├── Admission Chance.csv ├── README.md ├── final .ipynb ├── final.py └── outcomes ├── cgpa_vs_admit1.png ├── cgpa_vs_admit2.png ├── cgpa_vs_admit3.png ├── cgpa_vs_admit4.png ├── cgpa_vs_admit5.png ├── cgpa_vs_admit6.png ├── cgpa_vs_admit7.png ├── cgpa_vs_admit8.png ├── code1.jpeg ├── code2.jpeg └── code3.jpeg /Admission Chance.csv: -------------------------------------------------------------------------------- 1 | Serial No,GRE Score,TOEFL Score,University Rating, SOP,LOR ,CGPA,Research,Chance of Admit 2 | 1,337,118,4,4.5,4.5,9.65,1,0.92 3 | 2,324,107,4,4,4.5,8.87,1,0.76 4 | 3,316,104,3,3,3.5,8,1,0.72 5 | 4,322,110,3,3.5,2.5,8.67,1,0.8 6 | 5,314,103,2,2,3,8.21,0,0.65 7 | 6,330,115,5,4.5,3,9.34,1,0.9 8 | 7,321,109,3,3,4,8.2,1,0.75 9 | 8,308,101,2,3,4,7.9,0,0.68 10 | 9,302,102,1,2,1.5,8,0,0.5 11 | 10,323,108,3,3.5,3,8.6,0,0.45 12 | 11,325,106,3,3.5,4,8.4,1,0.52 13 | 12,327,111,4,4,4.5,9,1,0.84 14 | 13,328,112,4,4,4.5,9.1,1,0.78 15 | 14,307,109,3,4,3,8,1,0.62 16 | 15,311,104,3,3.5,2,8.2,1,0.61 17 | 16,314,105,3,3.5,2.5,8.3,0,0.54 18 | 17,317,107,3,4,3,8.7,0,0.66 19 | 18,319,106,3,4,3,8,1,0.65 20 | 19,318,110,3,4,3,8.8,0,0.63 21 | 20,303,102,3,3.5,3,8.5,0,0.62 22 | 21,312,107,3,3,2,7.9,1,0.64 23 | 22,325,114,4,3,2,8.4,0,0.7 24 | 23,328,116,5,5,5,9.5,1,0.94 25 | 24,334,119,5,5,4.5,9.7,1,0.95 26 | 25,336,119,5,4,3.5,9.8,1,0.97 27 | 26,340,120,5,4.5,4.5,9.6,1,0.94 28 | 27,322,109,5,4.5,3.5,8.8,0,0.76 29 | 28,298,98,2,1.5,2.5,7.5,1,0.44 30 | 29,295,93,1,2,2,7.2,0,0.46 31 | 30,310,99,2,1.5,2,7.3,0,0.54 32 | 31,300,97,2,3,3,8.1,1,0.65 33 | 32,327,103,3,4,4,8.3,1,0.74 34 | 33,338,118,4,3,4.5,9.4,1,0.91 35 | 34,340,114,5,4,4,9.6,1,0.9 36 | 35,331,112,5,4,5,9.8,1,0.94 37 | 36,320,110,5,5,5,9.2,1,0.88 38 | 37,299,106,2,4,4,8.4,0,0.64 39 | 38,300,105,1,1,2,7.8,0,0.58 40 | 39,304,105,1,3,1.5,7.5,0,0.52 41 | 40,307,108,2,4,3.5,7.7,0,0.48 42 | 41,308,110,3,3.5,3,8,1,0.46 43 | 42,316,105,2,2.5,2.5,8.2,1,0.49 44 | 43,313,107,2,2.5,2,8.5,1,0.53 45 | 44,332,117,4,4.5,4,9.1,0,0.87 46 | 45,326,113,5,4.5,4,9.4,1,0.91 47 | 46,322,110,5,5,4,9.1,1,0.88 48 | 47,329,114,5,4,5,9.3,1,0.86 49 | 48,339,119,5,4.5,4,9.7,0,0.89 50 | 49,321,110,3,3.5,5,8.85,1,0.82 51 | 50,327,111,4,3,4,8.4,1,0.78 52 | 51,313,98,3,2.5,4.5,8.3,1,0.76 53 | 52,312,100,2,1.5,3.5,7.9,1,0.56 54 | 53,334,116,4,4,3,8,1,0.78 55 | 54,324,112,4,4,2.5,8.1,1,0.72 56 | 55,322,110,3,3,3.5,8,0,0.7 57 | 56,320,103,3,3,3,7.7,0,0.64 58 | 57,316,102,3,2,3,7.4,0,0.64 59 | 58,298,99,2,4,2,7.6,0,0.46 60 | 59,300,99,1,3,2,6.8,1,0.36 61 | 60,311,104,2,2,2,8.3,0,0.42 62 | 61,309,100,2,3,3,8.1,0,0.48 63 | 62,307,101,3,4,3,8.2,0,0.47 64 | 63,304,105,2,3,3,8.2,1,0.54 65 | 64,315,107,2,4,3,8.5,1,0.56 66 | 65,325,111,3,3,3.5,8.7,0,0.52 67 | 66,325,112,4,3.5,3.5,8.92,0,0.55 68 | 67,327,114,3,3,3,9.02,0,0.61 69 | 68,316,107,2,3.5,3.5,8.64,1,0.57 70 | 69,318,109,3,3.5,4,9.22,1,0.68 71 | 70,328,115,4,4.5,4,9.16,1,0.78 72 | 71,332,118,5,5,5,9.64,1,0.94 73 | 72,336,112,5,5,5,9.76,1,0.96 74 | 73,321,111,5,5,5,9.45,1,0.93 75 | 74,314,108,4,4.5,4,9.04,1,0.84 76 | 75,314,106,3,3,5,8.9,0,0.74 77 | 76,329,114,2,2,4,8.56,1,0.72 78 | 77,327,112,3,3,3,8.72,1,0.74 79 | 78,301,99,2,3,2,8.22,0,0.64 80 | 79,296,95,2,3,2,7.54,1,0.44 81 | 80,294,93,1,1.5,2,7.36,0,0.46 82 | 81,312,105,3,2,3,8.02,1,0.5 83 | 82,340,120,4,5,5,9.5,1,0.96 