Upload 18 files

.gitattributes

@@ -33,3 +33,9 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Dataset/Rossmann[[:space:]]Stores[[:space:]]Data.csv filter=lfs diff=lfs merge=lfs -text
+Dataset/Rossmann_Cleaned_data.csv filter=lfs diff=lfs merge=lfs -text
+images/r2.png filter=lfs diff=lfs merge=lfs -text
+Notebooks/01[[:space:]]copy.ipynb filter=lfs diff=lfs merge=lfs -text
+Notebooks/01.ipynb filter=lfs diff=lfs merge=lfs -text
+Notebooks/SalesPrediction_ML.ipynb filter=lfs diff=lfs merge=lfs -text

Dataset/Rossmann Stores Data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f6e4597c142d7d909a13d53b68a8e85c00b9a4c7b5ff40adbb37d6829cc1f4cc
+size 38057952

Dataset/Rossmann_Cleaned_data.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7a26593831058de426161c0ee7e36cc3ee148b857930bd4111d8d10c6fbd93e
+size 75241817

Dataset/store.csv

@@ -0,0 +1,1116 @@
+"Store","StoreType","Assortment","CompetitionDistance","CompetitionOpenSinceMonth","CompetitionOpenSinceYear","Promo2","Promo2SinceWeek","Promo2SinceYear","PromoInterval"
+1,"c","a",1270,9,2008,0,,,""
+2,"a","a",570,11,2007,1,13,2010,"Jan,Apr,Jul,Oct"
+3,"a","a",14130,12,2006,1,14,2011,"Jan,Apr,Jul,Oct"
+4,"c","c",620,9,2009,0,,,""
+5,"a","a",29910,4,2015,0,,,""
+6,"a","a",310,12,2013,0,,,""
+7,"a","c",24000,4,2013,0,,,""
+8,"a","a",7520,10,2014,0,,,""
+9,"a","c",2030,8,2000,0,,,""
+10,"a","a",3160,9,2009,0,,,""
+11,"a","c",960,11,2011,1,1,2012,"Jan,Apr,Jul,Oct"
+12,"a","c",1070,,,1,13,2010,"Jan,Apr,Jul,Oct"
+13,"d","a",310,,,1,45,2009,"Feb,May,Aug,Nov"
+14,"a","a",1300,3,2014,1,40,2011,"Jan,Apr,Jul,Oct"
+15,"d","c",4110,3,2010,1,14,2011,"Jan,Apr,Jul,Oct"
+16,"a","c",3270,,,0,,,""
+17,"a","a",50,12,2005,1,26,2010,"Jan,Apr,Jul,Oct"
+18,"d","c",13840,6,2010,1,14,2012,"Jan,Apr,Jul,Oct"
+19,"a","c",3240,,,1,22,2011,"Mar,Jun,Sept,Dec"
+20,"d","a",2340,5,2009,1,40,2014,"Jan,Apr,Jul,Oct"
+21,"c","c",550,10,1999,1,45,2009,"Jan,Apr,Jul,Oct"
+22,"a","a",1040,,,1,22,2012,"Jan,Apr,Jul,Oct"
+23,"d","a",4060,8,2005,0,,,""
+24,"a","c",4590,3,2000,1,40,2011,"Jan,Apr,Jul,Oct"
+25,"c","a",430,4,2003,0,,,""
+26,"d","a",2300,,,0,,,""
+27,"a","a",60,1,2005,1,5,2011,"Jan,Apr,Jul,Oct"
+28,"a","a",1200,10,2014,1,6,2015,"Mar,Jun,Sept,Dec"
+29,"d","c",2170,,,0,,,""
+30,"a","a",40,2,2014,1,10,2014,"Mar,Jun,Sept,Dec"
+31,"d","c",9800,7,2012,0,,,""
+32,"a","a",2910,,,1,45,2009,"Feb,May,Aug,Nov"
+33,"a","c",1320,5,2013,0,,,""
+34,"c","a",2240,9,2009,0,,,""
+35,"d","c",7660,10,2000,1,1,2012,"Jan,Apr,Jul,Oct"
+36,"a","c",540,6,2003,1,40,2014,"Jan,Apr,Jul,Oct"
+37,"c","a",4230,12,2014,0,,,""
+38,"d","a",1090,4,2007,0,,,""
+39,"a","a",260,10,2006,1,31,2013,"Feb,May,Aug,Nov"
+40,"a","a",180,,,1,45,2009,"Feb,May,Aug,Nov"
+41,"d","c",1180,,,1,31,2013,"Jan,Apr,Jul,Oct"
+42,"a","c",290,,,1,40,2011,"Jan,Apr,Jul,Oct"
+43,"d","a",4880,,,1,37,2009,"Jan,Apr,Jul,Oct"
+44,"a","a",540,6,2011,0,,,""
+45,"d","a",9710,2,2014,0,,,""
+46,"c","a",1200,9,2005,1,14,2011,"Jan,Apr,Jul,Oct"
+47,"a","c",270,4,2013,1,14,2013,"Jan,Apr,Jul,Oct"
+48,"a","a",1060,5,2012,0,,,""
+49,"d","c",18010,9,2007,0,,,""
+50,"d","a",6260,11,2009,0,,,""
+51,"a","c",10570,7,2013,1,9,2011,"Jan,Apr,Jul,Oct"
+52,"d","c",450,4,2014,1,39,2010,"Jan,Apr,Jul,Oct"
+53,"a","c",30360,9,2013,0,,,""
+54,"d","c",7170,8,2014,1,5,2013,"Feb,May,Aug,Nov"
+55,"a","a",720,11,2004,0,,,""
+56,"d","c",6620,3,2012,1,10,2014,"Mar,Jun,Sept,Dec"
+57,"d","c",420,6,2014,0,,,""
+58,"a","c",7340,5,2008,1,27,2012,"Jan,Apr,Jul,Oct"
+59,"a","c",2840,6,2007,1,14,2011,"Jan,Apr,Jul,Oct"
+60,"d","c",5540,10,2009,0,,,""
+61,"a","c",350,12,2007,1,1,2012,"Jan,Apr,Jul,Oct"
+62,"a","a",2050,,,0,,,""
+63,"c","c",3700,6,2010,1,18,2010,"Feb,May,Aug,Nov"
+64,"d","c",22560,,,1,14,2013,"Jan,Apr,Jul,Oct"
+65,"a","c",13840,5,2010,1,1,2012,"Jan,Apr,Jul,Oct"
+66,"d","a",7660,,,1,37,2009,"Jan,Apr,Jul,Oct"
+67,"a","c",410,2,2006,0,,,""
+68,"a","c",250,,,1,35,2012,"Mar,Jun,Sept,Dec"
+69,"c","c",1130,,,1,40,2011,"Jan,Apr,Jul,Oct"
+70,"c","c",4840,,,0,,,""
+71,"a","a",17500,8,2008,1,37,2009,"Mar,Jun,Sept,Dec"
+72,"a","a",2200,12,2009,1,13,2010,"Jan,Apr,Jul,Oct"
+73,"a","c",1650,9,2008,0,,,""
+74,"a","a",330,,,0,,,""
+75,"d","c",22440,12,2013,0,,,""
+76,"d","c",19960,3,2006,0,,,""
+77,"d","c",1090,8,2009,1,10,2014,"Jan,Apr,Jul,Oct"
+78,"a","a",3510,11,2006,1,5,2013,"Feb,May,Aug,Nov"
+79,"a","a",3320,,,0,,,""
+80,"d","a",7910,,,0,,,""
+81,"a","a",2370,3,2011,1,40,2014,"Jan,Apr,Jul,Oct"
+82,"a","a",22390,4,2008,1,37,2009,"Jan,Apr,Jul,Oct"
+83,"a","a",2710,,,0,,,""
+84,"a","c",11810,8,2014,0,,,""
+85,"b","a",1870,10,2011,0,,,""
+86,"a","a",480,2,2005,1,31,2013,"Jan,Apr,Jul,Oct"
+87,"a","a",560,12,2010,0,,,""
+88,"a","a",10690,10,2005,0,,,""
+89,"a","a",2380,7,2004,1,40,2014,"Jan,Apr,Jul,Oct"
+90,"a","a",330,11,2007,0,,,""
+91,"c","a",2410,,,1,35,2011,"Jan,Apr,Jul,Oct"
+92,"c","a",240,,,1,45,2009,"Feb,May,Aug,Nov"
+93,"a","a",16690,,,1,14,2011,"Jan,Apr,Jul,Oct"
+94,"d","c",14620,,,0,,,""
+95,"a","a",1890,10,2014,0,,,""
+96,"a","a",8780,2,2005,1,37,2009,"Jan,Apr,Jul,Oct"
+97,"d","c",8980,,,0,,,""
+98,"d","c",15140,12,2006,1,1,2012,"Jan,Apr,Jul,Oct"
+99,"c","c",2030,11,2003,1,22,2012,"Mar,Jun,Sept,Dec"
+100,"d","a",17930,,,0,,,""
+101,"d","c",2440,,,1,22,2012,"Mar,Jun,Sept,Dec"
+102,"a","a",150,12,2007,1,10,2014,"Mar,Jun,Sept,Dec"
+103,"d","c",5210,5,2015,0,,,""
+104,"a","a",390,6,2009,0,,,""
+105,"a","c",6190,,,1,23,2013,"Mar,Jun,Sept,Dec"
+106,"a","a",1390,8,2013,0,,,""
+107,"a","a",1930,9,2009,0,,,""
+108,"d","c",2190,9,2003,0,,,""
+109,"a","c",3300,11,2010,0,,,""
+110,"a","c",46590,4,2013,0,,,""
+111,"d","c",7890,,,1,37,2009,"Jan,Apr,Jul,Oct"
+112,"a","a",1630,9,2009,0,,,""
+113,"d","c",20930,11,1999,0,,,""
+114,"c","a",4510,,,1,48,2011,"Mar,Jun,Sept,Dec"
+115,"d","c",5740,4,2007,1,40,2014,"Jan,Apr,Jul,Oct"
+116,"a","a",680,4,2013,0,,,""
+117,"a","a",3450,9,2011,0,,,""
+118,"d","c",3580,9,2012,0,,,""
+119,"a","c",2100,2,2010,0,,,""
+120,"d","a",2290,12,2014,1,37,2009,"Jan,Apr,Jul,Oct"
+121,"a","a",3570,11,2009,1,36,2013,"Mar,Jun,Sept,Dec"
+122,"a","c",58260,4,2013,0,,,""
+123,"a","a",16760,9,2011,0,,,""
+124,"a","a",1410,4,2003,0,,,""
+125,"a","a",760,12,2005,0,,,""
+126,"d","a",3370,10,2014,1,18,2011,"Feb,May,Aug,Nov"
+127,"d","a",1350,12,2005,1,13,2010,"Jan,Apr,Jul,Oct"
+128,"d","c",2000,,,1,1,2013,"Jan,Apr,Jul,Oct"
+129,"a","a",2460,,,1,14,2011,"Jan,Apr,Jul,Oct"
+130,"c","a",900,,,1,13,2010,"Jan,Apr,Jul,Oct"
+131,"c","a",920,7,2015,0,,,""
+132,"d","c",1040,,,1,27,2012,"Jan,Apr,Jul,Oct"
+133,"a","a",270,8,2013,1,10,2014,"Mar,Jun,Sept,Dec"
+134,"a","a",1200,9,2008,0,,,""
+135,"d","a",5190,,,1,1,2013,"Jan,Apr,Jul,Oct"
+136,"a","c",2200,12,2010,1,22,2012,"Feb,May,Aug,Nov"
+137,"a","a",1730,7,2015,1,40,2014,"Jan,Apr,Jul,Oct"
+138,"a","c",25360,10,2014,0,,,""
+139,"a","a",1700,1,2008,1,14,2011,"Jan,Apr,Jul,Oct"
+140,"a","c",1090,7,2010,1,1,2013,"Jan,Apr,Jul,Oct"
+141,"c","c",1540,,,1,22,2012,"Mar,Jun,Sept,Dec"
+142,"a","a",1090,7,2002,0,,,""
+143,"d","a",2930,12,2002,0,,,""
+144,"a","c",16570,,,0,,,""
+145,"a","a",280,,,1,45,2009,"Feb,May,Aug,Nov"
+146,"d","c",8050,10,1961,1,48,2012,"Jan,Apr,Jul,Oct"
+147,"d","c",8540,,,0,,,""
+148,"a","a",2090,12,2008,0,,,""
+149,"d","a",2610,7,2006,1,14,2011,"Jan,Apr,Jul,Oct"
+150,"c","c",31830,3,2010,0,,,""
+151,"d","c",4360,10,2005,0,,,""
+152,"a","a",1780,,,0,,,""
+153,"a","a",16240,10,2000,1,18,2011,"Feb,May,Aug,Nov"
+154,"d","c",16420,,,0,,,""
+155,"d","a",3050,,,1,35,2010,"Jan,Apr,Jul,Oct"
+156,"a","a",2020,2,2011,1,14,2011,"Mar,Jun,Sept,Dec"
+157,"a","c",2950,10,2004,0,,,""
+158,"d","c",11840,,,1,31,2009,"Feb,May,Aug,Nov"
+159,"d","a",8530,3,2013,0,,,""
+160,"d","c",17110,11,2005,0,,,""
+161,"a","c",2970,3,2005,0,,,""
+162,"d","c",5340,3,2012,1,13,2010,"Jan,Apr,Jul,Oct"
+163,"a","a",1480,4,2009,0,,,""
+164,"a","a",1160,9,2005,1,13,2010,"Jan,Apr,Jul,Oct"
+165,"a","a",3720,4,2005,1,13,2010,"Jan,Apr,Jul,Oct"
+166,"a","c",100,4,2014,1,31,2013,"Jan,Apr,Jul,Oct"
+167,"a","a",140,4,2008,0,,,""
+168,"a","a",12540,,,0,,,""
+169,"d","a",980,7,2014,1,18,2014,"Feb,May,Aug,Nov"
+170,"a","a",1070,5,2015,1,14,2011,"Jan,Apr,Jul,Oct"
+171,"a","a",2640,,,0,,,""
+172,"a","a",110,,,1,40,2014,"Jan,Apr,Jul,Oct"
+173,"a","a",350,12,2012,0,,,""
+174,"a","a",13090,,,1,22,2012,"Jan,Apr,Jul,Oct"
+175,"c","a",4130,,,0,,,""
+176,"a","a",3770,,,0,,,""
+177,"a","a",1250,2,2004,1,5,2013,"Feb,May,Aug,Nov"
+178,"d","a",1710,,,0,,,""
+179,"a","c",480,,,0,,,""
+180,"d","a",5800,9,2010,0,,,""
+181,"a","a",12610,3,2013,1,14,2011,"Jan,Apr,Jul,Oct"
+182,"d","c",1390,,,1,9,2011,"Mar,Jun,Sept,Dec"
+183,"a","a",9670,,,0,,,""
+184,"d","c",3560,,,0,,,""
+185,"d","c",1860,5,2015,0,,,""
+186,"a","a",290,10,2011,1,40,2014,"Jan,Apr,Jul,Oct"
+187,"a","c",19360,,,0,,,""
+188,"d","a",850,,,1,18,2011,"Feb,May,Aug,Nov"
+189,"d","a",5760,7,2014,0,,,""
+190,"a","a",1470,12,2006,1,40,2014,"Jan,Apr,Jul,Oct"
+191,"a","a",1100,8,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+192,"d","c",2770,3,2008,1,40,2014,"Jan,Apr,Jul,Oct"
+193,"a","a",520,,,0,,,""
+194,"d","c",16970,,,1,5,2013,"Feb,May,Aug,Nov"
+195,"a","c",220,,,0,,,""
+196,"c","a",3850,11,2005,1,14,2011,"Jan,Apr,Jul,Oct"
+197,"c","a",4210,3,2015,0,,,""
+198,"a","a",290,9,2002,1,13,2010,"Jan,Apr,Jul,Oct"
+199,"d","c",6360,12,2010,1,14,2012,"Mar,Jun,Sept,Dec"
+200,"a","a",1650,10,2000,0,,,""
+201,"d","a",20260,,,1,18,2014,"Mar,Jun,Sept,Dec"
+202,"d","c",5140,5,2010,0,,,""
+203,"c","c",490,11,2002,0,,,""
+204,"a","a",5630,12,2002,1,40,2014,"Jan,Apr,Jul,Oct"
+205,"a","a",110,12,2007,0,,,""
