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No,X1 transaction date,X2 house age,X3 distance to the nearest MRT station,X4 number of convenience stores,X5 latitude,X6 longitude,Y house price of unit area 1,2012.917,32,84.87882,10,24.98298,121.54024,37.9 2,2012.917,19.5,306.5947,9,24.98034,121.53951,42.2 3,2013.583,13.3,561.9845,5,24.98746,121.54391,47.3 4,2013.500,13.3,561.9845,5,24.98746,121.54391,54.8 5,2012.833,5,390.5684,5,24.97937,121.54245,43.1 6,2012.667,7.1,2175.03,3,24.96305,121.51254,32.1 7,2012.667,34.5,623.4731,7,24.97933,121.53642,40.3 8,2013.417,20.3,287.6025,6,24.98042,121.54228,46.7 9,2013.500,31.7,5512.038,1,24.95095,121.48458,18.8 10,2013.417,17.9,1783.18,3,24.96731,121.51486,22.1 11,2013.083,34.8,405.2134,1,24.97349,121.53372,41.4 12,2013.333,6.3,90.45606,9,24.97433,121.5431,58.1 13,2012.917,13,492.2313,5,24.96515,121.53737,39.3 14,2012.667,20.4,2469.645,4,24.96108,121.51046,23.8 15,2013.500,13.2,1164.838,4,24.99156,121.53406,34.3 16,2013.583,35.7,579.2083,2,24.9824,121.54619,50.5 17,2013.250,0,292.9978,6,24.97744,121.54458,70.1 18,2012.750,17.7,350.8515,1,24.97544,121.53119,37.4 19,2013.417,16.9,368.1363,8,24.9675,121.54451,42.3 20,2012.667,1.5,23.38284,7,24.96772,121.54102,47.7 21,2013.417,4.5,2275.877,3,24.96314,121.51151,29.3 22,2013.417,10.5,279.1726,7,24.97528,121.54541,51.6 23,2012.917,14.7,1360.139,1,24.95204,121.54842,24.6 24,2013.083,10.1,279.1726,7,24.97528,121.54541,47.9 25,2013.000,39.6,480.6977,4,24.97353,121.53885,38.8 26,2013.083,29.3,1487.868,2,24.97542,121.51726,27 27,2012.667,3.1,383.8624,5,24.98085,121.54391,56.2 28,2013.250,10.4,276.449,5,24.95593,121.53913,33.6 29,2013.500,19.2,557.478,4,24.97419,121.53797,47 30,2013.083,7.1,451.2438,5,24.97563,121.54694,57.1 31,2013.500,25.9,4519.69,0,24.94826,121.49587,22.1 32,2012.750,29.6,769.4034,7,24.98281,121.53408,25 33,2012.750,37.9,488.5727,1,24.97349,121.53451,34.2 34,2013.250,16.5,323.655,6,24.97841,121.54281,49.3 35,2012.750,15.4,205.367,7,24.98419,121.54243,55.1 36,2013.500,13.9,4079.418,0,25.01459,121.51816,27.3 37,2012.917,14.7,1935.009,2,24.96386,121.51458,22.9 38,2013.167,12,1360.139,1,24.95204,121.54842,25.3 39,2012.667,3.1,577.9615,6,24.97201,121.54722,47.7 40,2013.167,16.2,289.3248,5,24.98203,121.54348,46.2 41,2013.000,13.6,4082.015,0,24.94155,121.50381,15.9 42,2013.500,16.8,4066.587,0,24.94297,121.50342,18.2 43,2013.417,36.1,519.4617,5,24.96305,121.53758,34.7 44,2012.750,34.4,512.7871,6,24.98748,121.54301,34.1 45,2013.583,2.7,533.4762,4,24.97445,121.54765,53.9 46,2013.083,36.6,488.8193,8,24.97015,121.54494,38.3 47,2013.417,21.7,463.9623,9,24.9703,121.54458,42 48,2013.583,35.9,640.7391,3,24.97563,121.53715,61.5 49,2013.417,24.2,4605.749,0,24.94684,121.49578,13.4 50,2012.667,29.4,4510.359,1,24.94925,121.49542,13.2 51,2013.417,21.7,512.5487,4,24.974,121.53842,44.2 52,2013.083,31.3,1758.406,1,24.95402,121.55282,20.7 53,2013.583,32.1,1438.579,3,24.97419,121.5175,27 54,2013.083,13.3,492.2313,5,24.96515,121.53737,38.9 55,2013.083,16.1,289.3248,5,24.98203,121.54348,51.7 56,2012.833,31.7,1160.632,0,24.94968,121.53009,13.7 57,2013.417,33.6,371.2495,8,24.97254,121.54059,41.9 58,2012.917,3.5,56.47425,7,24.95744,121.53711,53.5 59,2013.500,30.3,4510.359,1,24.94925,121.49542,22.6 60,2013.083,13.3,336.0532,5,24.95776,121.53438,42.4 61,2013.417,11,1931.207,2,24.96365,121.51471,21.3 62,2013.500,5.3,259.6607,6,24.97585,121.54516,63.2 63,2012.917,17.2,2175.877,3,24.96303,121.51254,27.7 64,2013.583,2.6,533.4762,4,24.97445,121.54765,55 65,2013.333,17.5,995.7554,0,24.96305,121.54915,25.3 66,2013.417,40.1,123.7429,8,24.97635,121.54329,44.3 67,2013.000,1,193.5845,6,24.96571,121.54089,50.7 68,2013.500,8.5,104.8101,5,24.96674,121.54067,56.8 69,2013.417,30.4,464.223,6,24.97964,121.53805,36.2 70,2012.833,12.5,561.9845,5,24.98746,121.54391,42 71,2013.583,6.6,90.45606,9,24.97433,121.5431,59 72,2013.083,35.5,640.7391,3,24.97563,121.53715,40.8 73,2013.583,32.5,424.5442,8,24.97587,121.53913,36.3 74,2013.167,13.8,4082.015,0,24.94155,121.50381,20 75,2012.917,6.8,379.5575,10,24.98343,121.53762,54.4 76,2013.500,12.3,1360.139,1,24.95204,121.54842,29.5 77,2013.583,35.9,616.4004,3,24.97723,121.53767,36.8 78,2012.833,20.5,2185.128,3,24.96322,121.51237,25.6 79,2012.917,38.2,552.4371,2,24.97598,121.53381,29.8 80,2013.000,18,1414.837,1,24.95182,121.54887,26.5 