train.csv
row_id,x,y,accuracy,time,place_id
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1,5.9567,4.7968,13,186555,1757726713
2,8.3078,7.0407,74,322648,1137537235
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1400,7.8911,5.1416,3,747609,3652028137
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1421,7.0486,6.8208,6,95304,6359093924
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1424,5.5633,8.8118,72,99157,5345684144
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1520,7.6293,5.3122,7,539189,1593102183
1521,4.6865,2.9711,24,762703,3205140230
1522,6.1690,1.7649,178,461635,3629847180
1523,3.5374,8.8794,65,348985,9746520840
1524,4.5982,4.0721,58,384353,8171460405
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1527,4.5281,8.8479,66,95663,6413439287
1528,5.8770,3.0924,65,266813,2183629826
1529,8.9787,3.5414,75,210436,6166256510
1530,1.0087,7.9654,14,225548,9583081381
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1703,6.6151,9.4676,7,76378,5771361822
1704,3.7558,0.9279,56,751832,4284794705
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1713,8.9044,7.7247,70,773893,4653041632
1714,2.9825,2.4092,63,318645,4247340837
1715,9.2244,7.3066,125,426799,1782905093
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1717,6.6554,8.3008,4,312704,6538784431
1718,9.6496,4.6943,41,729572,7616491704
1719,5.9962,5.4381,68,49246,9824750417
1720,4.8576,4.9097,93,533232,3160797694
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1725,2.8142,6.5879,127,643078,7427437947
1726,2.2090,1.8434,1,95379,2515771940
1727,9.7907,0.6487,57,699016,1082383514
1728,8.0302,4.8733,346,634843,9275114134
1729,9.7352,9.4283,22,613652,6237389347
1730,4.4506,2.7546,19,8768,9638384552
1731,7.0020,0.8389,57,612184,9104101341
1732,9.0976,3.8167,59,147202,4903450124
1733,5.2962,7.0914,170,60821,7263445153
1734,5.2571,6.1663,85,739505,8940760079
1735,2.7413,5.5752,88,595552,7266425363
1736,1.4380,8.8132,184,264390,5624700903
1737,5.5461,1.9303,13,655587,6473489965
1738,2.8249,3.4647,7,93238,9676633174
1739,7.1036,7.7146,26,708624,4356993229
1740,2.8719,3.0707,72,449228,2432901905
1741,9.9086,3.8321,156,703730,1321254572
1742,2.7997,9.4286,156,177687,7501470727
1743,3.2783,8.1020,15,582948,9108032663
1744,2.2087,5.4139,30,373114,2217140005
1745,1.0979,4.7769,165,125639,4805803672
1746,6.3504,3.6681,162,741192,9990395184
1747,9.0289,8.0721,75,266365,6369557054
1748,6.5210,6.5077,8,724141,6129186794
1749,9.3368,8.8727,69,689110,7383903581
1750,5.0777,2.3418,64,120400,6830875272
1751,5.1101,4.7330,173,662741,7158127408
1752,2.0001,9.6814,3,360766,2621256752
1753,1.2712,2.1926,56,470758,1110401599
1754,5.9106,4.0648,232,39324,6954343417
1755,4.0393,6.9118,52,591081,8278100500
1756,8.1445,4.4762,91,712643,7657467776
1757,8.1993,0.6260,1,698832,9077312877
1758,9.7940,2.1729,23,247149,1036107777
1759,9.8722,8.6510,55,573046,2666848959