84 | 83,320,110,5,5,4.5,9.22,1,0.92 85 | 84,322,115,5,4,4.5,9.36,1,0.92 86 | 85,340,115,5,4.5,4.5,9.45,1,0.94 87 | 86,319,103,4,4.5,3.5,8.66,0,0.76 88 | 87,315,106,3,4.5,3.5,8.42,0,0.72 89 | 88,317,107,2,3.5,3,8.28,0,0.66 90 | 89,314,108,3,4.5,3.5,8.14,0,0.64 91 | 90,316,109,4,4.5,3.5,8.76,1,0.74 92 | 91,318,106,2,4,4,7.92,1,0.64 93 | 92,299,97,3,5,3.5,7.66,0,0.38 94 | 93,298,98,2,4,3,8.03,0,0.34 95 | 94,301,97,2,3,3,7.88,1,0.44 96 | 95,303,99,3,2,2.5,7.66,0,0.36 97 | 96,304,100,4,1.5,2.5,7.84,0,0.42 98 | 97,306,100,2,3,3,8,0,0.48 99 | 98,331,120,3,4,4,8.96,1,0.86 100 | 99,332,119,4,5,4.5,9.24,1,0.9 101 | 100,323,113,3,4,4,8.88,1,0.79 102 | 101,322,107,3,3.5,3.5,8.46,1,0.71 103 | 102,312,105,2,2.5,3,8.12,0,0.64 104 | 103,314,106,2,4,3.5,8.25,0,0.62 105 | 104,317,104,2,4.5,4,8.47,0,0.57 106 | 105,326,112,3,3.5,3,9.05,1,0.74 107 | 106,316,110,3,4,4.5,8.78,1,0.69 108 | 107,329,111,4,4.5,4.5,9.18,1,0.87 109 | 108,338,117,4,3.5,4.5,9.46,1,0.91 110 | 109,331,116,5,5,5,9.38,1,0.93 111 | 110,304,103,5,5,4,8.64,0,0.68 112 | 111,305,108,5,3,3,8.48,0,0.61 113 | 112,321,109,4,4,4,8.68,1,0.69 114 | 113,301,107,3,3.5,3.5,8.34,1,0.62 115 | 114,320,110,2,4,3.5,8.56,0,0.72 116 | 115,311,105,3,3.5,3,8.45,1,0.59 117 | 116,310,106,4,4.5,4.5,9.04,1,0.66 118 | 117,299,102,3,4,3.5,8.62,0,0.56 119 | 118,290,104,4,2,2.5,7.46,0,0.45 120 | 119,296,99,2,3,3.5,7.28,0,0.47 121 | 120,327,104,5,3,3.5,8.84,1,0.71 122 | 121,335,117,5,5,5,9.56,1,0.94 123 | 122,334,119,5,4.5,4.5,9.48,1,0.94 124 | 123,310,106,4,1.5,2.5,8.36,0,0.57 125 | 124,308,108,3,3.5,3.5,8.22,0,0.61 126 | 125,301,106,4,2.5,3,8.47,0,0.57 127 | 126,300,100,3,2,3,8.66,1,0.64 128 | 127,323,113,3,4,3,9.32,1,0.85 129 | 128,319,112,3,2.5,2,8.71,1,0.78 130 | 129,326,112,3,3.5,3,9.1,1,0.84 131 | 130,333,118,5,5,5,9.35,1,0.92 132 | 131,339,114,5,4,4.5,9.76,1,0.96 133 | 132,303,105,5,5,4.5,8.65,0,0.77 134 | 133,309,105,5,3.5,3.5,8.56,0,0.71 135 | 134,323,112,5,4,4.5,8.78,0,0.79 136 | 135,333,113,5,4,4,9.28,1,0.89 137 | 136,314,109,4,3.5,4,8.77,1,0.82 138 | 137,312,103,3,5,4,8.45,0,0.76 139 | 138,316,100,2,1.5,3,8.16,1,0.71 140 | 139,326,116,2,4.5,3,9.08,1,0.8 141 | 140,318,109,1,3.5,3.5,9.12,0,0.78 142 | 141,329,110,2,4,3,9.15,1,0.84 143 | 142,332,118,2,4.5,3.5,9.36,1,0.9 144 | 143,331,115,5,4,3.5,9.44,1,0.92 145 | 144,340,120,4,4.5,4,9.92,1,0.97 146 | 145,325,112,2,3,3.5,8.96,1,0.8 147 | 146,320,113,2,2,2.5,8.64,1,0.81 148 | 147,315,105,3,2,2.5,8.48,0,0.75 149 | 148,326,114,3,3,3,9.11,1,0.83 150 | 149,339,116,4,4,3.5,9.8,1,0.96 151 | 150,311,106,2,3.5,3,8.26,1,0.79 152 | 151,334,114,4,4,4,9.43,1,0.93 153 | 152,332,116,5,5,5,9.28,1,0.94 154 | 153,321,112,5,5,5,9.06,1,0.86 155 | 154,324,105,3,3,4,8.75,0,0.79 156 | 155,326,108,3,3,3.5,8.89,0,0.8 157 | 156,312,109,3,3,3,8.69,0,0.77 158 | 157,315,105,3,2,2.5,8.34,0,0.7 159 | 158,309,104,2,2,2.5,8.26,0,0.65 160 | 159,306,106,2,2,2.5,8.14,0,0.61 161 | 160,297,100,1,1.5,2,7.9,0,0.52 162 | 161,315,103,1,1.5,2,7.86,0,0.57 163 | 162,298,99,1,1.5,3,7.46,0,0.53 164 | 163,318,109,3,3,3,8.5,0,0.67 165 | 164,317,105,3,3.5,3,8.56,0,0.68 166 | 165,329,111,4,4.5,4,9.01,1,0.81 167 | 166,322,110,5,4.5,4,8.97,0,0.78 168 | 167,302,102,3,3.5,5,8.33,0,0.65 169 | 168,313,102,3,2,3,8.27,0,0.64 170 | 169,293,97,2,2,4,7.8,1,0.64 171 | 170,311,99,2,2.5,3,7.98,0,0.65 172 | 