81,2013.500,11.8,533.4762,4,24.97445,121.54765,40.3 82,2013.000,30.8,377.7956,6,24.96427,121.53964,36.8 83,2013.083,13.2,150.9347,7,24.96725,121.54252,48.1 84,2012.917,25.3,2707.392,3,24.96056,121.50831,17.7 85,2013.083,15.1,383.2805,7,24.96735,121.54464,43.7 86,2012.750,0,338.9679,9,24.96853,121.54413,50.8 87,2012.833,1.8,1455.798,1,24.9512,121.549,27 88,2013.583,16.9,4066.587,0,24.94297,121.50342,18.3 89,2012.917,8.9,1406.43,0,24.98573,121.52758,48 90,2013.500,23,3947.945,0,24.94783,121.50243,25.3 91,2012.833,0,274.0144,1,24.9748,121.53059,45.4 92,2013.250,9.1,1402.016,0,24.98569,121.5276,43.2 93,2012.917,20.6,2469.645,4,24.96108,121.51046,21.8 94,2012.917,31.9,1146.329,0,24.9492,121.53076,16.1 95,2012.917,40.9,167.5989,5,24.9663,121.54026,41 96,2012.917,8,104.8101,5,24.96674,121.54067,51.8 97,2013.417,6.4,90.45606,9,24.97433,121.5431,59.5 98,2013.083,28.4,617.4424,3,24.97746,121.53299,34.6 99,2013.417,16.4,289.3248,5,24.98203,121.54348,51 100,2013.417,6.4,90.45606,9,24.97433,121.5431,62.2 101,2013.500,17.5,964.7496,4,24.98872,121.53411,38.2 102,2012.833,12.7,170.1289,1,24.97371,121.52984,32.9 103,2013.083,1.1,193.5845,6,24.96571,121.54089,54.4 104,2012.750,0,208.3905,6,24.95618,121.53844,45.7 105,2012.667,32.7,392.4459,6,24.96398,121.5425,30.5 106,2012.833,0,292.9978,6,24.97744,121.54458,71 107,2013.083,17.2,189.5181,8,24.97707,121.54308,47.1 108,2013.333,12.2,1360.139,1,24.95204,121.54842,26.6 109,2013.417,31.4,592.5006,2,24.9726,121.53561,34.1 110,2013.583,4,2147.376,3,24.96299,121.51284,28.4 111,2013.083,8.1,104.8101,5,24.96674,121.54067,51.6 112,2013.583,33.3,196.6172,7,24.97701,121.54224,39.4 113,2013.417,9.9,2102.427,3,24.96044,121.51462,23.1 114,2013.333,14.8,393.2606,6,24.96172,121.53812,7.6 115,2012.667,30.6,143.8383,8,24.98155,121.54142,53.3 116,2013.083,20.6,737.9161,2,24.98092,121.54739,46.4 117,2013.000,30.9,6396.283,1,24.94375,121.47883,12.2 118,2013.000,13.6,4197.349,0,24.93885,121.50383,13 119,2013.500,25.3,1583.722,3,24.96622,121.51709,30.6 120,2013.500,16.6,289.3248,5,24.98203,121.54348,59.6 121,2013.167,13.3,492.2313,5,24.96515,121.53737,31.3 122,2013.500,13.6,492.2313,5,24.96515,121.53737,48 123,2013.250,31.5,414.9476,4,24.98199,121.54464,32.5 124,2013.417,0,185.4296,0,24.9711,121.5317,45.5 125,2012.917,9.9,279.1726,7,24.97528,121.54541,57.4 126,2013.167,1.1,193.5845,6,24.96571,121.54089,48.6 127,2013.083,38.6,804.6897,4,24.97838,121.53477,62.9 128,2013.250,3.8,383.8624,5,24.98085,121.54391,55 129,2013.083,41.3,124.9912,6,24.96674,121.54039,60.7 130,2013.417,38.5,216.8329,7,24.98086,121.54162,41 131,2013.250,29.6,535.527,8,24.98092,121.53653,37.5 132,2013.500,4,2147.376,3,24.96299,121.51284,30.7 133,2013.167,26.6,482.7581,5,24.97433,121.53863,37.5 134,2012.833,18,373.3937,8,24.9866,121.54082,39.5 135,2012.667,33.4,186.9686,6,24.96604,121.54211,42.2 136,2012.917,18.9,1009.235,0,24.96357,121.54951,20.8 137,2012.750,11.4,390.5684,5,24.97937,121.54245,46.8 138,2013.500,13.6,319.0708,6,24.96495,121.54277,47.4 139,2013.167,10,942.4664,0,24.97843,121.52406,43.5 140,2012.667,12.9,492.2313,5,24.96515,121.53737,42.5 141,2013.250,16.2,289.3248,5,24.98203,121.54348,51.4 142,2013.333,5.1,1559.827,3,24.97213,121.51627,28.9 143,2013.417,19.8,640.6071,5,24.97017,121.54647,37.5 144,2013.500,13.6,492.2313,5,24.96515,121.53737,40.1 145,2013.083,11.9,1360.139,1,24.95204,121.54842,28.4 146,2012.917,2.1,451.2438,5,24.97563,121.54694,45.5 147,2012.750,0,185.4296,0,24.9711,121.5317,52.2 148,2012.750,3.2,489.8821,8,24.97017,121.54494,43.2 149,2013.500,16.4,3780.59,0,24.93293,121.51203,45.1 150,2012.667,34.9,179.4538,8,24.97349,121.54245,39.7 151,2013.250,35.8,170.7311,7,24.96719,121.54269,48.5 152,2013.500,4.9,387.7721,9,24.98118,121.53788,44.7 153,2013.333,12,1360.139,1,24.95204,121.54842,28.9 154,2013.250,6.5,376.1709,6,24.95418,121.53713,40.9 155,2013.500,16.9,4066.587,0,24.94297,121.50342,20.7 156,2013.167,13.8,4082.015,0,24.94155,121.50381,15.6 157,2013.583,30.7,1264.73,0,24.94883,121.52954,18.3 158,2013.250,16.1,815.9314,4,24.97886,121.53464,35.6 159,2013.000,11.6,390.5684,5,24.97937,121.54245,39.4 160,2012.667,15.5,815.9314,4,24.97886,121.53464,37.4 161,2012.917,3.5,49.66105,8,24.95836,121.53756,57.8 162,2013.417,19.2,616.4004,3,24.97723,121.53767,39.6 163,2012.750,16,4066.587,0,24.94297,121.50342,11.6 