1760,6.3819,5.2655,63,544186,5758402063
1761,5.1943,7.1858,42,710500,1637845548
1762,0.3551,6.5221,125,508512,8339817597
1763,6.5813,0.5198,26,347373,9794345362
1764,1.3209,7.2171,69,666966,3260811985
1765,9.4197,4.1545,82,598764,5102221373
1766,3.9856,3.0691,14,179124,7411060516
1767,7.9361,3.7455,23,630143,7369252596
1768,0.0725,6.9235,168,295481,3640358817
1769,6.1939,2.1002,6,327996,5025973183
1770,4.7758,0.1676,5,132530,6815483548
1771,7.9430,1.5347,60,47835,7082434388
1772,9.9875,2.9512,158,742795,6780268197
1773,6.0924,9.3498,77,370011,3147494461
1774,0.3073,3.0930,56,124025,1248878048
1775,8.0346,9.6001,9,320112,3961040851
1776,3.6669,0.0104,165,619587,7783175166
1777,6.6644,6.9438,99,768399,7627595858
1778,0.4595,5.7156,54,574373,7385125974
1779,6.8162,3.7617,4,334781,1952920661
1780,9.3927,3.7599,109,521914,5619555438
1781,3.7737,7.2068,65,280510,2078678375
1782,0.6404,5.2067,71,543061,7709691165
1783,5.6796,6.9216,6,315827,6772654511
1784,1.3082,4.5923,103,122487,3311897395
1785,8.2687,3.9136,57,515824,7132982701
1786,9.0367,6.3665,199,389203,9532519929
1787,5.7681,7.2698,107,680756,4714124213
1788,2.4171,9.6384,66,545989,2600715348
1789,5.4533,1.2271,23,63425,7761956832
1790,7.9560,8.3202,1,193155,5413917249
1791,1.3139,4.7536,165,454461,3240715959
1792,9.5953,8.2950,13,282783,9516342314
1793,3.1576,4.4524,8,141512,1475935389
1794,9.7001,4.5028,65,475316,5364394541
1795,2.6579,2.5738,40,636109,6071219357
1796,7.2644,5.5773,20,64676,9839008624
1797,8.7314,9.3001,419,712079,8811665071
1798,2.1847,5.1852,74,239347,3529851616
1799,9.3534,6.4974,24,335217,5328618659
1800,3.6462,1.6352,59,258751,9068261680
1801,3.9880,5.3579,48,475698,7732402473
1802,1.2497,4.7014,59,157631,1749707373
1803,1.5633,2.5851,68,87501,5741988658
1804,2.9064,8.7190,177,209941,5081303106
1805,9.9108,8.5466,5,57941,8451477492
1806,5.7638,7.5913,71,462480,6703962978
1807,8.6867,1.9795,73,219031,5018017485
1808,1.5362,7.8808,71,649512,2545733992
1809,5.6292,8.1245,59,217367,3276321104
1810,9.4556,6.4391,8,184146,3412372944
1811,8.8528,6.3197,68,740215,1851189823
1812,0.6977,8.0106,7,662045,2374301416
1813,1.9318,9.8929,9,70083,4528569729
1814,8.0992,9.5756,19,380865,2948459696
1815,2.7995,6.0038,45,539549,4138309614
1816,7.7959,0.5374,40,48597,3365555272
1817,2.6989,4.7244,62,754919,1213151069
1818,1.7842,7.8552,65,354684,1101154737
1819,5.1104,3.0344,48,135605,2075904654
1820,8.2873,5.8036,60,250195,5886372895
1821,3.7349,5.5911,6,55028,3654807360
1822,9.8904,1.1594,58,652033,4845374300
1823,6.8929,6.0742,55,556588,8974940123
1824,3.8198,0.8926,67,503933,4899398465
1825,2.6684,7.5367,65,479442,4148168425
1826,2.2670,7.4159,9,82979,3046554979
1827,3.3445,1.3735,71,324264,1456201867
1828,2.0895,2.0542,65,98099,5831595982
1829,8.3250,7.3664,168,348060,8405652918
1830,8.6961,8.9064,10,66442,9656411113
1831,7.6917,7.3300,45,167234,6922768108
