Upload 11 files

Campus_Selection.csv

@@ -0,0 +1,216 @@
+sl_no,gender,ssc_p,ssc_b,hsc_p,hsc_b,hsc_s,degree_p,degree_t,workex,etest_p,specialisation,mba_p,status
+1,M,67.0,Others,91.0,Others,Commerce,58.0,Sci&Tech,No,55.0,Mkt&HR,58.8,Placed
+2,M,79.33,Central,78.33,Others,Science,77.48,Sci&Tech,Yes,86.5,Mkt&Fin,66.28,Placed
+3,M,65.0,Central,68.0,Central,Arts,64.0,Comm&Mgmt,No,75.0,Mkt&Fin,57.8,Placed
+4,M,56.0,Central,52.0,Central,Science,52.0,Sci&Tech,No,66.0,Mkt&HR,59.43,Not Placed
+5,M,85.8,Central,73.6,Central,Commerce,73.3,Comm&Mgmt,No,96.8,Mkt&Fin,55.5,Placed
+6,M,55.0,Others,49.8,Others,Science,67.25,Sci&Tech,Yes,55.0,Mkt&Fin,51.58,Not Placed
+7,F,46.0,Others,49.2,Others,Commerce,79.0,Comm&Mgmt,No,74.28,Mkt&Fin,53.29,Not Placed
+8,M,82.0,Central,64.0,Central,Science,66.0,Sci&Tech,Yes,67.0,Mkt&Fin,62.14,Placed
+9,M,73.0,Central,79.0,Central,Commerce,72.0,Comm&Mgmt,No,91.34,Mkt&Fin,61.29,Placed
+10,M,58.0,Central,70.0,Central,Commerce,61.0,Comm&Mgmt,No,54.0,Mkt&Fin,52.21,Not Placed
+11,M,58.0,Central,61.0,Central,Commerce,60.0,Comm&Mgmt,Yes,62.0,Mkt&HR,60.85,Placed
+12,M,69.6,Central,68.4,Central,Commerce,78.3,Comm&Mgmt,Yes,60.0,Mkt&Fin,63.7,Placed
+13,F,47.0,Central,55.0,Others,Science,65.0,Comm&Mgmt,No,62.0,Mkt&HR,65.04,Not Placed
+14,F,77.0,Central,87.0,Central,Commerce,59.0,Comm&Mgmt,No,68.0,Mkt&Fin,68.63,Placed
+15,M,62.0,Central,47.0,Central,Commerce,50.0,Comm&Mgmt,No,76.0,Mkt&HR,54.96,Not Placed
+16,F,65.0,Central,75.0,Central,Commerce,69.0,Comm&Mgmt,Yes,72.0,Mkt&Fin,64.66,Placed
+17,M,63.0,Central,66.2,Central,Commerce,65.6,Comm&Mgmt,Yes,60.0,Mkt&Fin,62.54,Placed
+18,F,55.0,Central,67.0,Central,Commerce,64.0,Comm&Mgmt,No,60.0,Mkt&Fin,67.28,Not Placed
+19,F,63.0,Central,66.0,Central,Commerce,64.0,Comm&Mgmt,No,68.0,Mkt&HR,64.08,Not Placed
+20,M,60.0,Others,67.0,Others,Arts,70.0,Comm&Mgmt,Yes,50.48,Mkt&Fin,77.89,Placed
+21,M,62.0,Others,65.0,Others,Commerce,66.0,Comm&Mgmt,No,50.0,Mkt&HR,56.7,Placed
+22,F,79.0,Others,76.0,Others,Commerce,85.0,Comm&Mgmt,No,95.0,Mkt&Fin,69.06,Placed
+23,F,69.8,Others,60.8,Others,Science,72.23,Sci&Tech,No,55.53,Mkt&HR,68.81,Placed
+24,F,77.4,Others,60.0,Others,Science,64.74,Sci&Tech,Yes,92.0,Mkt&Fin,63.62,Placed
+25,M,76.5,Others,97.7,Others,Science,78.86,Sci&Tech,No,97.4,Mkt&Fin,74.01,Placed
+26,F,52.58,Others,54.6,Central,Commerce,50.2,Comm&Mgmt,Yes,76.0,Mkt&Fin,65.33,Not Placed
+27,M,71.0,Others,79.0,Others,Commerce,66.0,Comm&Mgmt,Yes,94.0,Mkt&Fin,57.55,Placed
+28,M,63.0,Others,67.0,Others,Commerce,66.0,Comm&Mgmt,No,68.0,Mkt&HR,57.69,Placed
+29,M,76.76,Others,76.5,Others,Commerce,67.5,Comm&Mgmt,Yes,73.35,Mkt&Fin,64.15,Placed
+30,M,62.0,Central,67.0,Central,Commerce,58.0,Comm&Mgmt,No,77.0,Mkt&Fin,51.29,Not Placed
+31,F,64.0,Central,73.5,Central,Commerce,73.0,Comm&Mgmt,No,52.0,Mkt&HR,56.7,Placed
+32,F,67.0,Central,53.0,Central,Science,65.0,Sci&Tech,No,64.0,Mkt&HR,58.32,Not Placed
+33,F,61.0,Central,81.0,Central,Commerce,66.4,Comm&Mgmt,No,50.89,Mkt&HR,62.21,Placed
+34,F,87.0,Others,65.0,Others,Science,81.0,Comm&Mgmt,Yes,88.0,Mkt&Fin,72.78,Placed
+35,M,62.0,Others,51.0,Others,Science,52.0,Others,No,68.44,Mkt&HR,62.77,Not Placed
+36,F,69.0,Central,78.0,Central,Commerce,72.0,Comm&Mgmt,No,71.0,Mkt&HR,62.74,Placed
