【发布时间】：2020-09-30 23:35:03
【问题描述】：
我编写了一个回归模型，希望它能够预测未来的比特币价格。我试图让它工作，但遇到了多个问题。这是我的源代码：
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.tree import DecisionTreeRegressor
from matplotlib import pyplot as plt
import matplotlib
matplotlib.rcParams['figure.figsize'] = (20,10)
df = pd.read_csv("datasets_1869_18570_bitcoin_cash_price.csv")
price = df.Close
date = df.Date
date_format = pd.get_dummies(date)
# df_bitcoin = pd.concat([price, date_format], axis="columns")
# df_bitcoin.to_csv('test.csv')
X_train, X_test, y_train, y_test = train_test_split(date_format,price,test_size=0.2, random_state=10)
# print(X_train,y_train)
prediction = LinearRegression()
prediction.fit(X_train,y_train)
prediction.score(X_test,y_test)
print(prediction.score(X_test,y_test))
crossvalid = ShuffleSplit(n_splits=5,test_size=0.2, random_state=0)
print(cross_val_score(LinearRegression(), date_format, price, cv=crossvalid))
def find_best_model_using_gridsearchcv(x,y):
algos = {
'linear_regression': {
'model': LinearRegression(),
'params': {
'normalize': [True, False]
}
},
'lasso': {
'model': Lasso(),
'params': {
'alpha': [1,2],
'selection': ['random', 'cyclic']
}
},
'decision_tree': {
'model': DecisionTreeRegressor(),
'params': {
'criterion': ['mse', 'friedman_mse'],
'splitter': ['best', 'random']
}
}
}
scores = []
cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
for algo_name, config in algos.items():
gs = GridSearchCV(config['model'], config['params'], cv=cv, return_train_score=False)
gs.fit(date_format, price)
scores.append({
'model': algo_name,
'best_score': gs.best_score_,
'best_params': gs.best_params_
})
final = pd.DataFrame(scores, columns=['model', 'best_score', 'best_params'])
final.to_csv("final.csv")
find_best_model_using_gridsearchcv(date_format,price)
def predict_price(dates,price):
date_index = np.where(date_format.columns == dates)[0][0]
x = np.zeros(len(date_index.columns))
if date_index >= 0:
x[date_index] = 1
return prediction.predict([x])[0]
predict_price('Feb 20, 2018', 1000)
# print(cross_val_score(LinearRegression(), date_format, price, cv=crossvalid))
这是我的 csv 文件：
Date,Open,High,Low,Close,Volume,Market Cap
"Feb 20, 2018",1543.27,1569.03,1414.35,1418.73,"820,947,000","26,199,800,000"
"Feb 19, 2018",1483.34,1553.81,1483.34,1534.77,"578,906,000","25,179,700,000"
"Feb 18, 2018",1552.10,1641.40,1428.49,1487.46,"907,873,000","26,344,200,000"
"Feb 17, 2018",1548.48,1568.64,1517.14,1551.39,"641,719,000","26,280,100,000"
"Feb 16, 2018",1373.16,1558.66,1369.68,1552.20,"961,010,000","23,302,000,000"
"Feb 15, 2018",1358.65,1400.71,1330.18,1375.81,"502,454,000","23,053,300,000"