164,2013.500,8.5,104.8101,5,24.96674,121.54067,55.5 165,2012.833,0,185.4296,0,24.9711,121.5317,55.2 166,2012.917,13.7,1236.564,1,24.97694,121.55391,30.6 167,2013.417,0,292.9978,6,24.97744,121.54458,73.6 168,2013.417,28.2,330.0854,8,24.97408,121.54011,43.4 169,2013.083,27.6,515.1122,5,24.96299,121.5432,37.4 170,2013.417,8.4,1962.628,1,24.95468,121.55481,23.5 171,2013.333,24,4527.687,0,24.94741,121.49628,14.4 172,2013.083,3.6,383.8624,5,24.98085,121.54391,58.8 173,2013.583,6.6,90.45606,9,24.97433,121.5431,58.1 174,2013.083,41.3,401.8807,4,24.98326,121.5446,35.1 175,2013.417,4.3,432.0385,7,24.9805,121.53778,45.2 176,2013.083,30.2,472.1745,3,24.97005,121.53758,36.5 177,2012.833,13.9,4573.779,0,24.94867,121.49507,19.2 178,2013.083,33,181.0766,9,24.97697,121.54262,42 179,2013.500,13.1,1144.436,4,24.99176,121.53456,36.7 180,2013.083,14,438.8513,1,24.97493,121.5273,42.6 181,2012.667,26.9,4449.27,0,24.94898,121.49621,15.5 182,2013.167,11.6,201.8939,8,24.98489,121.54121,55.9 183,2013.500,13.5,2147.376,3,24.96299,121.51284,23.6 184,2013.500,17,4082.015,0,24.94155,121.50381,18.8 185,2012.750,14.1,2615.465,0,24.95495,121.56174,21.8 186,2012.750,31.4,1447.286,3,24.97285,121.5173,21.5 187,2013.167,20.9,2185.128,3,24.96322,121.51237,25.7 188,2013.000,8.9,3078.176,0,24.95464,121.56627,22 189,2012.917,34.8,190.0392,8,24.97707,121.54312,44.3 190,2012.917,16.3,4066.587,0,24.94297,121.50342,20.5 191,2013.500,35.3,616.5735,8,24.97945,121.53642,42.3 192,2013.167,13.2,750.0704,2,24.97371,121.54951,37.8 193,2013.167,43.8,57.58945,7,24.9675,121.54069,42.7 194,2013.417,9.7,421.479,5,24.98246,121.54477,49.3 195,2013.500,15.2,3771.895,0,24.93363,121.51158,29.3 196,2013.333,15.2,461.1016,5,24.95425,121.5399,34.6 197,2013.000,22.8,707.9067,2,24.981,121.54713,36.6 198,2013.250,34.4,126.7286,8,24.96881,121.54089,48.2 199,2013.083,34,157.6052,7,24.96628,121.54196,39.1 200,2013.417,18.2,451.6419,8,24.96945,121.5449,31.6 201,2013.417,17.4,995.7554,0,24.96305,121.54915,25.5 202,2013.417,13.1,561.9845,5,24.98746,121.54391,45.9 203,2012.917,38.3,642.6985,3,24.97559,121.53713,31.5 204,2012.667,15.6,289.3248,5,24.98203,121.54348,46.1 205,2013.000,18,1414.837,1,24.95182,121.54887,26.6 206,2013.083,12.8,1449.722,3,24.97289,121.51728,21.4 207,2013.250,22.2,379.5575,10,24.98343,121.53762,44 208,2013.083,38.5,665.0636,3,24.97503,121.53692,34.2 209,2012.750,11.5,1360.139,1,24.95204,121.54842,26.2 210,2012.833,34.8,175.6294,8,24.97347,121.54271,40.9 211,2013.500,5.2,390.5684,5,24.97937,121.54245,52.2 212,2013.083,0,274.0144,1,24.9748,121.53059,43.5 213,2013.333,17.6,1805.665,2,24.98672,121.52091,31.1 214,2013.083,6.2,90.45606,9,24.97433,121.5431,58 215,2013.583,18.1,1783.18,3,24.96731,121.51486,20.9 216,2013.333,19.2,383.7129,8,24.972,121.54477,48.1 217,2013.250,37.8,590.9292,1,24.97153,121.53559,39.7 218,2012.917,28,372.6242,6,24.97838,121.54119,40.8 219,2013.417,13.6,492.2313,5,24.96515,121.53737,43.8 220,2012.750,29.3,529.7771,8,24.98102,121.53655,40.2 221,2013.333,37.2,186.5101,9,24.97703,121.54265,78.3 222,2013.333,9,1402.016,0,24.98569,121.5276,38.5 223,2013.583,30.6,431.1114,10,24.98123,121.53743,48.5 224,2013.250,9.1,1402.016,0,24.98569,121.5276,42.3 225,2013.333,34.5,324.9419,6,24.97814,121.5417,46 226,2013.250,1.1,193.5845,6,24.96571,121.54089,49 227,2013.000,16.5,4082.015,0,24.94155,121.50381,12.8 228,2012.917,32.4,265.0609,8,24.98059,121.53986,40.2 229,2013.417,11.9,3171.329,0,25.00115,121.51776,46.6 230,2013.583,31,1156.412,0,24.9489,121.53095,19 231,2013.500,4,2147.376,3,24.96299,121.51284,33.4 232,2012.833,16.2,4074.736,0,24.94235,121.50357,14.7 233,2012.917,27.1,4412.765,1,24.95032,121.49587,17.4 234,2013.333,39.7,333.3679,9,24.98016,121.53932,32.4 235,2013.250,8,2216.612,4,24.96007,121.51361,23.9 236,2012.750,12.9,250.631,7,24.96606,121.54297,39.3 237,2013.167,3.6,373.8389,10,24.98322,121.53765,61.9 238,2013.167,13,732.8528,0,24.97668,121.52518,39 239,2013.083,12.8,732.8528,0,24.97668,121.52518,40.6 240,2013.500,18.1,837.7233,0,24.96334,121.54767,29.7 241,2013.083,11,1712.632,2,24.96412,121.5167,28.8 242,2013.500,13.7,250.631,7,24.96606,121.54297,41.4 243,2012.833,2,2077.39,3,24.96357,121.51329,33.4 244,2013.417,32.8,204.1705,8,24.98236,121.53923,48.2 245,2013.083,4.8,1559.827,3,24.97213,121.51627,21.7 