1832,6.5548,6.1272,94,406423,7765847674
1833,8.5511,2.9792,56,744065,1316775181
1834,6.4893,9.4083,55,573776,8943735546
1835,3.8499,7.4207,81,755952,5855430416
1836,8.3852,4.5327,12,14195,2156217931
1837,7.2231,0.0284,28,632143,6258702564
1838,2.5388,2.8528,41,727460,8046653696
1839,2.8387,7.0623,24,283400,2395977879
1840,3.4568,5.2198,6,717435,3223452506
1841,9.1797,3.8526,60,117678,3599371489
1842,6.6131,6.1612,77,701408,4903883549
1843,0.5550,6.0499,38,733444,7832221442
1844,4.3912,8.3174,6,332883,8150089907
1845,0.1293,5.2609,170,695506,1157153886
1846,7.2018,7.1552,78,381004,7165643669
1847,5.3722,2.3600,10,210251,9944937903
1848,0.0497,3.4271,69,468516,5628853546
1849,1.9817,7.1088,70,306672,7950720040
1850,4.7645,7.1879,52,779652,2511612968
1851,9.0866,1.7558,152,770914,8201322742
1852,0.4250,5.8933,16,101639,5395970822
1853,9.1217,5.2833,79,437505,1209629248
1854,3.2248,0.4979,78,533648,6824276209
1855,9.0337,9.5839,34,493106,9482995427
1856,7.3084,0.3638,67,750458,6061199897
1857,2.1082,5.6938,65,616039,2457833751
1858,1.3247,9.1174,34,462221,6076104226
1859,6.3121,9.7669,167,593986,2115802417
1860,6.8744,7.0311,5,619793,7819465203
1861,2.4872,8.2363,261,722880,8221199999
1862,6.4731,1.1389,4,384744,3679448739
1863,4.5257,2.1714,161,738326,3514717706
1864,0.8943,2.7269,915,782752,1284717734
1865,7.5020,7.0106,68,544506,9167426123
1866,9.9871,9.3952,69,747384,6719569458
1867,3.2217,0.1545,57,47284,4364807943
1868,3.0037,6.1324,106,550931,3853348262
1869,4.9073,1.0100,799,709808,5836872842
1870,2.4114,7.1059,71,343813,9830402103
1871,2.2531,2.3475,70,531916,3399431058
1872,5.3694,5.9384,80,135865,2203791192
1873,2.7300,0.7470,157,178604,8568995650
1874,9.3572,2.3627,9,317728,4147895777
1875,0.9307,2.6112,412,73095,5283227804
1876,3.7994,0.7791,106,314642,7779969108
1877,0.0899,2.0711,70,599051,8043067972
1878,6.2004,6.9786,7,161501,5122521800
1879,6.7558,1.6338,145,779262,5766195671
1880,7.2759,8.9054,265,556455,7144658616
1881,1.5595,0.9468,70,519094,8787907836
1882,9.8329,6.0578,65,318758,4666902338
1883,7.1925,9.6302,171,629015,2537247687
1884,5.7249,1.4727,53,754914,7007141812
1885,3.4343,5.7167,46,775479,1154057140
1886,9.2035,4.2608,72,606236,2294240532
1887,4.6581,4.6058,65,607841,1687945977
1888,9.1100,1.3403,26,420751,2282682575
1889,2.0705,3.1868,38,486892,3492905896
1890,5.5392,0.0063,22,525679,5752821706
1891,4.3313,7.3564,70,437124,9865220115
1892,3.7921,6.4771,75,574043,8775773350
1893,4.0445,7.9571,66,579641,3358414867
1894,6.4034,4.7256,15,199107,7005601709
1895,4.8789,7.6544,164,753344,1864086134
1896,1.7945,0.4303,1,745065,8401835004
1897,2.6315,4.3579,159,89614,3452265761
1898,0.0286,1.5254,68,332904,8751842175
1899,5.2268,2.1840,118,546035,3720520481
1900,2.7037,0.8258,12,157419,9958028601
1901,9.5142,5.2397,81,462234,6292126138
1902,4.4180,1.4440,32,691149,3224085352
1903,7.8679,0.1934,653,198888,6202875305