"Feb 14, 2018",1229.18,1384.06,1229.18,1362.27,"629,852,000","20,854,300,000"
"Feb 13, 2018",1285.23,1289.42,1205.73,1231.98,"442,663,000","21,803,000,000"
"Feb 12, 2018",1222.43,1302.66,1222.43,1283.91,"466,213,000","20,735,400,000"
"Feb 11, 2018",1255.59,1306.20,1197.87,1217.64,"651,555,000","21,295,600,000"
"Feb 10, 2018",1311.99,1394.81,1215.72,1257.31,"734,606,000","22,249,700,000"
"Feb 09, 2018",1273.87,1342.41,1206.39,1312.11,"1,133,760,000","21,601,200,000"
"Feb 08, 2018",951.57,1345.20,951.57,1284.56,"2,203,710,000","16,133,900,000"
"Feb 07, 2018",971.08,1040.49,902.49,957.96,"866,222,000","16,462,900,000"
"Feb 06, 2018",890.97,978.91,764.02,974.52,"777,420,000","15,103,100,000"
"Feb 05, 2018",1159.89,1172.70,839.95,887.41,"568,117,000","19,659,800,000"
"Feb 04, 2018",1273.17,1286.98,1101.31,1165.38,"656,034,000","21,577,500,000"
"Feb 03, 2018",1194.23,1314.43,1072.83,1272.50,"453,101,000","20,237,400,000"
"Feb 02, 2018",1272.50,1272.50,980.78,1191.14,"896,666,000","21,561,700,000"
"Feb 01, 2018",1491.12,1503.30,1214.88,1274.35,"678,020,000","25,263,200,000"
"Jan 31, 2018",1470.74,1522.87,1406.25,1486.89,"729,209,000","24,915,700,000"
"Jan 30, 2018",1657.69,1662.07,1468.17,1470.55,"649,823,000","28,079,700,000"
"Jan 29, 2018",1745.36,1763.24,1638.33,1657.14,"362,213,000","29,561,500,000"
"Jan 28, 2018",1636.41,1762.34,1636.41,1746.25,"580,544,000","27,713,300,000"
"Jan 27, 2018",1602.50,1665.81,1567.78,1628.63,"441,742,000","27,136,100,000"
"Jan 26, 2018",1637.19,1678.63,1500.37,1603.90,"594,667,000","27,720,500,000"
"Jan 25, 2018",1661.04,1707.70,1611.04,1642.12,"522,194,000","28,121,300,000"
"Jan 24, 2018",1622.15,1703.34,1575.42,1639.13,"488,161,000","27,460,200,000"
"Jan 23, 2018",1623.14,1700.34,1504.72,1621.46,"578,984,000","27,473,700,000"
"Jan 22, 2018",1794.46,1815.42,1525.50,1621.92,"681,350,000","30,370,200,000"
"Jan 21, 2018",2033.70,2033.70,1733.99,1776.64,"745,179,000","34,415,500,000"
"Jan 20, 2018",1784.26,2124.45,1780.45,2032.12,"1,116,240,000","30,191,500,000"
"Jan 19, 2018",1740.17,1870.78,1723.87,1769.62,"743,247,000","29,442,200,000"
"Jan 18, 2018",1752.27,1893.47,1696.25,1756.75,"1,024,060,000","29,643,600,000"
"Jan 17, 2018",1767.58,1875.08,1361.02,1748.05,"1,457,730,000","29,899,500,000"
"Jan 16, 2018",2406.86,2406.86,1555.97,1772.07,"1,704,030,000","40,708,800,000"
"Jan 15, 2018",2541.58,2576.13,2328.31,2401.54,"1,412,360,000","42,983,500,000"
"Jan 14, 2018",2687.10,2746.61,2490.04,2553.32,"993,310,000","45,439,500,000"
"Jan 13, 2018",2617.66,2850.27,2589.03,2685.29,"1,218,570,000","44,260,300,000"
"Jan 12, 2018",2475.90,2648.23,2413.33,2620.99,"1,110,220,000","41,859,100,000"
