diff --git a/Figure_1.png b/Figure_1.png index 2b14436..cdc09cb 100644 Binary files a/Figure_1.png and b/Figure_1.png differ diff --git "a/Hatal\304\261karsi.png" "b/Hatal\304\261karsi.png" new file mode 100644 index 0000000..7d99b62 Binary files /dev/null and "b/Hatal\304\261karsi.png" differ diff --git a/ISOC_EC_EVALN2$DEFAULTVIEW1608214139986.xlsx b/ISOC_EC_EVALN2$DEFAULTVIEW1608214139986.xlsx new file mode 100644 index 0000000..29b0399 Binary files /dev/null and b/ISOC_EC_EVALN2$DEFAULTVIEW1608214139986.xlsx differ diff --git a/Plots.ipynb b/Plots.ipynb index 5afec69..ca0fd5d 100644 --- a/Plots.ipynb +++ b/Plots.ipynb @@ -9,9 +9,24 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "RuntimeError", + "evalue": "The current Numpy installation ('C:\\\\Users\\\\BlkMrkT\\\\AppData\\\\Roaming\\\\Python\\\\Python38\\\\site-packages\\\\numpy\\\\__init__.py') fails to pass a sanity check due to a bug in the windows runtime. See this issue for more information: https://tinyurl.com/y3dm3h86", + "output_type": "error", + "traceback": [ + "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[1;31mRuntimeError\u001B[0m Traceback (most recent call last)", + "\u001B[1;32m\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[1;32mimport\u001B[0m \u001B[0mpandas\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mpd\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 2\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mmatplotlib\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 3\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mmatplotlib\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mpyplot\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0mplt\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 4\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mseaborn\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0msns\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\__init__.py\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 9\u001B[0m \u001B[1;32mfor\u001B[0m \u001B[0mdependency\u001B[0m \u001B[1;32min\u001B[0m \u001B[0mhard_dependencies\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 10\u001B[0m \u001B[1;32mtry\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m---> 11\u001B[1;33m \u001B[0m__import__\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mdependency\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 12\u001B[0m \u001B[1;32mexcept\u001B[0m \u001B[0mImportError\u001B[0m \u001B[1;32mas\u001B[0m \u001B[0me\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 13\u001B[0m \u001B[0mmissing_dependencies\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mappend\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;34mf\"{dependency}: {e}\"\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\numpy\\__init__.py\u001B[0m in \u001B[0;36m\u001B[1;34m\u001B[0m\n\u001B[0;32m 303\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 304\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0msys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mplatform\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;34m\"win32\"\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0msys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmaxsize\u001B[0m \u001B[1;33m>\u001B[0m \u001B[1;36m2\u001B[0m\u001B[1;33m**\u001B[0m\u001B[1;36m32\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[1;32m--> 305\u001B[1;33m \u001B[0m_win_os_check\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 306\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 307\u001B[0m \u001B[1;32mdel\u001B[0m \u001B[0m_win_os_check\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\numpy\\__init__.py\u001B[0m in \u001B[0;36m_win_os_check\u001B[1;34m()\u001B[0m\n\u001B[0;32m 300\u001B[0m \u001B[1;34m\"See this issue for more information: \"\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 301\u001B[0m \"https://tinyurl.com/y3dm3h86\")\n\u001B[1;32m--> 302\u001B[1;33m \u001B[1;32mraise\u001B[0m \u001B[0mRuntimeError\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mmsg\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mformat\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0m__file__\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m)\u001B[0m \u001B[1;32mfrom\u001B[0m \u001B[1;32mNone\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 303\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 304\u001B[0m \u001B[1;32mif\u001B[0m \u001B[0msys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mplatform\u001B[0m \u001B[1;33m==\u001B[0m \u001B[1;34m\"win32\"\u001B[0m \u001B[1;32mand\u001B[0m \u001B[0msys\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mmaxsize\u001B[0m \u001B[1;33m>\u001B[0m \u001B[1;36m2\u001B[0m\u001B[1;33m**\u001B[0m\u001B[1;36m32\u001B[0m\u001B[1;33m:\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n", + "\u001B[1;31mRuntimeError\u001B[0m: The current Numpy installation ('C:\\\\Users\\\\BlkMrkT\\\\AppData\\\\Roaming\\\\Python\\\\Python38\\\\site-packages\\\\numpy\\\\__init__.py') fails to pass a sanity check due to a bug in the windows runtime. See this issue for more information: https://tinyurl.com/y3dm3h86" + ] + } + ], "source": [ "import pandas as pd\n", "import matplotlib\n", @@ -194,8 +209,7 @@ "total_2019=0\n", "for price2 in turkey_2019:\n", " total_2019 += price2\n", - "df = pd.DataFrame([[total_2018,2018],[total_2019,2019]],columns=list(\"AB\"))\n", - "df" + "df = pd.DataFrame([[total_2018,2018],[total_2019,2019]],columns=list(\"AB\"))" ] }, { @@ -1104,9 +1118,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.2" } }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/ProjeTaslak (1).docx b/ProjeTaslak (1).docx new file mode 100644 index 0000000..7ce7cb3 Binary files /dev/null and b/ProjeTaslak (1).docx differ diff --git a/ProjeTaslak.docx b/ProjeTaslak.docx index bb7f740..c686116 100644 Binary files a/ProjeTaslak.docx and b/ProjeTaslak.docx differ diff --git a/kaynaklar/linkstoresearch.txt b/kaynaklar/linkstoresearch.txt new file mode 100644 index 0000000..3445896 --- /dev/null +++ b/kaynaklar/linkstoresearch.txt @@ -0,0 +1,19 @@ +https://data.worldbank.org/country/turkey +https://www.kaggle.com/huseyinkilic/turkeys-mobile-banking-user-commentary-analysis +---------------------------------------- +https://bkm.com.tr/en/e-commerce-transactions/(turkey) +https://bkm.com.tr/en/secilen-aya-ait-sektorel-gelisim/?filter_year=2020&filter_month=1&List=List +----------------------------------------------- +https://github.com/Glorf/recipenlg +---------------------------- +https://fred.stlouisfed.org/tags/series?t=e-commerce(usa) + +------------------------ +https://ec.europa.eu/eurostat/statistics-explained/index.php?title=E-commerce_statistics(europe) +---------------------------------- + +http://blog.sanalmimarlar.com/tag/e-ticaret-hacmi/ +https://www.digitaltalks.org/2020/09/13/bkm-2020-yili-agustos-ayi-verilerini-acikladi/ (makale icin) + +https://www.researchgate.net/publication/327348324_Turkiye%27de_E-Ticaret_Sektorunun_Yillara_Gore_Gelisimi + diff --git a/newtest.ipynb b/newtest.ipynb new file mode 100644 index 0000000..4fa358d --- /dev/null +++ b/newtest.ipynb @@ -0,0 +1,286 