171,312,101,2,2.5,3.5,8.04,1,0.68 173 | 172,334,117,5,4,4.5,9.07,1,0.89 174 | 173,322,110,4,4,5,9.13,1,0.86 175 | 174,323,113,4,4,4.5,9.23,1,0.89 176 | 175,321,111,4,4,4,8.97,1,0.87 177 | 176,320,111,4,4.5,3.5,8.87,1,0.85 178 | 177,329,119,4,4.5,4.5,9.16,1,0.9 179 | 178,319,110,3,3.5,3.5,9.04,0,0.82 180 | 179,309,108,3,2.5,3,8.12,0,0.72 181 | 180,307,102,3,3,3,8.27,0,0.73 182 | 181,300,104,3,3.5,3,8.16,0,0.71 183 | 182,305,107,2,2.5,2.5,8.42,0,0.71 184 | 183,299,100,2,3,3.5,7.88,0,0.68 185 | 184,314,110,3,4,4,8.8,0,0.75 186 | 185,316,106,2,2.5,4,8.32,0,0.72 187 | 186,327,113,4,4.5,4.5,9.11,1,0.89 188 | 187,317,107,3,3.5,3,8.68,1,0.84 189 | 188,335,118,5,4.5,3.5,9.44,1,0.93 190 | 189,331,115,5,4.5,3.5,9.36,1,0.93 191 | 190,324,112,5,5,5,9.08,1,0.88 192 | 191,324,111,5,4.5,4,9.16,1,0.9 193 | 192,323,110,5,4,5,8.98,1,0.87 194 | 193,322,114,5,4.5,4,8.94,1,0.86 195 | 194,336,118,5,4.5,5,9.53,1,0.94 196 | 195,316,109,3,3.5,3,8.76,0,0.77 197 | 196,307,107,2,3,3.5,8.52,1,0.78 198 | 197,306,105,2,3,2.5,8.26,0,0.73 199 | 198,310,106,2,3.5,2.5,8.33,0,0.73 200 | 199,311,104,3,4.5,4.5,8.43,0,0.7 201 | 200,313,107,3,4,4.5,8.69,0,0.72 202 | 201,317,103,3,2.5,3,8.54,1,0.73 203 | 202,315,110,2,3.5,3,8.46,1,0.72 204 | 203,340,120,5,4.5,4.5,9.91,1,0.97 205 | 204,334,120,5,4,5,9.87,1,0.97 206 | 205,298,105,3,3.5,4,8.54,0,0.69 207 | 206,295,99,2,2.5,3,7.65,0,0.57 208 | 207,315,99,2,3.5,3,7.89,0,0.63 209 | 208,310,102,3,3.5,4,8.02,1,0.66 210 | 209,305,106,2,3,3,8.16,0,0.64 211 | 210,301,104,3,3.5,4,8.12,1,0.68 212 | 211,325,108,4,4.5,4,9.06,1,0.79 213 | 212,328,110,4,5,4,9.14,1,0.82 214 | 213,338,120,4,5,5,9.66,1,0.95 215 | 214,333,119,5,5,4.5,9.78,1,0.96 216 | 215,331,117,4,4.5,5,9.42,1,0.94 217 | 216,330,116,5,5,4.5,9.36,1,0.93 218 | 217,322,112,4,4.5,4.5,9.26,1,0.91 219 | 218,321,109,4,4,4,9.13,1,0.85 220 | 219,324,110,4,3,3.5,8.97,1,0.84 221 | 220,312,104,3,3.5,3.5,8.42,0,0.74 222 | 221,313,103,3,4,4,8.75,0,0.76 223 | 222,316,110,3,3.5,4,8.56,0,0.75 224 | 223,324,113,4,4.5,4,8.79,0,0.76 225 | 224,308,109,2,3,4,8.45,0,0.71 226 | 225,305,105,2,3,2,8.23,0,0.67 227 | 226,296,99,2,2.5,2.5,8.03,0,0.61 228 | 227,306,110,2,3.5,4,8.45,0,0.63 229 | 228,312,110,2,3.5,3,8.53,0,0.64 230 | 229,318,112,3,4,3.5,8.67,0,0.71 231 | 230,324,111,4,3,3,9.01,1,0.82 232 | 231,313,104,3,4,4.5,8.65,0,0.73 233 | 232,319,106,3,3.5,2.5,8.33,1,0.74 234 | 233,312,107,2,2.5,3.5,8.27,0,0.69 235 | 234,304,100,2,2.5,3.5,8.07,0,0.64 236 | 235,330,113,5,5,4,9.31,1,0.91 237 | 236,326,111,5,4.5,4,9.23,1,0.88 238 | 237,325,112,4,4,4.5,9.17,1,0.85 239 | 238,329,114,5,4.5,5,9.19,1,0.86 240 | 239,310,104,3,2,3.5,8.37,0,0.7 241 | 240,299,100,1,1.5,2,7.89,0,0.59 242 | 241,296,101,1,2.5,3,7.68,0,0.6 243 | 242,317,103,2,2.5,2,8.15,0,0.65 244 | 243,324,115,3,3.5,3,8.76,1,0.7 245 | 244,325,114,3,3.5,3,9.04,1,0.76 246 | 245,314,107,2,2.5,4,8.56,0,0.63 247 | 246,328,110,4,4,2.5,9.02,1,0.81 248 | 247,316,105,3,3,3.5,8.73,0,0.72 249 | 248,311,104,2,2.5,3.5,8.48,0,0.71 250 | 249,324,110,3,3.5,4,8.87,1,0.8 251 | 250,321,111,3,3.5,4,8.83,1,0.77 252 | 251,320,104,3,3,2.5,8.57,1,0.74 253 | 252,316,99,2,2.5,3,9,0,0.7 254 | 253,318,100,2,2.5,3.5,8.54,1,0.71 255 | 254,335,115,4,4.5,4.5,9.68,1,0.93 256 | 255,321,114,4,4,5,9.12,0,0.85 257 | 256,307,110,4,4,4.5,8.37,0,0.79 258 | 257,309,99,3,4,4,8.56,0,0.76 259 | 