├── README.md ├── dolar.py └── 2016dolaralis.csv /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | # Dolar-Fiyat-n-Tahmin-Edelim 5 | Dolar fiyatini tahmin etmek = dersin ilgi çekmesi icin bir espri 6 | -------------------------------------------------------------------------------- /dolar.py: -------------------------------------------------------------------------------- 1 | 2 | #! /usr/bin/env python 3 | # -*- coding: UTF-8 -*- 4 | import numpy as np 5 | import pandas as pd 6 | from sklearn.linear_model import LinearRegression 7 | import matplotlib.pyplot as plt 8 | from sklearn.preprocessing import PolynomialFeatures 9 | 10 | 11 | 12 | veri = pd.read_csv("2016dolaralis.csv") 13 | 14 | 15 | x = veri["Gun"] 16 | y = veri["Fiyat"] 17 | 18 | x = x.reshape(251,1) 19 | y= y.reshape(251,1) 20 | 21 | plt.scatter(x,y) 22 | plt.show() 23 | 24 | #Lineer Reg. 25 | tahminlineer = LinearRegression() 26 | tahminlineer.fit(x,y) 27 | tahminlineer.predict(x) 28 | 29 | plt.plot(x,tahminlineer.predict(x),c="red") 30 | 31 | #Polinom Reg. 32 | 33 | tahminpolinom = PolynomialFeatures(degree=3) 34 | Xyeni = tahminpolinom.fit_transform(x) 35 | 36 | polinommodel = LinearRegression() 37 | polinommodel.fit(Xyeni,y) 38 | polinommodel.predict(Xyeni) 39 | 40 | plt.plot(x,polinommodel.predict(Xyeni)) 41 | 42 | plt.show() 43 | 44 | hatakaresilineer = 0 45 | hatakaresipolinom = 0 46 | 47 | for i in range(len(Xyeni)): 48 | hatakaresipolinom = hatakaresipolinom + (float(y[i])-float(polinommodel.predict(Xyeni)[i]))**2 49 | 50 | for i in range(len(y)): 51 | hatakaresilineer = hatakaresilineer + (float(y[i])-float(tahminlineer.predict(x)[i]))**2 52 | 53 | 54 | 55 | """ 56 | hatakaresipolinom = 0 57 | 58 | for a in range(150): 59 | 60 | tahminpolinom = PolynomialFeatures(degree=a+1) 61 | Xyeni = tahminpolinom.fit_transform(x) 62 | 63 | polinommodel = LinearRegression() 64 | polinommodel.fit(Xyeni,y) 65 | polinommodel.predict(Xyeni) 66 | for i in range(len(Xyeni)): 67 | hatakaresipolinom = hatakaresipolinom + (float(y[i])-float(polinommodel.predict(Xyeni)[i]))**2 68 | print(a+1,"inci dereceden fonksiyonda hata,", hatakaresipolinom) 69 | 70 | hatakaresipolinom = 0 71 | 72 | 73 | """ 74 | 75 | 76 | 77 | 78 | 79 | 80 | 81 | 82 | tahminpolinom8 = PolynomialFeatures(degree=8) 83 | Xyeni = tahminpolinom8.fit_transform(x) 84 | 85 | polinommodel8 = LinearRegression() 86 | polinommodel8.fit(Xyeni,y) 87 | polinommodel8.predict(Xyeni) 88 | 89 | plt.plot(x,polinommodel8.predict(Xyeni)) 90 | 91 | plt.show() 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | print((float(y[201])-float(polinommodel8.predict(Xyeni)[201]))) 106 | -------------------------------------------------------------------------------- /2016dolaralis.csv: -------------------------------------------------------------------------------- 1 | Gun,Fiyat 2 | 1,2.91810 3 | 2,2.94220 4 | 3,2.97500 5 | 4,3.00400 6 | 5,3.01670 7 | 6,2.98760 8 | 7,3.01880 9 | 8,3.03220 10 | 9,3.01380 11 | 10,3.02730 12 | 11,3.03620 13 | 12,3.03670 14 | 13,3.02430 15 | 14,3.04960 16 | 15,3.03870 17 | 16,3.01070 18 | 17,3.00970 19 | 18,3.02080 20 | 19,2.99780 21 | 20,2.97880 22 | 21,2.96090 23 | 22,2.96710 24 | 23,2.95240 25 | 24,2.94780 26 | 25,2.90440 27 | 26,2.90510 28 | 27,2.92950 29 | 28,2.94710 30 | 29,2.92390 31 | 30,2.92870 32 | 31,2.92280 33 | 32,2.94410 34 | 33,2.95170 35 | 34,2.96390 36 | 35,2.96060 37 | 36,2.96580 38 | 37,2.94490 39 | 38,2.93580 40 | 39,2.93860 41 | 40,2.92950 42 | 41,2.92930 43 | 42,2.96120 44 | 43,2.94510 45 | 44,2.93720 46 | 45,2.92150 47 | 46,2.91470 48 | 47,2.92010 49 | 48,2.92010 50 | 49,2.90810 51 | 50,2.88750 