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'fbprophet'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mfbprophet\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mProphet\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpyplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;31m#univariate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'fbprophet'" + ] + } + ], + "source": [ + "from fbprophet import Prophet\n", + "import pandas as pd\n", + "from pandas import DataFrame\n", + "from matplotlib import pyplot\n", + "#univariate" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(39, 2)\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "turkey = pd.read_excel(\"TurkeyData.xlsx\",header=0)\n", + "turkey.plot()\n", + "print(turkey.shape)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [], + "source": [ + "turkey.columns = ['ds', 'y']\n", + "turkey\n", + "turkey['ds']= pd.to_datetime(turkey['ds'])" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [], + "source": [ + "model = Prophet()" + ] + }, + { + "cell_type": "code", + "execution_count": 151, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.\n", + "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 151, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.fit(turkey)" + ] + }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ds yhat yhat_lower yhat_upper\n", + "0 2023-01-01 181601.405781 174003.131643 189095.823600\n", + "1 2023-04-01 188187.429927 180465.479630 196103.345740\n", + "2 2023-07-01 197399.786436 189167.045491 205659.826713\n", + "3 2023-10-01 200779.079616 191441.034888 210712.256492\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# define the period for which we want a prediction\n", + "future = list()\n", + "for i in range(1,13,3):\n", + "\tdate = '2023-%02d' % i\n", + "\tfuture.append([date])\n", + "future = DataFrame(future)\n", + "future.columns = ['ds']\n", + "future['ds']= pd.to_datetime(future['ds'])\n", + "# use the model to make a forecast\n", + "forecast = model.predict(future)\n", + "# summarize the forecast\n", + "print(forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']])\n", + "# plot forecast\n", + "model.plot(forecast)\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ds y\n", + "22 2016-07-01 35564.10\n", + "23 2016-10-01 36377.03\n", + "24 2017-01-01 41252.10\n", + "25 2017-04-01 47138.21\n", + "26 2017-07-01 56096.90\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "execution_count": 159, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# create test dataset, remove last 12 months\n", + "train = turkey.drop(turkey.index[-12:])\n", + "print(train.tail())\n", + "model.plot_components(forecast)" + ] + }, + { + "cell_type": "code", + "execution_count": 158, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Found input variables with inconsistent numbers of samples: [12, 2]", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0my_true\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mturkey\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'y'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mforecast\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'yhat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmean_absolute_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'MAE: %.3f'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mmae\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/bigdataodev/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 70\u001b[0m FutureWarning)\n\u001b[1;32m 71\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 