258,324,100,3,4,5,8.64,1,0.78 260 | 259,326,102,4,5,5,8.76,1,0.77 261 | 260,331,119,4,5,4.5,9.34,1,0.9 262 | 261,327,108,5,5,3.5,9.13,1,0.87 263 | 262,312,104,3,3.5,4,8.09,0,0.71 264 | 263,308,103,2,2.5,4,8.36,1,0.7 265 | 264,324,111,3,2.5,1.5,8.79,1,0.7 266 | 265,325,110,2,3,2.5,8.76,1,0.75 267 | 266,313,102,3,2.5,2.5,8.68,0,0.71 268 | 267,312,105,2,2,2.5,8.45,0,0.72 269 | 268,314,107,3,3,3.5,8.17,1,0.73 270 | 269,327,113,4,4.5,5,9.14,0,0.83 271 | 270,308,108,4,4.5,5,8.34,0,0.77 272 | 271,306,105,2,2.5,3,8.22,1,0.72 273 | 272,299,96,2,1.5,2,7.86,0,0.54 274 | 273,294,95,1,1.5,1.5,7.64,0,0.49 275 | 274,312,99,1,1,1.5,8.01,1,0.52 276 | 275,315,100,1,2,2.5,7.95,0,0.58 277 | 276,322,110,3,3.5,3,8.96,1,0.78 278 | 277,329,113,5,5,4.5,9.45,1,0.89 279 | 278,320,101,2,2.5,3,8.62,0,0.7 280 | 279,308,103,2,3,3.5,8.49,0,0.66 281 | 280,304,102,2,3,4,8.73,0,0.67 282 | 281,311,102,3,4.5,4,8.64,1,0.68 283 | 282,317,110,3,4,4.5,9.11,1,0.8 284 | 283,312,106,3,4,3.5,8.79,1,0.81 285 | 284,321,111,3,2.5,3,8.9,1,0.8 286 | 285,340,112,4,5,4.5,9.66,1,0.94 287 | 286,331,116,5,4,4,9.26,1,0.93 288 | 287,336,118,5,4.5,4,9.19,1,0.92 289 | 288,324,114,5,5,4.5,9.08,1,0.89 290 | 289,314,104,4,5,5,9.02,0,0.82 291 | 290,313,109,3,4,3.5,9,0,0.79 292 | 291,307,105,2,2.5,3,7.65,0,0.58 293 | 292,300,102,2,1.5,2,7.87,0,0.56 294 | 293,302,99,2,1,2,7.97,0,0.56 295 | 294,312,98,1,3.5,3,8.18,1,0.64 296 | 295,316,101,2,2.5,2,8.32,1,0.61 297 | 296,317,100,2,3,2.5,8.57,0,0.68 298 | 297,310,107,3,3.5,3.5,8.67,0,0.76 299 | 298,320,120,3,4,4.5,9.11,0,0.86 300 | 299,330,114,3,4.5,4.5,9.24,1,0.9 301 | 300,305,112,3,3,3.5,8.65,0,0.71 302 | 301,309,106,2,2.5,2.5,8,0,0.62 303 | 302,319,108,2,2.5,3,8.76,0,0.66 304 | 303,322,105,2,3,3,8.45,1,0.65 305 | 304,323,107,3,3.5,3.5,8.55,1,0.73 306 | 305,313,106,2,2.5,2,8.43,0,0.62 307 | 306,321,109,3,3.5,3.5,8.8,1,0.74 308 | 307,323,110,3,4,3.5,9.1,1,0.79 309 | 308,325,112,4,4,4,9,1,0.8 310 | 309,312,108,3,3.5,3,8.53,0,0.69 311 | 310,308,110,4,3.5,3,8.6,0,0.7 312 | 311,320,104,3,3,3.5,8.74,1,0.76 313 | 312,328,108,4,4.5,4,9.18,1,0.84 314 | 313,311,107,4,4.5,4.5,9,1,0.78 315 | 314,301,100,3,3.5,3,8.04,0,0.67 316 | 315,305,105,2,3,4,8.13,0,0.66 317 | 316,308,104,2,2.5,3,8.07,0,0.65 318 | 317,298,101,2,1.5,2,7.86,0,0.54 319 | 318,300,99,1,1,2.5,8.01,0,0.58 320 | 319,324,111,3,2.5,2,8.8,1,0.79 321 | 320,327,113,4,3.5,3,8.69,1,0.8 322 | 321,317,106,3,4,3.5,8.5,1,0.75 323 | 322,323,104,3,4,4,8.44,1,0.73 324 | 323,314,107,2,2.5,4,8.27,0,0.72 325 | 324,305,102,2,2,2.5,8.18,0,0.62 326 | 325,315,104,3,3,2.5,8.33,0,0.67 327 | 326,326,116,3,3.5,4,9.14,1,0.81 328 | 327,299,100,3,2,2,8.02,0,0.63 329 | 328,295,101,2,2.5,2,7.86,0,0.69 330 | 329,324,112,4,4,3.5,8.77,1,0.8 331 | 330,297,96,2,2.5,1.5,7.89,0,0.43 332 | 331,327,113,3,3.5,3,8.66,1,0.8 333 | 332,311,105,2,3,2,8.12,1,0.73 334 | 333,308,106,3,3.5,2.5,8.21,1,0.75 335 | 334,319,108,3,3,3.5,8.54,1,0.71 336 | 335,312,107,4,4.5,4,8.65,1,0.73 337 | 336,325,111,4,4,4.5,9.11,1,0.83 338 | 337,319,110,3,3,2.5,8.79,0,0.72 339 | 338,332,118,5,5,5,9.47,1,0.94 340 | 339,323,108,5,4,4,8.74,1,0.81 341 | 340,324,107,5,3.5,4,8.66,1,0.81 342 | 341,312,107,3,3,3,8.46,1,0.75 343 | 342,326,110,3,3.5,3.5,8.76,1,0.79 344 | 343,308,106,3,3,3,8.24,0,0.58 345 | 344,305,103,2,2.5,3.5,8.13,0,0.59 346 | 345,295,96,2,1.5,2,7.34,0,0.47 