52 | 51,2.87340 53 | 52,2.87960 54 | 53,2.88520 55 | 54,2.90840 56 | 55,2.85580 57 | 56,2.85540 58 | 57,2.86950 59 | 58,2.87230 60 | 59,2.86930 61 | 60,2.87890 62 | 61,2.87050 63 | 62,2.87330 64 | 63,2.86950 65 | 64,2.83340 66 | 65,2.82490 67 | 66,2.81970 68 | 67,2.81890 69 | 68,2.82770 70 | 69,2.84120 71 | 70,2.84280 72 | 71,2.85690 73 | 72,2.83380 74 | 73,2.82700 75 | 74,2.84820 76 | 75,2.85910 77 | 76,2.85450 78 | 77,2.85190 79 | 78,2.83290 80 | 79,2.82760 81 | 80,2.82100 82 | 81,2.82870 83 | 82,2.84570 84 | 83,2.83450 85 | 84,2.81750 86 | 85,2.81500 87 | 86,2.80140 88 | 87,2.79280 89 | 88,2.80650 90 | 89,2.85900 91 | 90,2.92010 92 | 91,2.91970 93 | 92,2.92290 94 | 93,2.93800 95 | 94,2.96230 96 | 95,2.94890 97 | 96,2.95870 98 | 97,2.97110 99 | 98,2.96450 100 | 99,2.97770 101 | 100,2.97680 102 | 101,2.98260 103 | 102,2.98210 104 | 103,2.94260 105 | 104,2.93490 106 | 105,2.93990 107 | 106,2.95600 108 | 107,2.95150 109 | 108,2.94890 110 | 109,2.93930 111 | 110,2.94620 112 | 111,2.90800 113 | 112,2.89780 114 | 113,2.88940 115 | 114,2.89410 116 | 115,2.91000 117 | 116,2.92190 118 | 117,2.93040 119 | 118,2.92690 120 | 119,2.92960 121 | 120,2.93000 122 | 121,2.89840 123 | 122,2.89620 124 | 123,2.90550 125 | 124,2.87990 126 | 125,2.92660 127 | 126,2.93650 128 | 127,2.91300 129 | 128,2.89360 130 | 129,2.88480 131 | 130,2.88460 132 | 131,2.88460 133 | 132,2.92140 134 | 133,2.89810 135 | 134,2.88850 136 | 135,2.89470 137 | 136,2.89130 138 | 137,2.88340 139 | 138,2.94850 140 | 139,2.97660 141 | 140,3.02810 142 | 141,3.07270 143 | 142,3.05730 144 | 143,3.03170 145 | 144,3.03530 146 | 145,3.03770 147 | 146,3.01670 148 | 147,3.01250 149 | 148,2.97970 150 | 149,2.99260 151 | 150,3.00750 152 | 151,3.02130 153 | 152,2.99960 154 | 153,2.98460 155 | 154,2.97810 156 | 155,2.95530 157 | 156,2.96210 158 | 157,2.95580 159 | 158,2.95100 160 | 159,2.92970 161 | 160,2.93390 162 | 161,2.92300 163 | 162,2.93550 164 | 163,2.94440 165 | 164,2.93590 166 | 165,2.95330 167 | 166,2.93900 168 | 167,2.93370 169 | 168,2.95450 170 | 169,2.95440 171 | 170,2.95580 172 | 171,2.95860 173 | 172,2.94410 174 | 173,2.93890 175 | 174,2.92860 176 | 175,2.93440 177 | 176,2.95520 178 | 177,2.96780 179 | 178,2.97400 180 | 179,2.97410 181 | 180,2.97390 182 | 181,2.94680 183 | 182,2.94740 184 | 183,2.98460 185 | 184,2.97090 186 | 185,2.97640 187 | 186,2.99590 188 | 187,3.00040 189 | 188,3.00360 190 | 189,3.02930 191 | 190,3.05370 192 | 191,3.05060 193 | 192,3.05050 194 | 193,3.05850 195 | 194,3.08080 196 | 195,3.08050 197 | 196,3.09670 198 | 197,3.08660 199 | 198,3.09570 200 | 199,3.09280 201 | 200,3.07850 202 | 201,3.06750 203 | 202,3.07360 204 | 203,3.07240 205 | 204,3.07790 206 | 205,3.07760 207 | 206,3.09980 208 | 207,3.09980 209 | 208,3.10240 210 | 209,3.09810 211 | 210,3.11170 212 | 211,3.11250 213 | 212,3.13480 214 | 213,3.15320 215 | 214,3.17360 216 | 215,3.18650 217 | 216,3.20480 218 | 217,3.25880 219 | 218,3.27870 220 | 219,3.27130 221 | 220,3.30550 222 | 221,3.31050 223 | 222,3.38350 224 | 223,3.37430 225 | 224,3.35840 226 | 225,3.38240 227 | 226,3.40470 228 | 227,3.44030 229 | 228,3.41800 230 | 229,3.41990 231 | 230,3.41740 232 | 231,3.44830 233 | 232,3.50670 234 | 233,3.53440 235 | 234,3.51020 236 | 235,3.43070 237 | 236,3.36690 238 | 237,3.45240 239 | 238,3.51720 240 | 239,3.47210 241 | 240,3.47860 242 | 241,3.51570 243 | 242,3.49400 244 | 243,3.49470 245 | 244,3.51160 246 | 245,3.51000 247 | 246,3.50550 248 | 247,3.50770 249 | 248,3.50410 250 | 249,3.51350 251 | 250,3.53290 252 | 251,3.53180 253 | --------------------------------------------------------------------------------