1904,3.1725,7.9490,66,325477,4499288325
1905,4.8691,1.9488,69,710522,7284656184
1906,7.0860,2.5336,71,485718,2465239230
1907,5.1775,9.4012,51,126192,5686488748
1908,3.8199,4.6126,156,484318,1547949267
1909,9.2256,6.8717,67,456711,1497820199
1910,4.7673,2.7380,10,629649,4791099657
1911,0.9439,2.4068,5,602773,2303079951
1912,7.4818,9.0846,80,611234,3866036195
1913,3.7995,9.7626,14,668791,5292680661
1914,6.1217,6.1253,65,592191,1087583500
1915,0.1280,0.2438,76,736574,5938943847
1916,1.3258,0.1929,71,390756,2960476965
1917,0.2371,1.5237,45,573220,2727523897
1918,0.9244,7.3307,35,318242,7567063317
1919,1.9953,3.6103,12,108790,8505968418
1920,4.7715,6.1875,44,728176,8940760079
1921,6.9283,1.6351,199,85753,5793243458
1922,3.0021,4.7123,32,739232,6018636234
1923,7.7655,0.2533,160,691442,3216664257
1924,7.5429,8.9210,41,559049,6103634843
1925,1.8185,1.3624,45,698517,8156816094
1926,8.0756,5.8688,14,716812,1734534938
1927,2.5757,7.5389,64,527353,4693861546
1928,0.1679,2.7328,72,440229,4614293027
1929,8.3772,0.6696,85,526498,9915896631
1930,8.0817,4.7472,20,754090,8637332977
1931,4.0708,7.5524,13,375557,9488323684
1932,5.8381,0.8759,40,542496,3796466244
1933,0.6337,8.1532,72,49166,2177919716
1934,7.6536,0.8200,64,385822,6255196095
1935,5.8364,1.5522,871,452593,6629422005
1936,5.7224,8.1379,24,490883,8085405325
1937,9.9746,1.9188,65,765488,1844540942
1938,2.9168,9.7408,106,645237,2437378352
1939,4.6499,7.9592,54,590514,7454926561
1940,5.4731,7.9401,76,293229,2185905685
1941,5.3526,9.6114,66,774738,8495657168
1942,0.2992,3.9362,61,138315,7493672345
1943,8.5705,1.6350,5,337069,2114506870
1944,5.7971,9.9660,27,68014,6592862434
1945,9.7283,6.8495,65,747566,5441758246
1946,8.1242,1.5114,84,329564,6066376844
1947,5.2284,0.2810,615,568311,8416401053
1948,3.5525,1.4584,172,144725,1678309867
1949,8.3973,2.8905,16,581267,6767351273
1950,6.9037,8.0795,74,207177,2395745713
1951,9.3773,9.2429,48,651623,9615450882
1952,2.7424,2.2145,73,312502,4886318537
1953,7.2789,6.1519,70,374760,1171323230
1954,7.4808,3.7314,65,311909,8581017422
1955,1.8686,6.3448,69,365649,3850527660
1956,1.1987,0.3023,59,461401,9657866318
1957,4.4342,8.7181,168,367517,9133987392
1958,1.0788,9.4770,55,596224,3293196015
1959,0.7154,2.0500,91,164274,1025585791
1960,2.6594,5.5701,26,284987,3384025382
1961,2.3191,4.5585,54,672836,9880420575
1962,0.4342,2.7167,93,637963,8345848508
1963,4.7673,8.4484,55,462453,6969767633
1964,3.9410,2.0398,10,166930,3806064363
1965,6.1283,4.2820,37,657782,2705496017
1966,2.5814,0.8001,165,454746,6877905134
1967,5.6023,4.3787,1,610004,8075045476
1968,8.3463,0.9111,202,272500,2001355114
1969,0.2508,7.0445,61,561831,5672660438
1970,9.4537,0.1443,67,765444,3748371379
1971,1.0778,9.4003,165,672895,6446301991
1972,0.7127,8.1129,83,89420,1771416121
1973,7.7086,1.9456,16,64624,9859718528
1974,8.7215,3.2745,81,634694,6683292031