+206,"a","c",380,,,1,14,2012,"Jan,Apr,Jul,Oct"
+207,"a","a",6870,,,0,,,""
+208,"c","a",300,4,2006,0,,,""
+209,"a","c",11680,9,2011,1,31,2013,"Jan,Apr,Jul,Oct"
+210,"d","a",970,11,1999,1,5,2013,"Feb,May,Aug,Nov"
+211,"a","c",350,11,2006,0,,,""
+212,"a","c",15050,9,2008,0,,,""
+213,"d","c",4030,3,2014,1,1,2014,"Jan,Apr,Jul,Oct"
+214,"d","a",8650,7,2013,1,10,2014,"Jan,Apr,Jul,Oct"
+215,"d","a",150,,,1,45,2009,"Feb,May,Aug,Nov"
+216,"c","a",190,,,1,45,2009,"Feb,May,Aug,Nov"
+217,"c","a",3150,,,0,,,""
+218,"a","c",640,,,1,9,2011,"Mar,Jun,Sept,Dec"
+219,"a","a",1640,2,2013,0,,,""
+220,"a","a",1000,9,2008,0,,,""
+221,"d","c",13530,9,2013,0,,,""
+222,"a","a",2170,11,2008,0,,,""
+223,"d","c",2920,10,1995,1,27,2011,"Jan,Apr,Jul,Oct"
+224,"d","c",7930,,,1,1,2013,"Jan,Apr,Jul,Oct"
+225,"d","a",10180,5,2015,0,,,""
+226,"a","a",450,,,0,,,""
+227,"a","a",2370,,,0,,,""
+228,"d","c",10800,,,1,18,2011,"Feb,May,Aug,Nov"
+229,"d","c",17410,4,2007,1,14,2011,"Jan,Apr,Jul,Oct"
+230,"d","c",6680,9,2013,0,,,""
+231,"d","c",3840,10,2008,1,39,2010,"Feb,May,Aug,Nov"
+232,"c","c",13570,5,2010,1,10,2013,"Mar,Jun,Sept,Dec"
+233,"a","a",1890,,,0,,,""
+234,"d","a",4370,,,0,,,""
+235,"a","a",5710,3,2012,1,37,2009,"Jan,Apr,Jul,Oct"
+236,"a","a",1000,11,2007,0,,,""
+237,"a","a",1420,11,2007,0,,,""
+238,"a","a",320,,,1,45,2009,"Feb,May,Aug,Nov"
+239,"d","c",610,,,0,,,""
+240,"a","a",1110,5,2009,0,,,""
+241,"d","c",780,,,0,,,""
+242,"d","a",6880,9,2001,1,14,2011,"Jan,Apr,Jul,Oct"
+243,"a","a",310,,,1,5,2013,"Feb,May,Aug,Nov"
+244,"d","a",710,3,2012,1,1,2012,"Jan,Apr,Jul,Oct"
+245,"a","c",1310,,,0,,,""
+246,"c","a",4660,4,2013,0,,,""
+247,"d","c",70,11,2010,1,5,2013,"Feb,May,Aug,Nov"
+248,"a","c",340,9,2012,1,40,2012,"Jan,Apr,Jul,Oct"
+249,"d","c",18010,9,2014,0,,,""
+250,"d","a",3520,,,1,18,2012,"Feb,May,Aug,Nov"
+251,"a","c",340,,,0,,,""
+252,"d","c",22330,,,1,5,2010,"Feb,May,Aug,Nov"
+253,"a","c",250,,,1,5,2013,"Feb,May,Aug,Nov"
+254,"d","a",330,3,2008,1,1,2012,"Mar,Jun,Sept,Dec"
+255,"c","c",4630,3,2011,1,35,2011,"Feb,May,Aug,Nov"
+256,"a","c",80,9,2005,1,10,2014,"Mar,Jun,Sept,Dec"
+257,"a","a",420,12,2012,0,,,""
+258,"a","a",27190,7,2010,1,37,2009,"Jan,Apr,Jul,Oct"
+259,"b","b",210,,,0,,,""
+260,"a","a",540,10,2011,0,,,""
+261,"d","c",15340,4,2015,1,39,2009,"Jan,Apr,Jul,Oct"
+262,"b","a",1180,5,2013,0,,,""
+263,"a","c",1140,5,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+264,"a","a",180,3,2014,0,,,""
+265,"a","a",4580,,,1,14,2015,"Jan,Apr,Jul,Oct"
+266,"a","c",360,3,2014,1,9,2011,"Mar,Jun,Sept,Dec"
+267,"c","a",2460,1,2012,0,,,""
+268,"a","a",4520,2,2014,0,,,""
+269,"a","c",60,6,2015,0,,,""
+270,"a","a",1450,7,2014,0,,,""
+271,"a","a",420,,,1,14,2011,"Jan,Apr,Jul,Oct"
+272,"a","a",16180,7,2003,1,14,2011,"Jan,Apr,Jul,Oct"
+273,"a","c",8480,,,0,,,""
+274,"b","b",3640,,,1,10,2013,"Jan,Apr,Jul,Oct"
+275,"d","a",300,5,2014,1,40,2014,"Jan,Apr,Jul,Oct"
+276,"a","a",2960,10,2014,1,36,2013,"Mar,Jun,Sept,Dec"
+277,"d","c",7840,,,1,31,2009,"Feb,May,Aug,Nov"
+278,"a","c",9260,2,2010,0,,,""
+279,"d","c",2320,,,1,40,2012,"Jan,Apr,Jul,Oct"
+280,"d","c",18640,9,2013,1,10,2014,"Mar,Jun,Sept,Dec"
+281,"d","c",6970,9,2011,0,,,""
+282,"a","a",1220,12,2010,0,,,""
+283,"a","a",2260,,,1,40,2014,"Jan,Apr,Jul,Oct"
+284,"d","a",1290,,,1,40,2014,"Jan,Apr,Jul,Oct"
+285,"a","a",2410,,,0,,,""
+286,"a","a",1460,4,2015,0,,,""
+287,"c","a",2740,5,2009,1,40,2014,"Jan,Apr,Jul,Oct"
+288,"d","a",800,,,1,14,2011,"Mar,Jun,Sept,Dec"
+289,"d","a",6540,12,2007,0,,,""
+290,"a","a",4150,5,2001,0,,,""
+291,"d","a",,,,0,,,""
+292,"a","a",1100,6,2009,0,,,""
+293,"c","c",140,11,2007,0,,,""
+294,"a","a",3150,5,2005,0,,,""
+295,"a","a",210,11,2000,1,36,2013,"Mar,Jun,Sept,Dec"
+296,"a","a",9580,5,2007,1,45,2014,"Feb,May,Aug,Nov"
+297,"a","a",2300,9,2010,0,,,""
+298,"d","a",19840,7,2009,0,,,""
+299,"d","c",38630,9,2012,0,,,""
+300,"a","c",120,4,2009,1,1,2012,"Jan,Apr,Jul,Oct"
+301,"a","c",4510,3,2015,0,,,""
+302,"d","c",2190,12,2007,1,9,2011,"Mar,Jun,Sept,Dec"
+303,"a","a",15430,11,2012,1,18,2011,"Feb,May,Aug,Nov"
+304,"a","a",1950,7,2015,0,,,""
+305,"c","c",2470,2,2005,1,31,2013,"Mar,Jun,Sept,Dec"
+306,"a","a",5100,4,2007,1,40,2014,"Jan,Apr,Jul,Oct"
+307,"a","a",18660,9,2002,0,,,""
+308,"a","a",1070,12,2006,1,13,2010,"Jan,Apr,Jul,Oct"
+309,"d","a",8740,,,1,37,2009,"Feb,May,Aug,Nov"
+310,"a","c",2290,,,1,10,2014,"Mar,Jun,Sept,Dec"
+311,"a","c",680,7,2005,0,,,""
+312,"d","a",11300,3,2012,0,,,""
+313,"d","c",14160,,,0,,,""
+314,"a","a",3560,10,2001,1,31,2013,"Feb,May,Aug,Nov"
+315,"a","c",38710,4,2013,0,,,""
+316,"d","a",9000,8,2001,0,,,""
+317,"d","a",3140,7,2013,1,14,2011,"Jan,Apr,Jul,Oct"
+318,"d","c",32330,3,2014,0,,,""
+319,"a","c",570,9,2012,1,27,2012,"Mar,Jun,Sept,Dec"
+320,"a","c",210,9,2012,0,,,""
+321,"c","c",8140,11,2013,1,10,2014,"Mar,Jun,Sept,Dec"
+322,"a","a",17500,4,2001,1,37,2009,"Jan,Apr,Jul,Oct"
+323,"d","c",8400,4,2012,1,5,2013,"Feb,May,Aug,Nov"
+324,"a","a",13140,,,1,14,2011,"Jan,Apr,Jul,Oct"
+325,"a","c",350,3,2011,1,22,2011,"Feb,May,Aug,Nov"
+326,"d","a",10070,5,2015,1,31,2013,"Feb,May,Aug,Nov"
+327,"c","c",1390,12,2004,0,,,""
+328,"a","a",3130,7,2002,0,,,""
+329,"a","a",1310,6,1990,1,22,2012,"Mar,Jun,Sept,Dec"
+330,"a","c",370,,,1,22,2012,"Mar,Jun,Sept,Dec"
+331,"a","c",670,,,1,14,2015,"Jan,Apr,Jul,Oct"
+332,"a","a",1840,3,2006,0,,,""
+333,"a","c",3720,2,2010,0,,,""
+334,"d","c",4040,8,2008,1,18,2013,"Mar,Jun,Sept,Dec"
+335,"b","a",90,,,1,31,2013,"Jan,Apr,Jul,Oct"
+336,"a","a",190,7,2014,0,,,""
+337,"d","c",10600,7,2005,1,45,2014,"Feb,May,Aug,Nov"
+338,"a","c",1590,,,1,37,2009,"Jan,Apr,Jul,Oct"
+339,"a","c",2280,,,1,10,2013,"Mar,Jun,Sept,Dec"
+340,"a","c",8080,,,0,,,""
+341,"a","a",190,9,2011,0,,,""
+342,"d","c",15770,,,1,40,2014,"Jan,Apr,Jul,Oct"
+343,"d","a",18650,4,2004,1,14,2014,"Jan,Apr,Jul,Oct"
+344,"a","c",300,4,2011,1,14,2011,"Jan,Apr,Jul,Oct"
+345,"a","a",120,,,1,22,2012,"Jan,Apr,Jul,Oct"
+346,"a","c",8090,,,0,,,""
+347,"d","c",9360,7,2013,1,22,2012,"Mar,Jun,Sept,Dec"
+348,"a","a",16490,,,1,22,2012,"Jan,Apr,Jul,Oct"
+349,"c","c",1490,4,2009,0,,,""
+350,"d","a",8880,,,1,14,2011,"Jan,Apr,Jul,Oct"
+351,"a","a",5290,11,2012,1,5,2013,"Feb,May,Aug,Nov"
+352,"d","c",6360,,,1,40,2012,"Mar,Jun,Sept,Dec"
+353,"b","b",900,,,1,14,2013,"Feb,May,Aug,Nov"
+354,"d","c",1500,10,2005,0,,,""
+355,"a","c",9720,8,2013,0,,,""
+356,"d","c",8970,12,2007,1,22,2012,"Feb,May,Aug,Nov"
+357,"a","a",2060,10,2008,0,,,""
+358,"a","a",2890,10,2003,0,,,""
+359,"d","c",4370,,,0,,,""
+360,"a","a",2040,6,2007,0,,,""
+361,"c","c",4490,5,2014,1,5,2013,"Feb,May,Aug,Nov"
+362,"c","c",340,,,0,,,""
+363,"a","a",250,9,2009,0,,,""
+364,"a","c",13620,,,1,10,2014,"Mar,Jun,Sept,Dec"
+365,"c","a",2410,,,1,45,2009,"Feb,May,Aug,Nov"
+366,"d","c",6470,12,2004,0,,,""
+367,"d","c",2640,9,2012,0,,,""
+368,"d","c",1450,4,2005,1,45,2009,"Feb,May,Aug,Nov"
+369,"d","c",5870,4,2014,0,,,""
+370,"d","a",8250,10,2000,1,31,2009,"Jan,Apr,Jul,Oct"
+371,"d","c",1970,7,2009,1,45,2014,"Feb,May,Aug,Nov"
+372,"d","c",4880,8,2010,1,18,2014,"Jan,Apr,Jul,Oct"
+373,"d","c",11120,,,1,22,2012,"Jan,Apr,Jul,Oct"
+374,"a","a",1150,9,2007,0,,,""
+375,"a","c",15710,2,2013,1,37,2009,"Jan,Apr,Jul,Oct"
+376,"a","a",160,8,2012,0,,,""
+377,"a","c",100,6,2010,1,18,2010,"Feb,May,Aug,Nov"
+378,"a","c",2140,8,2012,0,,,""
+379,"d","a",6630,,,0,,,""
+380,"a","a",2240,5,2013,1,10,2014,"Mar,Jun,Sept,Dec"
+381,"a","a",1800,11,2006,1,5,2013,"Feb,May,Aug,Nov"
+382,"c","c",26130,11,2002,0,,,""
+383,"a","c",350,,,0,,,""
+384,"a","c",130,,,1,14,2011,"Jan,Apr,Jul,Oct"
+385,"d","a",4580,9,2007,0,,,""
+386,"d","c",1460,4,2014,1,31,2013,"Jan,Apr,Jul,Oct"
+387,"c","a",210,,,1,36,2013,"Mar,Jun,Sept,Dec"
+388,"a","a",2260,,,0,,,""
+389,"a","c",6690,8,2011,0,,,""
+390,"a","c",1600,4,2009,0,,,""
+391,"a","a",460,11,2014,1,31,2013,"Feb,May,Aug,Nov"
+392,"a","a",2120,,,0,,,""
+393,"d","c",4820,3,2008,0,,,""
+394,"d","a",10850,,,0,,,""
+395,"a","a",3620,2,2013,0,,,""
+396,"a","c",23130,,,0,,,""
+397,"a","c",130,,,1,27,2013,"Feb,May,Aug,Nov"
+398,"c","c",1540,,,1,1,2012,"Jan,Apr,Jul,Oct"
+399,"a","a",5360,9,2012,1,40,2011,"Jan,Apr,Jul,Oct"
+400,"a","a",70,11,2004,1,14,2011,"Jan,Apr,Jul,Oct"
+401,"a","c",9200,10,2009,1,14,2012,"Jan,Apr,Jul,Oct"
+402,"c","c",5830,9,2011,1,13,2010,"Jan,Apr,Jul,Oct"
+403,"a","a",4970,7,2015,0,,,""
+404,"a","c",1420,,,1,10,2013,"Mar,Jun,Sept,Dec"
+405,"a","a",1080,4,2008,1,14,2011,"Jan,Apr,Jul,Oct"
+406,"d","c",8240,3,2001,1,10,2013,"Feb,May,Aug,Nov"
+407,"a","a",5890,10,2003,1,14,2011,"Feb,May,Aug,Nov"
+408,"c","a",1560,,,1,45,2009,"Feb,May,Aug,Nov"
+409,"d","c",840,,,1,1,2013,"Jan,Apr,Jul,Oct"
+410,"c","a",40,11,2011,1,22,2012,"Mar,Jun,Sept,Dec"
+411,"d","c",8460,,,0,,,""
+412,"d","c",4460,,,1,39,2010,"Jan,Apr,Jul,Oct"
+413,"a","c",760,9,2014,0,,,""
+414,"d","c",6210,,,1,1,2013,"Jan,Apr,Jul,Oct"
+415,"d","c",6910,4,2005,0,,,""
+416,"a","c",4650,6,2014,0,,,""
+417,"a","c",840,,,1,10,2014,"Jan,Apr,Jul,Oct"
+418,"a","a",1060,5,2009,1,27,2011,"Feb,May,Aug,Nov"
+419,"c","a",1620,9,2009,0,,,""
+420,"d","c",250,,,1,27,2012,"Jan,Apr,Jul,Oct"
+421,"c","c",3530,6,2012,1,35,2012,"Mar,Jun,Sept,Dec"
+422,"a","c",2880,,,0,,,""
+423,"b","a",1270,5,2014,0,,,""
+424,"d","c",1250,,,1,40,2011,"Jan,Apr,Jul,Oct"
+425,"d","c",1460,,,0,,,""
+426,"a","a",250,,,0,,,""
+427,"a","c",70,7,2005,1,13,2010,"Jan,Apr,Jul,Oct"
+428,"d","a",2960,12,2014,1,23,2015,"Mar,Jun,Sept,Dec"
+429,"d","c",16350,7,2005,1,31,2013,"Jan,Apr,Jul,Oct"
+430,"d","c",12870,10,2008,0,,,""
+431,"d","c",4520,,,0,,,""