246,2013.417,7.5,639.6198,5,24.97258,121.54814,40.8 247,2013.417,16.4,389.8219,6,24.96412,121.54273,40.6 248,2013.333,21.7,1055.067,0,24.96211,121.54928,23.1 249,2013.000,19,1009.235,0,24.96357,121.54951,22.3 250,2012.833,18,6306.153,1,24.95743,121.47516,15 251,2013.167,39.2,424.7132,7,24.97429,121.53917,30 252,2012.917,31.7,1159.454,0,24.9496,121.53018,13.8 253,2012.833,5.9,90.45606,9,24.97433,121.5431,52.7 254,2012.667,30.4,1735.595,2,24.96464,121.51623,25.9 255,2012.667,1.1,329.9747,5,24.98254,121.54395,51.8 256,2013.417,31.5,5512.038,1,24.95095,121.48458,17.4 257,2012.667,14.6,339.2289,1,24.97519,121.53151,26.5 258,2013.250,17.3,444.1334,1,24.97501,121.5273,43.9 259,2013.417,0,292.9978,6,24.97744,121.54458,63.3 260,2013.083,17.7,837.7233,0,24.96334,121.54767,28.8 261,2013.250,17,1485.097,4,24.97073,121.517,30.7 262,2013.167,16.2,2288.011,3,24.95885,121.51359,24.4 263,2012.917,15.9,289.3248,5,24.98203,121.54348,53 264,2013.417,3.9,2147.376,3,24.96299,121.51284,31.7 265,2013.167,32.6,493.657,7,24.96968,121.54522,40.6 266,2012.833,15.7,815.9314,4,24.97886,121.53464,38.1 267,2013.250,17.8,1783.18,3,24.96731,121.51486,23.7 268,2012.833,34.7,482.7581,5,24.97433,121.53863,41.1 269,2013.417,17.2,390.5684,5,24.97937,121.54245,40.1 270,2013.000,17.6,837.7233,0,24.96334,121.54767,23 271,2013.333,10.8,252.5822,1,24.9746,121.53046,117.5 272,2012.917,17.7,451.6419,8,24.96945,121.5449,26.5 273,2012.750,13,492.2313,5,24.96515,121.53737,40.5 274,2013.417,13.2,170.1289,1,24.97371,121.52984,29.3 275,2013.167,27.5,394.0173,7,24.97305,121.53994,41 276,2012.667,1.5,23.38284,7,24.96772,121.54102,49.7 277,2013.000,19.1,461.1016,5,24.95425,121.5399,34 278,2013.417,21.2,2185.128,3,24.96322,121.51237,27.7 279,2012.750,0,208.3905,6,24.95618,121.53844,44 280,2013.417,2.6,1554.25,3,24.97026,121.51642,31.1 281,2013.250,2.3,184.3302,6,24.96581,121.54086,45.4 282,2013.333,4.7,387.7721,9,24.98118,121.53788,44.8 283,2012.917,2,1455.798,1,24.9512,121.549,25.6 284,2013.417,33.5,1978.671,2,24.98674,121.51844,23.5 285,2012.917,15,383.2805,7,24.96735,121.54464,34.4 286,2013.167,30.1,718.2937,3,24.97509,121.53644,55.3 287,2012.917,5.9,90.45606,9,24.97433,121.5431,56.3 288,2013.000,19.2,461.1016,5,24.95425,121.5399,32.9 289,2013.583,16.6,323.6912,6,24.97841,121.5428,51 290,2013.333,13.9,289.3248,5,24.98203,121.54348,44.5 291,2013.083,37.7,490.3446,0,24.97217,121.53471,37 292,2012.833,3.4,56.47425,7,24.95744,121.53711,54.4 293,2013.083,17.5,395.6747,5,24.95674,121.534,24.5 294,2012.667,12.6,383.2805,7,24.96735,121.54464,42.5 295,2013.500,26.4,335.5273,6,24.9796,121.5414,38.1 296,2013.167,18.2,2179.59,3,24.96299,121.51252,21.8 297,2012.750,12.5,1144.436,4,24.99176,121.53456,34.1 298,2012.833,34.9,567.0349,4,24.97003,121.5458,28.5 299,2013.333,16.7,4082.015,0,24.94155,121.50381,16.7 300,2013.167,33.2,121.7262,10,24.98178,121.54059,46.1 301,2013.083,2.5,156.2442,4,24.96696,121.53992,36.9 302,2012.750,38,461.7848,0,24.97229,121.53445,35.7 303,2013.500,16.5,2288.011,3,24.95885,121.51359,23.2 304,2013.500,38.3,439.7105,0,24.97161,121.53423,38.4 305,2013.417,20,1626.083,3,24.96622,121.51668,29.4 306,2013.083,16.2,289.3248,5,24.98203,121.54348,55 307,2013.500,14.4,169.9803,1,24.97369,121.52979,50.2 308,2012.833,10.3,3079.89,0,24.9546,121.56627,24.7 309,2013.417,16.4,289.3248,5,24.98203,121.54348,53 310,2013.250,30.3,1264.73,0,24.94883,121.52954,19.1 311,2013.583,16.4,1643.499,2,24.95394,121.55174,24.7 312,2013.167,21.3,537.7971,4,24.97425,121.53814,42.2 313,2013.583,35.4,318.5292,9,24.97071,121.54069,78 314,2013.333,8.3,104.8101,5,24.96674,121.54067,42.8 315,2013.250,3.7,577.9615,6,24.97201,121.54722,41.6 316,2013.083,15.6,1756.411,2,24.9832,121.51812,27.3 317,2013.250,13.3,250.631,7,24.96606,121.54297,42 318,2012.750,15.6,752.7669,2,24.97795,121.53451,37.5 319,2013.333,7.1,379.5575,10,24.98343,121.53762,49.8 320,2013.250,34.6,272.6783,5,24.95562,121.53872,26.9 321,2012.750,13.5,4197.349,0,24.93885,121.50383,18.6 322,2012.917,16.9,964.7496,4,24.98872,121.53411,37.7 323,2013.000,12.9,187.4823,1,24.97388,121.52981,33.1 324,2013.417,28.6,197.1338,6,24.97631,121.54436,42.5 325,2012.667,12.4,1712.632,2,24.96412,121.5167,31.3 326,2013.083,36.6,488.8193,8,24.97015,121.54494,38.1 327,2013.500,4.1,56.47425,7,24.95744,121.53711,62.1 328,2013.417,3.5,757.3377,3,24.97538,121.54971,36.7 