72\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/bigdataodev/lib/python3.8/site-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36mmean_absolute_error\u001b[0;34m(y_true, y_pred, sample_weight, multioutput)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0;36m0.85\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 177\u001b[0m \"\"\"\n\u001b[0;32m--> 178\u001b[0;31m y_type, y_true, y_pred, multioutput = _check_reg_targets(\n\u001b[0m\u001b[1;32m 179\u001b[0m y_true, y_pred, multioutput)\n\u001b[1;32m 180\u001b[0m \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/bigdataodev/lib/python3.8/site-packages/sklearn/metrics/_regression.py\u001b[0m in \u001b[0;36m_check_reg_targets\u001b[0;34m(y_true, y_pred, multioutput, dtype)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m \"\"\"\n\u001b[0;32m---> 84\u001b[0;31m \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 85\u001b[0m \u001b[0my_true\u001b[0m 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\u001b[0muniques\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0m\u001b[1;32m 256\u001b[0m \" samples: %r\" % [int(l) for l in lengths])\n\u001b[1;32m 257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [12, 2]" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_absolute_error\n", + "# calculate MAE between expected and predicted values for december\n", + "y_true = turkey['y'][-12:].values\n", + "y_pred = forecast['yhat'][-2:].values\n", + "mae = mean_absolute_error(y_true, y_pred)\n", + "print('MAE: %.3f' % mae)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# plot expected vs actual\n", + "pyplot.plot(y_true, label='Actual')\n", + "pyplot.plot(y_pred, label='Predicted')\n", + "pyplot.legend()\n", + "pyplot.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.2" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/plots.py b/plots.py new file mode 100644 index 0000000..9079db7 --- /dev/null +++ b/plots.py @@ -0,0 +1,59 @@ +import pandas as pd +import matplotlib +import matplotlib.pyplot as plt +import seaborn as sns +import plotly.graph_objects as go + +# american ecommerce data +dataset_A = pd.read_excel("ecomretailfixed.xls") +dataset_turkey = pd.read_excel("TurkeyData.xlsx") + +date_A = dataset_A["observation_date"] +ecomsa_A = dataset_A["ECOMSA"] +to_tl = [] +for price in ecomsa_A: + to_tl.append(price * 7.84) + +# Türkiye ecommerse data +date_E = dataset_turkey["observation_date"] +ecomsa_E = dataset_turkey["ECOMSA"] + + +plt.style.use('seaborn-darkgrid') +#plt.plot(date_A,to_tl,label="Amerika",marker=3) +plt.plot(date_A,ecomsa_A,label="Amerika",marker=3) +plt.plot(date_E,ecomsa_E,label="Türkiye",marker=2) + +plt.title("Türkiye ve Amerika E-Ticaret Piyasa Hacmi Karşılaştırması (TRY)") +plt.xlabel("Tarihler") +plt.ylabel("Milyar (TRY)") + +plt.legend() +plt.show() +#plt.savefig('Matplotlib_save_plot.png') + +turkey_2018 = dataset_turkey["ECOMSA"][28:32] +total_2018=0 +for price in turkey_2018: + total_2018 += price + + +turkey_2019 = dataset_turkey["ECOMSA"][32:36] +total_2019=0 +for price2 in turkey_2019: + total_2019 += price2 +df = pd.DataFrame([[total_2018,2018],[total_2019,2019]],columns=list("AB")) + + + + +labels = df["B"] +values = df["A"] + + +fig = go.Figure(data=[go.Pie(labels=labels, values=values,title="Türkiye 2018-2019 E-Ticaret Hacmi\b", textinfo='label+percent', + insidetextorientation='radial' + )]) +fig.show() + + diff --git a/tensortest.py b/tensortest.py deleted file mode 100644 index 269593b..0000000 --- a/tensortest.py +++ /dev/null @@ -1,15 +0,0 @@ -import tensorflow as tf -import pandas as pd -from tensorflow import keras -import