Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of Quantum ML and Classical ML\n",
    "\n",
    "Simulation of Detection of Purchasing Behaviors of People Using Supervised Quantum Machine Learning Based on Continous-Variable Model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connecting to a Cloud Service:  https://qml.azurewebsites.net/api/Alisveris\n",
      "Connected to Cloud Service!\n",
      "The data consists of  211 samples.\n",
      "\n",
      "     AlisverisSayisi Cinsiyet  KullaniciID  SatinAldiMi   Yas\n",
      "201             33.0        K          202          1.0  63.0\n",
      "202             22.0        K          203          1.0  40.0\n",
      "203             25.0        K          204          1.0  45.0\n",
      "204             25.0        K          205          1.0  43.0\n",
      "205             15.0        K          206          1.0  20.0\n",
      "206             23.0        K          207          0.0  41.0\n",
      "207             26.0        K          208          1.0  41.0\n",
      "208             26.0        K          209          1.0  42.0\n",
      "209             27.0        K          210          1.0  49.0\n",
      "210             25.0        K          211          0.0  48.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "# Address of WebAPI or file with the JSON extension\n",
    "url = 'https://qml.azurewebsites.net/api/Alisveris'\n",
    "\n",
    "print(\"Connecting to a Cloud Service: \", url)\n",
    "\n",
    "try:\n",
    "    # load data that type of JSON to dataframe\n",
    "    df = pd.read_json(url, orient='columns')\n",
    "\n",
    "    if(df is None):\n",
    "        print(\"Data Not Available!\")\n",
    "    else:\n",
    "        # Yas = Age, AlisverisSayisi = ShoppingCount, SatinAldiMi = PurchaseStatus\n",
    "        df['Yas'] = df['Yas'].astype(float)\n",
    "        df['AlisverisSayisi'] = df['AlisverisSayisi'].astype(float)\n",
    "        df['SatinAldiMi'] = df['SatinAldiMi'].astype(float)\n",
    "\n",
    "        print(\"Connected to Cloud Service!\")\n",
    "        print(\"The data consists of \" ,len(df), \"samples.\\n\")\n",
    "\n",
    "        # show the last 10 data\n",
    "        print(df.tail(10))\n",
    "        # show the first 10 data\n",
    "        # df.head(10)\n",
    "except:\n",
    "    print(\"Not Connected to the Cloud Service!\")   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rta68Wf/yOax9rA9rH9uatY/1Yf3L5xR8XxLzAATdZpjYbH4DE6e7753HwYd/gVFjxnHw4V/g7nvnJR1S4uK8NNQA7KeqH4hIDfCoiNzrv3auqt7awWdza/3L57Bp5bWtljSyaeW1rAd67HR5y9Ik8tyDbjNMbHnO3zfxa1tb8NaKFUz98WUAub5kFNsZgXo+8J/W+A/3r0MlbNPK6wItTyLPPeg2w8Rm+fsmTlZbUFisWUMiUi0ii4FVwAOq+oT/0qUi8qyIXCkiBWewEJHTRWShiCzMQql7cI2BlieR5x50m2Fis/x9EyerLSgs1o5AVRtVtQ4YBIwRkeHA+cBuwN5Ab+C8Ip+9WlVHq+rovn37xhmmYwrPAdt2eRJ57kG3GSY2y983cbLagsIqUkegqu8BfwcOUdW3/MtGDcDvgTGViCEtuvQ/JdDyJPLcg24zTGyWv2/iZLUFhcWZNdRXRLb2f+4OHAD8U0QG+ssEOBJ4Lq4Y0qjHTpfTpf9pfHwGUE2X/qdtdqMYkpkHIOg2w8Rm8xuYOB126CFMueB8Bg4YgIgwcMAAG4SOGOsIRGQkcD3eb7QqYI6qXiIifwP6AgIsBr7e6qZyQVZHYIwxnZf45PWq+iywR4Hl+8W1TWOMMZ1nYw2ZWFhRmDHpYWMNmchZUZgx6WJnBCZyVhRmTLpYR2AiZ0VhxqRLyY5ARNpNXlpomTHNrCjMmHQJckYwt8AyGzDOFGVFYcakS9GbxSKyG/ApYCsRObrVS72A2sKfMsYmdTEmbTrKGtoVmARsDRzeavk64KtxBmXSr2u/Y+wXvzEpUbQjUNU7gTtFZLyqPl7BmEwBH626pd1f2JCuv7oLtSFMvFGvz6RblN+Hu++dl6spLoPUEbwsIhcAQ1q/X1VPjSsos7mCefkvnQkioB99vMzhXP2oawusVsG0FuX3IY+T15Qca0hEFgCPAItoNSi+qha6iRyLvI81tG7hCLTh9UDvlW6D6Tl6ScwRdV6xNpQbb9TrM+kW5ffh4MO/wFsrVrRbPnDAAO77y51lx5iEKMca6qGqBecMMJXRmfx7V3P1o64tsFoF01qU34c8Tl4TJH30LhH5fOyRmKI6k3/vaq5+1LUFVqtgWovy+5DHyWuCdATfwusMNojIWhFZJyJr4w7MfKxgXj41IF03X+Rwrn7UtQVWq2Bai/L7kMfJa0peGlLVnpUIxBRXLC+/0DJXb5RGXVtgtQqmtSi/D803hPOUNRTkZvFnCi1X1YdjiaiAvN8sNsaYcgS9WRzk0tC5rR4/BP4CXBwqOuMc1+cPWP/yOax9rA9rH9uatY/1Yf3L5yQdknPuvnceBx/+BUaNGcfBh3+Bu++dV/B9rh/rIIK2NW7Tps+gbux4Ru49lrqx45k2fUYicYQV5NJQ66piRGQw8JPYIjIV53pO/vqXz2HTymtbLWlk08prWQ/t5nLOq6C5764f6yBcyfOfNn0Gc+be1vK8qamp5fmF309XomWn5yz2J51/VlVHxBNSe3ZpKF6u5+SvfawPrUpYWqmm14R3Kh2Ok4Lmvrt+rINwJc+/bux4mpqa2i2vqqpi8RNuDMYQWR2BiFwFNPcWVUAd8Ey48IxL3M/JL9QJdLQ8f4Lmvrt/rEtzJc+/UCfQ0XKXBblHsBCvqngR8DhwnqqeGGtUpqLcz8mv7uTy/Ama++7+sS7NlTz/qqrCvz6LLXdZyYhV9XrgRryO4BngybiDMpXlek5+l/6ndGp5HgXNfXf9WAfhSp7/5KOO7NRylwW5NDQRuB5YBggwWEROrmT6qImX6zn5PXa6nPXAppXX4V0OqqZL/1PsRnErQXPfXT/WQbiS5998Q/jW2++gqamJqqoqJh91ZOpuFEOwOoJFwH+p6ov+812AG1V1rwrEB9jNYmOMKUeUdQQ1zZ0AgKr+C6gJE1wWZTHP3aV880J54y7Flxd33jyVAw/em1F7j+HAg/fmzpunRrp+V+oD8ibIGcHv8LKG/ugvOhGoVtUvxxxbC9fPCNrnuXu69D8ttZcv2uabA1DVndphMyt+GaFt3jhAbbcufO/YFRy0V6v00YTiy4s7b57KtF/cRcPGj/9+7FbTxIXfmsQXvjQl9PoLHufaWqZccH6mh3eIU9AzgiAdQTfgG8C+ePcIHgJmq2pDFIEG4XpHkMU8d5fyzYvljfffZiNzp/x7s2VpyodPmwMP3puV77a/iNC/dxMP3PeP0Ot3pT4gS0LXEYhIX6Cvqr4AXOE/EJHheBPYr44o1gzIXp67S/nmxfLDV61p//VNUz582qx6Vzq1vLNcqQ/Io47uEVwF9C2wfHvgF/GEk1bZy3N3Kd+8WH54v202tVuWpnz4tOnXu/DVg2LLO8uV+oA86qgjGKGqD7VdqKr3ASPjCyl9spjn7lK+ecG88W5d+Nqk9zd/Y8ry4dPmf06dRLeazatmu9U08T+nTopk/a7UB+RRR3UEHWUGWdZQK1nMc3cp37xY3viBe61zIr68aL4h/Mvf3cWqd4V+vZX/OTWaG8XgTn1AHhW9WSwidwO/UtV72iw/FDhLVQ/tcMUitcDDQDe8DudWVZ0iIkOBm4DewFPAf6vqRx2ty/WbxcYY46Io6gi+DfxcRK4TkW/6j+vx7g98K0AMDcB+qjoKb6C6Q0RkHDADuFJVdwbWAKcFWFfqFMpxjzrvPe48+jDrL/TZrOaIWz2DG+I+Dln9/kKJ9FE/dfS/gOH+oueBP6lqfdEPFV5PD+BR4AzgbmCAqm4SkfHAxap6cEefT9sZQcEcfGpABFqf/ITIe487zz/M+gt99v5FffjJnAHUN3x8gzcLOeIu1VvkWdzHIa01DpHVEYQMohpvsLqdgF8BPwX+T1V38l8fDNyrqsOLryV9HUGxHPxCys17jzvPP8z6C332i1M/wco17W8tpT1H3KV6izyL+ziktcYhyiEmyqaqjapaBwwCxgC7F3pboc+KyOkislBEFq5ena6Shc7kspeb9x53nn+Y9Rd6T6Gcf0h/jrhL9RZ5FvdxyHqNQ0