1975,2.3127,2.8493,257,424008,3537804084
1976,8.3912,5.4647,14,386487,5819240910
1977,9.3512,9.9318,9,71750,1518725320
1978,0.2587,5.9677,81,270643,6768981638
1979,1.4649,5.0336,79,700542,5228928538
1980,5.6440,1.8585,5,86099,9919909729
1981,2.5000,5.0890,68,327427,3870354112
1982,0.2843,2.4508,68,780018,2067031359
1983,1.8380,6.0534,1,757446,2448817709
1984,7.0440,4.6322,218,74444,6044559291
1985,3.1916,8.2938,31,335219,4218789610
1986,7.1887,6.7707,83,652436,5235146472
1987,6.4155,8.3558,61,231372,7726436145
1988,0.9471,4.1223,64,769074,3718399227
1989,8.8213,5.7879,54,464875,7453065256
1990,0.8883,2.5418,77,376733,2509622106
1991,0.4827,1.8538,848,656728,4683526794
1992,5.2975,5.7342,56,91368,1732994218
1993,5.8200,6.9377,66,588300,9078812279
1994,2.4011,1.8770,62,131296,4289466257
1995,9.7537,2.8280,106,611162,9116422694
1996,0.0884,0.2334,52,144551,4422663263
1997,4.1516,8.0211,27,451426,2365601990
1998,4.1175,1.7259,63,10445,1749365942
1999,3.0145,1.3430,156,268866,8899695417
2000,3.9114,2.9307,26,284565,7469100752
2001,6.7302,3.5061,168,681412,4440636877
2002,6.9262,1.8783,167,694981,2745717548
2003,3.1125,3.4077,160,699169,2887420487
2004,0.9344,3.6427,64,771666,6979875029
2005,3.3574,9.3722,61,463464,1444630961
2006,3.3478,1.1464,5,253283,6466419701
2007,9.0822,3.9128,84,209262,1588733974
2008,4.2706,5.4692,42,492161,6067112593
2009,5.9270,4.2359,168,529224,9301392023
2010,0.0773,8.1116,53,601134,7473230830
2011,6.4490,9.0886,30,623914,7722012093
2012,6.9315,5.4767,16,344837,2675332432
2013,7.7623,6.3635,18,61505,1677378106
2014,2.1148,8.2511,159,588978,6259530305
2015,2.8682,0.6004,163,591616,1078115477
2016,0.6506,4.2135,3,445413,1504989919
2017,7.2255,0.7320,52,36543,2334353687
2018,2.6215,0.9827,12,377767,6018277087
2019,8.9637,8.1347,70,711006,5163540988
2020,3.1408,0.2234,94,136053,1698117475
2021,7.5199,6.5085,5,92556,8365164817
2022,4.2519,6.9264,14,334438,4199109932
2023,2.1213,7.2166,737,769259,3260811985
2024,0.8890,2.4235,53,186157,5606371844
2025,5.7001,5.3679,65,352858,1432811536
2026,0.2458,7.5316,65,655965,4579943739
2027,6.3669,2.1854,66,427790,7156402933
2028,5.8692,6.5735,52,27424,3362611924
2029,7.3291,6.3665,49,22019,8423115999
2030,5.9701,8.5372,60,584736,2806044557
2031,5.4552,0.8722,53,145209,2158782730
2032,4.2359,4.3918,66,665922,1261864041
2033,4.7808,1.7686,78,212801,9213628882
2034,6.4891,5.8379,82,57879,9394771272
2035,4.3021,1.2134,7,418667,7477461025
2036,2.5116,8.1582,54,738478,7349145537
2037,6.8550,3.2401,73,324035,7550768699
2038,0.9492,7.6327,57,602909,3036050374
2039,3.7868,9.0090,72,321898,5976860748
2040,8.0971,5.0678,68,172285,7644492258
2041,3.5130,6.7432,167,354127,5508522491
2042,0.3584,7.7802,64,221012,2271456189
2043,3.0709,7.5045,8,714213,6079366016
2044,9.3876,1.1699,13,221458,4662611613
2045,1.2502,4.6661,38,475587,8963952624
2046,1.6211,8.3014,155,495425,3841548114
