "Age = 20 \n",
+      "Hypertention = 0 \n",
+      "Heart Disease = 0 \n",
+      "Ever Married = 0 \n",
+      "Avg Glucose Level = 80 \n",
+      "bmi = 27 \n",
+      "Work Type Govt Job = 0 \n",
+      "Work Type Private = 0 \n",
+      "Work Type Self-Employed = 0 \n",
+      "Work Type Children = 1 \n",
+      "Residence Type Rural = 0 \n",
+      "Residence Type Urban = 1 \n",
+      "Smoking Status Formerly Smoked = 0 \n",
+      "Smoking Status Never Smoked = 0 \n",
+      "Smoking Status Smokes = 1\n",
+      "\n",
+      "Model predicts NO STROKE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Gender = Male \\nAge = 20 \\nHypertention = 0 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
+    "Avg Glucose Level = 80 \\nbmi = 27 \\nWork Type Govt Job = 0 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
+    "Work Type Children = 1 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
+    "Smoking Status Never Smoked = 0 \\nSmoking Status Smokes = 1\")\n",
+    "\n",
+    "predictionOutcome = model.predict([[0,20,0,0,0,80,27,0,0,0,1,0,1,0,0,0,1]])\n",
+    "\n",
+    "if predictionOutcome == 0:\n",
+    "    print(\"\\nModel predicts NO STROKE\")\n",
+    "else:\n",
+    "    print(\"\\nModel predicts STROKE\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Case 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gender = Male \n",
+      "Age = 20 \n",
+      "Hypertention = 0 \n",
+      "Heart Disease = 0 \n",
+      "Ever Married = 0 \n",
+      "Avg Glucose Level = 80 \n",
+      "bmi = 22 \n",
+      "Work Type Govt Job = 0 \n",
+      "Work Type Private = 0 \n",
+      "Work Type Self-Employed = 0 \n",
+      "Work Type Children = 1 \n",
+      "Residence Type Rural = 0 \n",
+      "Residence Type Urban = 1 \n",
+      "Smoking Status Formerly Smoked = 0 \n",
+      "Smoking Status Never Smoked = 0 \n",
+      "Smoking Status Smokes = 1\n",
+      "\n",
+      "Model predicts NO STROKE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Gender = Male \\nAge = 20 \\nHypertention = 0 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
+    "Avg Glucose Level = 80 \\nbmi = 22 \\nWork Type Govt Job = 0 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
+    "Work Type Children = 1 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
+    "Smoking Status Never Smoked = 0 \\nSmoking Status Smokes = 1\")\n",
+    "\n",
+    "predictionOutcome = model.predict([[0,20,0,0,0,80,22,0,0,0,1,0,1,0,0,0,1]])\n",
+    "\n",
+    "if predictionOutcome == 0:\n",
+    "    print(\"\\nModel predicts NO STROKE\")\n",
+    "else:\n",
+    "    print(\"\\nModel predicts STROKE\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Case 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Gender = Male \n",
+      "Age = 20 \n",
+      "Hypertention = 1 \n",
+      "Heart Disease = 0 \n",
+      "Ever Married = 0 \n",
+      "Avg Glucose Level = 80 \n",
+      "bmi = 26 \n",
+      "Work Type Govt Job = 0 \n",
+      "Work Type Private = 0 \n",
+      "Work Type Self-Employed = 0 \n",
+      "Work Type Children = 1 \n",
+      "Residence Type Rural = 0 \n",
+      "Residence Type Urban = 1 \n",
+      "Smoking Status Unknown = 0  \n",
+      "Smoking Status Formerly Smoked = 0 \n",
+      "Smoking Status Never Smoked = 0 \n",
+      "Smoking Status Smokes = 1\n",
+      "\n",
+      "Model predicts NO STROKE\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Gender = Male \\nAge = 20 \\nHypertention = 1 \\nHeart Disease = 0 \\nEver Married = 0 \\n\\\n",
+    "Avg Glucose Level = 80 \\nbmi = 26 \\nWork Type Govt Job = 0 \\nWork Type Private = 0 \\nWork Type Self-Employed = 0 \\n\\\n",
+    "Work Type Children = 1 \\nResidence Type Rural = 0 \\nResidence Type Urban = 1 \\nSmoking Status Unknown = 0  \\nSmoking Status Formerly Smoked = 0 \\n\\\n",
+    "Smoking Status Never Smoked = 0 \\nSmoking Status Smokes = 1\")\n",
+    "\n",
+    "predictionOutcome = model.predict([[0,20,1,0,0,80,26,0,0,0,1,0,1,0,0,0,1]])\n",
+    "\n",
+    "if predictionOutcome == 0:\n",
+    "    print(\"\\nModel predicts NO STROKE\")\n",
+    "else:\n",
+    "    print(\"\\nModel predicts STROKE\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Automated Runs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "######################################################### PROGRAM STARTED #########################################################\n",
+      "\n",
+      "Array 0 = [  0   2   0   1   0 116  47   0   1   1   0   1   1   1   0   1   1]\n",
+      "Model predicts NO STROKE =  [0]\n",
+      "###################################################################################################################################\n",
+      "\n",
+      "Array 1 = [  1   6   1   1   0 241  18   1   0   0   0   1   0   1   0   1   0]\n",
+      "Model predicts NO STROKE =  [0]\n",
+      "###################################################################################################################################\n",
+      "\n",
+      "Array 2 = [ 0 19  0  1  0 94 21  1  0  1  1  0  1  1  1  0  1]\n",
+      "Model predicts NO STROKE =  [0]\n",
+      "###################################################################################################################################\n",
+      "\n",
+      "Array 3 = [ 1 45  0  1  1 50 30  0  1  1  0  1  0  1  1  1  0]\n",
+      "Model predicts STROKE =  [1]\n",
+      "######################################################### PROGRAM FINISHED #########################################################\n"
+     ]
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "count=0\n",
+    "array=[]\n",
+    "predictionOutcome=[0]\n",
+    "\n",
+    "print(\"######################################################### PROGRAM STARTED #########################################################\")\n",
+    "\n",
+    "while predictionOutcome == [0]:\n",
+    "    arr1=np.random.randint(2, size=1)\n",
+    "    arr2=np.random.randint(83, size=1)\n",
+    "    arr3=np.random.randint(2, size=3)\n",
+    "    arr4=np.random.randint(272, size=1)\n",
+    "    arr5=np.random.randint(49, size=1)\n",
+    "    arr6=np.random.randint(2, size=10)\n",
+    "    \n",
+    "    array = np.concatenate((arr1, arr2, arr3, arr4, arr5, arr6), axis=0)\n",
+    "    \n",
+    "    print(\"\\nArray %d =\" %count, array)\n",
+    "    \n",
+    "    predictionOutcome = model.predict([array])\n",
+    "    \n",
+    "    if predictionOutcome == 0:\n",
+    "        print(\"Model predicts NO STROKE = \", predictionOutcome)\n",
+    "        print(\"###################################################################################################################################\")\n",
+    "    else:\n",
+    "        print(\"Model predicts STROKE = \", predictionOutcome)\n",
+    "    \n",
+    "    count+=1\n",
+    "else:\n",
+    "    print(\"######################################################### PROGRAM FINISHED #########################################################\")\n"
+   ]
   }
  ],
  "metadata": {