+37,M,51.0,Central,44.0,Central,Commerce,57.0,Comm&Mgmt,No,64.0,Mkt&Fin,51.45,Not Placed
+38,F,79.0,Central,76.0,Central,Science,65.6,Sci&Tech,No,58.0,Mkt&HR,55.47,Placed
+39,F,73.0,Others,58.0,Others,Science,66.0,Comm&Mgmt,No,53.7,Mkt&HR,56.86,Placed
+40,M,81.0,Others,68.0,Others,Science,64.0,Sci&Tech,No,93.0,Mkt&Fin,62.56,Placed
+41,F,78.0,Central,77.0,Others,Commerce,80.0,Comm&Mgmt,No,60.0,Mkt&Fin,66.72,Placed
+42,F,74.0,Others,63.16,Others,Commerce,65.0,Comm&Mgmt,Yes,65.0,Mkt&HR,69.76,Not Placed
+43,M,49.0,Others,39.0,Central,Science,65.0,Others,No,63.0,Mkt&Fin,51.21,Not Placed
+44,M,87.0,Others,87.0,Others,Commerce,68.0,Comm&Mgmt,No,95.0,Mkt&HR,62.9,Placed
+45,F,77.0,Others,73.0,Others,Commerce,81.0,Comm&Mgmt,Yes,89.0,Mkt&Fin,69.7,Placed
+46,F,76.0,Central,64.0,Central,Science,72.0,Sci&Tech,No,58.0,Mkt&HR,66.53,Not Placed
+47,F,70.89,Others,71.98,Others,Science,65.6,Comm&Mgmt,No,68.0,Mkt&HR,71.63,Not Placed
+48,M,63.0,Central,60.0,Central,Commerce,57.0,Comm&Mgmt,Yes,78.0,Mkt&Fin,54.55,Placed
+49,M,63.0,Others,62.0,Others,Commerce,68.0,Comm&Mgmt,No,64.0,Mkt&Fin,62.46,Placed
+50,F,50.0,Others,37.0,Others,Arts,52.0,Others,No,65.0,Mkt&HR,56.11,Not Placed
+51,F,75.2,Central,73.2,Central,Science,68.4,Comm&Mgmt,No,65.0,Mkt&HR,62.98,Placed
+52,M,54.4,Central,61.12,Central,Commerce,56.2,Comm&Mgmt,No,67.0,Mkt&HR,62.65,Not Placed
+53,F,40.89,Others,45.83,Others,Commerce,53.0,Comm&Mgmt,No,71.2,Mkt&HR,65.49,Not Placed
+54,M,80.0,Others,70.0,Others,Science,72.0,Sci&Tech,No,87.0,Mkt&HR,71.04,Placed
+55,F,74.0,Central,60.0,Others,Science,69.0,Comm&Mgmt,No,78.0,Mkt&HR,65.56,Placed
+56,M,60.4,Central,66.6,Others,Science,65.0,Comm&Mgmt,No,71.0,Mkt&HR,52.71,Placed
+57,M,63.0,Others,71.4,Others,Commerce,61.4,Comm&Mgmt,No,68.0,Mkt&Fin,66.88,Placed
+58,M,68.0,Central,76.0,Central,Commerce,74.0,Comm&Mgmt,No,80.0,Mkt&Fin,63.59,Placed
+59,M,74.0,Central,62.0,Others,Science,68.0,Comm&Mgmt,No,74.0,Mkt&Fin,57.99,Placed
+60,M,52.6,Central,65.58,Others,Science,72.11,Sci&Tech,No,57.6,Mkt&Fin,56.66,Placed
+61,M,74.0,Central,70.0,Central,Science,72.0,Comm&Mgmt,Yes,60.0,Mkt&Fin,57.24,Placed
+62,M,84.2,Central,73.4,Central,Commerce,66.89,Comm&Mgmt,No,61.6,Mkt&Fin,62.48,Placed
+63,F,86.5,Others,64.2,Others,Science,67.4,Sci&Tech,No,59.0,Mkt&Fin,59.69,Placed
+64,M,61.0,Others,70.0,Others,Commerce,64.0,Comm&Mgmt,No,68.5,Mkt&HR,59.5,Not Placed
+65,M,80.0,Others,73.0,Others,Commerce,75.0,Comm&Mgmt,No,61.0,Mkt&Fin,58.78,Placed
+66,M,54.0,Others,47.0,Others,Science,57.0,Comm&Mgmt,No,89.69,Mkt&HR,57.1,Not Placed
+67,M,83.0,Others,74.0,Others,Science,66.0,Comm&Mgmt,No,68.92,Mkt&HR,58.46,Placed
+68,M,80.92,Others,78.5,Others,Commerce,67.0,Comm&Mgmt,No,68.71,Mkt&Fin,60.99,Placed
+69,F,69.7,Central,47.0,Central,Commerce,72.7,Sci&Tech,No,79.0,Mkt&HR,59.24,Not Placed
+70,M,73.0,Central,73.0,Central,Science,66.0,Sci&Tech,Yes,70.0,Mkt&Fin,68.07,Placed
+71,M,82.0,Others,61.0,Others,Science,62.0,Sci&Tech,No,89.0,Mkt&Fin,65.45,Placed
+72,M,75.0,Others,70.29,Others,Commerce,71.0,Comm&Mgmt,No,95.0,Mkt&Fin,66.94,Placed
+73,M,84.86,Others,67.0,Others,Science,78.0,Comm&Mgmt,No,95.5,Mkt&Fin,68.53,Placed
+74,M,64.6,Central,83.83,Others,Commerce,71.72,Comm&Mgmt,No,86.0,Mkt&Fin,59.75,Placed