329,2012.833,15.9,1497.713,3,24.97003,121.51696,23.6 330,2013.000,13.6,4197.349,0,24.93885,121.50383,19.2 331,2013.083,32,1156.777,0,24.94935,121.53046,12.8 332,2013.333,25.6,4519.69,0,24.94826,121.49587,15.6 333,2013.167,39.8,617.7134,2,24.97577,121.53475,39.6 334,2012.750,7.8,104.8101,5,24.96674,121.54067,38.4 335,2012.917,30,1013.341,5,24.99006,121.5346,22.8 336,2013.583,27.3,337.6016,6,24.96431,121.54063,36.5 337,2012.833,5.1,1867.233,2,24.98407,121.51748,35.6 338,2012.833,31.3,600.8604,5,24.96871,121.54651,30.9 339,2012.917,31.5,258.186,9,24.96867,121.54331,36.3 340,2013.333,1.7,329.9747,5,24.98254,121.54395,50.4 341,2013.333,33.6,270.8895,0,24.97281,121.53265,42.9 342,2013.000,13,750.0704,2,24.97371,121.54951,37 343,2012.667,5.7,90.45606,9,24.97433,121.5431,53.5 344,2013.000,33.5,563.2854,8,24.98223,121.53597,46.6 345,2013.500,34.6,3085.17,0,24.998,121.5155,41.2 346,2012.667,0,185.4296,0,24.9711,121.5317,37.9 347,2013.417,13.2,1712.632,2,24.96412,121.5167,30.8 348,2013.583,17.4,6488.021,1,24.95719,121.47353,11.2 349,2012.833,4.6,259.6607,6,24.97585,121.54516,53.7 350,2012.750,7.8,104.8101,5,24.96674,121.54067,47 351,2013.000,13.2,492.2313,5,24.96515,121.53737,42.3 352,2012.833,4,2180.245,3,24.96324,121.51241,28.6 353,2012.833,18.4,2674.961,3,24.96143,121.50827,25.7 354,2013.500,4.1,2147.376,3,24.96299,121.51284,31.3 355,2013.417,12.2,1360.139,1,24.95204,121.54842,30.1 356,2013.250,3.8,383.8624,5,24.98085,121.54391,60.7 357,2012.833,10.3,211.4473,1,24.97417,121.52999,45.3 358,2013.417,0,338.9679,9,24.96853,121.54413,44.9 359,2013.167,1.1,193.5845,6,24.96571,121.54089,45.1 360,2013.500,5.6,2408.993,0,24.95505,121.55964,24.7 361,2012.667,32.9,87.30222,10,24.983,121.54022,47.1 362,2013.083,41.4,281.205,8,24.97345,121.54093,63.3 363,2013.417,17.1,967.4,4,24.98872,121.53408,40 364,2013.500,32.3,109.9455,10,24.98182,121.54086,48 365,2013.417,35.3,614.1394,7,24.97913,121.53666,33.1 366,2012.917,17.3,2261.432,4,24.96182,121.51222,29.5 367,2012.750,14.2,1801.544,1,24.95153,121.55254,24.8 368,2012.833,15,1828.319,2,24.96464,121.51531,20.9 369,2013.417,18.2,350.8515,1,24.97544,121.53119,43.1 370,2012.667,20.2,2185.128,3,24.96322,121.51237,22.8 371,2012.750,15.9,289.3248,5,24.98203,121.54348,42.1 372,2013.500,4.1,312.8963,5,24.95591,121.53956,51.7 373,2013.000,33.9,157.6052,7,24.96628,121.54196,41.5 374,2013.083,0,274.0144,1,24.9748,121.53059,52.2 375,2013.250,5.4,390.5684,5,24.97937,121.54245,49.5 376,2013.250,21.7,1157.988,0,24.96165,121.55011,23.8 377,2013.417,14.7,1717.193,2,24.96447,121.51649,30.5 378,2013.333,3.9,49.66105,8,24.95836,121.53756,56.8 379,2013.333,37.3,587.8877,8,24.97077,121.54634,37.4 380,2013.333,0,292.9978,6,24.97744,121.54458,69.7 381,2013.333,14.1,289.3248,5,24.98203,121.54348,53.3 382,2013.417,8,132.5469,9,24.98298,121.53981,47.3 383,2013.000,16.3,3529.564,0,24.93207,121.51597,29.3 384,2012.667,29.1,506.1144,4,24.97845,121.53889,40.3 385,2012.750,16.1,4066.587,0,24.94297,121.50342,12.9 386,2013.000,18.3,82.88643,10,24.983,121.54026,46.6 387,2012.833,0,185.4296,0,24.9711,121.5317,55.3 388,2013.250,16.2,2103.555,3,24.96042,121.51462,25.6 389,2013.500,10.4,2251.938,4,24.95957,121.51353,27.3 390,2013.250,40.9,122.3619,8,24.96756,121.5423,67.7 391,2013.500,32.8,377.8302,9,24.97151,121.5435,38.6 392,2013.583,6.2,1939.749,1,24.95155,121.55387,31.3 393,2013.083,42.7,443.802,6,24.97927,121.53874,35.3 394,2013.000,16.9,967.4,4,24.98872,121.53408,40.3 395,2013.500,32.6,4136.271,1,24.95544,121.4963,24.7 396,2012.917,21.2,512.5487,4,24.974,121.53842,42.5 397,2012.667,37.1,918.6357,1,24.97198,121.55063,31.9 398,2013.417,13.1,1164.838,4,24.99156,121.53406,32.2 399,2013.417,14.7,1717.193,2,24.96447,121.51649,23 400,2012.917,12.7,170.1289,1,24.97371,121.52984,37.3 401,2013.250,26.8,482.7581,5,24.97433,121.53863,35.5 402,2013.083,7.6,2175.03,3,24.96305,121.51254,27.7 403,2012.833,12.7,187.4823,1,24.97388,121.52981,28.5 404,2012.667,30.9,161.942,9,24.98353,121.53966,39.7 405,2013.333,16.4,289.3248,5,24.98203,121.54348,41.2 406,2012.667,23,130.9945,6,24.95663,121.53765,37.2 407,2013.167,1.9,372.1386,7,24.97293,121.54026,40.5 408,2013.000,5.2,2408.993,0,24.95505,121.55964,22.3 409,2013.417,18.5,2175.744,3,24.9633,121.51243,28.1 410,2013.000,13.7,4082.015,0,24.94155,121.50381,15.4 411,2012.667,5.6,90.45606,9,24.97433,121.5431,50 412,2013.250,18.8,390.9696,7,24.97923,121.53986,40.6 413,2013.000,8.1,104.8101,5,24.96674,121.54067,52.5 414,2013.500,6.5,90.45606,9,24.97433,121.5431,63.9