numpy as np - -def house_model(y_new): - xs = np.array([20.11,20.12,20.13,20.14,20.15,20.16,20.17,20.18,20.19]) - ys = np.array([100.0,150.0,200.0,250.0,300.0,350.0,400.0,450.0,500.0]) - model = tf.keras.Sequential([keras.layers.Dense(units = 2,input_shape=[1])]) - model.compile(optimizer='sgd',loss='mean_squared_error') - model.fit(xs,ys,epochs=200) - return model.predict(y_new)[0] - -test = house_model([30.12]) -print(test) \ No newline at end of file diff --git a/test.py b/test.py old mode 100755 new mode 100644 index f1169cf..a507d47 --- a/test.py +++ b/test.py @@ -15,6 +15,7 @@ # europe ecommerse data date_E = dataset_turkey["observation_date"] ecomsa_E = dataset_turkey["ECOMSA"] +print(ecomsa_E) plt.plot(date_A,to_tl) plt.plot(date_E,ecomsa_E) diff --git a/xls_sanal_pos_ile_yapilan_eticaret_islemleri.asp.xls b/xls_sanal_pos_ile_yapilan_eticaret_islemleri.asp.xls new file mode 100644 index 0000000..5c04aaa --- /dev/null +++ b/xls_sanal_pos_ile_yapilan_eticaret_islemleri.asp.xls @@ -0,0 +1,2115 @@ + + + + + + + + + + + + +
+
+ +
+
+ + + + +
E-TİCARET İŞLEMLERİ
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Dönem İşlem Adedi İşlem Tutarı
Yerli Kartların Yurtiçi ve Yurtdışı KullanımıYerli ve Yabancı Kartların Yurtiçi KullanımıYerli Kartların Yurtiçi ve Yurtdışı KullanımıYerli ve Yabancı Kartların Yurtiçi Kullanımı
YurtiçiYurtdışıToplamYurtiçiYurtdışıToplamYurtiçiYurtdışıToplamYurtiçiYurtdışıToplam
  01-OCAK974.954 63.885 1.038.839 736.186 10.731 746.917 101,58 4,39 105,97 60,78 2,35 63,12 
  02-SUBAT1.013.948 70.273 1.084.221 748.668 9.114 757.782 99,47 4,81 104,27 56,52 2,54 59,06 
  03-MART1.488.439 65.705 1.554.144 1.928.753 20.557 1.949.310 138,08 9,45 147,53 113,41 7,18 120,59 
1. DÖNEM 3.477.341199.8633.677.2043.413.60740.4023.454.009339,1218,65357,77230,712,07242,77
  04-NISAN1.298.922 46.270 1.345.192 1.307.779 17.932 1.325.711 122,84 6,6 129,44 83,04 5,25 88,3 
  05-MAYIS1.396.431 45.009 1.441.440 1.379.150 19.397 1.398.547 148,1 7,01 155,1 105,94 5,94 111,88 
  06-HAZIRAN1.451.733 45.869 1.497.602 1.467.964 19.706 1.487.670 155,57 6,78 162,35 115,33 6,2 121,53 
2. DÖNEM 4.147.086137.1484.284.2344.154.89357.0354.211.928426,5120,39446,9304,3117,39321,71
  07-TEMMUZ1.510.997 43.497 1.554.494 1.509.011 20.009 1.529.020 166,79 5,59 172,38 125,61 6,08 131,69 
  08-AGUSTOS1.579.048 44.207 1.623.255 1.610.449 20.912 1.631.361 170,9 5,92 176,82 133,26 6,06 139,32 
  09-EYLÜL1.684.932 49.261 1.734.193 1.770.273 18.617 1.788.890 171,54 6,42 177,96 131,14 5,67 136,81 
3. DÖNEM 4.774.977136.9654.911.9424.889.73359.5384.949.271509,2217,94527,1639017,81407,81
  10-EKIM1.832.676 58.820 1.891.496 1.956.980 21.348 1.978.328 175,45 7,37 182,83 139,26 6,57 145,83 
  11-KASIM1.329.101 82.603 1.411.704 1.593.963 34.656 1.628.619 122,27 10,05 132,32 112,3 7,25 119,54 
  12-ARALIK1.400.968 91.039 1.492.007 2.029.431 35.190 2.064.621 143,6 11,34 154,94 143,11 7,61 150,72 
4. DÖNEM 4.562.745232.4624.795.2075.580.37491.1945.671.568441,3228,76470,09394,6721,42416,09
2005 YILI16.962.149706.43817.668.58718.038.607248.16918.286.7761.716,1885,741.801,921.319,6968,71.388,39
  01-OCAK1.084.611 81.609 1.166.220 1.216.593 41.596 1.258.189 119,97 8,58 128,55 109,73 10,78 120,51 
  02-SUBAT1.174.633 100.097 1.274.730 1.313.437 45.445 1.358.882 133,6 11,28 144,88 123,36 12,65 136,02 
  03-MART1.293.897 118.540 1.412.437 1.412.862 50.799 1.463.661 171,98 13,63 185,61 149,61 14,93 164,54 
1. DÖNEM 3.553.141300.2463.853.3873.942.892137.8404.080.732425,5533,49459,04382,7138,36421,07
  04-NISAN1.195.386 106.080 1.301.466 1.363.786 58.720 1.422.506 163,17 11,96 175,13 151,2 12,24 163,44 