+75,M,56.6,Central,64.8,Central,Commerce,70.2,Comm&Mgmt,No,84.27,Mkt&Fin,67.2,Placed
+76,F,59.0,Central,62.0,Others,Commerce,77.5,Comm&Mgmt,No,74.0,Mkt&HR,67.0,Not Placed
+77,F,66.5,Others,70.4,Central,Arts,71.93,Comm&Mgmt,No,61.0,Mkt&Fin,64.27,Placed
+78,M,64.0,Others,80.0,Others,Science,65.0,Sci&Tech,Yes,69.0,Mkt&Fin,57.65,Placed
+79,M,84.0,Others,90.9,Others,Science,64.5,Sci&Tech,No,86.04,Mkt&Fin,59.42,Placed
+80,F,69.0,Central,62.0,Central,Science,66.0,Sci&Tech,No,75.0,Mkt&HR,67.99,Not Placed
+81,F,69.0,Others,62.0,Others,Commerce,69.0,Comm&Mgmt,Yes,67.0,Mkt&HR,62.35,Placed
+82,M,81.7,Others,63.0,Others,Science,67.0,Comm&Mgmt,Yes,86.0,Mkt&Fin,70.2,Placed
+83,M,63.0,Central,67.0,Central,Commerce,74.0,Comm&Mgmt,No,82.0,Mkt&Fin,60.44,Not Placed
+84,M,84.0,Others,79.0,Others,Science,68.0,Sci&Tech,Yes,84.0,Mkt&Fin,66.69,Placed
+85,M,70.0,Central,63.0,Others,Science,70.0,Sci&Tech,Yes,55.0,Mkt&Fin,62.0,Placed
+86,F,83.84,Others,89.83,Others,Commerce,77.2,Comm&Mgmt,Yes,78.74,Mkt&Fin,76.18,Placed
+87,M,62.0,Others,63.0,Others,Commerce,64.0,Comm&Mgmt,No,67.0,Mkt&Fin,57.03,Placed
+88,M,59.6,Central,51.0,Central,Science,60.0,Others,No,75.0,Mkt&HR,59.08,Not Placed
+89,F,66.0,Central,62.0,Central,Commerce,73.0,Comm&Mgmt,No,58.0,Mkt&HR,64.36,Placed
+90,F,84.0,Others,75.0,Others,Science,69.0,Sci&Tech,Yes,62.0,Mkt&HR,62.36,Placed
+91,F,85.0,Others,90.0,Others,Commerce,82.0,Comm&Mgmt,No,92.0,Mkt&Fin,68.03,Placed
+92,M,52.0,Central,57.0,Central,Commerce,50.8,Comm&Mgmt,No,67.0,Mkt&HR,62.79,Not Placed
+93,F,60.23,Central,69.0,Central,Science,66.0,Comm&Mgmt,No,72.0,Mkt&Fin,59.47,Placed
+94,M,52.0,Central,62.0,Central,Commerce,54.0,Comm&Mgmt,No,72.0,Mkt&HR,55.41,Not Placed
+95,M,58.0,Central,62.0,Central,Commerce,64.0,Comm&Mgmt,No,53.88,Mkt&Fin,54.97,Placed
+96,M,73.0,Central,78.0,Others,Commerce,65.0,Comm&Mgmt,Yes,95.46,Mkt&Fin,62.16,Placed
+97,F,76.0,Central,70.0,Central,Science,76.0,Comm&Mgmt,Yes,66.0,Mkt&Fin,64.44,Placed
+98,F,70.5,Central,62.5,Others,Commerce,61.0,Comm&Mgmt,No,93.91,Mkt&Fin,69.03,Not Placed
+99,F,69.0,Central,73.0,Central,Commerce,65.0,Comm&Mgmt,No,70.0,Mkt&Fin,57.31,Placed
+100,M,54.0,Central,82.0,Others,Commerce,63.0,Sci&Tech,No,50.0,Mkt&Fin,59.47,Not Placed
+101,F,45.0,Others,57.0,Others,Commerce,58.0,Comm&Mgmt,Yes,56.39,Mkt&HR,64.95,Not Placed
+102,M,63.0,Central,72.0,Central,Commerce,68.0,Comm&Mgmt,No,78.0,Mkt&HR,60.44,Placed
+103,F,77.0,Others,61.0,Others,Commerce,68.0,Comm&Mgmt,Yes,57.5,Mkt&Fin,61.31,Placed
+104,M,73.0,Central,78.0,Central,Science,73.0,Sci&Tech,Yes,85.0,Mkt&HR,65.83,Placed
+105,M,69.0,Central,63.0,Others,Science,65.0,Comm&Mgmt,Yes,55.0,Mkt&HR,58.23,Placed
+106,M,59.0,Central,64.0,Others,Science,58.0,Sci&Tech,No,85.0,Mkt&HR,55.3,Not Placed
+107,M,61.08,Others,50.0,Others,Science,54.0,Sci&Tech,No,71.0,Mkt&Fin,65.69,Not Placed
+108,M,82.0,Others,90.0,Others,Commerce,83.0,Comm&Mgmt,No,80.0,Mkt&HR,73.52,Placed
+109,M,61.0,Central,82.0,Central,Commerce,69.0,Comm&Mgmt,No,84.0,Mkt&Fin,58.31,Placed
+110,M,52.0,Central,63.0,Others,Science,65.0,Sci&Tech,Yes,86.0,Mkt&HR,56.09,Not Placed
+111,F,69.5,Central,70.0,Central,Science,72.0,Sci&Tech,No,57.2,Mkt&HR,54.8,Placed
+112,M,51.0,Others,54.0,Others,Science,61.0,Sci&Tech,No,60.0,Mkt&HR,60.64,Not Placed