{ “cells”: [ { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “# Task6: Predicting Real Estate House Prices” ] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “## This task is provided to test your understanding of building a Linear Regression model for a provided dataset” ] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Dataset: Real_estate.csv” ] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Import the necessary libraries\n”, “#### Hint: Also import seaborn” ] }, { “cell_type”: “code”, “execution_count”: 1, “metadata”: {}, “outputs”: [], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Read the csv data into a pandas dataframe and display the first 5 samples” ] }, { “cell_type”: “code”, “execution_count”: 2, “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>No</th>\n”, ” <th>X1 transaction date</th>\n”, ” <th>X2 house age</th>\n”, ” <th>X3 distance to the nearest MRT station</th>\n”, ” <th>X4 number of convenience stores</th>\n”, ” <th>X5 latitude</th>\n”, ” <th>X6 longitude</th>\n”, ” <th>Y house price of unit area</th>\n”, ” </tr>\n”, ” </thead>\n”, ” <tbody>\n”, ” <tr>\n”, ” <th>0</th>\n”, ” <td>1</td>\n”, ” <td>2012.917</td>\n”, ” <td>32.0</td>\n”, ” <td>84.87882</td>\n”, ” <td>10</td>\n”, ” <td>24.98298</td>\n”, ” <td>121.54024</td>\n”, ” <td>37.9</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>1</th>\n”, ” <td>2</td>\n”, ” <td>2012.917</td>\n”, ” <td>19.5</td>\n”, ” <td>306.59470</td>\n”, ” <td>9</td>\n”, ” <td>24.98034</td>\n”, ” <td>121.53951</td>\n”, ” <td>42.2</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>2</th>\n”, ” <td>3</td>\n”, ” <td>2013.583</td>\n”, ” <td>13.3</td>\n”, ” <td>561.98450</td>\n”, ” <td>5</td>\n”, ” <td>24.98746</td>\n”, ” <td>121.54391</td>\n”, ” <td>47.3</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>3</th>\n”, ” <td>4</td>\n”, ” <td>2013.500</td>\n”, ” <td>13.3</td>\n”, ” <td>561.98450</td>\n”, ” <td>5</td>\n”, ” <td>24.98746</td>\n”, ” <td>121.54391</td>\n”, ” <td>54.8</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>4</th>\n”, ” <td>5</td>\n”, ” <td>2012.833</td>\n”, ” <td>5.0</td>\n”, ” <td>390.56840</td>\n”, ” <td>5</td>\n”, ” <td>24.97937</td>\n”, ” <td>121.54245</td>\n”, ” <td>43.1</td>\n”, ” </tr>\n”, ” </tbody>\n”, “</table>\n”, “</div>” ], “text/plain”: [ ” No X1 transaction date X2 house age \\\n”, “0 1 2012.917 32.0 \n”, “1 2 2012.917 19.5 \n”, “2 3 2013.583 13.3 \n”, “3 4 2013.500 13.3 \n”, “4 5 2012.833 5.0 \n”, “\n”, ” X3 distance to the nearest MRT station X4 number of convenience stores \\\n”, “0 84.87882 10 \n”, “1 306.59470 9 \n”, “2 561.98450 5 \n”, “3 561.98450 5 \n”, “4 390.56840 5 \n”, “\n”, ” X5 latitude X6 longitude Y house price of unit area \n”, “0 24.98298 121.54024 37.9 \n”, “1 24.98034 121.53951 42.2 \n”, “2 24.98746 121.54391 47.3 \n”, “3 24.98746 121.54391 54.8 \n”, “4 24.97937 121.54245 43.1 ” ] }, “execution_count”: 2, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Show more information about the dataset” ] }, { “cell_type”: “code”, “execution_count”: 3, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “<class ‘pandas.core.frame.DataFrame’>\n”, “RangeIndex: 414 entries, 0 to 413\n”, “Data columns (total 8 columns):\n”, ” # Column Non-Null Count Dtype \n”, “— —— ————– —– \n”, ” 0 No 414 non-null int64 \n”, ” 1 X1 transaction date 414 non-null float64\n”, ” 2 X2 house age 414 non-null float64\n”, ” 3 X3 distance to the nearest MRT station 414 non-null float64\n”, ” 4 X4 number of convenience stores 414 non-null int64 \n”, ” 5 X5 latitude 414 non-null float64\n”, ” 6 X6 longitude 414 non-null float64\n”, ” 7 Y house price of unit area 414 non-null float64\n”, “dtypes: float64(6), int64(2)\n”, “memory usage: 26.0 KB\n” ] } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Find how many samples are there and how many columns are there in the dataset” ] }, { “cell_type”: “code”, “execution_count”: 4, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “(414, 8)” ] }, “execution_count”: 4, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### What are the features available in the dataset?” ] }, { “cell_type”: “code”, “execution_count”: 5, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “Index([‘No’, ‘X1 transaction date’, ‘X2 house age’,\n”, ” ‘X3 distance to the nearest MRT station’,\n”, ” ‘X4 number of convenience stores’, ‘X5 latitude’, ‘X6 longitude’,\n”, ” ‘Y house price of unit area’],\n”, ” dtype=’object’)” ] }, “execution_count”: 5, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Check if any features have missing data” ] }, { “cell_type”: “code”, “execution_count”: 6, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “No 0\n”, “X1 transaction date 0\n”, “X2 house age 0\n”, “X3 distance to the nearest MRT station 0\n”, “X4 number of convenience stores 0\n”, “X5 latitude 0\n”, “X6 longitude 0\n”, “Y house price of unit area 0\n”, “dtype: int64” ] }, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Group all the features as dependent features in X” ] }, { “cell_type”: “code”, “execution_count”: 7, “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>No</th>\n”, ” <th>X1 transaction date</th>\n”, ” <th>X2 house age</th>\n”, ” <th>X3 distance to the nearest MRT station</th>\n”, ” <th>X4 number of convenience stores</th>\n”, ” <th>X5 latitude</th>\n”, ” <th>X6 longitude</th>\n”, ” </tr>\n”, ” </thead>\n”, ” <tbody>\n”, ” <tr>\n”, ” <th>0</th>\n”, ” <td>1</td>\n”, ” <td>2012.917</td>\n”, ” <td>32.0</td>\n”, ” <td>84.87882</td>\n”, ” <td>10</td>\n”, ” <td>24.98298</td>\n”, ” <td>121.54024</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>1</th>\n”, ” <td>2</td>\n”, ” <td>2012.917</td>\n”, ” <td>19.5</td>\n”, ” <td>306.59470</td>\n”, ” <td>9</td>\n”, ” <td>24.98034</td>\n”, ” <td>121.53951</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>2</th>\n”, ” <td>3</td>\n”, ” <td>2013.583</td>\n”, ” <td>13.3</td>\n”, ” <td>561.98450</td>\n”, ” <td>5</td>\n”, ” <td>24.98746</td>\n”, ” <td>121.54391</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>3</th>\n”, ” <td>4</td>\n”, ” <td>2013.500</td>\n”, ” <td>13.3</td>\n”, ” <td>561.98450</td>\n”, ” <td>5</td>\n”, ” <td>24.98746</td>\n”, ” <td>121.54391</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>4</th>\n”, ” <td>5</td>\n”, ” <td>2012.833</td>\n”, ” <td>5.0</td>\n”, ” <td>390.56840</td>\n”, ” <td>5</td>\n”, ” <td>24.97937</td>\n”, ” <td>121.54245</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”, ” </tr>\n”, ” <tr>\n”, ” <th>409</th>\n”, ” <td>410</td>\n”, ” <td>2013.000</td>\n”, ” <td>13.7</td>\n”, ” <td>4082.01500</td>\n”, ” <td>0</td>\n”, ” <td>24.94155</td>\n”, ” <td>121.50381</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>410</th>\n”, ” <td>411</td>\n”, ” <td>2012.667</td>\n”, ” <td>5.6</td>\n”, ” <td>90.45606</td>\n”, ” <td>9</td>\n”, ” <td>24.97433</td>\n”, ” <td>121.54310</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>411</th>\n”, ” <td>412</td>\n”, ” <td>2013.250</td>\n”, ” <td>18.8</td>\n”, ” <td>390.96960</td>\n”, ” <td>7</td>\n”, ” <td>24.97923</td>\n”, ” <td>121.53986</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>412</th>\n”, ” <td>413</td>\n”, ” <td>2013.000</td>\n”, ” <td>8.1</td>\n”, ” <td>104.81010</td>\n”, ” <td>5</td>\n”, ” <td>24.96674</td>\n”, ” <td>121.54067</td>\n”, ” </tr>\n”, ” <tr>\n”, ” <th>413</th>\n”, ” <td>414</td>\n”, ” <td>2013.500</td>\n”, ” <td>6.5</td>\n”, ” <td>90.45606</td>\n”, ” <td>9</td>\n”, ” <td>24.97433</td>\n”, ” <td>121.54310</td>\n”, ” </tr>\n”, ” </tbody>\n”, “</table>\n”, “<p>414 rows × 7 columns</p>\n”, “</div>” ], “text/plain”: [ ” No X1 transaction date X2 house age \\\n”, “0 1 2012.917 32.0 \n”, “1 2 2012.917 19.5 \n”, “2 3 2013.583 13.3 \n”, “3 4 2013.500 13.3 \n”, “4 5 2012.833 5.0 \n”, “.. … … … \n”, “409 410 2013.000 13.7 \n”, “410 411 2012.667 5.6 \n”, “411 412 2013.250 18.8 \n”, “412 413 2013.000 8.1 \n”, “413 414 2013.500 6.5 \n”, “\n”, ” X3 distance to the nearest MRT station X4 number of convenience stores \\\n”, “0 84.87882 10 \n”, “1 306.59470 9 \n”, “2 561.98450 5 \n”, “3 561.98450 5 \n”, “4 390.56840 5 \n”, “.. … … \n”, “409 4082.01500 0 \n”, “410 90.45606 9 \n”, “411 390.96960 7 \n”, “412 104.81010 5 \n”, “413 90.45606 9 \n”, “\n”, ” X5 latitude X6 longitude \n”, “0 24.98298 121.54024 \n”, “1 24.98034 121.53951 \n”, “2 24.98746 121.54391 \n”, “3 24.98746 121.54391 \n”, “4 24.97937 121.54245 \n”, “.. … … \n”, “409 24.94155 121.50381 \n”, “410 