UGzlbV94C/A+OArUWk+bfCIODNIp+5WlVHq+rovn0LlTO4qzO57OXmvced5x9m/YXeUyjnH9KfI+5SvUWexX0csl7jEFtHICJ9RWRr/+fuwAHAUuBBYLL/tpMBd8+rylQwB58akK6bLwqR9x53nn+Y9Rf67NcmvU9tt83PCrKQI+5SvUWexX0csl7jEGQ+giW0v3zzPt7MZdNUtdhgOgOB6/37BFXAHFW9S0ReAG4SkWnA00D70dpSrlgOfqFl5d7IijvPP8z6C332iOMuovvOPTOXI+5SvUWexX0csl7jEGTQuZ/gVUn9yV90nP/vWmBfVT08vvA8abtZbIwxLojyZvEEVT1fVZf4jx8AE1V1BjAkbKAmGMtVL832UfyynEufZyUvDQFbishYVX0CQETGAFv6rxW+A2gi1TZHWhte956DXYLw2T6KX9tc+rdWrGDqjy8DyMwlkrwKckbwFeC3IvKqiCwDfgt8VUS2AC6LMzjjaXjtkjbFaUDTBm+5AWwfVcLMWbM3K6gCqK+vZ+as2QlFZKJS8oxAVf8BjBCRrfDuKbzX6uU5sUVmWliuemm2j+KX9Vz6PAuSNdQN+CLe/YAuIt4kFKpqf2pViHQbVKRq0nLVm9k+it+A/v0LVtdmJZc+z4JcGroT+ALe/YAPWz1MhViuemm2j+KX9Vz6PAtys3iQqtqdoARZrnppto/il/Vc+jwLUkdwNXCVqiY2gpbVERhjTOdFWUewL7BIRF4UkWdFZImIPBs+RFMp5ebXh5lTIer3GWPiE+TSUIczkRm3lZtfX/BzL38DVIGNHa4r6DYt998YNxQ9IxCRXv6P64o8TAqUm19f8HP6Ec2dQEfrCrpNy/03xg0dnRH8CZiEN7GMAtLqNQU+EWNcJiLl5teHmVMh6DYt998YNxTtCFR1kv/v0MqFY6JWbn59sc8Ve28527Tcf2PcEGg+AhE5WkSuEJHLReTIuIMy0Sk3v77g56Qr0Ga6yQLrCrpNy/03xg0lOwIRmQV8HVgCPAd8XUR+FXdgJhpd+x1D7bCZSLfBgCDdBgea0Lvg53b6FbU7zyq5rqDbLDc2Y0y0gtQRPA8MV/+NIlIFLFHVT1UgPsDqCIwxphxR1hG8COzQ6vlgwOoISog6P97y7aOThX1ZiTbY3AP5EaSOoA+wVESe9J/vDTwuIn8GUNUj4gouraLOj7d8++hkYV9Wog0290C+BLk09NmOXlfVhyKNqIC0XRpat3BEkWyYwfQc3fmROqJeX55lYV9Wog0HH/6FgiONDhwwgPv+cmck2zDxC3ppKMh8BA+JyABgDF79wD9Utf03xLSIOj/e8u2jk4V9WYk22NwD+RIka+grwJPA0cBk4P9E5NS4A0uzYnnw5ebHR72+PMvCvqxEG4rNMWBzD2RTkJvF5wJ7qOopqnoysBdwXrxhpVvU+fGWbx+dLOzLSrTB5h7IlyA3i5ez+dhC64BgJac5FfXY+DbWfnSysC8r0QabeyBfgtws/gMwAm+mMsWbrexJ4F8AqnpFzDGm7maxMca4IMo6gleAO/A6AfA6hLeAnv7DRCxPNQhpi83leINKoj4g6H6z2oVklDwjaHmjSE9AVfWDeENqL09nBG1zxAGo6l720AtRry9KqYuNGhDxh+P2ORJvUG3rA8C79j/lgvNju+wT9DgnEVvWBT0jCHJpaDjwR6C3v+ht4CRVfT50lAHlqSPIUw1CGmMrxIV4g0qiPiDocbbahehFeWnoauA7qrqjqu4InANcEzZAU1ieahDSGFvY9yYtifqAoMfZaheSE6Qj2EJVH2x+oqp/B7aILaKcy1MNQhpjC/vepCVRHxD0OFvtQnKCdAT/FpEfisgQ/3Eh8GrcgeVVnmoQUhcbNf6cDK04Em9QSdQHBD3OVruQnCB1BKcCU4Hb8KarfBj4cpxB5VmeahDSGFuhZS7EG1QS9QFBj7PVLiQncNZQp1csMhj4AzAAaAKuVtVfiMjFwFeB1f5bL1DVezpaV55uFhtjTFQiu1ksIruIyNUicr+I/K35ESCGTcA5qro7MA74hoh80n/tSlWt8x8ddgJxcSVXP6txBJWFvPygbSi3rUFz65OYo2Da9BmBYgtTHxB1bYHVKrQXJH30GeDXwCKgsXm5qi7q1IZE7gR+CUwAPlDVnwX9bNRnBK7k6mc1jrjjdUnQNpTb1qC59ZXYl4ViaatQbGHqA6KuLchbrUKUdQSLVHWvkMEMwbu3MBz4DnAKsBZYiHfWsKajz0fdEbiSq5/VOIJyuY4gqKBtKLetQXPrk5yjoFRsYeoDoq4tyFutQuhLQyLSW0R6A38RkTNFZGDzMn950EC2BOYCZ6vqWmA2MAyowxuq4vIinztdRBaKyMLVq1cXekvZXMnVz2ocQblcRxBU0DaU29agufVJzlFQ6n1h6gOiri2wWoXCOrpHsAjvL/aT8YaiXuAva15ekojU4HUCN6jqbQCqulJVG1W1Ca8wbUyhz6rq1ao6WlVH9+3bN2h7AnElVz+rcQTlch1BUEHbUG5bg+bWJzlHQan3hakPiLq2wGoVCivaEajqUFX9hP9v28cnSq1YRAS4FljaeoRSERnY6m1HAc+FaUA5XMnVz2ocQblcRxBU0DaU29agufVJzVHQVqHYwtQHRF1bYLUKhRWtIxCRvYHXm6elFJGTgC8C/wEuVtV3S6x7AvDfwBIRWewvuwA4XkTq8EYzXQZ8LVQLyuBKrn5W44g7XpcEbUO5bQ2aW5/UHAWfnrAPjzy2oMPYwtQHRF1bYLUKhRW9WSwiTwEHqOq7IvIZ4Cbgm3jX9ndX1cmVCtLqCIwxpvOiqCOobvVX/5fwCsLmquoPgZ2iCNJUJvd76d+OYc0jW/P+o1uz5pGtWfq39n8lhomjEm3IQr1BXgTN07d8fnd02BGISPOlo/2B1kVkQYamMCU05357aX+KNrxO/StnRfpLbunfjmG7rg9QXeUNpV9dBdt1fWCzziBMHJVoQyW2YaLRnKf/1ooVqCpvrVjB1B9f1u6XfND3mcroqCO4EXjILwTbADwCICI7Ae9XILbMa3jtkjYTnwBNG7zlERlQ8wAimy8rDXorAAAftklEQVQT8ZZHEUcl2lCJbZhozJw1u13BWX19PTNnzS7rfaYyiv5lr6qXish8YCBwv358M6EK716BCakSud9VUnp5mDgq0YYs1BvkRdA8fcvnd0uHYw2p6v+p6u2q+mGrZf9S1afiDy37KpH73VSkcLz18jBxVKINWag3yIugefqWz++WIPMRmJhUIvd7xcYDaZsYpuotjyKOSrQhC/UGeRE0T9/y+d1iHUGCuvY7htphM5FugwFBug2OfMC13fe7hTc/OpDGJq8DaGyCNz86kN33+/hGa5g4KtGGSmzDROOwQw9hygXnM3DAAESEgQMGFBzQLej7TGUEmo9ARHYEdlbVv4pId6CLqq6LPTqf1REYY0znRTkfwVeBW4Hf+IsGAXeECy+fwuTCu5Jz7VI+f9yx5H0c/CjjnTZ9BnVjxzNy77HUjR3PtOkzIoy0c9J2HCohSD3AN/AGhnsCQFVfEpF+sUaVQW3Hi2/OhQdKXuJoO4Z6c841UNFT6TBtSFssUe9zV45hUFHGO236DObMva3leVNTU8vzC79/XkQRB5O241ApQeYjeEJVx4rI06q6h19k9pSqjqxMiNm4NBRmvHhXxlB3af6AuGPJ+zj4UcZbN3Y8TU1N7ZZXVVWx+InHy46xHGk7DmFFdmkIr6jsAqC7iBwI3AL8JWyAeRMmF96VnGuX8vnjjiXv4+BHGW+hTqCj5XFK23GolCAdwffxJppfgjdS6D3AhXEGlUVhcuFdybl2KZ8/7ljyPg5+lPFWVRX+NVNseZzSdhwqpeSRUNUmVb1GVY9R1cn+z6VTjcxmwuTCu5Jz7VI+f9yx5H0c/CjjnXzUkZ1aHqe0HYdKKXmzWESW4M0d0Nr7eLOUTVPVd+IILGvCjBfvyhjqLs0fEHcseR8HP8p4m28I33r7HTQ1NVFVVcXko46s+I1iSN9xqJQgN4t/AjQCf/IXHef/uxbYV1UPjy88TxZuFhtjTKUFvVkcJH10gqpOaPV8iYg8pqoTROTE8kM0xhjjgiB3a7YUkbHNT0RkDLCl/3RTLFFlgOsTvbgsifZndZ8XKuQqVFCVxGQyWSjsykIbINilob2B3+H98he8S0JfAZ4HDlPVOXEHmbZLQ22LnQCo6h5ofJwwn82CJNqf1X3etpCrWXV1NY2NjS3Pa2pqUFU2bfr477ra2tp2Y/+0LcYq9r4golxXUtLQhqCXhgKNNeSvcCv//e+FDa6z0tYRhCl2cqloKwlJtD+r+7xYIVdQbYusoizGykJhVxraEOU9AkTkMOBTQK34012pqk0PVYTrE724LIn2Z3Wfhy3YinMymSwUdmWhDc2CDDr3a7zJ67+Jd2noGGDHmONKNdcnenFZEu3P6j4PW7AV52QyWSjsykIbmgX5puyjqicBa1R1KjAeGBxvWOnm+kQvLkui/Vnd58UKtqqrqzd7XlNTQ5cum18ciHsymSwUdmWhDc2CXBpqvhOyXkS2A94BhsYXUvqFKXZyqWgrCUm0P6v7vFgh1x6jRrUrqILSRVZRFmNlobArC21oFiRr6IfAVcD+wK/wqoyvUdWK/bmUtpvFxhjjgkhGHxWRKmC+qr6nqnPx7g3sVslOwBQXNPc97hz5rObgZ1WUue+Fjn0lahLCTHQTZLtZqQ8IKsgZweOqOr5C8RRkZwTtBc19jztHPqs5+FkVZe57oWPfqF2ZcXN/7vm/Hh2uP0wcxeojjv3i0SXHLwqy3TTUBwQVWR2BiEwFngVuS2rUUesI2gua+x53jnxWc/CzKsrc92LHfsW7XZh8ybAO1x8mjjAT3QTZbhrqA4KKso7gO8AWQKOIbMBLIVVV7RUyRhNC0Nz3uHPks5qDn1VR5r4XO8b9tmk/8kyUNQlhJroJst0s1QcEFWQ+gp6qWqWqNaray39unUDCgua+x50jn9Uc/KyKMve92DFetab935dR1iSEmegmyHazVB8QVJCCMhGRE/3sIURksD/wnElQ0Nz3uHPks5qDn1VR5r4XOvaN2pXf3T+w5PrDxBFmopsg281SfUBQQS4NzQKagP2AHwEf4KWR7h1jXKaEoLnvcefIZzUHP6uizH0vdOy32OEiPv35njy9LL6ahDAT3QTZbpbqA4IKcrP4KVXdU0SeVtU9/GXPqOqoEp8bDPwBGIDXkVytqr8Qkd7AzcAQYBlwrKqu6WhddrPYGGM6L5I6At9GEanGn65SRPri/WIvZRNwjqruDowDviEinwS+j1ebsDMw33+eepZLnw8u55eHiS1oPUCY/P0ouXwc0ijIGcEJeIPO7QlcD0wGLlTVTv2mE5E7gV/6j4mq+paIDAT+rqq7dvRZ188ILJc+H1zOLw8TW6Hv7/2L+vCTOQOob/g4A6jtPAbNguTvR8nl4+CaSOcjEJHd8IaYELy/5pd2MpghwMPAcOA1Vd261WtrVHWbjj7vekdgufT54HJ+eZjYCn1/vzj1E6xcUxNo20Hy96Pk8nFwTWR1BCLyC+BmVf1VmYFsCcwFzlbVtc3zGQT43OnA6QA77LBDOZuuGMulzweX88vDxFboe1ooBbSYsPMedJbLxyGtgtwjeAq4UEReFpGfikjJ3qWZiNTgdQI3qGpzTfhK/5IQ/r+rCn1WVa9W1dGqOrpv375BN5kIy6XPB5fzy8PEVuh7WqgorJiw8x50lsvHIa2CFJRdr6qfB8YA/wJmiMhLpT4n3p/+1wJLVfWKVi/9GTjZ//lkIPXncpZLnw8u55eHia3Q9/drk96nttvmZwVt5zFoFiR/P0ouH4e0Cn7+BzsBu+Glfb4Q4P0TgP8GlojIYn/ZBcB0YI6InAa8hjfjWapZLn0+uJxfHia2Qt/fI467iO4792y3vqefeaas/P0ouXwc0ipI1tAM4GjgFWAO3uBzFZ3A3vWbxcYY46Io6wheBcar6iGq+rtKdwKVFGUtQNR1BVmYUyCJ3O+k8s0tz92kSclLQ6r66+afRWQYcDxwnKoOjzOwSmubS60Nr3vPodOXeKJcVxzrq/T6oX3u91srVjD1x5cBxHZKn8Q2k9yuMeUKMujcQBE5W0SeBJ4HqvE6g0xpeO2SzQvCAJo2eMsTXFcc66v0+sG7ntu6AAigvr6embNmR7YNF7aZ5HaNKVfRjkBEvioifwMeArYFvgK8papTVTVzVVJR1gJEXVeQhTkFksj9Tirf3PLcTdp0dEbwK7y//v9LVS9U1WfxxxvKoihrAaKuK8jCnAJJ5H4nlW9uee4mbTrqCLYDbgKuEJEXReRHQLCa8xSKshYg6rqCLMwpkETud1L55pbnbtKm6M1iVX0bmA3MFpFBwHHAKhFZCtyuqhdUKMaKiLIWIOq6gizMKZBE7ndS+eaW527SJtCgc5t9QGRXvKyhqfGE1J7VERhjTOdFWUewGVV9sZKdQFySmD/A5iwonys1CHmvD4i7/Xnfv0np9BlBEqI+I0hi/gCbs6B8SYw/X2ibNTU1qCqbNn08IFuexsGP+zjYPAPRi3Q+gqRF3REkMX+AzVlQviTGny+2zULyMg5+3MfB5hmIXuj5CERkz44+qKpPlROYC5KYP8DmLCifSzUIlY7DJXEfB6u/SE5HQ0xc3sFrCuwXcSwVI90GFfnrPL75A5LYZlYM6N+/4F+KcdcgBD0jyEt9QNzHIYnjbDxFbxar6uc6eKS2E4Bk5g+wOQvK50oNQk1NDV26bP63U57qA+I+DlZ/kZxA8xGIyHDgk0DLUVLVP8QVVNySmD/A5iwon0s1CJWOwyVxHwerv0hOkPkIpgAT8TqCe4BDgUdVdXLs0fmsjsAYYzovyjqCycD+wApV/TIwCugWMj6TQ1nNES+3XWH2h+3LdG/TNUEuDW1Q1SYR2SQivfAmm/9EzHGZjMnqGP3ltivM/rB9me5tuijIGcFCEdkauAZYBDwFPBlrVCZzsjpGf7ntCrM/bF+me5suCjJD2Zn+j78WkXlAL39IamMCy2qOeLntCrM/bF+me5suCjJD2WeaH8AOwNb+z8YEltUx+sttV5j9Yfsy3dt0UZBLQ+e2evwQ+AtwcYwxmQzKao54ue0Ksz9sX6Z7my4Kcmno8NbPRWQw8JPYIjKZlNUc8XLbFWZ/2L5M9zZdVM58BAI8q6oj4gmpPasjMMaYzousjkBErhKRmf7jl8AjwDNRBJklNteAaS2rcxkEaUPU7bT/W/ELUll8cqunm4BlqvpYrFG14foZgc01YFrL6lwGQeYLiHpOAfu/FY7NR1BBNteAaS2rcxkEmS8g6jkF7P9WOKHnI2i1ogl4WUI7+u8XQFXVqot9NteAaS2rcxkEybmPOi/f/m9VRpD00WuBK4B9gb2B0f6/xldsTgGbayCfOpODnqZ89SA591Hn5dv/rcoI0hG8r6r3quoqVX2n+RF7ZClicw2Y1rI6l0GQnPuo8/Lt/1ZlBBl07kER+SlwG9DQvDDNU1VGzeYaMK1ldS6DIDn3Uefl2/+tygiSNfRggcVaapYyEfkdMAlYparD/WUXA18FVvtvu0BV7ykVpOs3i40xxkWR1RGEmKryOqDQnwFXqmqd/yjZCSQtaA5zubnTruRIuxJHGEGPgSv5/FHHkbb2F+Ly9zAL+7eYIFlD3YAvAkNav19VL+noc6r6sIgMCRdestrmMGvD695z2OzUtNwxzYOuP26uxBFG0GPgyvjzUceRtvYX4vL3MAv7tyNBLg3NA97Hm4ugsXm5ql5ecuVeR3BXm0tDpwBrgYXAOaq6ptR6kro0FDSHudzcaVdypF2JI4ygxyDqPPdyRR1H2tpfiMvfw7Tu38jqCIBBqhpVVzYb+BGg/r+XA6cWeqOInA6cDrDDDjtEtPnOCZrDXG7utCs50q7EEUbQY+DK+PNRx5G29hfi8vcwC/u3I0HSRxeISCQDzKnqSlVtVNUmvBnPxnTw3qtVdbSqju7bt28Um++0oDnM5eZOu5Ij7UocYQQ9Bq6MPx91HGlrfyEufw+zsH87EqQj2BdYJCIvisizIrJERMqaoUxEBrZ6ehTwXDnrqZSgOczl5k67kiPtShxhBD0Grow/H3UcaWt/IS5/D7Owfzukqh0+8IaWaPcI8LkbgbeAjcBy4DTgj8AS4Fngz8DAUutRVfbaay9NSsPKObr2H8P1/Ue31rX/GK4NK+cUfN9d99yrB006QkfuPVYPmnSE3nXPvZGuP26uxBFG0GNQ7rGKWtRxpK39hbj8PUzj/gUWaoDfsZ0adE5EtgCOBP5LVQ+LpisqzeoIjDGm86Kcj6CriBwpInPw/sI/APh1BDEak8qc63Llqa1JsP1bvqJZQyJyIHA8cDDwIN5lnTGq+uUKxWYyLq051+XIU1uTYPs3nI7OCO4DhgH7quqJqvoXoKkyYZk8mDlr9mYTmADU19czc9bshCKKT57amgTbv+F0VEewF3Ac8FcR+TdwE1BdkahMLqQ157oceWprEmz/hlP0jEBVn1bV81R1GN7ENHsAXUXkXr/Yy5hQ0ppzXY48tTUJtn/DCVJHgKo+pqr/A2wP/BwYH2tUJhdSm3Ndhjy1NQm2f8MJMsREC/Uqgu/zH8aEEvXY9S7LU1uTYPs3HJu83hhjMiqyOgKTfq6M8Z6FPO9CbSi3XUntjywch7jlbR8FGYZ6GLBcVRtEZCIwEviDqr5XgfgAOyMIo+0Y7wBUdad22MyKjvHeNs8bvGu4Uy44PzWn74XaUFNTg6qyadOmlmVB2pXU/sjCcYhblvZR0DOCIB3BYmA03sQ09+GNEbSrqn4+gjgDsY6gfK6M8e7aOO3lKNaGQkq1K6n9kYXjELcs7aMoLw01qeomvNFCf66q3wYGlviMcYQrY7xnIc+7M7GWem9S+yMLxyFuedxHQTqCjSJyPHAycJe/rCa+kEyUXBnjPQt53p2JtdR7k9ofWTgOccvjPgrSEXwZr27gUlV9VUSGAv8bb1gmKq6M8Z6FPO9CbaipqaFLl82zsIO0K6n9kYXjELc87qOSdQSq+gJwVqvnrwLT4wzKRKf5hnDDa5egDcuRboPotsNFFZ8MPAt53sXaUGhZqXYltT+ycBzilsd9VPRmsYg8iDe38LuqOrmiUbVhN4uNMabzorhZfAreZaGzowoqK/KWY5wWLh2XILG4FO+06TOoGzuekXuPpW7seKZNn5FYLG25tJ+yquilIVX9TyUDSQsb99xNLh2XILG4FO+06TOYM/e2ludNTU0tzy/8/nkVjaUtl/ZTlgWpIzgamAH0A8R/qKr2ij88j0uXhrKUY5wlLh2XILG4FG/d2PE0NbWfaqSqqorFTzxe0Vjacmk/pVHQS0NBBp37CXC4qi4NH1b65THHOA1cOi5BYnEp3kKdQEfLK8ml/ZRlQdJHV1on8LE85hingUvHJUgsLsVbVVX410Cx5ZXk0n7KsiBHeqGI3Cwix4vI0c2P2CNzVB5zjNPApeMSJBaX4p181JGdWl5JLu2nLAtyaagXsB44qNUyBW4r/PZsy2OOcRq4dFyCxOJSvM03hG+9/Q6ampqoqqpi8lFHJn6jGNzaT1lm8xEYY0xGRTbonIgMEpHbRWSViKwUkbkiUtmBakKyPOTyuDKPQSW48h0JE4crbUgbl/ZbUrEEuTT0e+BPQPOYBCf6yw6MK6goWR5yedrOY6ANr3vPoeLDU8TNle9ImDhcaUPauLTfkowl0HwEqlpXalmcwlwasjzk8rgyj0EluPIdCROHK21IG5f2WxyxRDkfwdsicqKIVPuPE4F3yooqAZaHXB5X5jGoBFe+I2HicKUNaePSfksyliAdwanAscAK/zHZX5YKlodcHlfmMagEV74jYeJwpQ1p49J+SzKWkh2Bqr6mqkeoal//cWSaxiGyPOTyuDKPQSW48h0JE4crbUgbl/ZbkrGUvFksIj8BpgEbgHnAKOBsVU3F5DSWh1weV+YxqARXviNh4nClDWnj0n5LMpbAN4tF5CjgSODbwIOqOir26HxWR2CMMZ0X5c3i5vmJPw/cqKrvBgzgd37twXOtlvUWkQdE5CX/322CrMsYY0x8gnQEfxaRfwKjgfki0heoD/C564C25zTfB+ar6s7AfP+501wqNmkrCwVfLu/fqGNzua1BZaENpr0OLw2JSBUwDlgKrFXVRhHZAuipqu0TXtt/fghwl6oO95+/CExU1bdEZCDwd1XdtdR6kro01LbAA7ybN1MuOD/xa69tC74AqOpO7bCZqbmO7/L+jTo2l9saVBbakDdBLw0FuUfwuKqOLzOIIWzeEbynqlu3en2Nqpa8PJRUR+BSsUlbWSj4cnn/Rh2by20NKgttyJso7xHcLyJfFBGJIK7AROR0EVkoIgtXr15dyU23cKnYpK0sFHy5vH+jjs3ltgaVhTaYwoJ0BN8BbgE+EpG1IrJORNaWub2V/iUh/H9XFXujql6tqqNVdXTfvn3L3Fw4LhWbtJWFgi+X92/Usbnc1qCy0AZTWJCCsp6qWqWqNaray39e7nzFfwZO9n8+GXD6fNKlYpO2slDw5fL+jTo2l9saVBbaYAoLUlAmwAnAUFX9kYgMBgaq6pMlPncjMBHYVkSWA1OA6cAcETkNeI2PRzR1kkvFJm1loeDL5f0bdWwutzWoLLTBFBbkZvFsoAnYT1V393P/71fVvSsRIFhBmTHGlCPKm8VjVfUb+LUDqroG6BoyPtOBtOVqpy1el2VhX2ahDXkTZGKajSJSjTdPMX5BWVOsUeWYSxNlBJG2eF2WhX2ZhTbkUZAzgpnA7UA/EbkUeBT4caxR5djMWbM3K9gBqK+vZ+as2QlF1LG0xeuyLOzLLLQhj0qeEajqDSKyCNgfEOBIVV0ae2Q5lbZc7bTF67Is7MsstCGPip4RiEitiJwtIr8EPgv8RlV/aZ1AvNKWq522eF2WhX2ZhTbkUUeXhq7HG2huCXAo8LOKRJRzacvVTlu8LsvCvsxCG/Koo0tDn1TVEQAici3QYd2AiUbacrXTFq/LsrAvs9CGPCpaRyAiT6nqnsWeV5LVERhjTOdFUUcwyh9baK2IrANGRjDWkDEmxaxGIJuKXhpS1epKBmKMcZvVCGRXkDoCY4yxGoEMs47AGBOI1Qhkl3UExphArEYgu6wjMMYEYjUC2RVk0DljjLEagQwrOR+BC6yOwBhjOi/K+QhSb9r0GdSNHc/IvcdSN3Y806bPSDokY4xxRuYvDU2bPoM5c29red7U1NTy/MLvn5dUWMYY44zMnxHcevsdnVpujDF5k/mOoKmp8GRqxZYbY0zeZL4jqKoq3MRiy40xJm8y/9tw8lFHdmq5McbkTeZvFjffEL719jtoamqiqqqKyUcdaTeKjTHGZ3UExhiTUVZHEJFyaxBs3HZjTFpk/tJQGOXWINi47caYNLEzgg6UW4Ng47YbY9LEOoIOlFuDYOO2G2PSxDqCDpRbg2Djthtj0sQ6gg6UW4Ng47YbY9LEbhZ3oNwaBBu33RiTJonUEYjIMmAd0AhsKpXnanUExhjTeWmoI/icqtYFCTIOYfL8rUbAGJMlubw0FCbP32oEjDFZk9QZgQL3i8giETm90hsPk+dvNQLGmKxJ6oxggqq+KSL9gAdE5J+q+nDrN/gdxOkAO+ywQ6QbD5PnbzUCxpisSeSMQFXf9P9dBdwOjCnwnqtVdbSqju7bt2+k2w+T5281AsaYrKl4RyAiW4hIz+afgYOA5yoZQ5g8f6sRMMZkTRKXhvoDt4tI8/b/pKoVTbsJk+dvNQLGmKyx+QiMMSaj0lBHYIwxxgHWERhjTM5ZR2CMMTlnHYExxuScdQTGGJNz1hEYY0zOWUdgjDE5Zx2BMcbknHUEEbE5CowxaZXL+QiiZnMUGGPSzM4IImBzFBhj0sw6ggjYHAXGmDSzjiACNkeBMSbNrCOIgM1RYIxJM7tZHAGbo8AYk2Y2H4ExxmSUzUdgjDEmEOsIjDEm56wjMMaYnLOOwBhjcs46AmOMyTnrCIwxJuesIzDGmJyzjsAYY3LOOgJjjMk56wiMMSbnrCMwxpics47AGGNyzjoCY4zJuVSMPioiq4H/dPCWbYG3KxROXKwNbrA2uMHaEI0dVbVvqTeloiMoRUQWBhlq1WXWBjdYG9xgbagsuzRkjDE5Zx2BMcbkXFY6gquTDiAC1gY3WBvcYG2ooEzcIzDGGFO+rJwRGGOMKVPqOgIR+Z2IrBKR51ot6y0iD4jIS/6/2yQZYykiMlhEHhSRpSLyvIh8y1+emnaISK2IPCkiz/htmOovHyoiT/htuFlEuiYda0dEpFpEnhaRu/znqYofQESWicgSEVksIgv9ZWn6Lm0tIreKyD/9/xPjUxb/rv6+b36sFZGz09SG1HUEwHXAIW2WfR+Yr6o7A/P95y7bBJyjqrsD44BviMgnSVc7GoD9VHUUUAccIiLjgBnAlX4b1gCnJRhjEN8ClrZ6nrb4m31OVetapSum6bv0C2Cequ4GjMI7HqmJX1Vf9Pd9HbAXsB64nRS1AVVN3QMYAjzX6vmLwED/54HAi0nH2Mn23AkcmNZ2AD2Ap4CxeAU0Xfzl44H7ko6vg7gH4f0H3Q+4C5A0xd+qHcuAbdssS8V3CegFvIp/vzJt8Rdoz0HAY2lrQxrPCArpr6pvAfj/9ks4nsBEZAiwB/AEKWuHf1llMbAKeAB4BXhPVTf5b1kObJ9UfAH8HPge0OQ/70O64m+mwP0iskhETveXpeW79AlgNfB7/xLdb0VkC9ITf1vHATf6P6emDVnpCFJJRLYE5gJnq+rapOPpLFVtVO90eBAwBti90NsqG1UwIjIJWKWqi1ovLvBWJ+NvY4Kq7gkcineZ8TNJB9QJXYA9gdmqugfwIS5fQumAfz/pCOCWpGPprKx0BCtFZCCA/++qhOMpSURq8DqBG1T1Nn9x6toBoKrvAX/Hu9+xtYh08V8aBLyZVFwlTACOEJFlwE14l4d+Tnrib6Gqb/r/rsK7Nj2G9HyXlgPLVfUJ//mteB1DWuJv7VDgKVVd6T9PTRuy0hH8GTjZ//lkvGvuzhIRAa4FlqrqFa1eSk07RKSviGzt/9wdOADvJt+DwGT/bc62QVXPV9VBqjoE73T+b6p6AimJv5mIbCEiPZt/xrtG/Rwp+S6p6grgdRHZ1V+0P/ACKYm/jeP5+LIQpKkNSd+kKONmzI3AW8BGvL8mTsO7tjsfeMn/t3fScZZow754lxyeBRb7j8+nqR3ASOBpvw3PARf5yz8BPAm8jHeK3C3pWAO0ZSJwVxrj9+N9xn88D/zAX56m71IdsND/Lt0BbJOm+P029ADeAbZqtSw1bbDKYmOMybmsXBoyxhhTJusIjDEm56wjMMaYnLOOwBhjcs46AmOMyTnrCIwpQUSOEhEVkd2SjsWYOFhHYExpxwOP4hWeGZM51hEY0wF/PKgJeIWLx/nLqkRklj8Pw10ico+ITPZf20tEHvIHgLuveYgBY1xmHYExHTsSb6z8fwHvisiewNF4Q6GPAL6CN1x18/hRVwGTVXUv4HfApUkEbUxndCn9FmNy7Xi8wejAG5zueKAGuEVVm4AVIvKg//quwHDgAW84KarxhkMxxmnWERhThIj0wRuVdLiIKN4vdsUb4bPgR4DnVXV8hUI0JhJ2aciY4iYDf1DVHVV1iKoOxptN623gi/69gv54g9aBNyNVXxFpuVQkIp9KInBjOsM6AmOKO572f/3PBbbDG/n2OeA3eLPLva+qH+F1HjNE5Bm8UWX3qVy4xpTHRh81pgwisqWqfuBfPnoSb5awFUnHZUw57B6BMeW5y5+YpyvwI+sETJrZGYExxuSc3SMwxpics47AGGNyzjoCY4zJOesIjDEm56wjMMaYnLOOwBhjcu7/AZx1PPeZzrXTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graphical representation of the purchase status of the product\n",
    "\n",
    "import matplotlib.lines as mlines\n",
    "\n",
    "diziBoyut=len(df['SatinAldiMi'])\n",
    "plt.figure(figsize=(6,6))\n",
    "for i in range(diziBoyut):\n",
    "    if((df['SatinAldiMi'][i])==1.0):\n",
    "        plt.scatter(df['Yas'][i],df['AlisverisSayisi'][i], color='#f1c40f')\n",
    "    else:\n",
    "        plt.scatter(df['Yas'][i],df['AlisverisSayisi'][i], color='#2d3436')\n",
    "\n",
    "plt.ylabel('Person\\'s Annual Average Shopping Count')\n",
    "plt.xlabel('Age')\n",
    "\n",
    "alanlar_line = mlines.Line2D([], [], color='#f1c40f', marker='.', markersize=14, label='Was Bought')\n",
    "almayanlar_line = mlines.Line2D([], [], color='#2d3436', marker='.', markersize=14, label='Wasn\\'t Bought')\n",
    "plt.legend(handles=[alanlar_line,almayanlar_line],loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Amount of training data :\t 158\n",
      "Amount of testing data :\t 53\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We only get data which we need.\n",
    "\n",
    "# Age (Yas) and  Average Shopping Count (AlisverisSayisi) informations\n",
    "x = df.iloc[:, [4,0]].values\n",
    "\n",
    "# Purchase status\n",
    "y = df.iloc[:, 3].values\n",
    "\n",
    "# split data as test (25%) and training (75%)\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(x, y, test_size = 0.25, random_state = 0)\n",
    "\n",
    "# X_test_pure to be used when new data is tested\n",
    "X_test_pure=X_test\n",
    "print(\"Amount of training data :\\t {}\".format(len(X_train)))\n",
    "print(\"Amount of testing data :\\t {}\".format(len(X_test)))\n",
    "\n",
    "# Distribution of test and training data on graphics\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.ylabel('Person\\'s Annual Average Shopping Count')\n",
    "plt.xlabel('Age')\n",
    "\n",
    "plt.scatter(X_train[:,0],X_train[:,1], color='#2980b9', label='Train')\n",
    "plt.scatter(X_test[:,0],X_test[:,1], color='#c0392b', label='Test')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Normalization\n",
    "# Since the independent variables are not in the same unit, the normalization process is applied.\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "\n",
    "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "\n",
    "MinMaxScaler(copy=True, feature_range=(0, 1))\n",
    "X_test = scaler.fit_transform(X_test)\n",
    "\n",