+113,M,58.0,Others,61.0,Others,Commerce,61.0,Comm&Mgmt,No,58.0,Mkt&HR,53.94,Placed
+114,F,73.96,Others,79.0,Others,Commerce,67.0,Comm&Mgmt,No,72.15,Mkt&Fin,63.08,Placed
+115,M,65.0,Central,68.0,Others,Science,69.0,Comm&Mgmt,No,53.7,Mkt&HR,55.01,Placed
+116,F,73.0,Others,63.0,Others,Science,66.0,Comm&Mgmt,No,89.0,Mkt&Fin,60.5,Placed
+117,M,68.2,Central,72.8,Central,Commerce,66.6,Comm&Mgmt,Yes,96.0,Mkt&Fin,70.85,Placed
+118,M,77.0,Others,75.0,Others,Science,73.0,Sci&Tech,No,80.0,Mkt&Fin,67.05,Placed
+119,M,76.0,Central,80.0,Central,Science,78.0,Sci&Tech,Yes,97.0,Mkt&HR,70.48,Placed
+120,M,60.8,Central,68.4,Central,Commerce,64.6,Comm&Mgmt,Yes,82.66,Mkt&Fin,64.34,Placed
+121,M,58.0,Others,40.0,Others,Science,59.0,Comm&Mgmt,No,73.0,Mkt&HR,58.81,Not Placed
+122,F,64.0,Central,67.0,Others,Science,69.6,Sci&Tech,Yes,55.67,Mkt&HR,71.49,Placed
+123,F,66.5,Central,66.8,Central,Arts,69.3,Comm&Mgmt,Yes,80.4,Mkt&Fin,71.0,Placed
+124,M,74.0,Others,59.0,Others,Commerce,73.0,Comm&Mgmt,Yes,60.0,Mkt&HR,56.7,Placed
+125,M,67.0,Central,71.0,Central,Science,64.33,Others,Yes,64.0,Mkt&HR,61.26,Placed
+126,F,84.0,Central,73.0,Central,Commerce,73.0,Comm&Mgmt,No,75.0,Mkt&Fin,73.33,Placed
+127,F,79.0,Others,61.0,Others,Science,75.5,Sci&Tech,Yes,70.0,Mkt&Fin,68.2,Placed
+128,F,72.0,Others,60.0,Others,Science,69.0,Comm&Mgmt,No,55.5,Mkt&HR,58.4,Placed
+129,M,80.4,Central,73.4,Central,Science,77.72,Sci&Tech,Yes,81.2,Mkt&HR,76.26,Placed
+130,M,76.7,Central,89.7,Others,Commerce,66.0,Comm&Mgmt,Yes,90.0,Mkt&Fin,68.55,Placed
+131,M,62.0,Central,65.0,Others,Commerce,60.0,Comm&Mgmt,No,84.0,Mkt&Fin,64.15,Not Placed
+132,F,74.9,Others,57.0,Others,Science,62.0,Others,Yes,80.0,Mkt&Fin,60.78,Placed
+133,M,67.0,Others,68.0,Others,Commerce,64.0,Comm&Mgmt,Yes,74.4,Mkt&HR,53.49,Placed
+134,M,73.0,Central,64.0,Others,Commerce,77.0,Comm&Mgmt,Yes,65.0,Mkt&HR,60.98,Placed
+135,F,77.44,Central,92.0,Others,Commerce,72.0,Comm&Mgmt,Yes,94.0,Mkt&Fin,67.13,Placed
+136,F,72.0,Central,56.0,Others,Science,69.0,Comm&Mgmt,No,55.6,Mkt&HR,65.63,Placed
+137,F,47.0,Central,59.0,Central,Arts,64.0,Comm&Mgmt,No,78.0,Mkt&Fin,61.58,Not Placed
+138,M,67.0,Others,63.0,Central,Commerce,72.0,Comm&Mgmt,No,56.0,Mkt&HR,60.41,Placed
+139,F,82.0,Others,64.0,Others,Science,73.0,Sci&Tech,Yes,96.0,Mkt&Fin,71.77,Placed
+140,M,77.0,Central,70.0,Central,Commerce,59.0,Comm&Mgmt,Yes,58.0,Mkt&Fin,54.43,Placed
+141,M,65.0,Central,64.8,Others,Commerce,69.5,Comm&Mgmt,Yes,56.0,Mkt&Fin,56.94,Placed
+142,M,66.0,Central,64.0,Central,Science,60.0,Comm&Mgmt,No,60.0,Mkt&HR,61.9,Not Placed
+143,M,85.0,Central,60.0,Others,Science,73.43,Sci&Tech,Yes,60.0,Mkt&Fin,61.29,Placed
+144,M,77.67,Others,64.89,Others,Commerce,70.67,Comm&Mgmt,No,89.0,Mkt&Fin,60.39,Placed
+145,M,52.0,Others,50.0,Others,Arts,61.0,Comm&Mgmt,No,60.0,Mkt&Fin,58.52,Not Placed
+146,M,89.4,Others,65.66,Others,Science,71.25,Sci&Tech,No,72.0,Mkt&HR,63.23,Placed
+147,M,62.0,Central,63.0,Others,Science,66.0,Comm&Mgmt,No,85.0,Mkt&HR,55.14,Placed
+148,M,70.0,Central,74.0,Central,Commerce,65.0,Comm&Mgmt,No,83.0,Mkt&Fin,62.28,Placed
+149,F,77.0,Central,86.0,Central,Arts,56.0,Others,No,57.0,Mkt&Fin,64.08,Placed
+150,M,44.0,Central,58.0,Central,Arts,55.0,Comm&Mgmt,Yes,64.25,Mkt&HR,58.54,Not Placed