347 | 346,316,98,1,1.5,2,7.43,0,0.49 348 | 347,304,97,2,1.5,2,7.64,0,0.47 349 | 348,299,94,1,1,1,7.34,0,0.42 350 | 349,302,99,1,2,2,7.25,0,0.57 351 | 350,313,101,3,2.5,3,8.04,0,0.62 352 | 351,318,107,3,3,3.5,8.27,1,0.74 353 | 352,325,110,4,3.5,4,8.67,1,0.73 354 | 353,303,100,2,3,3.5,8.06,1,0.64 355 | 354,300,102,3,3.5,2.5,8.17,0,0.63 356 | 355,297,98,2,2.5,3,7.67,0,0.59 357 | 356,317,106,2,2,3.5,8.12,0,0.73 358 | 357,327,109,3,3.5,4,8.77,1,0.79 359 | 358,301,104,2,3.5,3.5,7.89,1,0.68 360 | 359,314,105,2,2.5,2,7.64,0,0.7 361 | 360,321,107,2,2,1.5,8.44,0,0.81 362 | 361,322,110,3,4,5,8.64,1,0.85 363 | 362,334,116,4,4,3.5,9.54,1,0.93 364 | 363,338,115,5,4.5,5,9.23,1,0.91 365 | 364,306,103,2,2.5,3,8.36,0,0.69 366 | 365,313,102,3,3.5,4,8.9,1,0.77 367 | 366,330,114,4,4.5,3,9.17,1,0.86 368 | 367,320,104,3,3.5,4.5,8.34,1,0.74 369 | 368,311,98,1,1,2.5,7.46,0,0.57 370 | 369,298,92,1,2,2,7.88,0,0.51 371 | 370,301,98,1,2,3,8.03,1,0.67 372 | 371,310,103,2,2.5,2.5,8.24,0,0.72 373 | 372,324,110,3,3.5,3,9.22,1,0.89 374 | 373,336,119,4,4.5,4,9.62,1,0.95 375 | 374,321,109,3,3,3,8.54,1,0.79 376 | 375,315,105,2,2,2.5,7.65,0,0.39 377 | 376,304,101,2,2,2.5,7.66,0,0.38 378 | 377,297,96,2,2.5,2,7.43,0,0.34 379 | 378,290,100,1,1.5,2,7.56,0,0.47 380 | 379,303,98,1,2,2.5,7.65,0,0.56 381 | 380,311,99,1,2.5,3,8.43,1,0.71 382 | 381,322,104,3,3.5,4,8.84,1,0.78 383 | 382,319,105,3,3,3.5,8.67,1,0.73 384 | 383,324,110,4,4.5,4,9.15,1,0.82 385 | 384,300,100,3,3,3.5,8.26,0,0.62 386 | 385,340,113,4,5,5,9.74,1,0.96 387 | 386,335,117,5,5,5,9.82,1,0.96 388 | 387,302,101,2,2.5,3.5,7.96,0,0.46 389 | 388,307,105,2,2,3.5,8.1,0,0.53 390 | 389,296,97,2,1.5,2,7.8,0,0.49 391 | 390,320,108,3,3.5,4,8.44,1,0.76 392 | 391,314,102,2,2,2.5,8.24,0,0.64 393 | 392,318,106,3,2,3,8.65,0,0.71 394 | 393,326,112,4,4,3.5,9.12,1,0.84 395 | 394,317,104,2,3,3,8.76,0,0.77 396 | 395,329,111,4,4.5,4,9.23,1,0.89 397 | 396,324,110,3,3.5,3.5,9.04,1,0.82 398 | 397,325,107,3,3,3.5,9.11,1,0.84 399 | 398,330,116,4,5,4.5,9.45,1,0.91 400 | 399,312,103,3,3.5,4,8.78,0,0.67 401 | 400,333,117,4,5,4,9.66,1,0.95 402 | 401,322,110,3,3.5,2.5,8.67,1,0.8 403 | 402,308,101,2,3,4,7.9,0,0.68 404 | 403,307,109,3,4,3,8,1,0.62 405 | 404,318,110,3,4,3,8.8,0,0.63 406 | 405,298,98,2,1.5,2.5,7.5,1,0.44 407 | 406,300,105,1,1,2,7.8,0,0.58 408 | 407,339,119,5,4.5,4,9.7,0,0.89 409 | 408,324,112,4,4,2.5,8.1,1,0.72 410 | 409,315,107,2,4,3,8.5,1,0.56 411 | 410,327,114,3,3,3,9.02,0,0.61 412 | 411,328,115,4,4.5,4,9.16,1,0.78 413 | 412,294,93,1,1.5,2,7.36,0,0.46 414 | 413,319,103,4,4.5,3.5,8.66,0,0.76 415 | 414,299,97,3,5,3.5,7.66,0,0.38 416 | 415,322,107,3,3.5,3.5,8.46,1,0.71 417 | 416,317,104,2,4.5,4,8.47,0,0.57 418 | 417,329,111,4,4.5,4.5,9.18,1,0.87 419 | 418,301,107,3,3.5,3.5,8.34,1,0.62 420 | 419,310,106,4,4.5,4.5,9.04,1,0.66 421 | 420,296,99,2,3,3.5,7.28,0,0.47 422 | 421,334,119,5,4.5,4.5,9.48,1,0.94 423 | 422,301,106,4,2.5,3,8.47,0,0.57 424 | 423,319,112,3,2.5,2,8.71,1,0.78 425 | 424,323,112,5,4,4.5,8.78,0,0.79 426 | 425,331,115,5,4,3.5,9.44,1,0.92 427 | 426,320,113,2,2,2.5,8.64,1,0.81 428 | 427,332,116,5,5,5,9.28,1,0.94 429 | 428,309,104,2,2,2.5,8.26,0,0.65 430 | 429,302,102,3,3.5,5,8.33,0,0.65 431 | 430,322,110,4,4,5,9.13,1,0.86 432 | 431,316,106,2,2.5,4,8.32,0,0.72 433 | 432,324,111,5,4.5,4,9.16,1,0.9 434 | 433,313,107,3,4,4.5,8.69,0,0.72 