+432,"a","a",810,5,2013,0,,,""
+433,"a","c",30030,11,2010,0,,,""
+434,"a","a",13020,8,2003,1,40,2014,"Jan,Apr,Jul,Oct"
+435,"a","a",910,,,0,,,""
+436,"d","a",2300,,,0,,,""
+437,"c","c",430,,,1,50,2010,"Jan,Apr,Jul,Oct"
+438,"d","c",1110,,,1,40,2012,"Jan,Apr,Jul,Oct"
+439,"a","a",1350,9,2009,0,,,""
+440,"d","a",3900,4,2005,1,45,2009,"Feb,May,Aug,Nov"
+441,"d","a",2530,,,0,,,""
+442,"c","a",500,,,1,45,2009,"Feb,May,Aug,Nov"
+443,"d","a",11400,12,2005,0,,,""
+444,"c","a",1700,11,2009,0,,,""
+445,"a","a",240,,,0,,,""
+446,"a","a",340,10,2000,1,31,2013,"Feb,May,Aug,Nov"
+447,"a","c",1510,9,2013,0,,,""
+448,"a","c",3970,9,2009,0,,,""
+449,"a","c",120,12,2014,1,37,2009,"Jan,Apr,Jul,Oct"
+450,"c","a",5780,11,1994,1,10,2014,"Mar,Jun,Sept,Dec"
+451,"a","a",2460,4,2009,1,13,2010,"Jan,Apr,Jul,Oct"
+452,"a","c",1850,8,2013,1,5,2011,"Feb,May,Aug,Nov"
+453,"a","c",75860,,,0,,,""
+454,"a","a",26450,,,0,,,""
+455,"d","c",7660,9,2010,0,,,""
+456,"a","c",140,,,0,,,""
+457,"d","c",13140,,,1,31,2013,"Jan,Apr,Jul,Oct"
+458,"c","a",3390,,,0,,,""
+459,"a","a",250,9,2008,1,5,2013,"Feb,May,Aug,Nov"
+460,"a","a",34050,4,2008,0,,,""
+461,"d","c",1790,7,2013,1,40,2013,"Jan,Apr,Jul,Oct"
+462,"a","a",44320,8,2008,0,,,""
+463,"a","a",4160,,,0,,,""
+464,"c","a",520,3,2009,0,,,""
+465,"d","c",10890,4,2005,0,,,""
+466,"a","c",3110,3,2003,0,,,""
+467,"a","c",20390,4,2008,0,,,""
+468,"c","c",5260,9,2012,0,,,""
+469,"c","c",710,9,2005,0,,,""
+470,"a","c",50,,,0,,,""
+471,"d","a",5300,,,1,45,2009,"Feb,May,Aug,Nov"
+472,"c","c",5030,8,2011,0,,,""
+473,"a","a",50,,,0,,,""
+474,"c","a",14810,,,1,14,2011,"Mar,Jun,Sept,Dec"
+475,"a","a",140,9,2005,0,,,""
+476,"d","a",8300,9,2006,0,,,""
+477,"d","a",770,7,2010,1,35,2010,"Jan,Apr,Jul,Oct"
+478,"d","c",1940,3,2012,0,,,""
+479,"a","a",320,12,2005,1,9,2011,"Feb,May,Aug,Nov"
+480,"a","a",300,11,2012,0,,,""
+481,"c","c",7470,,,1,44,2012,"Feb,May,Aug,Nov"
+482,"c","a",2550,10,2005,0,,,""
+483,"a","c",2310,9,2011,1,13,2010,"Jan,Apr,Jul,Oct"
+484,"a","c",14300,3,2011,0,,,""
+485,"d","c",3270,,,1,22,2012,"Jan,Apr,Jul,Oct"
+486,"a","a",2320,,,1,31,2013,"Jan,Apr,Jul,Oct"
+487,"d","c",2180,9,2012,1,40,2012,"Jan,Apr,Jul,Oct"
+488,"a","c",2890,4,2014,0,,,""
+489,"a","a",14960,11,2013,1,37,2009,"Jan,Apr,Jul,Oct"
+490,"a","a",660,4,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+491,"d","c",4680,,,1,22,2012,"Mar,Jun,Sept,Dec"
+492,"a","a",1740,3,2008,1,40,2012,"Jan,Apr,Jul,Oct"
+493,"d","c",1150,,,1,14,2011,"Jan,Apr,Jul,Oct"
+494,"b","a",1260,6,2011,0,,,""
+495,"d","a",5470,,,1,37,2009,"Jan,Apr,Jul,Oct"
+496,"d","c",2780,6,2015,0,,,""
+497,"a","c",1610,,,0,,,""
+498,"a","a",990,,,1,40,2014,"Jan,Apr,Jul,Oct"
+499,"c","c",450,,,1,5,2013,"Feb,May,Aug,Nov"
+500,"d","c",10690,9,2007,1,5,2013,"Jan,Apr,Jul,Oct"
+501,"a","c",1620,9,2006,0,,,""
+502,"a","a",220,2,2002,1,37,2009,"Jan,Apr,Jul,Oct"
+503,"d","c",13080,9,2006,1,14,2011,"Jan,Apr,Jul,Oct"
+504,"c","c",820,,,0,,,""
+505,"a","a",350,,,1,5,2013,"Feb,May,Aug,Nov"
+506,"a","a",1850,12,2014,1,18,2011,"Feb,May,Aug,Nov"
+507,"a","c",9070,4,2011,1,37,2009,"Jan,Apr,Jul,Oct"
+508,"a","c",1280,,,1,40,2011,"Jan,Apr,Jul,Oct"
+509,"a","a",4740,7,2008,1,37,2009,"Jan,Apr,Jul,Oct"
+510,"a","c",8260,,,0,,,""
+511,"a","a",2060,9,2009,0,,,""
+512,"b","b",590,,,1,5,2013,"Mar,Jun,Sept,Dec"
+513,"a","a",400,8,2013,0,,,""
+514,"c","c",1200,7,2012,1,27,2012,"Jan,Apr,Jul,Oct"
+515,"d","c",11260,,,1,9,2011,"Feb,May,Aug,Nov"
+516,"a","c",20,,,1,35,2010,"Mar,Jun,Sept,Dec"
+517,"a","c",22490,,,0,,,""
+518,"d","c",3330,3,2015,1,13,2010,"Jan,Apr,Jul,Oct"
+519,"c","c",2510,8,2009,1,1,2012,"Jan,Apr,Jul,Oct"
+520,"a","c",6900,,,1,40,2012,"Mar,Jun,Sept,Dec"
+521,"d","a",18610,11,2002,1,48,2011,"Mar,Jun,Sept,Dec"
+522,"d","c",7160,11,2012,1,9,2011,"Jan,Apr,Jul,Oct"
+523,"c","c",50,11,2013,0,,,""
+524,"a","c",40860,9,2013,0,,,""
+525,"d","c",1870,9,2013,0,,,""
+526,"a","a",120,,,1,48,2011,"Mar,Jun,Sept,Dec"
+527,"d","c",5830,4,2008,0,,,""
+528,"a","c",20620,,,1,37,2009,"Jan,Apr,Jul,Oct"
+529,"d","c",12920,9,2012,0,,,""
+530,"a","c",18160,,,0,,,""
+531,"a","c",4030,,,0,,,""
+532,"a","c",1070,8,2010,0,,,""
+533,"a","c",5950,,,0,,,""
+534,"d","a",1200,9,2009,1,45,2009,"Jan,Apr,Jul,Oct"
+535,"a","a",3570,4,2007,0,,,""
+536,"a","c",4700,9,2002,1,31,2013,"Feb,May,Aug,Nov"
+537,"a","a",600,5,2002,1,1,2012,"Jan,Apr,Jul,Oct"
+538,"a","a",990,2,2010,0,,,""
+539,"a","a",770,5,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+540,"d","c",810,,,1,48,2012,"Jan,Apr,Jul,Oct"
+541,"a","c",650,7,1990,0,,,""
+542,"a","a",7280,9,2012,1,1,2013,"Jan,Apr,Jul,Oct"
+543,"c","a",1080,12,2012,0,,,""
+544,"a","a",250,12,2001,1,13,2010,"Jan,Apr,Jul,Oct"
+545,"a","c",5020,5,2006,1,18,2014,"Feb,May,Aug,Nov"
+546,"a","a",580,1,2005,0,,,""
+547,"d","c",8990,11,2009,1,35,2010,"Mar,Jun,Sept,Dec"
+548,"d","c",3760,2,2009,0,,,""
+549,"a","c",2330,,,0,,,""
+550,"d","c",50,6,2015,0,,,""
+551,"a","c",2190,9,2013,0,,,""
+552,"a","a",4260,7,2008,1,37,2009,"Jan,Apr,Jul,Oct"
+553,"c","a",3040,9,2002,1,13,2010,"Jan,Apr,Jul,Oct"
+554,"c","c",1200,,,0,,,""
+555,"d","a",1560,1,2014,1,10,2013,"Mar,Jun,Sept,Dec"
+556,"d","c",1140,11,2014,0,,,""
+557,"a","a",250,,,0,,,""
+558,"a","a",3000,2,2010,0,,,""
+559,"d","a",3910,11,2006,1,5,2013,"Feb,May,Aug,Nov"
+560,"c","c",1910,7,2013,0,,,""
+561,"d","a",14300,,,0,,,""
+562,"b","c",1210,,,0,,,""
+563,"a","a",700,3,2015,1,10,2014,"Jan,Apr,Jul,Oct"
+564,"d","c",6540,,,1,14,2013,"Jan,Apr,Jul,Oct"
+565,"a","c",160,7,2007,0,,,""
+566,"a","a",3620,,,1,40,2013,"Jan,Apr,Jul,Oct"
+567,"c","a",1010,9,2012,1,18,2011,"Feb,May,Aug,Nov"
+568,"d","c",4270,,,1,1,2013,"Jan,Apr,Jul,Oct"
+569,"a","a",1340,9,2006,0,,,""
+570,"a","a",180,11,2006,0,,,""
+571,"d","a",2110,11,1995,1,40,2014,"Jan,Apr,Jul,Oct"
+572,"d","c",9230,4,2004,1,37,2009,"Jan,Apr,Jul,Oct"
+573,"a","a",1190,11,2012,1,36,2013,"Jan,Apr,Jul,Oct"
+574,"d","a",4400,,,0,,,""
+575,"a","a",960,5,2008,1,13,2010,"Jan,Apr,Jul,Oct"
+576,"c","a",50,11,2006,0,,,""
+577,"a","c",2270,,,1,35,2012,"Mar,Jun,Sept,Dec"
+578,"d","a",12700,4,2013,0,,,""
+579,"c","a",20970,11,2012,0,,,""
+580,"a","c",170,2,2009,0,,,""
+581,"a","a",7250,12,2013,0,,,""
+582,"a","a",120,,,0,,,""
+583,"a","a",2640,11,2002,0,,,""
+584,"d","a",1360,,,1,35,2010,"Mar,Jun,Sept,Dec"
+585,"d","c",440,4,2014,0,,,""
+586,"a","c",250,10,2008,0,,,""
+587,"d","c",330,9,2006,1,14,2011,"Jan,Apr,Jul,Oct"
+588,"d","c",15720,3,2010,0,,,""
+589,"a","c",360,,,1,18,2013,"Feb,May,Aug,Nov"
+590,"d","c",4520,9,2010,1,31,2013,"Jan,Apr,Jul,Oct"
+591,"a","c",3340,,,1,22,2012,"Mar,Jun,Sept,Dec"
+592,"a","a",2540,6,2005,0,,,""
+593,"a","c",33060,3,2010,0,,,""
+594,"a","a",1790,10,2011,0,,,""
+595,"c","c",1130,6,2015,0,,,""
+596,"c","a",290,9,2006,0,,,""
+597,"a","a",150,9,2008,1,1,2012,"Mar,Jun,Sept,Dec"
+598,"c","a",550,12,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+599,"d","c",580,11,2014,0,,,""
+600,"d","c",17340,6,2010,1,9,2011,"Feb,May,Aug,Nov"
+601,"d","a",8220,4,2014,1,14,2011,"Jan,Apr,Jul,Oct"
+602,"a","a",2710,7,2001,1,22,2012,"Mar,Jun,Sept,Dec"
+603,"a","a",340,4,2007,1,13,2010,"Jan,Apr,Jul,Oct"
+604,"d","a",10950,3,2014,0,,,""
+605,"d","a",10310,4,2003,1,37,2009,"Jan,Apr,Jul,Oct"
+606,"a","a",2260,11,2007,0,,,""
+607,"a","a",350,8,1999,1,14,2011,"Jan,Apr,Jul,Oct"
+608,"a","c",18370,2,2013,1,14,2011,"Jan,Apr,Jul,Oct"
+609,"a","a",2070,9,2007,0,,,""
+610,"a","a",660,1,2007,0,,,""
+611,"a","a",8080,12,2002,1,40,2014,"Jan,Apr,Jul,Oct"
+612,"d","c",2490,11,2012,1,31,2009,"Jan,Apr,Jul,Oct"
+613,"c","a",250,6,2007,1,14,2011,"Jan,Apr,Jul,Oct"
+614,"a","a",1160,12,2012,0,,,""
+615,"d","a",730,8,2007,0,,,""
+616,"a","c",3040,8,2011,1,35,2010,"Mar,Jun,Sept,Dec"
+617,"a","c",8940,,,1,9,2011,"Jan,Apr,Jul,Oct"
+618,"d","c",9910,,,0,,,""
+619,"a","a",1600,6,2006,1,45,2009,"Feb,May,Aug,Nov"
+620,"d","c",5440,,,1,40,2014,"Jan,Apr,Jul,Oct"
+621,"a","a",30,7,2002,0,,,""
+622,"a","c",,,,0,,,""
+623,"a","a",4080,3,2011,1,13,2010,"Jan,Apr,Jul,Oct"
+624,"a","c",6920,9,2011,0,,,""
+625,"a","a",1170,4,2011,1,22,2012,"Feb,May,Aug,Nov"
+626,"c","c",10740,11,2013,0,,,""
+627,"c","c",3970,3,2010,0,,,""
+628,"a","c",2180,,,0,,,""
+629,"d","a",510,7,2014,1,23,2015,"Mar,Jun,Sept,Dec"
+630,"a","a",1690,4,2015,0,,,""
+631,"d","c",2870,,,1,35,2012,"Mar,Jun,Sept,Dec"
+632,"a","a",3350,2,2010,0,,,""
+633,"d","a",11640,4,2005,1,22,2011,"Jan,Apr,Jul,Oct"
+634,"d","a",18610,,,0,,,""
+635,"a","a",27530,4,2014,0,,,""
+636,"c","a",720,10,2004,1,13,2010,"Jan,Apr,Jul,Oct"
+637,"d","c",9790,,,1,31,2009,"Feb,May,Aug,Nov"
+638,"d","a",10170,11,2001,0,,,""
+639,"a","a",7780,11,2006,0,,,""
+640,"d","c",8040,,,0,,,""
+641,"a","c",610,12,2003,1,36,2013,"Mar,Jun,Sept,Dec"
+642,"c","c",530,4,2005,1,35,2010,"Mar,Jun,Sept,Dec"
+643,"a","a",230,6,2010,1,18,2010,"Feb,May,Aug,Nov"
+644,"c","a",4030,12,2004,1,14,2011,"Jan,Apr,Jul,Oct"
+645,"a","a",90,,,1,45,2009,"Feb,May,Aug,Nov"
+646,"a","a",620,9,2014,0,,,""
+647,"a","c",7420,4,2013,0,,,""
+648,"d","a",2130,12,2008,0,,,""
+649,"a","a",14570,11,2002,0,,,""
+650,"a","a",1420,10,2012,1,40,2014,"Jan,Apr,Jul,Oct"
+651,"a","a",200,,,0,,,""
+652,"a","a",20390,,,0,,,""
+653,"d","c",7520,7,2014,1,45,2009,"Feb,May,Aug,Nov"
+654,"c","a",6930,9,2006,0,,,""
+655,"d","c",960,11,2012,1,5,2013,"Feb,May,Aug,Nov"
+656,"d","a",410,4,2009,1,13,2010,"Jan,Apr,Jul,Oct"
+657,"c","c",80,1,2006,1,10,2014,"Jan,Apr,Jul,Oct"
+658,"d","c",520,,,1,37,2009,"Jan,Apr,Jul,Oct"
+659,"d","a",1590,3,2012,0,,,""
+660,"a","a",1200,11,2006,1,40,2014,"Jan,Apr,Jul,Oct"
+661,"d","c",2140,7,2013,0,,,""