+151,M,71.0,Central,58.66,Central,Science,58.0,Sci&Tech,Yes,56.0,Mkt&Fin,61.3,Placed
+152,M,65.0,Central,65.0,Central,Commerce,75.0,Comm&Mgmt,No,83.0,Mkt&Fin,58.87,Placed
+153,F,75.4,Others,60.5,Central,Science,84.0,Sci&Tech,No,98.0,Mkt&Fin,65.25,Placed
+154,M,49.0,Others,59.0,Others,Science,65.0,Sci&Tech,Yes,86.0,Mkt&Fin,62.48,Placed
+155,M,53.0,Central,63.0,Others,Science,60.0,Comm&Mgmt,Yes,70.0,Mkt&Fin,53.2,Placed
+156,M,51.57,Others,74.66,Others,Commerce,59.9,Comm&Mgmt,Yes,56.15,Mkt&HR,65.99,Not Placed
+157,M,84.2,Central,69.4,Central,Science,65.0,Sci&Tech,Yes,80.0,Mkt&HR,52.72,Placed
+158,M,66.5,Central,62.5,Central,Commerce,60.9,Comm&Mgmt,No,93.4,Mkt&Fin,55.03,Placed
+159,M,67.0,Others,63.0,Others,Science,64.0,Sci&Tech,No,60.0,Mkt&Fin,61.87,Not Placed
+160,M,52.0,Central,49.0,Others,Commerce,58.0,Comm&Mgmt,No,62.0,Mkt&HR,60.59,Not Placed
+161,M,87.0,Central,74.0,Central,Science,65.0,Sci&Tech,Yes,75.0,Mkt&HR,72.29,Placed
+162,M,55.6,Others,51.0,Others,Commerce,57.5,Comm&Mgmt,No,57.63,Mkt&HR,62.72,Not Placed
+163,M,74.2,Central,87.6,Others,Commerce,77.25,Comm&Mgmt,Yes,75.2,Mkt&Fin,66.06,Placed
+164,M,63.0,Others,67.0,Others,Science,64.0,Sci&Tech,No,75.0,Mkt&Fin,66.46,Placed
+165,F,67.16,Central,72.5,Central,Commerce,63.35,Comm&Mgmt,No,53.04,Mkt&Fin,65.52,Placed
+166,F,63.3,Central,78.33,Others,Commerce,74.0,Comm&Mgmt,No,80.0,Mkt&Fin,74.56,Not Placed
+167,M,62.0,Others,62.0,Others,Commerce,60.0,Comm&Mgmt,Yes,63.0,Mkt&HR,52.38,Placed
+168,M,67.9,Others,62.0,Others,Science,67.0,Sci&Tech,Yes,58.1,Mkt&Fin,75.71,Not Placed
+169,F,48.0,Central,51.0,Central,Commerce,58.0,Comm&Mgmt,Yes,60.0,Mkt&HR,58.79,Not Placed
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app.py

@@ -0,0 +1,350 @@
+# -*- coding: utf-8 -*-
+# ==================================================
+# app.py - Gradio App for Hugging Face Spaces
+# Campus Placement Prediction
+# ==================================================
+
+import gradio as gr
+import pandas as pd
+import joblib
+import numpy as np
+import matplotlib.pyplot as plt # Only needed for figure type hint potentially
+import seaborn as sns # Not directly used if images are pre-generated
+import os
+import warnings
+
+warnings.filterwarnings('ignore')
+
+# --- Configuration: Relative Paths for HF Spaces ---
+# Ensure these files are uploaded to your HF Space repository
+MODEL_FILENAME = 'placement_model_pipeline.joblib'
+LABEL_ENCODER_FILENAME = 'placement_label_encoder.joblib'
+FEATURES_FILENAME = 'placement_model_features.joblib'
+DATA_FILE = 'Campus_Selection.csv' # Original data file
+PLOT_DIR = 'plots' # Subdirectory for plots
+FEATURE_IMPORTANCE_PLOT = os.path.join(PLOT_DIR, 'feature_importance.png')
+PLACEMENT_PIE_CHART = os.path.join(PLOT_DIR, 'placement_distribution.png')
+CORRELATION_HEATMAP = os.path.join(PLOT_DIR, 'correlation_heatmap.png')
+
+# --- Global Variables to Hold Loaded Objects ---
+pipeline = None
+label_encoder = None
+feature_names = None
+df_original = None
+df_head = pd.DataFrame() # Default empty dataframe
+dataset_stats = "Dataset information not available."
+
+# --- Load Model and Preprocessing Objects ---
+print("Attempting to load model artifacts...")