435 | 434,295,99,2,2.5,3,7.65,0,0.57 436 | 435,328,110,4,5,4,9.14,1,0.82 437 | 436,321,109,4,4,4,9.13,1,0.85 438 | 437,313,103,3,4,4,8.75,0,0.76 439 | 438,306,110,2,3.5,4,8.45,0,0.63 440 | 439,324,111,4,3,3,9.01,1,0.82 441 | 440,310,104,3,2,3.5,8.37,0,0.7 442 | 441,314,107,2,2.5,4,8.56,0,0.63 443 | 442,311,104,2,2.5,3.5,8.48,0,0.71 444 | 443,320,104,3,3,2.5,8.57,1,0.74 445 | 444,335,115,4,4.5,4.5,9.68,1,0.93 446 | 445,331,119,4,5,4.5,9.34,1,0.9 447 | 446,308,103,2,2.5,4,8.36,1,0.7 448 | 447,327,113,4,4.5,5,9.14,0,0.83 449 | 448,299,96,2,1.5,2,7.86,0,0.54 450 | 449,315,100,1,2,2.5,7.95,0,0.58 451 | 450,320,101,2,2.5,3,8.62,0,0.7 452 | 451,311,102,3,4.5,4,8.64,1,0.68 453 | 452,307,105,2,2.5,3,7.65,0,0.58 454 | 453,305,112,3,3,3.5,8.65,0,0.71 455 | 454,321,109,3,3.5,3.5,8.8,1,0.74 456 | 455,312,108,3,3.5,3,8.53,0,0.69 457 | 456,305,105,2,3,4,8.13,0,0.66 458 | 457,305,102,2,2,2.5,8.18,0,0.62 459 | 458,297,96,2,2.5,1.5,7.89,0,0.43 460 | 459,325,111,4,4,4.5,9.11,1,0.83 461 | 460,326,110,3,3.5,3.5,8.76,1,0.79 462 | 461,295,96,2,1.5,2,7.34,0,0.47 463 | 462,300,102,3,3.5,2.5,8.17,0,0.63 464 | 463,321,107,2,2,1.5,8.44,0,0.81 465 | 464,330,114,4,4.5,3,9.17,1,0.86 466 | 465,324,110,3,3.5,3,9.22,1,0.89 467 | 466,315,105,2,2,2.5,7.65,0,0.39 468 | 467,290,100,1,1.5,2,7.56,0,0.47 469 | 468,300,100,3,3,3.5,8.26,0,0.62 470 | 469,302,101,2,2.5,3.5,7.96,0,0.46 471 | 470,320,108,3,3.5,4,8.44,1,0.76 472 | 471,324,110,3,3.5,3.5,9.04,1,0.82 473 | 472,330,116,4,5,4.5,9.45,1,0.91 474 | 473,333,117,4,5,4,9.66,1,0.95 475 | 474,298,98,2,1.5,2.5,7.5,1,0.44 476 | 475,300,105,1,1,2,7.8,0,0.58 477 | 476,339,119,5,4.5,4,9.7,0,0.89 478 | 477,324,112,4,4,2.5,8.1,1,0.72 479 | 478,315,107,2,4,3,8.5,1,0.56 480 | 479,327,114,3,3,3,9.02,0,0.61 481 | 480,328,115,4,4.5,4,9.16,1,0.78 482 | 481,294,93,1,1.5,2,7.36,0,0.46 483 | 482,319,103,4,4.5,3.5,8.66,0,0.76 484 | 483,299,97,3,5,3.5,7.66,0,0.38 485 | 484,322,107,3,3.5,3.5,8.46,1,0.71 486 | 485,317,104,2,4.5,4,8.47,0,0.57 487 | 486,329,111,4,4.5,4.5,9.18,1,0.87 488 | 487,301,107,3,3.5,3.5,8.34,1,0.62 489 | 488,310,106,4,4.5,4.5,9.04,1,0.66 490 | 489,309,104,2,2,2.5,8.26,0,0.65 491 | 490,302,102,3,3.5,5,8.33,0,0.65 492 | 491,322,110,4,4,5,9.13,1,0.86 493 | 492,316,106,2,2.5,4,8.32,0,0.72 494 | 493,324,111,5,4.5,4,9.16,1,0.9 495 | 494,313,107,3,4,4.5,8.69,0,0.72 496 | 495,295,99,2,2.5,3,7.65,0,0.57 497 | 496,328,110,4,5,4,9.14,1,0.82 498 | 497,321,109,4,4,4,9.13,1,0.85 499 | 498,313,103,3,4,4,8.75,0,0.76 500 | 499,306,110,2,3.5,4,8.45,0,0.63 501 | 500,324,111,4,3,3,9.01,1,0.82 502 | 501,310,104,3,2,3.5,8.37,0,0.7 503 | 502,314,107,2,2.5,4,8.56,0,0.63 504 | 503,311,104,2,2.5,3.5,8.48,0,0.71 505 | 504,320,104,3,3,2.5,8.57,1,0.74 506 | 505,335,115,4,4.5,4.5,9.68,1,0.93 507 | 506,331,119,4,5,4.5,9.34,1,0.9 508 | 507,308,103,2,2.5,4,8.36,1,0.7 509 | 508,320,108,3,3.5,4,8.44,1,0.76 510 | 509,314,102,2,2,2.5,8.24,0,0.64 511 | 510,318,106,3,2,3,8.65,0,0.71 512 | 511,326,112,4,4,3.5,9.12,1,0.84 513 | 512,317,104,2,3,3,8.76,0,0.77 514 | 513,329,111,4,4.5,4,9.23,1,0.89 515 | 514,324,110,3,3.5,3.5,9.04,1,0.82 516 | 515,325,107,3,3,3.5,9.11,1,0.84 517 | 516,330,116,4,5,4.5,9.45,1,0.91 518 | 517,312,103,3,3.5,4,8.78,0,0.67 519 | 518,333,117,4,5,4,9.66,1,0.95 520 | 519,322,110,3,3.5,2.5,8.67,1,0.8 521 | 520,308,101,2,3,4,7.9,0,0.68 