+662,"d","a",1070,,,0,,,""
+663,"a","c",7860,5,2005,0,,,""
+664,"d","c",1680,10,2005,0,,,""
+665,"a","a",90,12,2012,1,14,2011,"Jan,Apr,Jul,Oct"
+666,"c","c",2700,,,1,9,2011,"Mar,Jun,Sept,Dec"
+667,"d","c",2870,9,2012,0,,,""
+668,"c","a",1270,9,2010,0,,,""
+669,"d","a",17080,7,2012,1,31,2013,"Jan,Apr,Jul,Oct"
+670,"a","a",2060,,,1,45,2009,"Feb,May,Aug,Nov"
+671,"a","c",2070,2,2008,1,39,2010,"Jan,Apr,Jul,Oct"
+672,"c","a",240,9,2002,0,,,""
+673,"d","c",15170,,,1,5,2013,"Feb,May,Aug,Nov"
+674,"a","a",2640,12,2005,1,31,2013,"Feb,May,Aug,Nov"
+675,"a","a",2100,8,2013,1,14,2011,"Jan,Apr,Jul,Oct"
+676,"b","b",1410,9,2008,0,,,""
+677,"d","a",1740,6,2014,1,45,2009,"Feb,May,Aug,Nov"
+678,"c","a",3250,,,1,40,2011,"Jan,Apr,Jul,Oct"
+679,"a","a",4140,9,2012,0,,,""
+680,"c","a",170,,,1,35,2012,"Mar,Jun,Sept,Dec"
+681,"a","c",620,,,1,1,2014,"Mar,Jun,Sept,Dec"
+682,"b","a",150,9,2006,0,,,""
+683,"a","a",2850,7,2014,0,,,""
+684,"d","c",680,,,1,22,2012,"Jan,Apr,Jul,Oct"
+685,"a","a",650,11,2013,1,37,2009,"Jan,Apr,Jul,Oct"
+686,"a","a",20050,4,2002,0,,,""
+687,"d","c",2770,,,0,,,""
+688,"a","a",18760,8,2015,1,14,2011,"Jan,Apr,Jul,Oct"
+689,"d","a",15040,10,2004,0,,,""
+690,"a","a",100,,,0,,,""
+691,"d","c",3030,,,1,37,2009,"Jan,Apr,Jul,Oct"
+692,"a","a",40,8,2001,0,,,""
+693,"d","a",450,,,1,40,2011,"Jan,Apr,Jul,Oct"
+694,"a","c",460,11,2012,1,40,2014,"Jan,Apr,Jul,Oct"
+695,"a","a",550,7,2011,1,1,2012,"Jan,Apr,Jul,Oct"
+696,"a","c",430,,,0,,,""
+697,"d","a",3780,,,1,40,2011,"Jan,Apr,Jul,Oct"
+698,"a","a",1790,5,2011,0,,,""
+699,"a","a",180,,,1,5,2013,"Jan,Apr,Jul,Oct"
+700,"a","c",830,,,1,27,2013,"Jan,Apr,Jul,Oct"
+701,"d","a",1450,3,2012,1,14,2011,"Jan,Apr,Jul,Oct"
+702,"a","a",8550,9,2001,1,45,2009,"Feb,May,Aug,Nov"
+703,"a","a",80,6,2005,0,,,""
+704,"d","c",1910,4,2009,0,,,""
+705,"a","a",4140,9,2012,1,18,2011,"Feb,May,Aug,Nov"
+706,"d","a",7830,9,2014,1,10,2014,"Mar,Jun,Sept,Dec"
+707,"a","c",2900,7,1990,0,,,""
+708,"c","c",11470,10,2009,1,18,2014,"Jan,Apr,Jul,Oct"
+709,"a","a",500,12,2010,0,,,""
+710,"d","a",1500,9,2008,1,14,2011,"Jan,Apr,Jul,Oct"
+711,"d","a",17110,3,2007,1,5,2010,"Jan,Apr,Jul,Oct"
+712,"a","a",4870,9,2007,1,45,2009,"Jan,Apr,Jul,Oct"
+713,"a","c",220,,,1,10,2014,"Jan,Apr,Jul,Oct"
+714,"d","c",12070,10,2005,1,10,2013,"Jan,Apr,Jul,Oct"
+715,"a","a",14810,6,2014,0,,,""
+716,"d","a",3200,1,2008,1,22,2011,"Jan,Apr,Jul,Oct"
+717,"d","c",310,,,1,40,2011,"Jan,Apr,Jul,Oct"
+718,"a","a",1100,6,2015,0,,,""
+719,"c","c",8190,,,1,45,2009,"Feb,May,Aug,Nov"
+720,"a","c",15320,3,2011,1,14,2013,"Feb,May,Aug,Nov"
+721,"a","c",3590,9,2012,1,22,2012,"Mar,Jun,Sept,Dec"
+722,"a","a",50,,,0,,,""
+723,"d","c",5650,9,2008,1,5,2013,"Feb,May,Aug,Nov"
+724,"d","c",5900,,,0,,,""
+725,"d","c",17540,6,2012,0,,,""
+726,"a","c",40540,2,2002,0,,,""
+727,"a","a",2050,3,2007,0,,,""
+728,"d","a",13990,,,1,14,2011,"Jan,Apr,Jul,Oct"
+729,"c","c",8980,6,2011,0,,,""
+730,"c","a",1190,9,2013,0,,,""
+731,"a","a",15270,,,1,10,2014,"Jan,Apr,Jul,Oct"
+732,"a","c",35280,,,0,,,""
+733,"b","b",860,10,1999,0,,,""
+734,"a","a",220,,,1,36,2013,"Mar,Jun,Sept,Dec"
+735,"d","c",1920,4,2005,0,,,""
+736,"c","c",1650,,,1,14,2011,"Jan,Apr,Jul,Oct"
+737,"a","a",100,5,2007,1,31,2013,"Jan,Apr,Jul,Oct"
+738,"d","c",5980,10,2005,0,,,""
+739,"d","c",2770,6,2008,1,22,2011,"Jan,Apr,Jul,Oct"
+740,"d","a",6400,3,2014,0,,,""
+741,"d","c",11900,,,0,,,""
+742,"d","c",4380,,,0,,,""
+743,"a","a",6710,11,2003,1,14,2012,"Jan,Apr,Jul,Oct"
+744,"a","a",1370,12,2011,1,40,2014,"Jan,Apr,Jul,Oct"
+745,"a","a",17650,11,2013,1,37,2009,"Jan,Apr,Jul,Oct"
+746,"d","c",4330,2,2011,1,35,2011,"Mar,Jun,Sept,Dec"
+747,"c","c",45740,8,2008,0,,,""
+748,"d","a",2380,3,2010,1,14,2011,"Jan,Apr,Jul,Oct"
+749,"a","a",3410,8,2011,1,14,2015,"Jan,Apr,Jul,Oct"
+750,"d","a",8670,2,2002,1,14,2011,"Jan,Apr,Jul,Oct"
+751,"a","a",650,10,2006,0,,,""
+752,"a","a",970,3,2013,1,31,2013,"Feb,May,Aug,Nov"
+753,"d","c",540,11,2012,1,35,2010,"Mar,Jun,Sept,Dec"
+754,"c","c",380,5,2008,1,10,2014,"Mar,Jun,Sept,Dec"
+755,"d","c",13130,12,2003,0,,,""
+756,"a","c",50,,,1,40,2011,"Jan,Apr,Jul,Oct"
+757,"a","c",3450,,,0,,,""
+758,"a","c",19780,6,2008,0,,,""
+759,"a","a",110,11,2012,1,31,2013,"Feb,May,Aug,Nov"
+760,"a","a",560,1,2011,0,,,""
+761,"a","a",2390,9,2012,0,,,""
+762,"d","c",1280,,,1,10,2013,"Mar,Jun,Sept,Dec"
+763,"d","c",32240,3,2010,0,,,""
+764,"a","c",26490,,,1,10,2014,"Mar,Jun,Sept,Dec"
+765,"a","c",25430,5,1999,1,37,2009,"Jan,Apr,Jul,Oct"
+766,"d","c",9820,,,0,,,""
+767,"a","c",13080,,,0,,,""
+768,"a","c",2630,9,2012,0,,,""
+769,"b","b",840,,,1,48,2012,"Jan,Apr,Jul,Oct"
+770,"a","c",100,4,2015,0,,,""
+771,"a","a",20640,9,2007,0,,,""
+772,"d","c",1850,,,0,,,""
+773,"a","a",200,,,0,,,""
+774,"a","c",640,9,2013,0,,,""
+775,"d","c",6970,12,2005,1,22,2011,"Jan,Apr,Jul,Oct"
+776,"c","a",700,4,2009,1,14,2011,"Jan,Apr,Jul,Oct"
+777,"d","c",8250,10,2004,1,10,2013,"Mar,Jun,Sept,Dec"
+778,"a","a",340,6,2003,1,40,2014,"Jan,Apr,Jul,Oct"
+779,"a","a",16990,4,2004,0,,,""
+780,"a","a",18160,,,0,,,""
+781,"a","a",630,4,2007,0,,,""
+782,"c","c",5390,8,2003,1,1,2012,"Jan,Apr,Jul,Oct"
+783,"d","c",15490,,,0,,,""
+784,"a","a",560,10,2014,1,10,2014,"Jan,Apr,Jul,Oct"
+785,"d","c",970,7,2005,1,31,2013,"Feb,May,Aug,Nov"
+786,"a","a",290,11,2006,1,5,2013,"Feb,May,Aug,Nov"
+787,"c","c",3210,6,2009,0,,,""
+788,"a","c",1530,3,2013,0,,,""
+789,"a","c",9770,7,2003,0,,,""
+790,"d","c",9070,12,2003,0,,,""
+791,"a","a",5950,4,2007,0,,,""
+792,"d","a",17280,10,2009,1,18,2011,"Feb,May,Aug,Nov"
+793,"d","a",2710,7,2006,0,,,""
+794,"c","c",5090,9,2006,0,,,""
+795,"d","a",510,,,1,35,2010,"Mar,Jun,Sept,Dec"
+796,"a","c",7180,11,2012,0,,,""
+797,"a","a",2090,10,2012,1,40,2014,"Jan,Apr,Jul,Oct"
+798,"a","a",9560,4,2001,0,,,""
+799,"a","c",2700,,,0,,,""
+800,"d","a",2020,7,2014,0,,,""
+801,"d","a",48330,4,2013,0,,,""
+802,"a","c",910,,,1,22,2011,"Feb,May,Aug,Nov"
+803,"d","a",1760,,,1,10,2014,"Mar,Jun,Sept,Dec"
+804,"c","c",2100,,,1,1,2013,"Jan,Apr,Jul,Oct"
+805,"d","a",24770,10,2011,1,10,2014,"Mar,Jun,Sept,Dec"
+806,"d","a",260,,,1,44,2010,"Feb,May,Aug,Nov"
+807,"a","a",3870,4,2008,0,,,""
+808,"a","a",18620,,,1,31,2009,"Feb,May,Aug,Nov"
+809,"a","a",12770,10,2000,0,,,""
+810,"d","c",9640,11,2013,0,,,""
+811,"a","a",410,9,2012,0,,,""
+812,"d","a",2590,9,2012,0,,,""
+813,"a","a",1560,9,2003,0,,,""
+814,"d","c",24530,7,2013,0,,,""
+815,"a","a",590,1,1900,1,40,2014,"Jan,Apr,Jul,Oct"
+816,"c","c",460,,,0,,,""
+817,"a","a",140,3,2006,0,,,""
+818,"d","a",490,,,1,35,2010,"Mar,Jun,Sept,Dec"
+819,"a","c",720,10,2014,0,,,""
+820,"a","c",1650,,,1,40,2014,"Jan,Apr,Jul,Oct"
+821,"a","a",1700,9,2009,0,,,""
+822,"a","c",410,11,2010,1,48,2010,"Mar,Jun,Sept,Dec"
+823,"a","c",16210,11,2010,0,,,""
+824,"a","a",17570,,,0,,,""
+825,"a","a",380,5,2011,1,40,2014,"Jan,Apr,Jul,Oct"
+826,"a","c",7980,6,2005,0,,,""
+827,"a","c",250,1,2005,0,,,""
+828,"d","c",3290,12,2014,0,,,""
+829,"c","a",110,,,0,,,""
+830,"a","c",6320,,,1,5,2011,"Jan,Apr,Jul,Oct"
+831,"a","a",800,6,2007,0,,,""
+832,"d","a",5070,,,1,45,2009,"Feb,May,Aug,Nov"
+833,"d","c",3290,12,1999,1,35,2010,"Mar,Jun,Sept,Dec"
+834,"a","a",3470,3,2012,0,,,""
+835,"a","a",2890,12,2007,1,10,2014,"Mar,Jun,Sept,Dec"
+836,"a","a",2720,9,2012,0,,,""
+837,"a","c",14600,4,2015,0,,,""
+838,"d","c",6890,,,1,48,2011,"Mar,Jun,Sept,Dec"
+839,"c","a",240,1,2015,0,,,""
+840,"a","a",1070,9,2009,0,,,""
+841,"a","a",27650,8,2004,0,,,""
+842,"d","c",1200,11,2007,0,,,""
+843,"c","a",60,4,2006,0,,,""
+844,"a","a",2030,9,2012,1,18,2011,"Feb,May,Aug,Nov"
+845,"d","a",7860,11,2005,1,14,2011,"Jan,Apr,Jul,Oct"
+846,"a","c",8860,4,2004,1,37,2009,"Jan,Apr,Jul,Oct"
+847,"c","c",190,,,1,31,2013,"Feb,May,Aug,Nov"
+848,"a","c",370,7,2007,1,14,2011,"Jan,Apr,Jul,Oct"
+849,"c","c",5000,,,0,,,""
+850,"d","a",1120,5,2007,1,31,2013,"Jan,Apr,Jul,Oct"
+851,"d","c",2330,,,1,49,2014,"Mar,Jun,Sept,Dec"
+852,"c","a",940,4,2004,1,14,2011,"Jan,Apr,Jul,Oct"
+853,"a","a",14040,,,0,,,""
+854,"c","a",4770,,,1,13,2010,"Jan,Apr,Jul,Oct"
+855,"a","a",3440,,,1,45,2009,"Feb,May,Aug,Nov"
+856,"a","a",3020,2,2010,0,,,""
+857,"c","a",6270,8,2005,1,23,2014,"Mar,Jun,Sept,Dec"
+858,"a","a",3370,12,2008,1,40,2014,"Jan,Apr,Jul,Oct"
+859,"c","a",21770,7,2015,0,,,""
+860,"c","c",5980,2,2010,0,,,""
+861,"c","c",740,,,1,14,2013,"Mar,Jun,Sept,Dec"
+862,"a","c",2840,3,2010,1,14,2011,"Jan,Apr,Jul,Oct"
+863,"a","c",21370,11,2010,0,,,""
+864,"a","a",1020,10,2012,1,45,2009,"Feb,May,Aug,Nov"
+865,"d","c",2640,,,0,,,""
+866,"d","a",9680,,,1,5,2013,"Feb,May,Aug,Nov"
+867,"d","c",21810,9,2012,0,,,""
+868,"d","c",1360,8,2005,1,10,2014,"Jan,Apr,Jul,Oct"
+869,"c","a",230,10,2001,1,14,2011,"Jan,Apr,Jul,Oct"
+870,"a","a",780,4,2009,0,,,""
+871,"d","c",10620,,,0,,,""
+872,"a","c",3860,9,2014,1,23,2015,"Mar,Jun,Sept,Dec"
+873,"a","a",2040,11,2008,0,,,""
+874,"a","a",3210,,,0,,,""
+875,"d","a",5070,11,2007,1,18,2015,"Feb,May,Aug,Nov"
+876,"a","a",21790,4,2005,1,18,2015,"Feb,May,Aug,Nov"
+877,"a","c",29190,,,0,,,""
+878,"d","c",1100,12,2014,0,,,""
+879,"d","a",,,,1,5,2013,"Feb,May,Aug,Nov"
+880,"a","c",4570,,,0,,,""
+881,"a","a",180,3,2008,1,31,2013,"Feb,May,Aug,Nov"
+882,"a","a",30,4,2013,0,,,""
+883,"a","a",3200,6,2005,0,,,""
+884,"d","c",7550,,,0,,,""
+885,"a","a",480,12,2005,0,,,""
+886,"a","c",12430,10,2004,0,,,""
+887,"d","a",19700,,,1,37,2009,"Jan,Apr,Jul,Oct"
+888,"d","a",4450,6,2012,1,35,2012,"Mar,Jun,Sept,Dec"
+889,"d","a",18670,12,2005,0,,,""
+890,"a","a",4450,,,1,14,2011,"Jan,Apr,Jul,Oct"
+891,"a","c",350,,,1,31,2013,"Feb,May,Aug,Nov"