  05-MAYIS1.353.790 110.037 1.463.827 1.539.504 56.138 1.595.642 185,91 13,87 199,78 167,91 8,79 176,7 
  06-HAZIRAN1.363.584 110.093 1.473.677 1.428.037 55.804 1.483.841 183,48 13,97 197,44 186,58 9,9 196,47 
2. DÖNEM 3.912.760326.2104.238.9704.331.327170.6624.501.989532,5639,8572,36505,6930,93536,61
  07-TEMMUZ1.405.446 113.287 1.518.733 1.452.303 53.794 1.506.097 216,38 12,3 228,67 198,8 9,9 208,7 
  08-AGUSTOS1.387.907 104.096 1.492.003 1.464.640 57.489 1.522.129 216,03 13,14 229,17 202,48 9,48 211,97 
  09-EYLÜL1.400.932 112.873 1.513.805 1.425.560 37.447 1.463.007 205,33 14,56 219,89 180,31 8,08 188,4 
3. DÖNEM 4.194.285330.2564.524.5414.342.503148.7304.491.233637,7439,99677,73581,627,47609,07
  10-EKIM2.509.583 120.576 2.630.159 2.555.201 41.774 2.596.975 277,57 15,57 293,14 253,43 11,52 264,95 
  11-KASIM2.673.927 118.509 2.792.436 2.710.529 41.344 2.751.873 283,85 14,94 298,79 257,86 11,53 269,39 
  12-ARALIK3.090.008 130.241 3.220.249 3.223.564 41.671 3.265.235 314,47 22,22 336,69 301,33 10,27 311,59 
4. DÖNEM 8.273.518369.3268.642.8448.489.294124.7898.614.083875,8852,73928,62812,6133,33845,93
2006 YILI19.933.7041.326.03821.259.74221.106.016582.02121.688.0372.471,74166,022.637,752.282,6130,082.412,68
  01-OCAK2.977.777 153.229 3.131.006 3.178.945 47.070 3.226.015 305,97 17,7 323,67 308,55 9,36 317,91 
  02-SUBAT3.139.121 147.884 3.287.005 3.366.143 53.693 3.419.836 321,83 16,88 338,71 325,69 11,45 337,15 
  03-MART3.293.341 146.355 3.439.696 3.671.755 62.972 3.734.727 357,29 18,82 376,11 392,28 15,72 408 
1. DÖNEM 9.410.239447.4689.857.70710.216.843163.73510.380.578985,0953,41.038,51.026,5236,541.063,06
  04-NISAN3.404.872 141.158 3.546.030 3.667.854 60.434 3.728.288 395,59 18,48 414,07 404,54 14,92 419,45 
  05-MAYIS3.794.624 147.350 3.941.974 4.130.647 73.640 4.204.287 431,95 18,77 450,72 448,21 18,77 466,98 
  06-HAZIRAN3.571.779 138.280 3.710.059 3.846.533 69.882 3.916.415 434,29 17,15 451,44 445,22 18,81 464,04 
2. DÖNEM 10.771.275426.78811.198.06311.645.034203.95611.848.9901.261,8454,391.316,231.297,9752,51.350,47
  07-TEMMUZ5.110.233 152.809 5.263.042 5.391.352 80.086 5.471.438 507,61 17,34 524,94 518,88 23,08 541,97 
  08-AGUSTOS4.551.284 152.180 4.703.464 4.886.725 77.027 4.963.752 493,24 21,15 514,39 503,7 20,52 524,22 
  09-EYLÜL4.552.951 154.505 4.707.456 4.908.163 63.505 4.971.668 452,18 18,66 470,84 464,86 14,57 479,42 
3. DÖNEM 14.214.468459.49414.673.96215.186.240220.61815.406.8581.453,0357,151.510,181.487,4458,171.545,61
  10-EKIM4.929.842 194.118 5.123.960 5.314.584 66.783 5.381.367 471,14 21,07 492,2 488,08 14,21 502,28 
  11-KASIM5.067.394 202.752 5.270.146 5.398.316 63.691 5.462.007 491,35 22,16 513,5 500,83 15,76 516,59 
  12-ARALIK5.198.184 219.499 5.417.683 5.605.565 60.439 5.666.004 517,69 21,47 539,16 544,54 14,61 559,15 
4. DÖNEM 15.195.420616.36915.811.78916.318.465190.91316.509.3781.480,1764,71.544,871.533,4544,581.578,03
2007 YILI49.591.4021.950.11951.541.52153.366.582779.22254.145.8045.180,13229,645.409,775.345,38191,795.537,17
  01-OCAK4.623.339 293.964 4.917.303 5.012.222 145.971 5.158.193 560,14 29,67 589,81 604,37 50,09 654,46 
  02-SUBAT4.486.585 271.200 4.757.785 4.886.172 151.130 5.037.302 549,1 29,61 578,71 591,96 55,7 647,65 
  03-MART4.731.364 264.556 4.995.920 5.115.888 196.344 5.312.232 603,39 29,15 632,54 646,64 77,36 724 
1. DÖNEM 13.841.288829.72014.671.00815.014.282493.44515.507.7271.712,6388,431.801,061.842,97183,152.026,12
  04-NISAN4.788.670 251.773 5.040.443 5.104.058 208.752 5.312.810 639,06 28,91 667,97 672,27 86,41 758,68 