    "# Graph of training and test data after normalization\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.ylabel('Person\\'s Annual Average Shopping Count')\n",
    "plt.xlabel('Age')\n",
    "\n",
    "plt.scatter(X_train[:,0],X_train[:,1], color='#2980b9', label='Train')\n",
    "plt.scatter(X_test[:,0],X_test[:,1], color='#c0392b', label='Test')\n",
    "plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantum Machine Learning\n",
    "\n",
    "### Supervised Learning\n",
    "\n",
    "In this sample of Supervised Learning; between the two classes, the **variational circuit** for 2D pattern recognition task is trained.\n",
    "\n",
    "Firstly, the **Strawberry Fields** library, a few gates, learner and helper imported in the code.\n",
    "\n",
    "##### **Source: ** https://qmlt.readthedocs.io/en/latest/tutorials/numerical.html#supervised-learning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import strawberryfields as sf\n",
    "from strawberryfields.ops import Dgate, BSgate\n",
    "import numpy as np\n",
    "from qmlt.numerical import CircuitLearner\n",
    "from qmlt.numerical.helpers import make_param\n",
    "from qmlt.numerical.losses import square_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = 101\n",
    "batch_size = 2\n",
    "\n",
    "my_init_params = [make_param(name='phi', constant=2.,monitor=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The function which representing the variational circuit is defined.\n",
    "def circuit(X, params):\n",
    "\n",
    "    eng, q = sf.Engine(2)\n",
    "    \n",
    "    # Since X is a data stack, a circuit is defined for a single input.\n",
    "    def single_input_circuit(x):\n",
    "\n",
    "        eng.reset()\n",
    "        with eng:\n",
    "            Dgate(x[0], 0.) | q[0]\n",
    "            Dgate(x[1], 0.) | q[1]\n",
    "            BSgate(phi=params[0]) | (q[0], q[1])\n",
    "            BSgate() | (q[0], q[1])\n",
    "        state = eng.run('fock', cutoff_dim=10, eval=True)\n",
    "    \n",
    "        # The output is defined as the probability of measuring |2,0> as opposed to |0,2>.\n",
    "        p0 = state.fock_prob([0,2])\n",
    "        p1 = state.fock_prob([2,0])\n",
    "        normalization = p0 + p1 + 1e-10\n",
    "        outp = p1 / normalization\n",
    "        \n",
    "        # print(\"x0:\",x[0],\" - x1:\",x[1],\" - Cikti:\",outp)\n",
    "        return outp\n",
    "\n",
    "    # Implament of data stack to the circuit one by one.\n",
    "    circuit_output = [single_input_circuit(x) for x in X]\n",
    "\n",
    "    return circuit_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the loss function: Comparing output of variational circuit with targets.\n",
    "def myloss(circuit_output, targets):\n",
    "    # Use of function of square_loss contained in the MLT (Machine Learning Tool).\n",
    "    return square_loss(outputs=circuit_output, targets=targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Round the output values of the circuit.\n",
    "def outputs_to_predictions(circuit_output):\n",
    "    return round(circuit_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the model hyperparams and training algorithm.\n",
    "hyperparams = {'circuit': circuit,\n",
    "               'init_circuit_params': my_init_params,\n",
    "               'task': 'supervised',\n",
    "               'loss': myloss,\n",
    "               'optimizer': 'SGD',\n",
    "               'init_learning_rate': 0.5,\n",
    "               'decay': 0.01,\n",
    "               'log_every': 5,\n",
    "               'warm_start': False # Not using previous results in the next training.\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------------------------- \n",
      " HYPERPARAMETERS: \n",
      "\n",
      "adaptive_learning_rate_threshold - 0.0 (default value)\n",
      "circuit - User defined function.\n",
      "decay - 0.01\n",
      "epsilon - 1e-06 (default value)\n",
      "init_circuit_params - Values: [2.0].\n",
      "init_learning_rate - 0.5\n",
      "log_every - 5\n",
      "loss - User defined function.\n",
      "optimizer - SGD\n",
      "plot - False (default value)\n",
      "print_log - True (default value)\n",
      "regularization_strength - 0.1 (default value)\n",
      "regularizer - No regularizer provided.\n",
      "task - supervised\n",
      "warm_start - False\n",
      "\n",
      " -----------------------------\n",
      "Initial loss = 34.7467510743608 | Initial regularizer = 0.0 |  \n",
      "\n",
      "Global step 1 | Loss = 34.85269770540879 | Regularizer = 0.0 |  learning rate = 0.5 |  \n",
      "Global step 6 | Loss = 21.349592903041696 | Regularizer = 0.0 |  learning rate = 0.47619047619047616 |  \n",
      "Global step 11 | Loss = 19.84071968674987 | Regularizer = 0.0 |  learning rate = 0.45454545454545453 |  \n",
      "Global step 16 | Loss = 11.597700770215319 | Regularizer = 0.0 |  learning rate = 0.4347826086956522 |  \n",
      "Global step 21 | Loss = 11.018537856348898 | Regularizer = 0.0 |  learning rate = 0.4166666666666667 |  \n",
      "Global step 26 | Loss = 11.387420147340244 | Regularizer = 0.0 |  learning rate = 0.4 |  \n",
      "Global step 31 | Loss = 11.55806925766317 | Regularizer = 0.0 |  learning rate = 0.3846153846153846 |  \n",
      "Global step 36 | Loss = 15.960791241623102 | Regularizer = 0.0 |  learning rate = 0.37037037037037035 |  \n",