+try:
+    if os.path.exists(MODEL_FILENAME):
+        pipeline = joblib.load(MODEL_FILENAME)
+        print(f"- Loaded: {MODEL_FILENAME}")
+    else:
+        print(f"Error: Model file not found at {MODEL_FILENAME}")
+        # gr.Error(f"Model file '{MODEL_FILENAME}' not found. Cannot make predictions.") # Use if you want error banner on load
+
+    if os.path.exists(LABEL_ENCODER_FILENAME):
+        label_encoder = joblib.load(LABEL_ENCODER_FILENAME)
+        print(f"- Loaded: {LABEL_ENCODER_FILENAME}")
+    else:
+        print(f"Error: Label encoder file not found at {LABEL_ENCODER_FILENAME}")
+        # gr.Error(f"Label encoder file '{LABEL_ENCODER_FILENAME}' not found.")
+
+    if os.path.exists(FEATURES_FILENAME):
+        feature_names = joblib.load(FEATURES_FILENAME)
+        print(f"- Loaded: {FEATURES_FILENAME}")
+    else:
+        print(f"Error: Feature names file not found at {FEATURES_FILENAME}")
+        # gr.Error(f"Feature names file '{FEATURES_FILENAME}' not found.")
+
+    if pipeline and label_encoder and feature_names:
+        print("All essential model artifacts loaded successfully.")
+    else:
+        print("Warning: One or more essential model artifacts failed to load. Prediction functionality may be limited.")
+
+except Exception as e:
+    print(f"Error loading model artifacts: {e}")
+    # Optionally raise a Gradio error to be visible in the UI on load
+    # gr.Error(f"Failed to load model artifacts: {e}")
+
+
+# --- Load Original Data for Overview Tab ---
+print("Attempting to load original dataset...")
+try:
+    if os.path.exists(DATA_FILE):
+        df_original = pd.read_csv(DATA_FILE)
+        df_head = df_original.head(10)
+        dataset_stats = f"**Number of Records:** {len(df_original)}\n\n**Columns:** {len(df_original.columns)}"
+        print(f"- Loaded: {DATA_FILE}")
+    else:
+        print(f"Warning: Original data file '{DATA_FILE}' not found for overview tab.")
+        dataset_stats = f"Original dataset file '{DATA_FILE}' not found."
+
+except Exception as e:
+    print(f"Error loading original dataset: {e}")
+    dataset_stats = f"Error loading original dataset: {e}"
+
+# --- Check if Plot Files Exist (for warnings in UI) ---
+plots_exist = {
+    "feature_importance": os.path.exists(FEATURE_IMPORTANCE_PLOT),
+    "pie_chart": os.path.exists(PLACEMENT_PIE_CHART),
+    "heatmap": os.path.exists(CORRELATION_HEATMAP)
+}
+print(f"Plot file existence check: {plots_exist}")
+
+
+# --- Define Prediction Function ---
+def predict_placement(*args):
+    """
+    Predicts placement status based on input features.
+    Returns:
+        - Profile Summary (Markdown)
+        - Prediction Result (Markdown)
+        - Probability Plot (Matplotlib Figure or None)
+    """
+    # Check if essential objects are loaded
+    if pipeline is None or label_encoder is None or feature_names is None:
+         message = "⚠️ **Error:** Model artifacts not loaded correctly. Cannot perform prediction."
+         print(message)
+         return (message, "", None) # Return error message and no plot
+
+    # Create a DataFrame from the inputs with correct column names
+    try:
+        input_data = pd.DataFrame([args], columns=feature_names)
+    except ValueError as e:
+         message = f"⚠️ **Error:** Input data mismatch with expected features. Details: {e}"
+         print(message)
+         return (message, "", None)
+
+    # Prepare Profile Summary String
+    profile_md = "### 🧑‍🎓 Student Profile Summary\n" + "-"*25 + "\n"
+    for i, feature in enumerate(feature_names):
+        label = feature.replace('_p', ' %').replace('_b', ' Board').replace('_s', ' Stream').replace('_t', ' Type').replace('workex', 'Work Experience').replace('etest', 'Employability Test').replace('ssc', 'SSC').replace('hsc', 'HSC').replace('mba', 'MBA').replace('degree','Degree').replace('specialisation','Specialisation').replace('gender','Gender').replace('_',' ').title()
+        profile_md += f"**{label}:** {args[i]}\n"
+
+    # Convert numerical inputs (sliders/numbers) to numeric types
+    numerical_cols_in_features = [
+        'ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'
+    ]
+    try:
+        for col in numerical_cols_in_features:
+            if col in input_data.columns:
+               input_data[col] = pd.to_numeric(input_data[col])
+    except ValueError as e:
+        error_msg = f"Error: Invalid numeric value provided. Details: {e}"
+        print(error_msg)
+        return (profile_md, f"⚠️ **Prediction Error:**\n{error_msg}", None)
+
+    # Make prediction probability
+    try:
+        pred_proba = pipeline.predict_proba(input_data)[0]
+        predicted_class_index = np.argmax(pred_proba)
+        predicted_status = label_encoder.inverse_transform([predicted_class_index])[0]
+        confidence = pred_proba[predicted_class_index]
+
+        # Format prediction result
+        if predicted_status == 'Placed':
+            result_md = f"## ✅ Prediction: PLACED\n**Confidence:** {confidence:.2%}"
+        else:
+            result_md = f"## ❌ Prediction: NOT PLACED\n**Confidence:** {confidence:.2%}"
+
+        # Create probability bar chart
+        fig, ax = plt.subplots(figsize=(5, 3)) # Smaller plot for UI
+        statuses = label_encoder.classes_
+        probabilities = pred_proba
+        colors = ['#ff9999', '#66b3ff'] # Ensure colors match labels if needed
+        # Ensure correct color mapping if classes aren't always ['Not Placed', 'Placed']