  05-MAYIS5.042.902 251.236 5.294.138 5.517.030 272.815 5.789.845 678,96 30,47 709,43 729,92 104,09 834,01 
  06-HAZIRAN4.846.570 238.991 5.085.561 4.935.292 274.897 5.210.189 681,21 29,47 710,68 717,01 104,42 821,43 
2. DÖNEM 14.678.142742.00015.420.14215.556.380756.46416.312.8441.999,2288,852.088,072.119,2294,922.414,11
  07-TEMMUZ5.224.090 238.870 5.462.960 5.794.764 325.517 6.120.281 750,36 28,62 778,97 801,74 117,19 918,94 
  08-AGUSTOS4.919.165 234.377 5.153.542 5.337.704 345.752 5.683.456 696,48 27,65 724,13 739,71 109,62 849,33 
  09-EYLÜL5.144.649 275.484 5.420.133 5.616.463 299.395 5.915.858 682,98 33,9 716,88 706,87 97,29 804,16 
3. DÖNEM 15.287.904748.73116.036.63516.748.931970.66417.719.5952.129,8190,162.219,982.248,32324,112.572,43
  10-EKIM4.553.643 290.322 4.843.965 5.051.577 278.930 5.330.507 568,61 35,6 604,21 624,17 104,13 728,3 
  11-KASIM4.552.082 311.854 4.863.936 4.552.082 197.383 4.749.465 586,19 36,45 622,63 586,18 81,44 667,62 
  12-ARALIK4.776.353 394.029 5.170.382 4.776.353 164.257 4.940.610 616,57 47,89 664,46 616,58 63,53 680,11 
4. DÖNEM 13.882.078996.20514.878.28314.380.012640.57015.020.5821.771,37119,941.891,311.826,93249,12.076,03
2008 YILI57.689.4123.316.65661.006.06861.699.6052.861.14364.560.7487.613,04387,398.000,438.037,411.051,279.088,68
  01-OCAK4.564.418 293.960 4.858.378 4.564.418 153.619 4.718.037 599,45 45,28 644,73 605,45 52,35 657,8 
  02-SUBAT4.255.547 280.431 4.535.978 4.255.547 174.054 4.429.601 547,85 46,43 594,27 547,85 82,92 630,77 
  03-MART5.034.600 290.611 5.325.211 5.034.600 210.840 5.245.440 643,39 33,93 677,32 643,39 101,76 745,15 
1. DÖNEM 13.854.565865.00214.719.56713.854.565538.51314.393.0781.790,68125,631.916,311.796,69237,032.033,72
  04-NISAN5.076.907 271.174 5.348.081 5.076.907 251.858 5.328.765 706,83 30,72 737,55 706,84 121,05 827,89 
  05-MAYIS4.779.977 292.012 5.071.989 4.779.977 302.243 5.082.220 563,91 35,29 599,2 563,91 135,84 699,74 
  06-HAZIRAN5.541.533 321.530 5.863.063 5.541.533 317.294 5.858.827 762,71 37,62 800,33 762,7 143,01 905,72 
2. DÖNEM 15.398.417884.71616.283.13315.398.417871.39516.269.8122.033,45103,632.137,082.033,44399,92.433,35
  07-TEMMUZ5.688.158 301.875 5.990.033 5.688.158 360.987 6.049.145 862,62 33,88 896,5 862,62 266,38 1.129 
  08-AGUSTOS4.972.910 313.512 5.286.422 4.972.910 379.535 5.352.445 683,63 34,87 718,5 683,64 199,39 883,03 
  09-EYLÜL5.256.518 354.626 5.611.144 5.256.518 341.254 5.597.772 728,88 39,23 768,11 728,87 205,71 934,58 
3. DÖNEM 15.917.586970.01316.887.59915.917.5861.081.77616.999.3622.275,13107,982.383,112.275,13671,482.946,6
  10-EKIM6.252.909 503.929 6.756.838 6.252.909 404.042 6.656.951 781,46 61,6 843,06 781,46 223,44 1.004,89 
  11-KASIM5.506.992 443.263 5.950.255 5.506.992 234.748 5.741.740 698,47 46,1 744,56 698,47 171,42 869,89 
  12-ARALIK6.218.654 495.542 6.714.196 6.218.654 207.526 6.426.180 852,16 49,75 901,91 852,16 133,07 985,22 
4. DÖNEM 17.978.5551.442.73419.421.28917.978.555846.31618.824.8712.332,08157,452.489,532.332,08527,932.860,01
2009 YILI63.149.1234.162.46567.311.58863.149.1233.338.00066.487.1238.431,35494,698.926,048.437,331.836,3410.273,68
  01-OCAK6.670.800 512.340 7.183.140 6.670.800 220.722 6.891.522 989,67 51,81 1.041,48 989,67 147,43 1.137,1 
  02-SUBAT6.023.682 465.173 6.488.855 6.023.682 223.361 6.247.043 837,14 49,69 886,83 837,14 152,88 990,03 
  03-MART6.858.709 531.931 7.390.640 6.858.709 285.337 7.144.046 966,79 63,36 1.030,16 966,79 241,07 1.207,87 
1. DÖNEM 19.553.1911.509.44421.062.63519.553.191729.42020.282.6112.793,6164,862.958,472.793,6541,383.334,99