      "Global step 41 | Loss = 10.921344545832596 | Regularizer = 0.0 |  learning rate = 0.35714285714285715 |  \n",
      "Global step 46 | Loss = 11.740304901002705 | Regularizer = 0.0 |  learning rate = 0.3448275862068966 |  \n",
      "Global step 51 | Loss = 11.733649235206009 | Regularizer = 0.0 |  learning rate = 0.3333333333333333 |  \n",
      "Global step 56 | Loss = 11.926618398924775 | Regularizer = 0.0 |  learning rate = 0.3225806451612903 |  \n",
      "Global step 61 | Loss = 11.827954126640774 | Regularizer = 0.0 |  learning rate = 0.3125 |  \n",
      "Global step 66 | Loss = 11.938262202115551 | Regularizer = 0.0 |  learning rate = 0.30303030303030304 |  \n",
      "Global step 71 | Loss = 12.088584322565486 | Regularizer = 0.0 |  learning rate = 0.2941176470588235 |  \n",
      "Global step 76 | Loss = 12.13235976953501 | Regularizer = 0.0 |  learning rate = 0.2857142857142857 |  \n",
      "Global step 81 | Loss = 11.799882895232114 | Regularizer = 0.0 |  learning rate = 0.2777777777777778 |  \n",
      "Global step 86 | Loss = 11.346074084778923 | Regularizer = 0.0 |  learning rate = 0.27027027027027023 |  \n",
      "Global step 91 | Loss = 11.965180361039836 | Regularizer = 0.0 |  learning rate = 0.2631578947368421 |  \n",
      "Global step 96 | Loss = 11.50585895783151 | Regularizer = 0.0 |  learning rate = 0.2564102564102564 |  \n",
      "Global step 101 | Loss = 11.833952660310572 | Regularizer = 0.0 |  learning rate = 0.25 |  \n"
     ]
    }
   ],
   "source": [
    "# Creation of learner.\n",
    "learner = CircuitLearner(hyperparams=hyperparams)\n",
    "\n",
    "# Training of the learner.\n",
    "learner.train_circuit(X=X_train, Y=Y_train, steps=steps, batch_size=batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In Quantum, the results are always probabilistic, so (if necessary) process (learning) can be repeated.\n",
    "# Re-setting the model hyperparams and training algorithm.\n",
    "hyperparams = {'circuit': circuit,\n",
    "               'init_circuit_params': my_init_params,\n",
    "               'task': 'supervised',\n",
    "               'loss': myloss,\n",
    "               'optimizer': 'SGD',\n",
    "               'init_learning_rate': 0.5,\n",
    "               'decay': 0.01,\n",
    "               'log_every': 500,\n",
    "               'warm_start': True # Using previous results in the next training.              \n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----------------------------- \n",
      " HYPERPARAMETERS: \n",
      "\n",
      "adaptive_learning_rate_threshold - 0.0 (default value)\n",
      "circuit - User defined function.\n",
      "decay - 0.01\n",
      "epsilon - 1e-06 (default value)\n",
      "init_circuit_params - Values: [0.23109943157232526].\n",
      "init_learning_rate - 0.5\n",
      "log_every - 500\n",
      "loss - User defined function.\n",
      "optimizer - SGD\n",
      "plot - False (default value)\n",
      "print_log - True (default value)\n",
      "regularization_strength - 0.1 (default value)\n",
      "regularizer - No regularizer provided.\n",
      "task - supervised\n",
      "warm_start - True\n",
      "\n",
      " -----------------------------\n",
      "Quantum ML Accuracy Rate:\t % 92.45283018867924\n"
     ]
    }
   ],
   "source": [
    "# Re-Creation of learner with new paremeters.\n",
    "learner = CircuitLearner(hyperparams=hyperparams)\n",
    "\n",
    "# Evaluation of the score of the test data set.\n",
    "test_score = learner.score_circuit(X=X_test, Y=Y_test, outputs_to_predictions=outputs_to_predictions)\n",
    "kuantum_dogruluk_orani=100*test_score['accuracy']\n",
    "\n",
    "# If the desired result is not achieved, continue with the training.\n",
    "while(kuantum_dogruluk_orani<92):\n",
    "    # Training of the learner.\n",
    "    learner.train_circuit(X=X_train, Y=Y_train, steps=steps, batch_size=batch_size)\n",
    "    # Evaluation of the score of the test data set.\n",
    "    test_score = learner.score_circuit(X=X_test, Y=Y_test, outputs_to_predictions=outputs_to_predictions)\n",
    "    kuantum_dogruluk_orani=100*test_score['accuracy']\n",
    "    \n",
    "print(\"Quantum ML Accuracy Rate:\\t % {}\".format(kuantum_dogruluk_orani))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classical Machine Learning\n",
    "## Supervised Learning\n",
    "Classification with Support Vector Machine (SVM) algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=0, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating and training SVM model\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "classifier_c = SVC(kernel='linear', random_state = 0)\n",
    "classifier_c.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "Total count of test dataset :  53\n",
      "\n",
      "\n",
      "Values of Classical ML Confusion Matrix\n",
      "_____________________________________\n",
      "\n",
      "True Prediction Count (TP+TN)  :  46\n",
      "False Prediction Count (FP+FN) :  7\n",
      "TP :  17\n",
      "TN :  29\n",
      "FP :  1\n",
      "FN :  6\n",
      "\n",
      "\n",
      "\n",
      "Values of Quantum ML Confusion Matrix\n",
      "_____________________________________\n",
      "\n",
      "True Prediction Count (TP+TN)  :  49\n",
      "False Prediction Count (FP+FN) :  4\n",
      "TP : 21\n",
      "TN : 28\n",
      "FP : 2\n",
      "FN : 2\n",
      "\n",
      "\n",
      "\n",
      "Classical ML Accuracy Rate:\t %86.8\n",
      "Quantum ML Accuracy Rate:\t %92.5\n"