"Jan 11, 2018",2891.55,2976.49,2396.84,2462.61,"1,947,950,000","48,880,800,000"
"Jan 10, 2018",2390.02,2961.20,2332.48,2895.38,"2,578,810,000","40,398,000,000"
"Jan 09, 2018",2412.36,2502.87,2346.68,2391.56,"1,084,750,000","40,770,900,000"
"Jan 08, 2018",2786.60,2810.32,2275.07,2421.47,"1,428,900,000","47,091,600,000"
"Jan 07, 2018",2784.68,3071.16,2730.31,2786.88,"1,444,780,000","47,054,000,000"
"Jan 06, 2018",2583.71,2829.69,2481.36,2786.65,"1,669,160,000","43,653,200,000"
"Jan 05, 2018",2400.74,2648.32,2370.59,2584.48,"2,115,710,000","40,557,600,000"
"Jan 04, 2018",2608.87,2610.20,2359.30,2430.18,"5,377,260,000","44,069,100,000"
"Jan 03, 2018",2700.55,2958.47,2592.56,2608.69,"4,688,840,000","45,613,300,000"
"Jan 02, 2018",2434.50,2867.14,2434.50,2711.00,"1,831,230,000","41,114,800,000"
"Jan 01, 2018",2534.82,2534.86,2389.52,2432.54,"922,813,000","42,804,800,000"
"Dec 31, 2017",2376.96,2627.95,2350.03,2533.01,"1,300,820,000","40,134,900,000"
"Dec 30, 2017",2791.55,2791.55,2329.47,2392.41,"1,257,760,000","47,129,800,000"
"Dec 29, 2017",2587.30,2922.41,2389.04,2779.95,"2,479,420,000","43,677,000,000"
"Dec 28, 2017",2888.64,2896.78,2481.30,2566.64,"3,221,300,000","48,758,700,000"
"Dec 27, 2017",3108.32,3108.32,2771.05,2889.52,"1,406,450,000","52,461,200,000"
"Dec 26, 2017",2947.74,3112.64,2915.80,3108.89,"1,544,430,000","49,745,700,000"
"Dec 25, 2017",2929.35,3099.62,2755.60,2954.69,"1,355,440,000","49,430,800,000"
"Dec 24, 2017",3260.55,3260.55,2573.44,2903.12,"1,712,160,000","55,013,200,000"
"Dec 23, 2017",2746.95,3479.71,2684.82,3257.08,"2,843,310,000","46,342,000,000"
"Dec 22, 2017",3324.00,3369.55,1938.36,2696.69,"3,877,770,000","56,071,800,000"
"Dec 21, 2017",3909.00,3909.00,3038.01,3267.17,"3,664,330,000","65,933,700,000"
"Dec 20, 2017",2825.03,4355.62,2825.03,3923.07,"11,889,600,000","47,644,600,000"
"Dec 19, 2017",2199.06,2918.93,2160.92,2805.89,"3,913,340,000","37,083,300,000"
"Dec 18, 2017",1869.21,2353.64,1799.12,2196.64,"2,538,650,000","31,516,900,000"
"Dec 17, 2017",1812.33,1939.93,1806.65,1862.88,"1,020,590,000","30,554,500,000"
"Dec 16, 2017",1824.85,1890.06,1769.50,1801.88,"971,255,000","30,762,600,000"
"Dec 15, 2017",1974.66,1974.66,1678.06,1815.43,"1,457,230,000","33,284,800,000"
"Dec 14, 2017",1600.67,2080.41,1593.15,1966.14,"2,717,410,000","26,977,700,000"
"Dec 13, 2017",1616.99,1702.53,1497.94,1607.65,"1,245,190,000","27,249,600,000"
"Dec 12, 2017",1427.38,1638.29,1427.38,1615.03,"1,386,250,000","24,051,800,000"
"Dec 11, 2017",1320.00,1453.49,1316.85,1423.38,"922,792,000","22,239,900,000"
"Dec 10, 2017",1411.84,1411.84,1226.21,1323.07,"885,886,000","23,784,900,000"
"Dec 09, 2017",1464.63,1526.40,1353.90,1410.06,"1,059,610,000","24,671,600,000"
"Dec 08, 2017",1329.16,1731.32,1247.93,1464.13,"2,553,980,000","22,386,700,000"
"Dec 07, 2017",1430.71,1454.73,1323.75,1330.93,"1,285,210,000","24,095,900,000"
"Dec 06, 2017",1498.49,1530.65,1396.16,1430.10,"1,154,620,000","25,234,400,000"
"Dec 05, 2017",1580.16,1623.16,1498.00,1501.85,"1,032,800,000","26,606,900,000"
"Dec 04, 2017",1560.16,1580.11,1498.98,1576.92,"780,634,000","26,267,300,000"
"Dec 03, 2017",1435.36,1649.30,1420.60,1559.93,"1,476,740,000","24,163,600,000"
"Dec 02, 2017",1462.06,1502.37,1415.49,1434.98,"574,510,000","24,610,500,000"
"Dec 01, 2017",1381.81,1482.68,1300.46,1462.68,"965,151,000","23,257,100,000"
"Nov 30, 2017",1380.36,1468.75,1222.10,1389.78,"1,211,730,000","23,230,400,000"
"Nov 29, 2017",1541.63,1673.23,1332.14,1359.71,"2,345,250,000","25,941,800,000"
"Nov 28, 2017",1621.17,1629.48,1543.08,1546.18,"1,274,880,000","27,277,200,000"
"Nov 27, 2017",1722.93,1742.40,1607.79,1622.57,"1,391,460,000","28,986,800,000"
"Nov 26, 2017",1571.39,1758.61,1563.35,1735.66,"1,593,050,000","26,434,500,000"
"Nov 25, 2017",1634.00,1656.55,1535.77,1571.27,"1,560,040,000","27,484,900,000"
"Nov 24, 2017",1651.86,1782.83,1575.76,1648.51,"3,128,960,000","27,782,400,000"
"Nov 23, 2017",1302.23,1683.81,1299.95,1683.81,"4,112,090,000","21,899,400,000"
"Nov 22, 2017",1175.06,1335.25,1171.60,1303.31,"1,517,620,000","19,758,600,000"
"Nov 21, 2017",1220.78,1224.15,1140.88,1176.97,"772,859,000","20,525,100,000"
"Nov 20, 2017",1172.43,1236.01,1167.28,1236.01,"800,053,000","19,710,200,000"
"Nov 19, 2017",1260.95,1262.34,1144.08,1172.05,"1,285,910,000","21,196,200,000"
"Nov 18, 2017",1197.43,1388.11,1197.43,1254.53,"3,053,770,000","20,125,900,000"
"Nov 17, 2017",909.16,1217.42,863.98,1185.48,"3,203,430,000","15,279,000,000"
"Nov 16, 2017",1202.17,1210.36,876.57,900.78,"2,034,690,000","20,202,000,000"
"Nov 15, 2017",1269.79,1326.39,1191.98,1212.40,"1,321,780,000","21,335,600,000"
"Nov 14, 2017",1346.73,1390.21,1211.39,1273.53,"1,697,910,000","22,626,200,000"
"Nov 13, 2017",1381.82,1501.16,1043.00,1353.99,"4,850,570,000","23,214,700,000"
"Nov 12, 2017",1346.97,2477.65,1215.58,1388.86,"8,371,320,000","22,614,400,000"
"Nov 11, 2017",1001.64,1403.66,910.72,1340.45,"5,139,770,000","16,807,400,000"
"Nov 10, 2017",651.77,1034.77,651.77,1007.42,"5,195,420,000","10,933,000,000"
"Nov 09, 2017",622.31,670.14,619.26,654.30,"710,387,000","10,437,700,000"
"Nov 08, 2017",615.24,646.38,565.60,622.46,"837,646,000","10,317,500,000"
"Nov 07, 2017",602.68,626.21,602.45,616.30,"375,367,000","10,105,200,000"
"Nov 06, 2017",630.31,657.34,602.65,602.65,"794,105,000","10,566,600,000"