2047,1.8833,2.0772,31,730737,4419654085
2048,7.0636,3.8915,32,782984,1730088572
2049,6.5596,6.1047,12,782836,7621312921
2050,1.8199,6.8839,28,531687,7859819736
2051,5.4399,9.4855,75,426543,2665395306
2052,6.0315,1.0712,6,109059,9682745228
2053,2.0920,4.7508,56,347942,9921704596
2054,4.3903,6.0191,62,605866,3091765722
2055,2.6649,1.1123,66,243655,6817575027
2056,2.5678,6.2776,60,678329,1052628400
2057,2.2515,8.4235,10,346099,5963481258
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5003,2.0783,7.4297,18,184285,8667199565
5004,6.8268,1.6812,13,66728,2979978170
5005,2.2546,3.6986,32,486436,4293566395
5006,1.4158,6.7140,13,344154,4585255502
5007,8.2408,0.0525,70,41449,3987779213
5008,1.1177,0.4179,64,613314,9258583741
5009,7.6028,8.8639,160,762216,9957917415
5010,8.7992,6.2247,77,684967,7377864058
5011,0.8609,4.1498,64,355945,4825016246
5012,5.7162,1.4523,69,209894,2576063010
5013,9.6064,9.2852,66,624541,1935881237
5014,4.5980,4.8103,42,322305,9479838528
5015,9.5432,6.0588,63,56058,7570358258
5016,4.0471,1.0600,80,233758,1023098207
5017,8.3526,8.5972,68,460926,1273800364
5018,1.0011,9.6792,53,616783,7577921465
5019,8.1949,7.9373,27,695475,6095772132
5020,8.7868,3.1896,64,212279,1082412324
5021,8.2437,1.4508,69,623889,3089380445
5022,0.7315,3.9815,157,271564,8854408802
5023,6.7352,0.4388,65,372304,7424547688
5024,5.8091,5.8573,26,422148,2486283105
5025,8.5865,1.6778,242,324898,3703369140
5026,9.8412,9.0187,13,316182,8339381387
5027,5.7554,0.0873,283,404105,3102660966
5028,1.2544,9.1368,96,81336,3159063060
5029,3.6813,3.6286,290,303300,7545123531
5030,7.1663,7.0296,17,213619,7493530239
5031,8.1262,0.8069,76,349575,7227290562
5032,8.3097,6.7607,71,499761,7139647538
# -*- coding: utf-8 -*-
from sklearn.neighbors import KNeighborsRegressor
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
def knntest():
# 读取数据
data=pd.read_csv("./train.csv")
# 缩小数据的范围
data=data.query("x>1.0&x<1.25&y>2.5&y<2.75")
# 处理时间数据 字符串时间转换为时间类型
data_time=pd.to_datetime(data["time"],unit='s')
# 转换成字典格式
data_time=pd.DatetimeIndex(data_time)
# 添加特征
data.loc[:, ('year')]=data_time.year
# data['year']=data_time.year
data.loc[:, ('hour')]=data_time.hour
data.loc[:, ('weekday')]=data_time.weekday
# 删除时间列a
data=data.drop(['time'],axis=1)
# 获取签到大于3的
place_count=data.groupby("place_id").count()
tf=place_count[place_count.row_id>3].reset_index()
# 过滤数据
data=data[data["place_id"].isin(tf.place_id)]
# 获取特征值与目标值
y=data['place_id']
x=data.drop(["place_id",'row_id'],axis=1)
# 进行数据的分割
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.25)
# 进行标准化
std=StandardScaler()
x_train=std.fit_transform(x_train)
x_test=std.transform(x_test)
# KNeighborsRegressor实例
k=KNeighborsRegressor(n_neighbors=5)
k.fit(x_train,y_train)
# 获取预测结果
y_predict=k.predict(x_test)
print("预测目标的位置",y_predict)
# 获取准确率
print("准确率",k.score(x_test,y_test))
return None
if __name__=="__main__":
knntest()