+892,"a","a",19370,4,2002,0,,,""
+893,"a","a",130,,,1,1,2013,"Jan,Apr,Jul,Oct"
+894,"a","a",190,11,2012,0,,,""
+895,"a","c",4150,,,0,,,""
+896,"a","c",170,9,2012,0,,,""
+897,"c","c",3290,1,2007,1,5,2013,"Feb,May,Aug,Nov"
+898,"a","a",18540,,,0,,,""
+899,"d","a",2590,,,1,13,2010,"Jan,Apr,Jul,Oct"
+900,"a","a",3920,4,2005,1,40,2014,"Jan,Apr,Jul,Oct"
+901,"a","c",3170,4,2014,0,,,""
+902,"a","a",310,5,2015,1,40,2014,"Jan,Apr,Jul,Oct"
+903,"d","c",7290,9,2014,0,,,""
+904,"d","c",570,7,2013,1,14,2011,"Jan,Apr,Jul,Oct"
+905,"a","a",90,6,2014,0,,,""
+906,"a","a",90,7,2010,0,,,""
+907,"a","c",250,,,0,,,""
+908,"a","a",1980,7,2010,1,37,2009,"Jan,Apr,Jul,Oct"
+909,"a","c",1680,,,1,45,2009,"Feb,May,Aug,Nov"
+910,"d","c",12480,,,1,1,2013,"Jan,Apr,Jul,Oct"
+911,"a","c",16490,,,0,,,""
+912,"c","c",3100,5,2010,0,,,""
+913,"a","a",280,,,0,,,""
+914,"c","c",2640,4,2011,1,22,2012,"Mar,Jun,Sept,Dec"
+915,"d","c",650,3,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+916,"a","a",90,11,2012,0,,,""
+917,"a","a",7240,2,2010,0,,,""
+918,"a","c",18710,4,2015,0,,,""
+919,"a","a",2620,,,1,45,2009,"Feb,May,Aug,Nov"
+920,"a","a",850,2,2012,1,40,2014,"Jan,Apr,Jul,Oct"
+921,"a","a",840,9,2006,0,,,""
+922,"d","a",2110,3,2006,0,,,""
+923,"a","a",280,9,2008,0,,,""
+924,"a","a",6420,4,2011,1,1,2012,"Jan,Apr,Jul,Oct"
+925,"c","a",470,3,2007,1,1,2012,"Jan,Apr,Jul,Oct"
+926,"d","c",5150,3,2011,1,13,2010,"Jan,Apr,Jul,Oct"
+927,"a","a",480,,,0,,,""
+928,"d","c",1090,,,1,31,2013,"Feb,May,Aug,Nov"
+929,"a","c",4820,9,2013,0,,,""
+930,"a","a",70,,,0,,,""
+931,"a","c",1480,9,2011,1,1,2012,"Jan,Apr,Jul,Oct"
+932,"a","a",15700,,,1,13,2010,"Jan,Apr,Jul,Oct"
+933,"a","c",6270,2,2005,0,,,""
+934,"a","c",5460,,,1,14,2011,"Jan,Apr,Jul,Oct"
+935,"a","c",22350,6,2010,0,,,""
+936,"a","a",580,2,2008,0,,,""
+937,"d","a",2810,,,1,10,2014,"Jan,Apr,Jul,Oct"
+938,"a","a",2820,9,2009,0,,,""
+939,"d","a",1340,,,0,,,""
+940,"d","c",6470,9,2012,0,,,""
+941,"a","a",1200,12,2011,1,31,2013,"Jan,Apr,Jul,Oct"
+942,"d","c",6860,,,1,18,2011,"Jan,Apr,Jul,Oct"
+943,"d","c",18020,,,0,,,""
+944,"c","a",1670,7,2015,0,,,""
+945,"a","c",12480,3,2011,0,,,""
+946,"a","a",2220,12,2011,1,14,2015,"Jan,Apr,Jul,Oct"
+947,"a","a",460,3,2014,1,13,2010,"Jan,Apr,Jul,Oct"
+948,"b","b",1430,,,0,,,""
+949,"a","a",870,3,2006,0,,,""
+950,"a","a",8460,11,1994,0,,,""
+951,"d","c",710,,,1,40,2011,"Jan,Apr,Jul,Oct"
+952,"d","c",6300,10,2013,0,,,""
+953,"a","a",19830,4,2006,1,22,2011,"Mar,Jun,Sept,Dec"
+954,"a","a",390,2,2013,1,10,2014,"Jan,Apr,Jul,Oct"
+955,"d","c",1690,7,2009,1,36,2013,"Mar,Jun,Sept,Dec"
+956,"a","a",2330,10,2014,1,18,2011,"Feb,May,Aug,Nov"
+957,"d","c",1420,11,2012,0,,,""
+958,"a","a",440,11,2013,0,,,""
+959,"a","c",1060,12,2005,0,,,""
+960,"d","a",8990,,,1,31,2009,"Feb,May,Aug,Nov"
+961,"d","c",9430,,,0,,,""
+962,"c","a",260,,,0,,,""
+963,"a","c",23620,11,2013,0,,,""
+964,"a","a",270,,,1,5,2013,"Feb,May,Aug,Nov"
+965,"a","c",110,,,0,,,""
+966,"a","a",760,2,2008,0,,,""
+967,"a","c",3560,9,2013,1,36,2013,"Jan,Apr,Jul,Oct"
+968,"c","a",1190,,,0,,,""
+969,"a","c",600,11,1999,1,10,2013,"Jan,Apr,Jul,Oct"
+970,"a","a",910,12,2014,1,37,2009,"Jan,Apr,Jul,Oct"
+971,"c","a",1140,5,2011,1,14,2012,"Mar,Jun,Sept,Dec"
+972,"a","a",14960,,,0,,,""
+973,"d","c",330,,,1,28,2012,"Jan,Apr,Jul,Oct"
+974,"a","a",150,3,2011,1,40,2014,"Jan,Apr,Jul,Oct"
+975,"a","c",9630,,,1,14,2011,"Jan,Apr,Jul,Oct"
+976,"a","a",4180,,,0,,,""
+977,"a","a",520,9,2005,1,13,2010,"Jan,Apr,Jul,Oct"
+978,"c","c",3890,,,0,,,""
+979,"a","c",2270,11,2005,1,14,2011,"Jan,Apr,Jul,Oct"
+980,"a","a",4420,9,2005,0,,,""
+981,"d","c",2620,11,2002,0,,,""
+982,"d","a",21930,,,0,,,""
+983,"a","a",40,3,2014,1,1,2014,"Jan,Apr,Jul,Oct"
+984,"c","a",440,,,1,1,2013,"Jan,Apr,Jul,Oct"
+985,"c","c",490,5,2007,1,13,2010,"Jan,Apr,Jul,Oct"
+986,"a","a",620,10,2014,1,18,2014,"Feb,May,Aug,Nov"
+987,"c","a",1690,6,2007,0,,,""
+988,"a","a",30,11,2012,0,,,""
+989,"a","a",1640,6,2006,1,40,2011,"Jan,Apr,Jul,Oct"
+990,"d","a",20930,,,0,,,""
+991,"a","a",1010,,,0,,,""
+992,"a","a",2480,7,1990,0,,,""
+993,"d","c",3460,10,2013,1,10,2014,"Jan,Apr,Jul,Oct"
+994,"a","a",2290,7,2011,1,1,2012,"Jan,Apr,Jul,Oct"
+995,"d","a",6560,12,2013,0,,,""
+996,"c","a",2870,7,2015,1,13,2010,"Jan,Apr,Jul,Oct"
+997,"d","c",5840,7,2010,1,37,2009,"Jan,Apr,Jul,Oct"
+998,"a","a",780,9,2005,1,5,2013,"Feb,May,Aug,Nov"
+999,"d","c",15140,2,2002,1,37,2009,"Jan,Apr,Jul,Oct"
+1000,"a","c",2230,5,2009,1,40,2014,"Jan,Apr,Jul,Oct"
+1001,"c","a",19640,,,1,14,2011,"Jan,Apr,Jul,Oct"
+1002,"d","c",1130,11,2008,0,,,""
+1003,"a","a",170,7,2013,1,27,2013,"Jan,Apr,Jul,Oct"
+1004,"d","c",970,,,1,9,2011,"Mar,Jun,Sept,Dec"
+1005,"a","a",6480,,,0,,,""
+1006,"c","c",3890,11,2006,1,5,2013,"Feb,May,Aug,Nov"
+1007,"c","c",4180,9,2012,0,,,""
+1008,"a","c",30,9,2010,0,,,""
+1009,"a","a",230,7,2004,1,10,2014,"Jan,Apr,Jul,Oct"
+1010,"d","c",4610,6,2010,1,18,2010,"Feb,May,Aug,Nov"
+1011,"a","c",490,9,2012,1,18,2011,"Feb,May,Aug,Nov"
+1012,"d","c",6330,6,2004,1,39,2010,"Jan,Apr,Jul,Oct"
+1013,"a","a",630,2,2015,1,31,2013,"Feb,May,Aug,Nov"
+1014,"a","c",210,,,1,31,2013,"Jan,Apr,Jul,Oct"
+1015,"d","c",9910,12,2010,1,9,2011,"Mar,Jun,Sept,Dec"
+1016,"c","c",550,,,1,35,2010,"Mar,Jun,Sept,Dec"
+1017,"c","a",110,11,2008,0,,,""
+1018,"c","c",140,9,2012,0,,,""
+1019,"d","c",2740,7,2014,1,13,2010,"Jan,Apr,Jul,Oct"
+1020,"a","a",40,8,2015,0,,,""
+1021,"a","a",1080,5,2011,0,,,""
+1022,"a","c",1520,,,0,,,""
+1023,"c","a",3740,2,2002,1,14,2011,"Jan,Apr,Jul,Oct"
+1024,"c","c",1990,1,2012,0,,,""
+1025,"a","a",720,11,2009,0,,,""
+1026,"c","a",450,6,2011,1,48,2012,"Mar,Jun,Sept,Dec"
+1027,"a","c",190,6,2008,1,40,2011,"Jan,Apr,Jul,Oct"
+1028,"a","a",150,,,1,31,2013,"Jan,Apr,Jul,Oct"
+1029,"a","a",1590,3,2006,0,,,""
+1030,"a","a",36410,4,2008,0,,,""
+1031,"d","a",590,5,2001,0,,,""
+1032,"d","c",270,2,2013,1,40,2012,"Jan,Apr,Jul,Oct"
+1033,"a","a",7680,3,2006,0,,,""
+1034,"a","a",13750,4,2015,0,,,""
+1035,"a","a",27150,,,0,,,""
+1036,"d","c",9560,,,1,36,2013,"Jan,Apr,Jul,Oct"
+1037,"a","c",150,,,0,,,""
+1038,"d","a",17290,10,2013,0,,,""
+1039,"a","c",70,6,1990,1,22,2012,"Mar,Jun,Sept,Dec"
+1040,"a","a",4030,2,2013,1,10,2014,"Jan,Apr,Jul,Oct"
+1041,"c","a",1600,8,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+1042,"a","a",3440,,,1,31,2013,"Feb,May,Aug,Nov"
+1043,"c","a",420,3,2006,0,,,""
+1044,"c","a",240,4,2015,1,13,2010,"Jan,Apr,Jul,Oct"
+1045,"a","c",26990,12,2013,0,,,""
+1046,"d","c",29070,4,2005,0,,,""
+1047,"a","a",3750,,,1,45,2009,"Feb,May,Aug,Nov"
+1048,"d","c",1860,9,2012,1,40,2012,"Jan,Apr,Jul,Oct"
+1049,"a","a",370,7,2012,1,14,2011,"Jan,Apr,Jul,Oct"
+1050,"d","c",13170,8,2014,1,9,2011,"Mar,Jun,Sept,Dec"
+1051,"c","a",200,7,1998,1,1,2012,"Jan,Apr,Jul,Oct"
+1052,"a","c",5080,,,1,31,2013,"Feb,May,Aug,Nov"
+1053,"a","a",1710,7,2015,0,,,""
+1054,"a","c",13190,,,1,45,2013,"Feb,May,Aug,Nov"
+1055,"c","a",1980,4,2009,0,,,""
+1056,"d","c",5350,,,1,40,2012,"Jan,Apr,Jul,Oct"
+1057,"d","c",3230,11,2011,0,,,""
+1058,"a","c",180,,,1,35,2010,"Mar,Jun,Sept,Dec"
+1059,"c","a",3380,4,2013,0,,,""
+1060,"a","c",3430,,,1,31,2013,"Feb,May,Aug,Nov"
+1061,"d","c",8110,,,0,,,""
+1062,"d","a",190,9,2012,1,40,2012,"Feb,May,Aug,Nov"
+1063,"a","c",6250,,,0,,,""
+1064,"a","c",420,,,0,,,""
+1065,"a","a",1290,,,1,35,2011,"Mar,Jun,Sept,Dec"
+1066,"a","a",3350,,,0,,,""
+1067,"d","c",12020,7,2009,0,,,""
+1068,"d","c",5010,,,1,5,2013,"Jan,Apr,Jul,Oct"
+1069,"a","c",18050,,,1,14,2011,"Jan,Apr,Jul,Oct"
+1070,"c","c",400,10,2008,0,,,""
+1071,"a","a",820,3,2012,1,35,2012,"Mar,Jun,Sept,Dec"
+1072,"a","c",5380,8,2015,1,5,2010,"Feb,May,Aug,Nov"
+1073,"a","c",1710,,,1,44,2012,"Jan,Apr,Jul,Oct"
+1074,"c","c",3330,10,2001,1,14,2011,"Jan,Apr,Jul,Oct"
+1075,"a","c",1410,10,2013,0,,,""
+1076,"a","c",90,,,1,1,2013,"Jan,Apr,Jul,Oct"
+1077,"a","a",3750,11,2001,1,35,2010,"Mar,Jun,Sept,Dec"
+1078,"d","c",670,,,1,40,2011,"Jan,Apr,Jul,Oct"
+1079,"a","a",16680,,,1,37,2009,"Jan,Apr,Jul,Oct"
+1080,"a","a",2410,,,1,40,2014,"Jan,Apr,Jul,Oct"
+1081,"b","a",400,3,2006,0,,,""
+1082,"c","a",440,4,2002,0,,,""
+1083,"d","c",11540,,,1,5,2013,"Feb,May,Aug,Nov"
+1084,"a","a",190,,,1,13,2010,"Jan,Apr,Jul,Oct"
+1085,"c","a",4030,2,2015,0,,,""
+1086,"a","a",180,11,2013,1,18,2011,"Feb,May,Aug,Nov"
+1087,"d","c",2210,11,2011,0,,,""
+1088,"a","a",4300,3,2009,1,27,2013,"Jan,Apr,Jul,Oct"
+1089,"d","a",5220,5,2009,0,,,""
+1090,"a","a",330,,,1,14,2011,"Jan,Apr,Jul,Oct"
+1091,"a","c",9990,,,0,,,""
+1092,"a","a",300,7,2000,1,40,2014,"Jan,Apr,Jul,Oct"
+1093,"c","c",10450,6,2009,0,,,""
+1094,"d","a",2380,3,2013,1,40,2014,"Jan,Apr,Jul,Oct"
+1095,"a","a",690,6,2007,1,14,2011,"Jan,Apr,Jul,Oct"
+1096,"a","c",1130,,,1,10,2014,"Mar,Jun,Sept,Dec"
+1097,"b","b",720,3,2002,0,,,""
+1098,"a","a",1830,11,2004,0,,,""
+1099,"a","c",200,4,2013,1,14,2013,"Jan,Apr,Jul,Oct"
+1100,"a","a",540,,,1,14,2011,"Jan,Apr,Jul,Oct"
+1101,"d","c",4060,9,2012,0,,,""
+1102,"a","a",850,11,2012,1,40,2014,"Jan,Apr,Jul,Oct"
+1103,"d","c",1340,10,2006,1,5,2013,"Feb,May,Aug,Nov"
+1104,"d","a",260,2,2012,1,14,2011,"Jan,Apr,Jul,Oct"
+1105,"c","c",330,11,2008,1,5,2013,"Feb,May,Aug,Nov"
+1106,"a","c",5330,9,2011,1,31,2013,"Jan,Apr,Jul,Oct"
+1107,"a","a",1400,6,2012,1,13,2010,"Jan,Apr,Jul,Oct"
+1108,"a","a",540,4,2004,0,,,""
+1109,"c","a",3490,4,2011,1,22,2012,"Jan,Apr,Jul,Oct"
+1110,"c","c",900,9,2010,0,,,""
+1111,"a","a",1900,6,2014,1,31,2013,"Jan,Apr,Jul,Oct"
+1112,"c","c",1880,4,2006,0,,,""
+1113,"a","c",9260,,,0,,,""
+1114,"a","c",870,,,0,,,""
+1115,"d","c",5350,,,1,22,2012,"Mar,Jun,Sept,Dec"