"Nov 05, 2017",619.91,635.72,579.13,630.70,"816,028,000","10,390,900,000"
"Nov 04, 2017",635.35,642.05,571.12,620.45,"1,161,370,000","10,647,200,000"
"Nov 03, 2017",587.32,684.53,577.86,625.32,"1,682,210,000","9,839,760,000"
"Nov 02, 2017",527.68,610.39,514.52,587.22,"1,632,060,000","8,840,010,000"
"Nov 01, 2017",438.30,538.26,437.91,531.83,"1,152,230,000","7,342,250,000"
"Oct 31, 2017",449.37,454.07,431.76,439.05,"343,799,000","7,527,260,000"
"Oct 30, 2017",458.45,478.42,417.62,448.23,"796,409,000","7,679,000,000"
"Oct 29, 2017",420.47,526.12,408.40,451.92,"2,002,440,000","7,042,260,000"
"Oct 28, 2017",369.54,425.44,369.06,423.35,"781,037,000","6,183,520,000"
"Oct 27, 2017",338.30,372.81,338.24,367.57,"501,989,000","5,655,870,000"
"Oct 26, 2017",332.11,350.94,331.21,338.20,"234,967,000","5,550,700,000"
"Oct 25, 2017",326.59,334.38,320.38,332.13,"151,935,000","5,457,720,000"
"Oct 24, 2017",315.90,340.45,311.48,326.89,"267,442,000","5,278,410,000"
"Oct 23, 2017",330.67,332.28,309.87,315.60,"191,558,000","5,524,790,000"
"Oct 22, 2017",323.50,355.52,323.50,331.66,"355,546,000","5,405,020,000"
"Oct 21, 2017",326.75,328.45,317.46,323.36,"129,938,000","5,458,980,000"
"Oct 20, 2017",330.90,332.98,323.08,327.44,"160,204,000","5,527,860,000"
"Oct 19, 2017",339.27,339.27,326.27,330.81,"195,578,000","5,667,070,000"
"Oct 18, 2017",373.65,376.18,313.77,339.17,"477,184,000","6,240,990,000"
"Oct 17, 2017",313.70,394.96,309.64,371.73,"1,008,160,000","5,239,520,000"
"Oct 16, 2017",315.26,318.02,310.52,313.64,"117,797,000","5,265,400,000"
"Oct 15, 2017",322.42,323.59,307.68,314.95,"133,385,000","5,384,760,000"
"Oct 14, 2017",321.54,327.42,317.35,321.96,"164,137,000","5,369,720,000"
"Oct 13, 2017",314.15,323.08,304.38,321.46,"288,210,000","5,242,630,000"
"Oct 12, 2017",315.47,330.13,310.71,313.21,"262,066,000","5,262,390,000"
"Oct 11, 2017",320.20,321.26,312.13,314.90,"131,577,000","5,341,000,000"
"Oct 10, 2017",313.22,352.02,311.70,321.59,"367,809,000","5,224,170,000"
"Oct 09, 2017",342.76,342.76,289.04,314.16,"288,075,000","5,716,320,000"
"Oct 08, 2017",360.09,362.46,337.00,342.21,"203,754,000","6,004,940,000"
"Oct 07, 2017",363.60,364.88,356.06,360.78,"91,004,000","6,062,860,000"
"Oct 06, 2017",355.44,371.28,354.96,363.01,"182,031,000","5,926,540,000"
"Oct 05, 2017",358.42,371.19,348.45,355.75,"268,341,000","5,971,260,000"
"Oct 04, 2017",404.20,404.45,354.55,358.45,"251,584,000","6,729,640,000"
"Oct 03, 2017",421.79,421.79,395.74,404.18,"130,247,000","7,022,370,000"
"Oct 02, 2017",415.87,430.86,411.84,421.19,"219,590,000","6,921,580,000"
"Oct 01, 2017",433.38,436.94,415.15,415.15,"164,290,000","7,207,770,000"