  04-NISAN6.723.094 524.473 7.247.567 6.723.094 294.187 7.017.281 934,02 59,4 993,42 934,02 231,62 1.165,64 
  05-MAYIS6.653.465 494.153 7.147.618 6.653.465 354.436 7.007.901 978,6 57,44 1.036,04 978,6 220,75 1.199,35 
  06-HAZIRAN6.840.617 569.813 7.410.430 6.840.617 367.977 7.208.594 1.012,34 64,83 1.077,17 1.012,34 179,6 1.191,94 
2. DÖNEM 20.217.1761.588.43921.805.61520.217.1761.016.60021.233.7762.924,97181,663.106,632.924,97631,963.556,93
  07-TEMMUZ8.160.110 560.136 8.720.246 8.244.435 413.332 8.657.767 1.387,96 64,1 1.452,07 1.396,55 182,02 1.578,57 
  08-AGUSTOS8.164.404 752.630 8.917.034 8.164.404 414.553 8.578.957 1.257,75 83,39 1.341,14 1.257,75 177,01 1.434,76 
  09-EYLÜL7.598.446 777.177 8.375.623 7.598.446 372.619 7.971.065 1.126,35 84,75 1.211,1 1.126,34 162,2 1.288,55 
3. DÖNEM 23.922.9602.089.94326.012.90324.007.2851.200.50425.207.7893.772,06232,254.004,313.780,64521,234.301,87
  10-EKIM8.003.513 926.221 8.929.734 8.003.513 384.230 8.387.743 1.158,6 102,07 1.260,67 1.158,6 179,48 1.338,09 
  11-KASIM7.436.464 997.552 8.434.016 7.436.464 275.539 7.712.003 1.102,93 96,55 1.199,49 1.102,93 132,66 1.235,59 
  12-ARALIK8.862.672 1.030.582 9.893.254 8.862.672 236.687 9.099.359 1.348,55 244,94 1.593,48 1.348,55 109,09 1.457,63 
4. DÖNEM 24.302.6492.954.35527.257.00424.302.649896.45625.199.1053.610,08443,564.053,643.610,08421,234.031,31
2010 YILI87.995.9768.142.18196.138.15788.080.3013.842.98091.923.28113.100,711.022,3314.123,0413.109,292.115,815.225,1
  01-OCAK9.764.404 1.006.980 10.771.384 9.764.404 251.695 10.016.099 1.693,95 113,6 1.807,55 1.693,95 133,1 1.827,05 
  02-SUBAT8.242.303 915.426 9.157.729 8.242.303 255.595 8.497.898 1.273,53 96,22 1.369,75 1.273,53 145,11 1.418,65 
  03-MART9.265.226 959.771 10.224.997 9.265.226 317.467 9.582.693 1.411,31 123,16 1.534,47 1.411,31 186,49 1.597,81 
1. DÖNEM 27.271.9332.882.17730.154.11027.271.933824.75728.096.6904.378,8332,984.711,774.378,8464,714.843,51
  04-NISAN9.210.480 966.220 10.176.700 9.210.480 352.997 9.563.477 1.442,85 119,18 1.562,03 1.442,85 200,42 1.643,27 
  05-MAYIS9.836.291 1.088.065 10.924.356 9.836.291 417.732 10.254.023 1.635,86 116,47 1.752,33 1.635,86 219,96 1.855,82 
  06-HAZIRAN9.890.114 1.007.093 10.897.207 9.890.114 369.833 10.259.947 1.837,77 120,58 1.958,36 1.837,77 186,02 2.023,79 
2. DÖNEM 28.936.8853.061.37831.998.26328.936.8851.140.56230.077.4474.916,48356,245.272,714.916,48606,45.522,88
  07-TEMMUZ11.514.988 993.043 12.508.031 11.514.988 425.811 11.940.799 2.246,03 118,5 2.364,54 2.246,03 200,66 2.446,7 
  08-AGUSTOS10.622.574 1.152.424 11.774.998 10.622.574 407.424 11.029.998 1.945,54 130,24 2.075,78 1.945,54 222,09 2.167,63 
  09-EYLÜL10.205.391 1.131.380 11.336.771 10.205.391 385.160 10.590.551 1.794,94 119,26 1.914,2 1.794,94 195,59 1.990,53 
3. DÖNEM 32.342.9533.276.84735.619.80032.342.9531.218.39533.561.3485.986,51368,016.354,525.986,51618,356.604,86
  10-EKIM10.964.181 1.188.903 12.153.084 10.964.181 365.683 11.329.864 1.765,66 126,79 1.892,45 1.765,66 192,19 1.957,86 
  11-KASIM10.531.307 1.207.724 11.739.031 10.531.307 258.002 10.789.309 1.721,04 121,15 1.842,2 1.721,04 155,64 1.876,69 
2011 YILI110.047.25911.617.029121.664.288110.047.2593.807.399113.854.65818.768,491.305,1620.073,6618.768,492.037,320.805,79
+
+ "Yerli ve Yabancı Kartların Yurtiçi Kullanımı" bilgilerinde; "Yurtiçi", yerli kartların yurtiçi kullanımını, "Yurtdışı", yabancı kartların yurtiçi kullanımını gösterir.
+ "Yerli Kartların Yurtiçi ve Yurtdışı Kullanımı" bilgilerinde; "Yurtiçi" yerli kartların yurtiçi kullanımını, "Yurtdışı", yerli kartların yurtdışı kullanımını gösterir. +
+ + \ No newline at end of file