Notebooks/01 copy.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:906fe9e08e8405ddb727e310a9b4191ffd607831c2c29e1d978778bbf3ed948e
+size 20758656

Notebooks/01.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:906fe9e08e8405ddb727e310a9b4191ffd607831c2c29e1d978778bbf3ed948e
+size 20758656

Notebooks/SalesPrediction_ML.ipynb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d55ad0de185923b6a16d3c7c06bf59c155a6ac0a33e8aa85f5b52969c5556e57
+size 19598159

Notebooks/pipeline.ipynb

@@ -0,0 +1,1924 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<h1> ----- PIPELINE NOTEBOOK ----- </h1>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.pipeline import Pipeline\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.preprocessing import OneHotEncoder\n",
+    "from xgboost import XGBRegressor\n",
+    "\n",
+    "from sklearn.compose import ColumnTransformer\n",
+    "\n",
+    "from sklearn import set_config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>DayOfWeek</th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Sales</th>\n",
+       "      <th>Customers</th>\n",
+       "      <th>Promo</th>\n",
+       "      <th>StateHoliday</th>\n",
+       "      <th>SchoolHoliday</th>\n",
+       "      <th>StoreType</th>\n",
+       "      <th>Assortment</th>\n",
+       "      <th>CompetitionDistance</th>\n",
+       "      <th>CompetitionOpenSinceMonth</th>\n",
+       "      <th>CompetitionOpenSinceYear</th>\n",
+       "      <th>Promo2</th>\n",
+       "      <th>Promo2SinceWeek</th>\n",
+       "      <th>Promo2SinceYear</th>\n",
+       "      <th>PromoInterval</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015-07-31</td>\n",
+       "      <td>5263</td>\n",
+       "      <td>555</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Large Store</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>1270</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015-07-31</td>\n",
+       "      <td>6064</td>\n",
+       "      <td>625</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>570</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015-07-31</td>\n",
+       "      <td>8314</td>\n",
+       "      <td>821</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>14130</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015-07-31</td>\n",
+       "      <td>13995</td>\n",
+       "      <td>1498</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Large Store</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>620</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2015-07-31</td>\n",
+       "      <td>4822</td>\n",
+       "      <td>559</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>29910</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  Store  DayOfWeek        Date  Sales  Customers  Promo  \\\n",
+       "0           0      1          5  2015-07-31   5263        555      1   \n",
+       "1           1      2          5  2015-07-31   6064        625      1   \n",
+       "2           2      3          5  2015-07-31   8314        821      1   \n",
+       "3           3      4          5  2015-07-31  13995       1498      1   \n",
+       "4           4      5          5  2015-07-31   4822        559      1   \n",
+       "\n",
+       "   StateHoliday  SchoolHoliday    StoreType Assortment  CompetitionDistance  \\\n",
+       "0             0              1  Large Store      basic                 1270   \n",
+       "1             0              1   Small Shop      basic                  570   \n",
+       "2             0              1   Small Shop      basic                14130   \n",
+       "3             0              1  Large Store   extended                  620   \n",
+       "4             0              1   Small Shop      basic                29910   \n",
+       "\n",
+       "   CompetitionOpenSinceMonth  CompetitionOpenSinceYear  Promo2  \\\n",
+       "0                          9                      2008       0   \n",
+       "1                         11                      2007       1   \n",
+       "2                         12                      2006       1   \n",
+       "3                          9                      2009       0   \n",
+       "4                          4                      2015       0   \n",
+       "\n",
+       "   Promo2SinceWeek  Promo2SinceYear    PromoInterval  \n",
+       "0                0                0                0  \n",
+       "1               13             2010  Jan,Apr,Jul,Oct  \n",
+       "2               14             2011  Jan,Apr,Jul,Oct  \n",
+       "3                0                0                0  \n",
+       "4                0                0                0  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(r\"../Dataset/Rossmann_Cleaned_data.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PromoInterval</th>\n",
+       "      <th>StoreType</th>\n",
+       "      <th>Assortment</th>\n",
+       "      <th>StateHoliday</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>Customers</th>\n",
+       "      <th>Promo</th>\n",
+       "      <th>SchoolHoliday</th>\n",
+       "      <th>CompetitionDistance</th>\n",
+       "      <th>CompetitionOpenSinceMonth</th>\n",
+       "      <th>CompetitionOpenSinceYear</th>\n",
+       "      <th>Sales</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Large Store</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>555</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1270</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>5263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>625</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>570</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>6064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>821</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>14130</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2006</td>\n",
+       "      <td>8314</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Large Store</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1498</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>620</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>13995</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>559</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>29910</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>4822</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PromoInterval    StoreType Assortment  StateHoliday  Store  Customers  \\\n",
+       "0                0  Large Store      basic             0      1        555   \n",
+       "1  Jan,Apr,Jul,Oct   Small Shop      basic             0      2        625   \n",
+       "2  Jan,Apr,Jul,Oct   Small Shop      basic             0      3        821   \n",
+       "3                0  Large Store   extended             0      4       1498   \n",
+       "4                0   Small Shop      basic             0      5        559   \n",
+       "\n",
+       "   Promo  SchoolHoliday  CompetitionDistance  CompetitionOpenSinceMonth  \\\n",
+       "0      1              1                 1270                          9   \n",
+       "1      1              1                  570                         11   \n",
+       "2      1              1                14130                         12   \n",
+       "3      1              1                  620                          9   \n",
+       "4      1              1                29910                          4   \n",
+       "\n",
+       "   CompetitionOpenSinceYear  Sales  \n",
+       "0                      2008   5263  \n",
+       "1                      2007   6064  \n",
+       "2                      2006   8314  \n",
+       "3                      2009  13995  \n",
+       "4                      2015   4822  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[[\"PromoInterval\",\"StoreType\",\"Assortment\",\"StateHoliday\",\"Store\",\"Customers\",\"Promo\",\"SchoolHoliday\",\"CompetitionDistance\",\"CompetitionOpenSinceMonth\",\"CompetitionOpenSinceYear\",\"Sales\"]]\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n",
+      "7388\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[\"Customers\"].min())\n",
+    "print(df[\"Customers\"].max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 844338 entries, 0 to 844337\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column                     Non-Null Count   Dtype \n",
+      "---  ------                     --------------   ----- \n",
+      " 0   PromoInterval              844338 non-null  object\n",
+      " 1   StoreType                  844338 non-null  object\n",
+      " 2   Assortment                 844338 non-null  object\n",
+      " 3   StateHoliday               844338 non-null  int64 \n",
+      " 4   Store                      844338 non-null  int64 \n",
+      " 5   Customers                  844338 non-null  int64 \n",
+      " 6   Promo                      844338 non-null  int64 \n",
+      " 7   SchoolHoliday              844338 non-null  int64 \n",
+      " 8   CompetitionDistance        844338 non-null  int64 \n",
+      " 9   CompetitionOpenSinceMonth  844338 non-null  int64 \n",
+      " 10  CompetitionOpenSinceYear   844338 non-null  int64 \n",
+      " 11  Sales                      844338 non-null  int64 \n",
+      "dtypes: int64(9), object(3)\n",
+      "memory usage: 77.3+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(844338, 12)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique values in PromoInterval: ['0' 'Jan,Apr,Jul,Oct' 'Feb,May,Aug,Nov' 'Mar,Jun,Sept,Dec']\n",
+      "Unique values in StoreType: ['Large Store' 'Small Shop' 'Hypermarket' 'Medium Store']\n",
+      "Unique values in Assortment: ['basic' 'extended' 'extra']\n",
+      "Unique values in StateHoliday: [0 1]\n",
+      "Unique values in Store: [   1    2    3 ... 1115  876  292]\n",
+      "Unique values in Customers: [ 555  625  821 ... 3900   36 4065]\n",
+      "Unique values in Promo: [1 0]\n",
+      "Unique values in SchoolHoliday: [1 0]\n",
+      "Unique values in CompetitionDistance: [ 1270   570 14130   620 29910   310 24000  7520  2030  3160   960  1070\n",
+      "  1300  4110  3270    50 13840  3240  2340   550  1040  4060  4590   430\n",
+      "  2300    60  1200  2170    40  9800  2910  1320  2240  7660   540  4230\n",
+      "  1090   260   180  1180   290  4880  9710   270  1060 18010  6260 10570\n",
+      "   450 30360  7170   720  6620   420  7340  2840  5540   350  2050  3700\n",
+      " 22560   410   250  1130  4840 17500  2200  1650   330 22440 19960  3510\n",
+      "  3320  7910  2370 22390  2710 11810  1870   480   560 10690  2380  2410\n",
+      "   240 16690 14620  1890  8780  8980 15140 17930  2440   150  5210   390\n",
+      "  6190  1390  1930  2190  3300 46590  7890  1630 20930  4510  5740   680\n",
+      "  3450  3580  2100  2290  3570 58260 16760  1410   760  3370  1350  2000\n",
+      "  2460   900   920  5190  1730 25360  1700  1540  2930 16570   280  8050\n",
+      "  8540  2090  2610 31830  4360  1780 16240 16420  3050  2020  2950 11840\n",
+      "  8530 17110  2970  5340  1480  1160  3720   100   140 12540   980  2640\n",
+      "   110 13090  4130  3770  1250  1710  5800 12610  9670  3560  1860 19360\n",
+      "   850  5760  1470  1100  2770   520 16970   220  3850  4210  6360 20260\n",
+      "  5140   490  5630   380  6870   300 11680   970 15050  4030  8650   190\n",
+      "  3150   640  1640  1000 13530  2920  7930 10180 10800 17410  6680  3840\n",
+      " 13570  4370  5710  1420   320   610  1110   780  6880   710  1310  4660\n",
+      "    70   340  3520 22330  4630    80 27190   210 15340  1140  4580   360\n",
+      "  4520  1450 16180  8480  3640  2960  7840  9260  2320 18640  6970  1220\n",
+      "  2260  1290  1460  2740   800  6540  4150  2325  9580 19840 38630   120\n",
+      " 15430  1950  2470  5100 18660  8740 11300 14160 38710  9000  3140 32330\n",
+      "  8140  8400 13140 10070  3130   370   670  1840  4040    90 10600  1590\n",
+      "  2280  8080 15770 18650  8090  9360 16490  1490  8880  5290  1500  9720\n",
+      "  8970  2060  2890  2040  4490 13620  6470  5870  8250  1970 11120  1150\n",
+      " 15710   160  2140  6630  1800 26130   130  6690  1600   460  2120  4820\n",
+      " 10850  3620 23130  5360  9200  5830  4970  1080  8240  5890  1560   840\n",
+      "  8460  4460  6210  6910  4650  1620  3530  2880 16350 12870   810 30030\n",
+      " 13020   910  3900  2530   500 11400  1510  3970  5780  1850 75860 26450\n",
+      "  3390 34050  1790 44320  4160 10890  3110 20390  5260  5300  5030 14810\n",
+      "  8300   770  1940  7470  2550  2310 14300  2180 14960   660  4680  1740\n",
+      "  1260  5470  2780  1610   990 13080   820  9070  1280  4740  8260   590\n",
+      "   400 11260    20 22490  3330  2510  6900 18610  7160 40860 20620 12920\n",
+      " 18160  5950  4700   600   650  7280  5020   580  8990  3760  2330  4260\n",
+      "  3040  3000  3910  1910  1210   700  1010  4270  1340  2110  9230  1190\n",
+      "  4400  2270 12700 20970   170  7250  1360   440 15720  3340  2540 33060\n",
+      " 17340  8220 10950 10310 18370  2070  2490   730  8940  9910  5440    30\n",
+      "  4080  6920  1170 10740   510  1690  2870  3350 11640 27530  9790 10170\n",
+      "  7780  8040   530   230  7420  2130 14570   200  6930  7860  1680  2700\n",
+      " 17080 15170  3250  4140  2850 20050 18760 15040  3030  3780   830  8550\n",
+      "  7830  2900 11470  4870 12070  3200  8190 15320  3590  5650  5900 17540\n",
+      " 40540 13990 15270 35280   860  1920  5980  6400 11900  4380  6710  1370\n",
+      " 17650  4330 45740  3410  8670 13130 19780  2390 32240 26490 25430  9820\n",
+      "  2630 20640 16990   630  5390 15490  3210  1530  9770 17280  5090  7180\n",
+      "  9560 48330  1760 24770  3870 18620 12770  9640  2590 24530 16210 17570\n",
+      "  7980  3290  6320  5070  3470  2720 14600  6890 27650  8860  5000  1120\n",
+      "   940 14040  4770  3440  3020  6270 21770   740 21370  1020  9680 21810\n",
+      " 10620  3860 29190  4570  7550 12430 19700  4450 18670 19370 18540  3920\n",
+      "  3170  7290  1980 12480  3100  7240 18710  2620  6420   470  5150 15700\n",
+      "  5460 22350  2810  2820  6860 18020  1670  2220  1430   870  6300 19830\n",
+      "  9430 23620  9630  4180  3890  4420 21930  2480  3460  6560  5840  2230\n",
+      " 19640  6480  4610  6330  1520  3740  1990 36410  7680 13750 27150 17290\n",
+      " 26990 29070  3750 13170  5080 13190  5350  3230  3380  3430  8110  6250\n",
+      " 12020  5010 18050  5380 16680 11540  2210  4300  5220  9990 10450   690\n",
+      "  1830  5330  1400  3490  1900  1880 21790]\n",
+      "Unique values in CompetitionOpenSinceMonth: [ 9 11 12  4 10  8  3  6  5  1  2  7]\n",
+      "Unique values in CompetitionOpenSinceYear: [2008 2007 2006 2009 2015 2013 2014 2000 2011 2010 2005 1999 2003 2012\n",
+      " 2004 2002 1961 1995 2001 1990 1994 1900 1998]\n",
+      "Unique values in Sales: [ 5263  6064  8314 ...   660 17815 23303]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_unique_values(dataframe):\n",
+    "    for column in dataframe.columns:\n",
+    "        unique_values = dataframe[column].unique()\n",
+    "        print(f\"Unique values in {column}: {unique_values}\")\n",
+    "\n",
+    "# Example usage:\n",
+    "print_unique_values(df)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df.drop(columns = [\"Sales\"])\n",
+    "y = df[\"Sales\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((633253, 11), (211085, 11), (633253,), (211085,))"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25, random_state=42)\n",
+    "\n",
+    "# Checking the shape after spliting\n",
+    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PromoInterval</th>\n",
+       "      <th>StoreType</th>\n",
+       "      <th>Assortment</th>\n",
+       "      <th>StateHoliday</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>Customers</th>\n",
+       "      <th>Promo</th>\n",
+       "      <th>SchoolHoliday</th>\n",
+       "      <th>CompetitionDistance</th>\n",
+       "      <th>CompetitionOpenSinceMonth</th>\n",
+       "      <th>CompetitionOpenSinceYear</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>795018</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>650</td>\n",
+       "      <td>636</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1420</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463276</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72</td>\n",
+       "      <td>261</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268352</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Medium Store</td>\n",
+       "      <td>extra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>733</td>\n",
+       "      <td>3567</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>860</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67308</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>0</td>\n",
+       "      <td>796</td>\n",
+       "      <td>791</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7180</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482458</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>0</td>\n",
+       "      <td>301</td>\n",
+       "      <td>480</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4510</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259178</th>\n",
+       "      <td>Feb,May,Aug,Nov</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1013</td>\n",
+       "      <td>217</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>630</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>365838</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>0</td>\n",
+       "      <td>11</td>\n",
+       "      <td>1394</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>960</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2011</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>131932</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>376</td>\n",
+       "      <td>796</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>160</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>671155</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Hypermarket</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>885</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19960</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>121958</th>\n",
+       "      <td>Feb,May,Aug,Nov</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>446</td>\n",
+       "      <td>684</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>340</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>633253 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          PromoInterval     StoreType Assortment  StateHoliday  Store  \\\n",
+       "795018  Jan,Apr,Jul,Oct    Small Shop      basic             0    650   \n",
+       "463276  Jan,Apr,Jul,Oct    Small Shop      basic             0     72   \n",
+       "268352                0  Medium Store      extra             0    733   \n",
+       "67308                 0    Small Shop   extended             0    796   \n",
+       "482458                0    Small Shop   extended             0    301   \n",
+       "...                 ...           ...        ...           ...    ...   \n",
+       "259178  Feb,May,Aug,Nov    Small Shop      basic             0   1013   \n",
+       "365838  Jan,Apr,Jul,Oct    Small Shop   extended             0     11   \n",
+       "131932                0    Small Shop      basic             0    376   \n",
+       "671155                0   Hypermarket   extended             0     76   \n",
+       "121958  Feb,May,Aug,Nov    Small Shop      basic             0    446   \n",
+       "\n",
+       "        Customers  Promo  SchoolHoliday  CompetitionDistance  \\\n",
+       "795018        636      1              0                 1420   \n",
+       "463276        261      0              0                 2200   \n",
+       "268352       3567      1              0                  860   \n",
+       "67308         791      1              0                 7180   \n",
+       "482458        480      0              0                 4510   \n",
+       "...           ...    ...            ...                  ...   \n",
+       "259178        217      0              0                  630   \n",
+       "365838       1394      1              0                  960   \n",
+       "131932        796      0              0                  160   \n",
+       "671155        885      0              0                19960   \n",
+       "121958        684      1              0                  340   \n",
+       "\n",
+       "        CompetitionOpenSinceMonth  CompetitionOpenSinceYear  \n",
+       "795018                         10                      2012  \n",
+       "463276                         12                      2009  \n",
+       "268352                         10                      1999  \n",
+       "67308                          11                      2012  \n",
+       "482458                          3                      2015  \n",
+       "...                           ...                       ...  \n",
+       "259178                          2                      2015  \n",
+       "365838                         11                      2011  \n",
+       "131932                          8                      2012  \n",