+        status_color_map = {label_encoder.classes_[0]: colors[0], label_encoder.classes_[1]: colors[1]}
+        bar_colors = [status_color_map[status] for status in statuses]
+
+        bars = ax.bar(statuses, probabilities, color=bar_colors)
+        ax.set_ylim(0, 1)
+        ax.set_ylabel('Probability')
+        ax.set_title('Placement Probability')
+        for bar in bars:
+            height = bar.get_height()
+            ax.text(bar.get_x() + bar.get_width()/2., height, f'{height:.2%}',
+                    ha='center', va='bottom', fontsize=9)
+        plt.tight_layout()
+
+        # IMPORTANT: Close the plot to prevent it from displaying in logs or consuming memory
+        # We return the figure object for Gradio to render
+        # plt.close(fig) # DO NOT CLOSE HERE - Gradio needs the figure object
+
+        return profile_md, result_md, fig # Return figure object
+
+    except Exception as e:
+        error_msg = f"An error occurred during prediction: {e}"
+        print(f"Error during prediction: {e}")
+        print(f"Input data:\n{input_data.to_string()}")
+        print(f"Input data types:\n{input_data.dtypes}")
+        # Ensure plot is closed if an error occurs before returning
+        try: plt.close(fig)
+        except NameError: pass # fig might not be defined if error happened early
+        return (profile_md, f"⚠️ **Prediction Error:**\n{error_msg}", None)
+
+
+# --- Build Gradio Interface using Blocks ---
+app_title = "🎓 Campus Placement Predictor"
+app_description = """
+Predict student placement based on academic performance, background, work experience, and MBA specialization.
+Input the details below and click 'Predict'. Explore other tabs for insights.
+"""
+
+css = """
+.gradio-container { font-family: 'IBM Plex Sans', sans-serif; max-width: 1200px; margin: auto; }
+.gr-button { color: white; border-color: #007bff; background: #007bff; }
+footer { visibility: hidden; }
+.gr-label { font-weight: bold; }
+h1 { text-align: center; }
+"""
+
+# Define default values (can be adjusted)
+default_ssc_p = 70.0
+default_hsc_p = 70.0
+default_degree_p = 70.0
+default_etest_p = 70.0
+default_mba_p = 65.0
+
+# Start Gradio Blocks UI Definition
+app_ui = gr.Blocks(theme=gr.themes.Soft(primary_hue=gr.themes.colors.blue, secondary_hue=gr.themes.colors.sky), title=app_title, css=css)
+
+with app_ui:
+    gr.Markdown(f"<h1>{app_title}</h1>")
+    gr.Markdown(app_description)
+
+    # Define Input Components (organized)
+    input_components_map = {}
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("**Personal & Secondary**")
+            input_components_map['gender'] = gr.Radio(label="Gender", choices=['M', 'F'], value='M')
+            input_components_map['ssc_p'] = gr.Slider(label="SSC Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_ssc_p)
+            input_components_map['ssc_b'] = gr.Dropdown(label="SSC Board", choices=['Central', 'Others'], value='Central')
+        with gr.Column(scale=1):
+            gr.Markdown("**Higher Secondary**")
+            input_components_map['hsc_p'] = gr.Slider(label="HSC Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_hsc_p)
+            input_components_map['hsc_b'] = gr.Dropdown(label="HSC Board", choices=['Central', 'Others'], value='Central')
+            input_components_map['hsc_s'] = gr.Dropdown(label="HSC Stream", choices=['Commerce', 'Science', 'Arts'], value='Commerce')
+        with gr.Column(scale=1):
+            gr.Markdown("**Degree & Experience**")
+            input_components_map['degree_p'] = gr.Slider(label="Degree Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_degree_p)
+            input_components_map['degree_t'] = gr.Dropdown(label="Degree Type", choices=['Comm&Mgmt', 'Sci&Tech', 'Others'], value='Comm&Mgmt')
+            input_components_map['workex'] = gr.Radio(label="Work Experience", choices=['No', 'Yes'], value='No')
+        with gr.Column(scale=1):
+            gr.Markdown("**Employability & MBA**")
+            input_components_map['etest_p'] = gr.Slider(label="Employability Test %", minimum=0.0, maximum=100.0, step=0.1, value=default_etest_p)
+            input_components_map['specialisation'] = gr.Dropdown(label="MBA Specialization", choices=['Mkt&Fin', 'Mkt&HR'], value='Mkt&Fin')
+            input_components_map['mba_p'] = gr.Slider(label="MBA Percentage", minimum=0.0, maximum=100.0, step=0.1, value=default_mba_p)
+
+    # --- Order Input Components based on loaded feature_names ---
+    ordered_input_components = []
+    if feature_names:
+        missing_features = []
+        for name in feature_names:
+            component = input_components_map.get(name)
+            if component:
+                ordered_input_components.append(component)
+            else:
+                missing_features.append(name)
+                print(f"Warning: UI component for feature '{name}' not defined in input_components_map.")
+        if missing_features:
+             gr.Warning(f"Missing UI components for features: {', '.join(missing_features)}. Predictions might fail.")
+        elif len(ordered_input_components) != len(feature_names):
+             gr.Warning("Mismatch between number of UI components and expected features.")
+    else:
+        # Fallback if feature_names couldn't load - order might be wrong!
+        ordered_input_components = list(input_components_map.values())
+        gr.Warning("Feature names file not loaded. Input order may be incorrect, predictions might fail.")