24.97433 121.54310 \n”, “411 24.97923 121.53986 \n”, “412 24.96674 121.54067 \n”, “413 24.97433 121.54310 \n”, “\n”, “[414 rows x 7 columns]” ] }, “execution_count”: 7, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Group feature(s) as independent features in y” ] }, { “cell_type”: “code”, “execution_count”: 8, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “0 37.9\n”, “1 42.2\n”, “2 47.3\n”, “3 54.8\n”, “4 43.1\n”, ” … \n”, “409 15.4\n”, “410 50.0\n”, “411 40.6\n”, “412 52.5\n”, “413 63.9\n”, “Name: Y house price of unit area, Length: 414, dtype: float64” ] }, “execution_count”: 8, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Split the dataset into train and test data” ] }, { “cell_type”: “code”, “execution_count”: 9, “metadata”: {}, “outputs”: [], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Choose the model (Linear Regression)” ] }, { “cell_type”: “code”, “execution_count”: 10, “metadata”: {}, “outputs”: [], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Create an Estimator object” ] }, { “cell_type”: “code”, “execution_count”: 11, “metadata”: {}, “outputs”: [], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Train the model” ] }, { “cell_type”: “code”, “execution_count”: 12, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “LinearRegression()” ] }, “execution_count”: 12, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Apply the model” ] }, { “cell_type”: “code”, “execution_count”: 13, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “array([39.09184855, 12.82614768, 41.76173828, 12.09424855, 41.68156196,\n”, ” 39.36666405, 40.57898977, 33.8846898 , 50.05358688, 45.550874 ,\n”, ” 45.51385485, 40.95300388, 38.33135922, 39.89670897, 46.59039145,\n”, ” 38.21070994, 39.78636257, 40.74697578, 41.30341084, 42.31380145,\n”, ” 49.36104125])” ] }, “execution_count”: 13, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Display the coefficients” ] }, { “cell_type”: “code”, “execution_count”: 14, “metadata”: {}, “outputs”: [ { “data”: { “text/plain”: [ “array([-3.28281240e-03, 5.18980590e+00, -2.70389960e-01, -4.34076666e-03,\n”, ” 1.16918512e+00, 2.26156387e+02, -5.63794054e+00])” ] }, “execution_count”: 14, “metadata”: {}, “output_type”: “execute_result” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Find how well the trained model did with testing data” ] }, { “cell_type”: “code”, “execution_count”: 15, “metadata”: {}, “outputs”: [ { “name”: “stdout”, “output_type”: “stream”, “text”: [ “r2 Score : 0.7065810721724328\n” ] } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Plot House Age Vs Price\n”, “#### Hint: Use regplot in sns” ] }, { “cell_type”: “code”, “execution_count”: 16, “metadata”: {}, “outputs”: [ { “data”: { “image/png”: “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\n”, “text/plain”: [ “<Figure size 432×288 with 1 Axes>” ] }, “metadata”: { “needs_background”: “light” }, “output_type”: “display_data” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Plot Distance to MRT station Vs Price” ] }, { “cell_type”: “code”, “execution_count”: 17, “metadata”: { “scrolled”: true }, “outputs”: [ { “data”: { “image/png”: 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\n”, “text/plain”: [ “<Figure size 432×288 with 1 Axes>” ] }, “metadata”: { “needs_background”: “light” }, “output_type”: “display_data” } ], “source”: [] }, { “cell_type”: “markdown”, “metadata”: {}, “source”: [ “### Plot Number of Convienience Stores Vs Price” ] }, { “cell_type”: “code”, “execution_count”: 18, “metadata”: {}, “outputs”: [ { “data”: { “image/png”: 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\n”, “text/plain”: [ “<Figure size 432×288 with 1 Axes>” ] }, “metadata”: { “needs_background”: “light” }, “output_type”: “display_data” } ], “source”: [] }, { “cell_type”: “code”, “execution_count”: null, “metadata”: {}, “outputs”: [], “source”: [] }, { “cell_type”: “code”, “execution_count”: null, “metadata”: {}, “outputs”: [], “source”: [] } ], “metadata”: { “kernelspec”: { “display_name”: “testing”, “language”: “python”, “name”: “testing” }, “language_info”: { “codemirror_mode”: { “name”: “ipython”, “version”: 3 }, “file_extension”: “.py”, “mimetype”: “text/x-python“, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.8.5” } }, “nbformat”: 4, “nbformat_minor”: 4 }