+       "671155                          3                      2006  \n",
+       "121958                         10                      2000  \n",
+       "\n",
+       "[633253 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PromoInterval\n",
+       "0                   423292\n",
+       "Jan,Apr,Jul,Oct     242397\n",
+       "Feb,May,Aug,Nov      97998\n",
+       "Mar,Jun,Sept,Dec     80651\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"PromoInterval\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;encoding&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;ohe&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
+       "                                                  [&#x27;PromoInterval&#x27;, &#x27;StoreType&#x27;,\n",
+       "                                                   &#x27;Assortment&#x27;])])),\n",
+       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "                              colsample_bylevel=None, colsample_bynode=None,\n",
+       "                              colsample_bytree=None, device=None,...\n",
+       "                              feature_types=None, gamma=None, grow_policy=None,\n",
+       "                              importance_type=None,\n",
+       "                              interaction_constraints=None, learning_rate=0.1,\n",
+       "                              max_bin=None, max_cat_threshold=None,\n",
+       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
+       "                              max_depth=13, max_leaves=None,\n",
+       "                              min_child_weight=None, missing=nan,\n",
+       "                              monotone_constraints=None, multi_strategy=None,\n",
+       "                              n_estimators=None, n_jobs=None,\n",
+       "                              num_parallel_tree=None, random_state=None, ...))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;encoding&#x27;,\n",
+       "                 ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                                   transformers=[(&#x27;ohe&#x27;,\n",
+       "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
+       "                                                  [&#x27;PromoInterval&#x27;, &#x27;StoreType&#x27;,\n",
+       "                                                   &#x27;Assortment&#x27;])])),\n",
+       "                (&#x27;scaler&#x27;, StandardScaler()),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "                              colsample_bylevel=None, colsample_bynode=None,\n",
+       "                              colsample_bytree=None, device=None,...\n",
+       "                              feature_types=None, gamma=None, grow_policy=None,\n",
+       "                              importance_type=None,\n",
+       "                              interaction_constraints=None, learning_rate=0.1,\n",
+       "                              max_bin=None, max_cat_threshold=None,\n",
+       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
+       "                              max_depth=13, max_leaves=None,\n",
+       "                              min_child_weight=None, missing=nan,\n",
+       "                              monotone_constraints=None, multi_strategy=None,\n",
+       "                              n_estimators=None, n_jobs=None,\n",
+       "                              num_parallel_tree=None, random_state=None, ...))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;encoding: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for encoding: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(remainder=&#x27;passthrough&#x27;,\n",
+       "                  transformers=[(&#x27;ohe&#x27;, OneHotEncoder(handle_unknown=&#x27;ignore&#x27;),\n",
+       "                                 [&#x27;PromoInterval&#x27;, &#x27;StoreType&#x27;, &#x27;Assortment&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">ohe</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;PromoInterval&#x27;, &#x27;StoreType&#x27;, &#x27;Assortment&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OneHotEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">remainder</label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;StateHoliday&#x27;, &#x27;Store&#x27;, &#x27;Customers&#x27;, &#x27;Promo&#x27;, &#x27;SchoolHoliday&#x27;, &#x27;CompetitionDistance&#x27;, &#x27;CompetitionOpenSinceMonth&#x27;, &#x27;CompetitionOpenSinceYear&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">passthrough</label><div class=\"sk-toggleable__content fitted\"><pre>passthrough</pre></div> </div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;StandardScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "             colsample_bylevel=None, colsample_bynode=None,\n",
+       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
+       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
+       "             gamma=None, grow_policy=None, importance_type=None,\n",
+       "             interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
+       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
+       "             max_delta_step=None, max_depth=13, max_leaves=None,\n",
+       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
+       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
+       "             num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('encoding',\n",
+       "                 ColumnTransformer(remainder='passthrough',\n",
+       "                                   transformers=[('ohe',\n",
+       "                                                  OneHotEncoder(handle_unknown='ignore'),\n",
+       "                                                  ['PromoInterval', 'StoreType',\n",
+       "                                                   'Assortment'])])),\n",
+       "                ('scaler', StandardScaler()),\n",
+       "                ('model',\n",
+       "                 XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
+       "                              colsample_bylevel=None, colsample_bynode=None,\n",
+       "                              colsample_bytree=None, device=None,...\n",
+       "                              feature_types=None, gamma=None, grow_policy=None,\n",
+       "                              importance_type=None,\n",
+       "                              interaction_constraints=None, learning_rate=0.1,\n",
+       "                              max_bin=None, max_cat_threshold=None,\n",
+       "                              max_cat_to_onehot=None, max_delta_step=None,\n",
+       "                              max_depth=13, max_leaves=None,\n",
+       "                              min_child_weight=None, missing=nan,\n",
+       "                              monotone_constraints=None, multi_strategy=None,\n",
+       "                              n_estimators=None, n_jobs=None,\n",
+       "                              num_parallel_tree=None, random_state=None, ...))])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Define the ColumnTransformer\n",
+    "ohe_col = [\"PromoInterval\", \"StoreType\", \"Assortment\"]\n",
+    "\n",
+    "ct_encoding = ColumnTransformer(\n",
+    "    transformers=[\n",
+    "        (\"ohe\", OneHotEncoder(handle_unknown=\"ignore\"), ohe_col)\n",
+    "    ],\n",
+    "    remainder=\"passthrough\"\n",
+    ")\n",
+    "\n",
+    "\n",
+    "# Define the XGBRegressor model\n",
+    "model = XGBRegressor(learning_rate=0.1, max_depth=13)\n",
+    "\n",
+    "# Define the pipeline\n",
+    "pipe = Pipeline(steps=[\n",
+    "    (\"encoding\", ct_encoding),\n",
+    "    (\"scaler\", StandardScaler()),\n",
+    "    (\"model\", model)\n",
+    "])\n",
+    "\n",
+    "# Now you can fit your pipeline with your data\n",
+    "pipe.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([5674.2217, 7922.6377, 9180.126 , ..., 7287.449 , 3228.0945,\n",
+       "       4453.9897], dtype=float32)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = pipe.predict(X_test)\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "43879      5934\n",
+       "562681     7800\n",
+       "239643     9111\n",
+       "689976     7831\n",
+       "397240    10046\n",
+       "          ...  \n",
+       "512864    13692\n",
+       "750784     6958\n",
+       "192729     6785\n",
+       "755727     2925\n",
+       "604917     4178\n",
+       "Name: Sales, Length: 211085, dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PromoInterval', 'StoreType', 'Assortment', 'StateHoliday', 'Store',\n",
+       "       'Customers', 'Promo', 'SchoolHoliday', 'CompetitionDistance',\n",
+       "       'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PromoInterval</th>\n",
+       "      <th>StoreType</th>\n",
+       "      <th>Assortment</th>\n",
+       "      <th>StateHoliday</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>Customers</th>\n",
+       "      <th>Promo</th>\n",
+       "      <th>SchoolHoliday</th>\n",
+       "      <th>CompetitionDistance</th>\n",
+       "      <th>CompetitionOpenSinceMonth</th>\n",
+       "      <th>CompetitionOpenSinceYear</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>795018</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>650</td>\n",
+       "      <td>636</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1420</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>463276</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72</td>\n",
+       "      <td>261</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2200</td>\n",
+       "      <td>12</td>\n",
+       "      <td>2009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268352</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Medium Store</td>\n",
+       "      <td>extra</td>\n",
+       "      <td>0</td>\n",
+       "      <td>733</td>\n",
+       "      <td>3567</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>860</td>\n",
+       "      <td>10</td>\n",
+       "      <td>1999</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67308</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>0</td>\n",
+       "      <td>796</td>\n",
+       "      <td>791</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7180</td>\n",
+       "      <td>11</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482458</th>\n",
+       "      <td>0</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>extended</td>\n",
+       "      <td>0</td>\n",
+       "      <td>301</td>\n",
+       "      <td>480</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4510</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2015</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          PromoInterval     StoreType Assortment  StateHoliday  Store  \\\n",
+       "795018  Jan,Apr,Jul,Oct    Small Shop      basic             0    650   \n",
+       "463276  Jan,Apr,Jul,Oct    Small Shop      basic             0     72   \n",
+       "268352                0  Medium Store      extra             0    733   \n",
+       "67308                 0    Small Shop   extended             0    796   \n",
+       "482458                0    Small Shop   extended             0    301   \n",
+       "\n",
+       "        Customers  Promo  SchoolHoliday  CompetitionDistance  \\\n",
+       "795018        636      1              0                 1420   \n",
+       "463276        261      0              0                 2200   \n",
+       "268352       3567      1              0                  860   \n",
+       "67308         791      1              0                 7180   \n",
+       "482458        480      0              0                 4510   \n",
+       "\n",
+       "        CompetitionOpenSinceMonth  CompetitionOpenSinceYear  \n",
+       "795018                         10                      2012  \n",
+       "463276                         12                      2009  \n",
+       "268352                         10                      1999  \n",
+       "67308                          11                      2012  \n",
+       "482458                          3                      2015  "
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PromoInterval</th>\n",
+       "      <th>StoreType</th>\n",
+       "      <th>Assortment</th>\n",
+       "      <th>StateHoliday</th>\n",
+       "      <th>Store</th>\n",
+       "      <th>Customers</th>\n",
+       "      <th>Promo</th>\n",
+       "      <th>SchoolHoliday</th>\n",
+       "      <th>CompetitionDistance</th>\n",
+       "      <th>CompetitionOpenSinceMonth</th>\n",
+       "      <th>CompetitionOpenSinceYear</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Jan,Apr,Jul,Oct</td>\n",
+       "      <td>Small Shop</td>\n",
+       "      <td>basic</td>\n",
+       "      <td>0</td>\n",
+       "      <td>650</td>\n",
+       "      <td>636</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1420</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2012</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PromoInterval   StoreType Assortment StateHoliday Store Customers Promo  \\\n",
+       "0  Jan,Apr,Jul,Oct  Small Shop      basic            0   650       636     1   \n",
+       "\n",
+       "  SchoolHoliday CompetitionDistance CompetitionOpenSinceMonth  \\\n",
+       "0             0                1420                        10   \n",
+       "\n",
+       "  CompetitionOpenSinceYear  \n",
+       "0                     2012  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 795018\n",
+    "temp_df = pd.DataFrame(data =  [[\"Jan,Apr,Jul,Oct\",\"Small Shop\",\"basic\",\"0\",\"650\",\"636\",\"1\",\"0\",\"1420\",\"10\",\"2012\"]], columns = X_test.columns)\n",
+    "temp_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6357.158], dtype=float32)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pipe.predict(temp_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Record at index 795018:\n",
+      "PromoInterval                Jan,Apr,Jul,Oct\n",
+      "StoreType                         Small Shop\n",
+      "Assortment                             basic\n",
+      "StateHoliday                               0\n",
+      "Store                                    650\n",
+      "Customers                                636\n",
+      "Promo                                      1\n",
+      "SchoolHoliday                              0\n",
+      "CompetitionDistance                     1420\n",
+      "CompetitionOpenSinceMonth                 10\n",
+      "CompetitionOpenSinceYear                2012\n",
+      "Sales                                   6322\n",
+      "Name: 795018, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Assuming your DataFrame is named df\n",
+    "record = df.iloc[795018]\n",
+    "\n",
+    "print(\"Record at index 795018:\")\n",
+    "print(record)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unique values in PromoInterval: ['0' 'Jan,Apr,Jul,Oct' 'Feb,May,Aug,Nov' 'Mar,Jun,Sept,Dec']\n",
+      "Unique values in StoreType: ['Large Store' 'Small Shop' 'Hypermarket' 'Medium Store']\n",
+      "Unique values in Assortment: ['basic' 'extended' 'extra']\n",
+      "Unique values in StateHoliday: [0 1]\n",
+      "Unique values in Store: [   1    2    3 ... 1115  876  292]\n",
+      "Unique values in Customers: [ 555  625  821 ... 3900   36 4065]\n",
+      "Unique values in Promo: [1 0]\n",
+      "Unique values in SchoolHoliday: [1 0]\n",
+      "Unique values in CompetitionDistance: [ 1270   570 14130   620 29910   310 24000  7520  2030  3160   960  1070\n",
+      "  1300  4110  3270    50 13840  3240  2340   550  1040  4060  4590   430\n",
+      "  2300    60  1200  2170    40  9800  2910  1320  2240  7660   540  4230\n",
+      "  1090   260   180  1180   290  4880  9710   270  1060 18010  6260 10570\n",
+      "   450 30360  7170   720  6620   420  7340  2840  5540   350  2050  3700\n",
+      " 22560   410   250  1130  4840 17500  2200  1650   330 22440 19960  3510\n",
+      "  3320  7910  2370 22390  2710 11810  1870   480   560 10690  2380  2410\n",
+      "   240 16690 14620  1890  8780  8980 15140 17930  2440   150  5210   390\n",
+      "  6190  1390  1930  2190  3300 46590  7890  1630 20930  4510  5740   680\n",
+      "  3450  3580  2100  2290  3570 58260 16760  1410   760  3370  1350  2000\n",
+      "  2460   900   920  5190  1730 25360  1700  1540  2930 16570   280  8050\n",
+      "  8540  2090  2610 31830  4360  1780 16240 16420  3050  2020  2950 11840\n",
+      "  8530 17110  2970  5340  1480  1160  3720   100   140 12540   980  2640\n",
+      "   110 13090  4130  3770  1250  1710  5800 12610  9670  3560  1860 19360\n",
+      "   850  5760  1470  1100  2770   520 16970   220  3850  4210  6360 20260\n",
+      "  5140   490  5630   380  6870   300 11680   970 15050  4030  8650   190\n",
+      "  3150   640  1640  1000 13530  2920  7930 10180 10800 17410  6680  3840\n",
+      " 13570  4370  5710  1420   320   610  1110   780  6880   710  1310  4660\n",
+      "    70   340  3520 22330  4630    80 27190   210 15340  1140  4580   360\n",
+      "  4520  1450 16180  8480  3640  2960  7840  9260  2320 18640  6970  1220\n",
+      "  2260  1290  1460  2740   800  6540  4150  2325  9580 19840 38630   120\n",
+      " 15430  1950  2470  5100 18660  8740 11300 14160 38710  9000  3140 32330\n",
+      "  8140  8400 13140 10070  3130   370   670  1840  4040    90 10600  1590\n",
+      "  2280  8080 15770 18650  8090  9360 16490  1490  8880  5290  1500  9720\n",
+      "  8970  2060  2890  2040  4490 13620  6470  5870  8250  1970 11120  1150\n",
+      " 15710   160  2140  6630  1800 26130   130  6690  1600   460  2120  4820\n",
+      " 10850  3620 23130  5360  9200  5830  4970  1080  8240  5890  1560   840\n",
+      "  8460  4460  6210  6910  4650  1620  3530  2880 16350 12870   810 30030\n",
+      " 13020   910  3900  2530   500 11400  1510  3970  5780  1850 75860 26450\n",
+      "  3390 34050  1790 44320  4160 10890  3110 20390  5260  5300  5030 14810\n",
+      "  8300   770  1940  7470  2550  2310 14300  2180 14960   660  4680  1740\n",
+      "  1260  5470  2780  1610   990 13080   820  9070  1280  4740  8260   590\n",
+      "   400 11260    20 22490  3330  2510  6900 18610  7160 40860 20620 12920\n",
+      " 18160  5950  4700   600   650  7280  5020   580  8990  3760  2330  4260\n",
+      "  3040  3000  3910  1910  1210   700  1010  4270  1340  2110  9230  1190\n",
+      "  4400  2270 12700 20970   170  7250  1360   440 15720  3340  2540 33060\n",
+      " 17340  8220 10950 10310 18370  2070  2490   730  8940  9910  5440    30\n",
+      "  4080  6920  1170 10740   510  1690  2870  3350 11640 27530  9790 10170\n",
+      "  7780  8040   530   230  7420  2130 14570   200  6930  7860  1680  2700\n",
+      " 17080 15170  3250  4140  2850 20050 18760 15040  3030  3780   830  8550\n",
+      "  7830  2900 11470  4870 12070  3200  8190 15320  3590  5650  5900 17540\n",
+      " 40540 13990 15270 35280   860  1920  5980  6400 11900  4380  6710  1370\n",
+      " 17650  4330 45740  3410  8670 13130 19780  2390 32240 26490 25430  9820\n",
+      "  2630 20640 16990   630  5390 15490  3210  1530  9770 17280  5090  7180\n",
+      "  9560 48330  1760 24770  3870 18620 12770  9640  2590 24530 16210 17570\n",
+      "  7980  3290  6320  5070  3470  2720 14600  6890 27650  8860  5000  1120\n",
+      "   940 14040  4770  3440  3020  6270 21770   740 21370  1020  9680 21810\n",
+      " 10620  3860 29190  4570  7550 12430 19700  4450 18670 19370 18540  3920\n",
+      "  3170  7290  1980 12480  3100  7240 18710  2620  6420   470  5150 15700\n",
+      "  5460 22350  2810  2820  6860 18020  1670  2220  1430   870  6300 19830\n",
+      "  9430 23620  9630  4180  3890  4420 21930  2480  3460  6560  5840  2230\n",
+      " 19640  6480  4610  6330  1520  3740  1990 36410  7680 13750 27150 17290\n",
+      " 26990 29070  3750 13170  5080 13190  5350  3230  3380  3430  8110  6250\n",
+      " 12020  5010 18050  5380 16680 11540  2210  4300  5220  9990 10450   690\n",
+      "  1830  5330  1400  3490  1900  1880 21790]\n",
+      "Unique values in CompetitionOpenSinceMonth: [ 9 11 12  4 10  8  3  6  5  1  2  7]\n",
+      "Unique values in CompetitionOpenSinceYear: [2008 2007 2006 2009 2015 2013 2014 2000 2011 2010 2005 1999 2003 2012\n",
+      " 2004 2002 1961 1995 2001 1990 1994 1900 1998]\n",
+      "Unique values in Sales: [ 5263  6064  8314 ...   660 17815 23303]\n"
+     ]
+    }
+   ],
+   "source": [
+    "def print_unique_values(dataframe):\n",
+    "    for column in dataframe.columns:\n",
+    "        unique_values = dataframe[column].unique()\n",
+    "        print(f\"Unique values in {column}: {unique_values}\")\n",
+    "\n",
+    "# Example usage:\n",
+    "print_unique_values(df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Save The Model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['model2.pkl']"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import joblib\n",
+    "\n",
+    "# joblib.dump(pipe, 'model2.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model1 = joblib.load(\"../models/model2.pkl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([6357.158], dtype=float32)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model1.predict(temp_df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<hr>\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Requirements.txt