+
+
+    predict_button = gr.Button("🚀 Predict Placement Status")
+
+    # Define Output Components within Tabs
+    with gr.Tabs():
+        with gr.TabItem("📊 Prediction Results"):
+            with gr.Row():
+                out_profile = gr.Markdown(label="Input Summary")
+                with gr.Column():
+                     out_prediction = gr.Markdown(label="Prediction")
+                     out_plot = gr.Plot(label="Probability Distribution") # Displays the matplotlib fig
+
+        with gr.TabItem("💡 Feature Importance"):
+            gr.Markdown("## Feature Importance Analysis")
+            gr.Markdown("Shows which factors most influence the placement prediction (based on the trained model). Higher values indicate greater influence.")
+            if plots_exist["feature_importance"]:
+                gr.Image(FEATURE_IMPORTANCE_PLOT, label="Feature Importance Plot", show_label=False)
+            else:
+                gr.Warning(f"Feature importance plot not found at '{FEATURE_IMPORTANCE_PLOT}'. Please ensure it was generated and uploaded.")
+            gr.Markdown("""
+            *Insights based on typical results for this type of problem:*
+            - **Academic Performance:** SSC %, HSC %, and Degree % are often strong predictors.
+            - **Employability Test:** Performance in standardized tests (etest_p) is usually critical.
+            - **Work Experience:** Can provide a significant advantage.
+            - **MBA Performance:** MBA % reinforces the importance of consistent academic achievement.
+            """)
+
+        with gr.TabItem("📈 Dataset Overview"):
+            gr.Markdown("## Dataset Overview")
+            gr.Markdown("A quick look at the data used to train the model.")
+            with gr.Row():
+                with gr.Column(scale=2): # Give more space to dataframe
+                    gr.Markdown("**Data Sample**")
+                    if df_original is not None:
+                         gr.DataFrame(df_head, label="First 10 Rows", row_count=(10, "fixed"), wrap=True, interactive=False)
+                    else:
+                         gr.Warning(f"Original dataset '{DATA_FILE}' not found.")
+                    gr.Markdown("**Basic Stats**")
+                    gr.Markdown(dataset_stats)
+                with gr.Column(scale=1):
+                     gr.Markdown("**Placement Distribution**")
+                     if plots_exist["pie_chart"]:
+                         gr.Image(PLACEMENT_PIE_CHART, label="Placement Distribution", show_label=False)
+                     else:
+                         gr.Warning(f"Placement distribution plot not found at '{PLACEMENT_PIE_CHART}'.")
+                     gr.Markdown("**Correlation Analysis**")
+                     if plots_exist["heatmap"]:
+                         gr.Image(CORRELATION_HEATMAP, label="Correlation Heatmap", show_label=False)
+                     else:
+                         gr.Warning(f"Correlation heatmap not found at '{CORRELATION_HEATMAP}'.")
+
+    # --- Link Button Click to Function ---
+    predict_button.click(
+        fn=predict_placement,
+        inputs=ordered_input_components, # Use the ordered list
+        outputs=[out_profile, out_prediction, out_plot]
+    )
+
+    # --- Add Examples ---
+    # Ensure example values match the order and type of ordered_input_components
+    if feature_names: # Only add examples if we know the correct feature order
+        example_list = [
+            # M, ssc_p, ssc_b, hsc_p, hsc_b, hsc_s, degree_p, degree_t, workex, etest_p, specialisation, mba_p -> default order if no feature_names
+            ['M', 67.0, 'Others', 91.0, 'Others', 'Commerce', 58.0, 'Sci&Tech', 'No', 55.0, 'Mkt&HR', 58.8], # Row 1 (Placed)
+            ['M', 56.0, 'Central', 52.0, 'Central', 'Science', 52.0, 'Sci&Tech', 'No', 66.0, 'Mkt&HR', 59.43], # Row 4 (Not Placed)
+            ['F', 77.0, 'Central', 87.0, 'Central', 'Commerce', 59.0, 'Comm&Mgmt', 'No', 68.0, 'Mkt&Fin', 68.63], # Row 14 (Placed)
+            ['F', 52.0, 'Central', 64.0, 'Central', 'Commerce', 61.0, 'Comm&Mgmt', 'No', 55.0, 'Mkt&Fin', 62.93], # Row 187 (Not Placed)
+            ['M', 84.0, 'Others', 90.9, 'Others', 'Science', 64.5, 'Sci&Tech', 'No', 86.04, 'Mkt&Fin', 59.42]  # Row 79 (Placed)
+        ]
+        # Remap examples based on actual feature_names order if necessary (though the default order matches here)
+        # This step is complex if the order differs significantly. Assuming the order defined in UI matches feature_names for simplicity now.
+        final_examples = example_list
+
+        gr.Examples(
+            examples=final_examples,
+            inputs=ordered_input_components,
+            outputs=[out_profile, out_prediction, out_plot],
+            fn=predict_placement,
+            cache_examples=False # Caching might be ok if function is pure
+        )
+
+# --- Launch the App ---
+# This is the standard way to launch in HF Spaces (app variable must be defined)
+# app_ui.launch() # No debug=True for production on Spaces
+
+# If running locally for testing before pushing to HF:
+if __name__ == "__main__":
+    print("Launching Gradio app locally...")
+    app_ui.launch(debug=True) # Use debug=True for local testing
+    # app_ui.launch() # Use this for standard local deployment without debug prints

placement_label_encoder.joblib

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+version https://git-lfs.github.com/spec/v1
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