522 | 521,307,109,3,4,3,8,1,0.62 523 | 522,318,110,3,4,3,8.8,0,0.63 524 | 523,298,98,2,1.5,2.5,7.5,1,0.44 525 | 524,300,105,1,1,2,7.8,0,0.58 526 | 525,339,119,5,4.5,4,9.7,0,0.89 527 | 526,324,114,5,5,4.5,9.08,1,0.89 528 | 527,314,104,4,5,5,9.02,0,0.82 529 | 528,313,109,3,4,3.5,9,0,0.79 530 | 529,307,105,2,2.5,3,7.65,0,0.58 531 | 530,300,102,2,1.5,2,7.87,0,0.56 532 | 531,302,99,2,1,2,7.97,0,0.56 533 | 532,312,98,1,3.5,3,8.18,1,0.64 534 | 533,316,101,2,2.5,2,8.32,1,0.61 535 | 534,317,100,2,3,2.5,8.57,0,0.68 536 | 535,310,107,3,3.5,3.5,8.67,0,0.76 537 | 536,320,120,3,4,4.5,9.11,0,0.86 538 | 537,330,114,3,4.5,4.5,9.24,1,0.9 539 | 538,305,112,3,3,3.5,8.65,0,0.71 540 | 539,309,106,2,2.5,2.5,8,0,0.62 541 | 540,319,108,2,2.5,3,8.76,0,0.66 542 | 541,322,105,2,3,3,8.45,1,0.65 543 | 542,323,107,3,3.5,3.5,8.55,1,0.73 544 | 543,313,106,2,2.5,2,8.43,0,0.62 545 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # INT375-project 2 | 1 3 | -------------------------------------------------------------------------------- /final.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import matplotlib.pyplot as plt 4 | import seaborn as sns 5 | 6 | # Load data 7 | df = pd.read_csv("Admission Chance.csv") 8 | df.describe() 9 | df.info() 10 | print(df.head(5)) 11 | 12 | # Clean column names 13 | df.columns = df.columns.str.strip() 14 | 15 | df.fillna(df.mean(numeric_only=True), inplace=True) 16 | 17 | df = df.drop(columns=['Serial No']) 18 | 19 | 20 | # Objective 1 21 | # Correlation analysis 22 | corr_matrix = df.corr() 23 | 24 | 25 | plt.figure(figsize=(10, 7)) 26 | sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', fmt=".2f", linewidths=0.5) 27 | plt.title("Analyzing Interdependence Among Admission Factors", fontsize=14) 28 | plt.xticks(rotation=45) 29 | plt.yticks(rotation=0) 30 | plt.tight_layout() 31 | # plt.savefig("cgpa_vs_admit1.png", dpi=300, bbox_inches='tight') 32 | plt.show() 33 | 34 | # Objective 2 35 | #Analyze the Impact of GRE Score on Admission Chances 36 | 37 | 38 | plt.figure(figsize=(8, 6)) 39 | sns.regplot(data=df, x="GRE Score", y="Chance of Admit", scatter_kws={'alpha':0.6}, line_kws={'color':'red'}) 40 | plt.title("GRE Score vs Chance of Admission") 41 | plt.xlabel("GRE Score") 42 | plt.ylabel("Chance of Admit") 43 | plt.grid(True) 44 | plt.tight_layout() 45 | # plt.savefig("cgpa_vs_admit2.png", dpi=300, bbox_inches='tight') 46 | plt.show() 47 | 48 | 49 | # Objective 3: Distribution of University Ratings 50 | 51 | 52 | plt.figure(figsize=(8, 6)) 53 | ax = sns.countplot(data=df, x="University Rating", palette="viridis") 54 | 55 | 56 | plt.title("Distribution of University Ratings") 57 | plt.xlabel("University Rating") 58 | plt.ylabel("Number of Applicants") 59 | 60 | plt.tight_layout() 61 | # plt.savefig("cgpa_vs_admit3.png", dpi=300, bbox_inches='tight') 62 | plt.show() 63 | 64 | avg_scores = df.groupby("University Rating")[["GRE Score", "CGPA"]].mean().reset_index() 65 | 66 | ax = avg_scores.plot( 67 | x="University Rating", 68 | kind="bar", 69 | figsize=(9, 6), 70 | colormap="Set2", 71 | title="Average GRE Score and CGPA by University Rating" 72 | ) 73 | 74 | # Add data labels on each bar 75 | for container in ax.containers: 76 | for bar in container: 77 | height = bar.get_height() 78 | ax.annotate(f'{height:.1f}', (bar.get_x() + bar.get_width() / 2., height), 79 | ha='center', va='bottom', fontsize=10) 80 | 81 | plt.ylabel("Average Score") 82 | plt.xticks(rotation=0) 83 | plt.grid(axis='y') 84 | plt.tight_layout() 85 | # plt.savefig("cgpa_vs_admit4.png", dpi=300, bbox_inches='tight') 86 | plt.show() 87 | 88 | 89 | #Objective 4 90 | 91 | # Objective 4: Percentage of Students with Research Experience 92 | research_counts = df['Research'].value_counts() 93 | plt.figure(figsize=(5, 5)) 94 | plt.pie(research_counts, labels=['No Research', 'Has Research'], autopct='%1.1f%%', startangle=90, colors=['skyblue', 'lightgreen']) 95 | plt.title('Research Participation') 96 | # plt.savefig("cgpa_vs_admit5.png", dpi=300, bbox_inches='tight') 97 | plt.show() 98 | 99 | # Select feature columns 100 | feature_cols = ['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR', 'CGPA'] 101 | 102 | # normalize 1-100 103 | df_normalized = df.copy() 104 | for col in feature_cols: 105 | min_val = df[col].min() 106 | max_val = df[col].max() 107 | df_normalized[col] = ((df[col] - min_val) / (max_val - min_val)) * 100 108 | 109 | 110 | research_profile = df_normalized.groupby('Research')[feature_cols].mean().T 111 | research_profile.columns = ['No Research', 'Research'] 112 | 113 | ax = research_profile.plot(kind='bar', figsize=(10, 6), colormap='coolwarm') 114 | 115 | 116 | for p in ax.patches: 117 | value = p.get_height() 118 | ax.text(p.get_x() + p.get_width() / 2, value + 1.5, f'{value:.1f}', 119 | ha='center', va='bottom', fontsize=9, color='black') 120 | 121 | plt.title("Normalized (0–100) Feature Comparison: Research vs No Research") 122 | plt.ylabel("Average Normalized Score") 123 | plt.xticks(rotation=45) 124 | plt.grid(axis='y') 125 | plt.tight_layout() 126 | # plt.savefig("cgpa_vs_admit6.png", dpi=300, bbox_inches='tight') 127 | plt.show() 128 | 129 | 130 | 131 | 132 | 133 | # Objective 5: Relationship Between CGPA and Admission 134 | 135 | df_sorted = df.sort_values('CGPA') 136 | plt.figure(figsize=(6, 4)) 137 | sns.lineplot(x='CGPA', y='Chance of Admit', data=df_sorted) 138 | plt.title('CGPA vs Chance of Admit') 139 | # plt.savefig("cgpa_vs_admit7.png", dpi=300, bbox_inches='tight') 140 | plt.show() 141 | 142 | 143 | # objective 6 144 | # Distribution of TOEFL Scores 145 | plt.figure(figsize=(10, 6)) 146 | hist = sns.histplot(df['TOEFL Score'], kde=True, bins=15, color='skyblue', edgecolor='black') 147 | 148 | 149 | for patch in hist.patches: 150 | height = patch.get_height() 151 | if height > 0: 152 | plt.text(patch.get_x() + patch.get_width() / 2, height + 0.5, 153 | int(height), ha='center', va='bottom', fontsize=9, color='black') 154 | 155 | plt.title("Distribution of TOEFL Scores") 156 | plt.xlabel("TOEFL Score") 157 | plt.ylabel("Number of Students") 158 | plt.grid(axis='y') 159 | plt.tight_layout() 160 | # plt.savefig("cgpa_vs_admit8.png", dpi=300, bbox_inches='tight') 161 | plt.show() 162 | 163 | 164 | 165 | 166 | 167 | 168 | 169 | 170 | -------------------------------------------------------------------------------- /outcomes/cgpa_vs_admit1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/manikant27/INT375-project/245c50cf286089177cff85721fd666a41c753225/outcomes/cgpa_vs_admit1.png 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