     ]
    }
   ],
   "source": [
    "# Definition of the Confusion Matrix\n",
    "\n",
    "# confusion_matrix(y_true, y_pred, labels=None, sample_weight=None)\n",
    "# Confusion Matrix: Used to calculate the accuracy of a classification process.\n",
    "# For binary classification;\n",
    "# Count of true negatives  C[0,0] -> count of of matches 0-0 \n",
    "# Count of false negatives C[1,0] -> count of of matches 1-0 \n",
    "# Count of true positives  C[1,1] -> count of of matches 1-1 \n",
    "# Count of false positives C[0,1] -> count of of matches 0-1 \n",
    "\n",
    "# Creation of Confusion Matrix for Classial Machine Learning\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "klasik_TP = klasik_TN = klasik_FP = klasik_FN = 0\n",
    "\n",
    "Y_pred_class = classifier_c.predict(X_test)\n",
    "cm = confusion_matrix(Y_test, Y_pred_class)\n",
    "# print(cm)\n",
    "\n",
    "klasik_TP = cm[1][1]\n",
    "klasik_TN = cm[0][0]\n",
    "klasik_FP = cm[0][1]\n",
    "klasik_FN = cm[1][0]\n",
    "\n",
    "true_p = cm[0][0]+cm[1][1]\n",
    "false_p = cm[0][1]+cm[1][0]\n",
    "klasik_dogruluk_orani=(true_p*100)/len(Y_test)\n",
    "\n",
    "#############################################################\n",
    "\n",
    "# Creation of Confusion Matrix for Quantum Machine Learning\n",
    "\n",
    "TahminiDeger = GercekDeger = 0.0\n",
    "TestVeriSayisi = Bildi = Bilemedi = 0\n",
    "kuantum_TP = kuantum_TN = kuantum_FP = kuantum_FN = 0\n",
    "\n",
    "\n",
    "kuantum_y_tahmin=[]\n",
    "\n",
    "for i in range(len(X_test)):\n",
    "    GercekDeger=Y_test[i]\n",
    "    outcomes = learner.run_circuit(X=X_test[[i]], outputs_to_predictions=outputs_to_predictions)\n",
    "    TahminiDeger=outcomes['predictions'][0]\n",
    "    kuantum_y_tahmin.append(outcomes['predictions'][0])\n",
    "    if GercekDeger==TahminiDeger:\n",
    "        Bildi+=1\n",
    "        if(GercekDeger==1.0):\n",
    "            kuantum_TP+=1;\n",
    "        else:\n",
    "            kuantum_TN+=1;\n",
    "    else:\n",
    "        Bilemedi+=1\n",
    "        if(GercekDeger==1.0):\n",
    "            kuantum_FP+=1;\n",
    "        else:\n",
    "            kuantum_FN+=1;\n",
    "\n",
    "TestVeriSayisi = len(X_test)\n",
    "            \n",
    "print(\"\\n\")\n",
    "print(\"Total count of test dataset : \", TestVeriSayisi)\n",
    "print(\"\\n\")\n",
    "\n",
    "print(\"Values of Classical ML Confusion Matrix\\n_____________________________________\\n\")\n",
    "print(\"True Prediction Count (TP+TN)  : \",true_p)\n",
    "print(\"False Prediction Count (FP+FN) : \",false_p)\n",
    "print(\"TP : \",klasik_TP)\n",
    "print(\"TN : \",klasik_TN)\n",
    "print(\"FP : \",klasik_FP)\n",
    "print(\"FN : \",klasik_FN)\n",
    "\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "print(\"Values of Quantum ML Confusion Matrix\\n_____________________________________\\n\")\n",
    "print(\"True Prediction Count (TP+TN)  : \",Bildi)\n",
    "print(\"False Prediction Count (FP+FN) : \",Bilemedi)\n",
    "print(\"TP :\",kuantum_TP)\n",
    "print(\"TN :\",kuantum_TN)\n",
    "print(\"FP :\",kuantum_FP)\n",
    "print(\"FN :\",kuantum_FN)\n",
    "\n",
    "print(\"\\n\\n\")\n",
    "\n",
    "print(\"Classical ML Accuracy Rate:\\t %{}\".format(round(klasik_dogruluk_orani,1)))\n",
    "print(\"Quantum ML Accuracy Rate:\\t %{}\".format(round(kuantum_dogruluk_orani,1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy\n",
      "___________________\n",
      "\n",
      "Classical :  0.87\n",
      "Quantum   :  0.92\n",
      "\n",
      "\n",
      "Error Rate\n",
      "___________________\n",
      "\n",
      "Classical :  0.13\n",
      "Quantum   :  0.08\n",
      "\n",
      "\n",
      "Recall - Sensivity - True Positive Rate\n",
      "___________________\n",
      "\n",
      "Classical :  0.74\n",
      "Quantum   :  0.91\n",
      "\n",
      "\n",
      "Specificity - True Negative Rate\n",
      "___________________\n",
      "\n",
      "Classical :  0.97\n",
      "Quantum   :  0.93\n",
      "\n",
      "\n",
      "False Positive Rate (1 - Specificity)\n",
      "___________________\n",
      "\n",
      "Classical :  0.03\n",
      "Quantum   :  0.07\n",
      "\n",
      "\n",
      "Precision\n",
      "___________________\n",
      "\n",
      "Classical :  0.94\n",
      "Quantum   :  0.91\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Accuracy rate of the Classifier\n",
    "print(\"Accuracy\\n___________________\\n\")\n",
    "print(\"Classical : \",round((klasik_TP+klasik_TN)/TestVeriSayisi, 2))\n",
    "print(\"Quantum   : \",round((kuantum_TP+kuantum_TN)/TestVeriSayisi, 2))\n",
    "\n",
    "print(\"\\n\")\n",
    "# Error Rate of the Classifier\n",
    "print(\"Error Rate\\n___________________\\n\")\n",
    "print(\"Classical : \",round((klasik_FP+klasik_FN)/TestVeriSayisi, 2))\n",
    "print(\"Quantum   : \",round((kuantum_FP+kuantum_FN)/TestVeriSayisi, 2))\n",
    "\n",
    "print(\"\\n\")\n",
    "# When the \"Positive\" is real, which is often estimated \"Positive\"\n",
    "print(\"Recall - Sensivity - True Positive Rate\\n___________________\\n\")\n",
    "print(\"Classical : \",round((klasik_TP)/(klasik_TP+klasik_FN), 2))\n",
    "print(\"Quantum   : \",round((kuantum_TP)/(kuantum_TP+kuantum_FN), 2))\n",
    "\n",
    "print(\"\\n\")\n",
    "# When the \"Negative\" is real, which is often estimated \"Negative\"\n",