"Sep 30, 2017",436.64,445.62,432.53,432.63,"150,565,000","7,258,880,000"
"Sep 29, 2017",447.66,447.92,426.99,436.77,"148,725,000","7,441,960,000"
"Sep 28, 2017",456.71,465.20,433.50,447.81,"300,421,000","7,592,000,000"
"Sep 27, 2017",441.86,460.00,439.41,457.31,"197,885,000","7,343,490,000"
"Sep 26, 2017",445.36,458.25,441.83,441.83,"165,303,000","7,400,020,000"
"Sep 25, 2017",419.65,450.13,419.65,445.80,"302,272,000","6,971,280,000"
"Sep 24, 2017",428.80,433.04,419.25,421.03,"114,527,000","7,121,950,000"
"Sep 23, 2017",415.31,432.96,407.45,428.50,"189,127,000","6,895,970,000"
"Sep 22, 2017",417.92,435.44,402.77,415.09,"241,905,000","6,937,170,000"
"Sep 21, 2017",475.83,480.00,413.75,416.26,"316,731,000","7,898,300,000"
"Sep 20, 2017",526.74,527.72,471.09,476.05,"669,264,000","8,741,670,000"
"Sep 19, 2017",480.64,550.41,450.05,527.88,"802,321,000","7,975,010,000"
"Sep 18, 2017",422.09,479.69,422.09,479.32,"458,550,000","7,001,640,000"
"Sep 17, 2017",438.90,438.90,384.06,419.86,"221,828,000","7,279,520,000"
"Sep 16, 2017",424.49,450.98,388.20,440.22,"313,583,000","7,039,590,000"
"Sep 15, 2017",369.49,448.39,301.69,424.02,"707,231,000","6,126,800,000"
"Sep 14, 2017",504.22,510.47,367.04,367.04,"257,431,000","8,359,650,000"
"Sep 13, 2017",509.47,519.20,471.22,503.61,"340,344,000","8,445,540,000"
"Sep 12, 2017",539.03,559.22,505.01,510.41,"273,825,000","8,934,220,000"
"Sep 11, 2017",537.19,556.25,515.67,537.81,"274,712,000","8,902,570,000"
"Sep 10, 2017",546.48,546.75,484.09,537.07,"289,508,000","9,055,430,000"
"Sep 09, 2017",584.73,586.18,539.15,547.47,"234,578,000","9,688,150,000"
"Sep 08, 2017",654.37,687.30,557.50,583.10,"809,763,000","10,840,500,000"
"Sep 07, 2017",636.85,707.98,603.50,652.86,"1,082,380,000","10,548,500,000"
"Sep 06, 2017",541.28,642.69,539.96,638.18,"693,240,000","8,964,700,000"
"Sep 05, 2017",514.90,550.95,458.78,541.71,"338,978,000","8,527,100,000"
"Sep 04, 2017",608.26,608.26,500.75,517.24,"328,957,000","10,072,200,000"
"Sep 03, 2017",578.27,617.41,563.59,607.43,"344,862,000","9,574,520,000"
"Sep 02, 2017",621.96,642.05,560.58,575.90,"350,478,000","10,297,000,000"
"Sep 01, 2017",588.40,645.52,586.73,622.17,"393,839,000","9,740,460,000"
"Aug 31, 2017",576.25,603.69,572.14,588.17,"298,144,000","9,538,750,000"
"Aug 30, 2017",549.32,604.69,535.92,575.21,"443,856,000","9,092,300,000"
"Aug 29, 2017",596.13,599.30,528.32,552.93,"370,017,000","9,866,110,000"
"Aug 28, 2017",619.70,626.88,595.52,596.18,"216,273,000","10,254,800,000"
"Aug 27, 2017",625.89,654.87,589.63,620.90,"402,718,000","10,357,000,000"
"Aug 26, 2017",641.88,646.93,622.36,625.32,"193,414,000","10,616,100,000"
"Aug 25, 2017",627.06,665.41,624.30,641.05,"348,632,000","10,365,600,000"