@@ -0,0 +1 @@
+Joblib

app.py

@@ -0,0 +1,100 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import pickle
+
+# Load your trained model
+model = joblib.load('models\model1.pkl')
+
+
+# with open('models/model1.pkl', 'rb') as file:
+#     model = pickle.load(file)
+
+# Function to predict sales
+def predict_sales(input_data):
+    # Make predictions using the loaded model
+    sales_prediction = model.predict(input_data)
+    return sales_prediction
+
+# Streamlit app
+def main():
+    st.title('Sales Prediction App')
+    st.image("images\\r1.jpg", caption="Sunrise by the mountains")
+
+    # Input widgets
+    PromoInterval = st.selectbox("Promo Interval", ['No Promotion', 'Jan,Apr,Jul,Oct', 'Feb,May,Aug,Nov', 'Mar,Jun,Sept,Dec'])
+
+    # -----------------------------------------------------------------------------------------------
+    StoreType = st.radio("StoreType", ["Small Shop", "Medium Store", "Large Store", "Hypermarket"])
+    Assortment = st.radio("Assortment", ["basic", "extra", "extended"])
+    
+
+    # Encode StateHoliday as 1 for 'Yes' and 0 for 'No' --------------------------------------
+    StateHoliday = st.radio("State Holiday", ["Yes", "No"])
+    StateHoliday = 1 if StateHoliday == "Yes" else 0
+
+    SchoolHoliday = st.radio("School Holiday", ["Yes", "No"])
+    SchoolHoliday = 1 if SchoolHoliday == "Yes" else 0
+
+    Promo = st.radio("Promotion", ["store is participating", "store is not participating"])
+    Promo = 1 if Promo == "store is participating" else 0
+    # ----------------------------------------------------------------------------------------
+    
+
+    Store = st.slider("Store", 1, 1115)
+    Customers = st.slider("Customers", 0, 7388)
+    CompetitionDistance = st.slider("Competition Distance", 20, 75860)
+    CompetitionOpenSinceMonth = st.slider("Competition Open Since Month", 1, 12)
+    CompetitionOpenSinceYear = st.slider("Competition Open Since Year", 1998, 2015)
+    # ----------------------------------------------------------------------------------------
+
+    # Store user inputs
+    input_data = pd.DataFrame({
+        'PromoInterval': [PromoInterval],
+        'StoreType': [StoreType],
+        'Assortment': [Assortment],
+        'StateHoliday': [StateHoliday],
+        'Store': [Store],
+        'Customers': [Customers],
+        'Promo': [Promo],
+        'SchoolHoliday': [SchoolHoliday],
+        'CompetitionDistance': [CompetitionDistance],
+        'CompetitionOpenSinceMonth': [CompetitionOpenSinceMonth],
+        'CompetitionOpenSinceYear': [CompetitionOpenSinceYear]
+    })
+
+    # Display input data
+    st.subheader('Input Data:')
+    st.write(input_data)
+
+    # Predict sales
+    # if st.button('Predict Sales'):
+    #     prediction = predict_sales(input_data)
+    #     st.write('Predicted Sales:', prediction)
+
+    if st.button('Predict Sales'):
+        prediction = predict_sales(input_data)[0]
+        formatted_prediction = "{:.2f}".format(prediction)  # Format prediction to display two decimal points
+        st.write('Predicted Sales:', formatted_prediction)
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+# Record at index 795018:
+# PromoInterval                Jan,Apr,Jul,Oct
+# StoreType                         Small Shop
+# Assortment                             basic
+# StateHoliday                               0
+# Store                                    650
+# Customers                                636
+# Promo                                      1
+# SchoolHoliday                              0
+# CompetitionDistance                     1420
+# CompetitionOpenSinceMonth                 10
+# CompetitionOpenSinceYear                2012
+# Sales                                   6322
+# Name: 795018, dtype: object

models/Rossmann_Model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a4264097df040b74694426b0b55cd70008790bec56cdeefa4eb7de0144096d1
+size 22628002

models/model1.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f44f412a489f3fe693806f91c0b379f6fa5ba685d29f905dacaf223449ac6279
+size 22632250

pages/Data Overview.py

@@ -0,0 +1,16 @@
+import streamlit as st
+import pandas as pd
+
+# st.title("hi")
+
+# Function for data overview
+def show_data_overview():
+    # Load data from CSV file
+    data = pd.read_csv("Dataset\Rossmann_Cleaned_data.csv")
+    
+    # Display data overview
+    st.subheader("Data Overview")
+    st.write(data)
+
+
+show_data_overview()  # Call the function to show data overview

pages/app.py

@@ -0,0 +1,100 @@
+
+import streamlit as st
+import pandas as pd
+import joblib
+import pickle
+
+# Load your trained model
+model = joblib.load('models\model1.pkl')
+
+
+# with open('models/model1.pkl', 'rb') as file:
+#     model = pickle.load(file)
+
+# Function to predict sales
+def predict_sales(input_data):
+    # Make predictions using the loaded model
+    sales_prediction = model.predict(input_data)
+    return sales_prediction
+
+# Streamlit app
+def main():
+    st.title('Sales Prediction App')
+    st.image("images\\r1.jpg", caption="Sunrise by the mountains")
+
+    # Input widgets
+    PromoInterval = st.selectbox("Promo Interval", ['No Promotion', 'Jan,Apr,Jul,Oct', 'Feb,May,Aug,Nov', 'Mar,Jun,Sept,Dec'])
+
+    # -----------------------------------------------------------------------------------------------
+    StoreType = st.radio("StoreType", ["Small Shop", "Medium Store", "Large Store", "Hypermarket"])
+    Assortment = st.radio("Assortment", ["basic", "extra", "extended"])
+    
+
+    # Encode StateHoliday as 1 for 'Yes' and 0 for 'No' --------------------------------------
+    StateHoliday = st.radio("State Holiday", ["Yes", "No"])
+    StateHoliday = 1 if StateHoliday == "Yes" else 0
+
+    SchoolHoliday = st.radio("School Holiday", ["Yes", "No"])
+    SchoolHoliday = 1 if SchoolHoliday == "Yes" else 0
+
+    Promo = st.radio("Promotion", ["store is participating", "store is not participating"])
+    Promo = 1 if Promo == "store is participating" else 0
+    # ----------------------------------------------------------------------------------------
+    
+
+    Store = st.slider("Store", 1, 1115)
+    Customers = st.slider("Customers", 0, 7388)
+    CompetitionDistance = st.slider("Competition Distance", 20, 75860)
+    CompetitionOpenSinceMonth = st.slider("Competition Open Since Month", 1, 12)
+    CompetitionOpenSinceYear = st.slider("Competition Open Since Year", 1998, 2015)
+    # ----------------------------------------------------------------------------------------
+
+    # Store user inputs
+    input_data = pd.DataFrame({
+        'PromoInterval': [PromoInterval],
+        'StoreType': [StoreType],
+        'Assortment': [Assortment],
+        'StateHoliday': [StateHoliday],
+        'Store': [Store],
+        'Customers': [Customers],
+        'Promo': [Promo],
+        'SchoolHoliday': [SchoolHoliday],
+        'CompetitionDistance': [CompetitionDistance],
+        'CompetitionOpenSinceMonth': [CompetitionOpenSinceMonth],
+        'CompetitionOpenSinceYear': [CompetitionOpenSinceYear]
+    })
+
+    # Display input data
+    st.subheader('Input Data:')
+    st.write(input_data)
+
+    # Predict sales
+    # if st.button('Predict Sales'):
+    #     prediction = predict_sales(input_data)
+    #     st.write('Predicted Sales:', prediction)
+
+    if st.button('Predict Sales'):
+        prediction = predict_sales(input_data)[0]
+        formatted_prediction = "{:.2f}".format(prediction)  # Format prediction to display two decimal points
+        st.write('Predicted Sales:', formatted_prediction)
+
+
+if __name__ == '__main__':
+    main()
+
+
+
+# Record at index 795018:
+# PromoInterval                Jan,Apr,Jul,Oct
+# StoreType                         Small Shop
+# Assortment                             basic
+# StateHoliday                               0
+# Store                                    650
+# Customers                                636
+# Promo                                      1
+# SchoolHoliday                              0
+# CompetitionDistance                     1420
+# CompetitionOpenSinceMonth                 10
+# CompetitionOpenSinceYear                2012
+# Sales                                   6322
+# Name: 795018, dtype: object