    "print(\"Specificity - True Negative Rate\\n___________________\\n\")\n",
    "print(\"Classical : \",round((klasik_TN)/(klasik_FP+klasik_TN), 2))\n",
    "print(\"Quantum   : \",round((kuantum_TN)/(kuantum_FP+kuantum_TN), 2))\n",
    "\n",
    "print(\"\\n\")\n",
    "# When the \"Negative\" is real, which is often estimated \"Positive\"\n",
    "print(\"False Positive Rate (1 - Specificity)\\n___________________\\n\")\n",
    "print(\"Classical : \",round((klasik_FP)/(klasik_FP+klasik_TN), 2))\n",
    "print(\"Quantum   : \",round((kuantum_FP)/(kuantum_FP+kuantum_TN), 2))\n",
    "\n",
    "print(\"\\n\")\n",
    "# \"Positive\" forecast accuracy rate\n",
    "print(\"Precision\\n___________________\\n\")\n",
    "print(\"Classical : \",round((klasik_TP)/(klasik_TP+klasik_FP), 2))\n",
    "print(\"Quantum   : \",round((kuantum_TP)/(kuantum_TP+kuantum_FP), 2))\n",
    "\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classical AUC : 0.853\n",
      "Quantum AUC   : 0.923\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "\n",
    "klasik_y_tahmin = Y_pred_class #Classial ML predictions\n",
    "\n",
    "\n",
    "klasik_auc = roc_auc_score(Y_test, klasik_y_tahmin)\n",
    "print('Classical AUC : %.3f' % klasik_auc)\n",
    "fpr, tpr, thresholds = roc_curve(Y_test, klasik_y_tahmin)\n",
    "\n",
    "kuantum_auc = roc_auc_score(Y_test, kuantum_y_tahmin)\n",
    "print('Quantum AUC   : %.3f' % kuantum_auc)\n",
    "qfpr, qtpr, qthresholds = roc_curve(Y_test, kuantum_y_tahmin)\n",
    "\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(13,6))\n",
    "\n",
    "ax1.plot([0, 1], [0, 1], linestyle='--', color='#f39c12')\n",
    "ax1.plot(fpr, tpr, marker='.',color='#c0392b')\n",
    "ax1.set_title(\"Classical Machine Learning ROC Curve\", fontsize=15)\n",
    "ax1.set_ylabel('True Positive Rate', fontsize=13)\n",
    "ax1.set_xlabel('False Positive Rate', fontsize=13)\n",
    "ax1.grid()\n",
    "ax1.legend([\"Separation Line\",\"Classical AUC = %.3f\"%klasik_auc], fontsize=13, loc=4)\n",
    "\n",
    "ax2.plot([0, 1], [0, 1], linestyle='--', color='#f39c12')\n",
    "ax2.plot(qfpr, qtpr, marker='.',color='#2980b9')\n",
    "ax2.set_title(\"Quantum Machine Learning ROC Curve\", fontsize=15)\n",
    "ax2.set_ylabel('True Positive Rate', fontsize=13)\n",
    "ax2.set_xlabel('False Positive Rate', fontsize=13)\n",
    "ax2.grid()\n",
    "ax2.legend([\"Separation Line\", \"Quantum AUC = %.3f\"%kuantum_auc], fontsize=13, loc=4)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Age : 35\n",
      "Annual Average Shopping Count : 30\n",
      "The normalization process is applied to new inputs :  [[0.40350877 0.73529412]]\n",
      "\n",
      "According to Classical ML which have 86.8 accurancy rate, customer; May buy product.\n",
      "\n",
      "According to Quantum ML which have 92.5 accurancy rate, customer  ; May buy product.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predict to whether or not this user is buying by user information entered from external.\n",
    "\n",
    "yas =float(input(\"Age : \")) \n",
    "maas = float (input(\"Annual Average Shopping Count : \"))\n",
    "\n",
    "newrow = [[yas, maas]]\n",
    "\n",
    "# Adding the latest data to the test dataset for normalization\n",
    "X_test_pure = np.concatenate((X_test_pure,newrow))\n",
    "\n",
    "\n",
    "# Normalization of test dataset\n",
    "X_test_sc = scaler.fit_transform(X_test_pure)\n",
    "\n",
    "# The normalization process is applied to new inputs:\n",
    "print(\"The normalization process is applied to new inputs : \",X_test_sc[[-1]])\n",
    "\n",
    "# Send to Quantum ML the latest added data X_test_sc[-1]\n",
    "outcomes = learner.run_circuit(X=X_test_sc[[-1]], outputs_to_predictions=outputs_to_predictions)\n",
    "\n",
    "\n",
    "sonuc=\"\"\n",
    "def sonucugor(i):\n",
    "    sonuc=\"\"\n",
    "    if i==1.0:\n",
    "        sonuc=\" May buy product.\"\n",
    "    else:\n",
    "        sonuc=\" Dont may buy product.\"\n",
    "    return sonuc\n",
    "     \n",
    "\n",
    "print(\"\\nAccording to Classical ML which have {} accurancy rate, customer;{}\".format(round(klasik_dogruluk_orani,1),sonucugor(classifier_c.predict(X_test_sc[[-1]]))))\n",
    "print(\"\\nAccording to Quantum ML which have {} accurancy rate, customer  ;{}\".format(round(kuantum_dogruluk_orani,1),sonucugor(outcomes['predictions'][0])))      \n",
    "\n",
    "new_data=X_test_sc[-1]\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "\n",
    "for i in range(len(X_train)):\n",
    "    if((Y_train[i])==1.0):\n",
    "        plt.scatter(X_train[i][0],X_train[i][1], color='#f1c40f')\n",
    "    else:\n",
    "        plt.scatter(X_train[i][0],X_train[i][1],  color='#2d3436')\n",
    "\n",
    "plt.scatter(new_data[0],new_data[1], color='#2ecc71')\n",
    "\n",
    "plt.ylabel('Tahmini Maas')\n",
    "plt.xlabel('Yas')\n",
    "\n",
    "alanlar_line = mlines.Line2D([], [], color='#f1c40f', marker='.', markersize=14, label='Was bought')\n",
    "almayanlar_line = mlines.Line2D([], [], color='#2d3436', marker='.', markersize=14, label='Wasn\\'t bought')\n",
    "new = mlines.Line2D([], [], color='#2ecc71', marker='.', markersize=14, label='New Customer')\n",
    "\n",
    "plt.legend(handles=[alanlar_line,almayanlar_line,new],loc='lower right')\n",
    "\n",
    "plt.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": "3.6.5"
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