"Aug 24, 2017",670.03,697.32,617.31,628.11,"407,177,000","11,071,100,000"
"Aug 23, 2017",690.96,715.67,651.32,669.40,"501,983,000","11,416,800,000"
"Aug 22, 2017",596.19,734.88,570.27,690.88,"1,393,260,000","9,844,620,000"
"Aug 21, 2017",723.70,756.59,573.47,599.63,"1,123,400,000","11,943,100,000"
"Aug 20, 2017",772.42,858.96,683.94,712.87,"1,494,020,000","12,742,600,000"
"Aug 19, 2017",697.04,1091.97,625.16,754.56,"3,196,230,000","11,498,100,000"
"Aug 18, 2017",458.67,764.07,458.67,690.24,"3,087,490,000","7,565,590,000"
"Aug 17, 2017",301.02,460.53,293.10,460.53,"744,605,000","4,964,980,000"
"Aug 16, 2017",297.97,307.62,290.21,300.21,"106,436,000","4,914,350,000"
"Aug 15, 2017",298.19,306.52,292.88,297.86,"133,924,000","4,917,480,000"
"Aug 14, 2017",296.10,327.39,293.50,297.68,"174,968,000","4,882,720,000"
"Aug 13, 2017",316.29,317.47,298.05,298.05,"120,722,000","5,215,330,000"
"Aug 12, 2017",327.82,341.85,313.70,317.09,"126,007,000","5,405,200,000"
"Aug 11, 2017",275.88,351.16,275.02,328.24,"233,971,000","4,548,600,000"
"Aug 10, 2017",305.21,311.68,274.68,275.95,"136,637,000","5,031,790,000"
"Aug 09, 2017",345.28,349.32,298.61,303.89,"165,032,000","5,691,960,000"
"Aug 08, 2017",321.35,386.29,309.61,345.49,"274,880,000","5,297,390,000"
"Aug 07, 2017",223.76,373.87,223.76,319.69,"346,546,000","3,688,360,000"
"Aug 06, 2017",212.18,223.70,203.44,220.66,"107,606,000","3,497,290,000"
"Aug 05, 2017",231.11,273.04,200.98,213.15,"144,043,000","3,809,330,000"
"Aug 04, 2017",362.18,386.93,233.05,233.05,"185,038,000","5,969,720,000"
"Aug 03, 2017",448.49,519.28,364.05,364.05,"161,518,000","7,392,030,000"
"Aug 02, 2017",382.38,756.93,309.33,452.66,"416,207,000","6,302,360,000"
"Aug 01, 2017",294.60,426.11,210.38,380.01,"65,988,800",-
"Jul 31, 2017",346.36,347.82,266.19,294.46,"1,075,960",-
"Jul 30, 2017",385.14,385.14,316.25,345.66,"606,695",-
"Jul 29, 2017",410.56,423.73,323.73,384.77,"737,815",-
"Jul 28, 2017",386.65,465.18,217.06,406.05,"1,230,160",-
"Jul 27, 2017",417.10,460.97,367.78,385.48,"533,207",-
"Jul 26, 2017",407.08,486.16,321.79,365.82,"1,784,640",-
"Jul 25, 2017",441.35,541.66,338.09,406.90,"524,908",-
"Jul 24, 2017",412.58,578.89,409.21,440.70,"190,952",-
"Jul 23, 2017",555.89,578.97,411.78,413.06,"85,013",-
我的错误是：
ttributeError: 'numpy.int64' object has no attribute 'columns'
和
AttributeError: 'numpy.int64' object has no attribute 'columns'
我的回归似乎有负分，即 -0.12086295635446963。我在做 LinearRegression().score
【问题讨论】：
-
你遇到了什么问题？
-
好的。您现在可以回答我的问题或给出提示吗？
-
默认评分我们为 R²，它可以是负数（参见 LinearRegression 文档）。错误是您在整数上调用
columns方法，正如错误所说，因此需要修复。
标签： python pandas numpy machine-learning scikit-learn