What's_Cooking_SVM_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from nltk.tokenize import TweetTokenizer\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tokenize = TweetTokenizer().tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['submission3.csv', 'Cooking.ipynb', 'test.json', 'submission2.csv', 'test.json.zip', 'submissionSVM.csv', 'sample_submission.csv.zip', \"What's_Cooking_SVM.ipynb\", 'submission.csv', 'train.json', \"What's_Cooking_SVM_2.ipynb\", 'sample_submission.csv', 'Cooking-4.ipynb', 'train.json.zip', 'values.csv', '.ipynb_checkpoints', 'Cooking_blog.ipynb', 'Cooking-2.ipynb']\n",
      "       cuisine     id                                        ingredients\n",
      "0        greek  10259  [romaine lettuce, black olives, grape tomatoes...\n",
      "1  southern_us  25693  [plain flour, ground pepper, salt, tomatoes, g...\n",
      "2     filipino  20130  [eggs, pepper, salt, mayonaise, cooking oil, g...\n",
      "3       indian  22213                [water, vegetable oil, wheat, salt]\n",
      "4       indian  13162  [black pepper, shallots, cornflour, cayenne pe...\n",
      "0          greek\n",
      "1    southern_us\n",
      "2       filipino\n",
      "3         indian\n",
      "4         indian\n",
      "Name: cuisine, dtype: object\n",
      "0    18009\n",
      "1    28583\n",
      "2    41580\n",
      "3    29752\n",
      "4    35687\n",
      "Name: id, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir())\n",
    "train = pd.read_json('train.json')\n",
    "test = pd.read_json('test.json')\n",
    "\n",
    "print(train.head())\n",
    "\n",
    "ytrain = train['cuisine']\n",
    "print(ytrain.head(5))\n",
    "\n",
    "Id = test['id']\n",
    "print(Id.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f926235d550>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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d/T7z53g3aWoOSEF9z5Jl2ILU6LkWqbrh28ArKnyflcuR/ycG3kpX1Xbjb5L3\nGwe8vuJv9Nf5mHUyaTDbd4A/lMzjP/Lv4j3AX0lB+bgq5Wncat8LSKm729rALyPi2S7me1NElGp4\nq/Aeq5F+2JtHxNR81rp1RJSdA6ejOV8kzQX+KSKezs9XAW6IiNcV3P9C4DMRUWnQkaTZwE6k+uXt\nG2Uq+v4t8utobqVuqvr7VIW5mQbsvzp91R+leodJWisiHlPfeIB+olpV5bCQ9C5S0Hk0P18HeEtE\n/E/JfFYnXZ2LNJ5hbeDcqDgLQL6aWiWWrbIsZVRVAUn6r4g4UtKltKibjQKDVHI+E0kNMI9HxDW5\np8L2wHXdLO4Q7//FiPi6lh2ABJQahNWYBG2X/Lz0JGidVr80leM6SY1ql/0pMO1A099xTSoOOsqe\niYhnlUdEK3WBrNLDovLcSpJ+FxG7qX/3YKjY57zFAXwTUuNhkX2rzs3UbCap6uaJ/HxVUqeFXQbd\no8+PSVe3N7LseIC2g6gkfSgizpH0uVavR8GBkl36m0yLiBeqEyPikfz9lgoAA04oyo6xGXTSRKWB\nikXnzFrGqAoA9I0I/GaH+ZxCGizU8GSLtE4NdQBqjNad1eF7dGMStI7nfImIb0u6mr4BO4dExM0F\ndv1m3v4EUtBoaKQVdY2kLwGrKg1I+xRwaYn9GyrPrRQRu+X70t1PB+rCAbzS3EwDrBIRjYM/EfFE\nvuJsKyL2y/dV66cbbQ0dfZdd+pu0arMofNzsUhB6M92ZNHEZoyoARMSN+b7taMQ21HzAi4h/5LPG\nbhr0QBwRl+b7UmcCLXRjErRuzPkCqb738cYZqwpMo9z4O0paaeDfNH+uoo4idaedC3yctOb0j0qV\nvq9Mnc6t1Kgu6WSK704P4B3PzUTuHRa5wbVK7zBJMyNiUru0gSLiB/m+0Ij4NmUYQ2qPKjyj6wCz\nJH2bNMYngMMpMR1FN4JQdG/SxGWMqgCQ65oHPVON4sOq75L0GdJZP6QzxtIDTNq4cLAXBqvCaihR\n9TGNzidBqzznS0PVM1ZJnyR99y9XGs3bsCYlusjleuofUmL+oUFUnlupQR1O8Z11egDvaG6mrHLv\nsNwGtBqwgaR16TsZWgt4WdECSHo5qbF0Iuk7/D/gsxFR+H81n9z9UdLmJYNww+Gkxtfz6Ztu5bCy\nmeSTs4UR8YzSNNevB86KiEcK7NuyKqyhaJVYy7xHUyOwBhlO3RDF537ZEDiJNHYgSPWdR0bEgyXK\nUnmOcfXNs/Ju0tn3Ofn5+4F7IuJLJcqxPn2ToF0bJSZBG1CWfspcZeVG2O2Bm5oaYee0C8iS1ib1\nYvpP0ll8w+NlGgrVpXnS1cFMecICAAARaklEQVTcSk15zAd2rtq4l/P4F1JPpreRvpuPAD+Ogt1q\ncx6V52ZqymMl+qaTuCMKTieh1C34SNLBflHTS48BP4yI7xbM51rSmfdPctJBwOERsXOxT/BCPleS\neg5eT6ruBcqd5HQq/49MIB0vriBVM24dEW8vsO+0oV7v5EppVAWAkUTSH0ijG/vNzR0RPy2Rx28i\n4k3t0trk0Y3VvLYgNTj+OtfzrhARj5fY//qI2Em551M+Y/2/EldkHVEaC7HMPOklD9wrkHoindhh\nWa4C3hYRSzvMpxsH8LXo/7soE1QPI/VSeSQ/Xxd4f0R8v0Qeh5cJWi32v27gwV7StRFRZrBkpZOc\nbnU4acqv8b/xBdI4j5Ml3dw4YRouo6oKqCH34jkZeDVpZOEKwJPtGlS62PsGujPv+1hJL29c0kra\nkjRzYyHqW83rVvpXN5RZzetjwFTSNBJbkXqbnEoaqVhUN6ocOtHxPOkR8bzS4j4dBQA6nOI7B6Ir\nImIPoPRBP+fxceBYUp39P8hXRJQbwfqxiHhhbquIeDj/bdsGgEavFdJAsGV6rpTotXKVpKNI80IF\n6bf+C+XupUUDWsU2w251OGl4LnfWmEJfY26hwWhdPm71MyoDAPBd0uXghaTLqoNJ85a0063eN9Cd\ned8/SzpYNOo0x5EaMYvqxmpeh5H70ANExLxcRVZYRHwzn7E+Rqoy+H9Vzlg70K0Ro7+X9F1SfW9z\nVUGZfO7Lt5WpNv3C85KekrR2VO/j/S/Aa8pWBw4wRtILnSVyYCr6ebrVa6XR5jDwf+IjFOtOWrkH\nThc7nDQcAnwC+GpE3J1P9s5ps09DN49b/YzKKiBJsyJiQnM9s6Q/RESRPspIWiXyoKWmtA3K/MOo\nS2sKKA3oeFV+ekeZg7mk/wUOiKbuemWpbyrmmyNie6XeUDcVrb4ZcMY6LNSledKb8mn8UzT+pj1d\nAFzSBaR2nRn0D0SFzvQk/ZI0HXblKb1zQB1HuhoM0sFrQUR8vuD+Y0hrEFxQtQwjgdLgyv9k2emg\nK6/Dm6vTNosS6zPk/d5IGj38fFPaCz21qhitVwCdzlNyvaSpEXEtgKT3kP7IryyysySRzrCq9CoY\naEf6GpK3VRrYUXQQVjdW87pGHfSh79IZa0ciTZjWDZfRf+BSAI9J2i4iZhfJIHcO+CLLLm9ZJoj8\nIt+qOpq0AtV1VP9d/CvpzPuT9DWIF+5am3vffBooHQA0xMCnnHflfu8VnEnq1XUiaTroQxiii/dg\nlMbJvJP0fz4bWCzpmogYsofPAFcAN0g6MCIeyGk/ooPxS6M1AHyYNEDj06RqlM1IPWqK+iBwRv6j\nvIw0zWzhf9CICKVRr2UXp+5HnY/CvSTfOtGNPvRPA3MlVTpj7QZ1tqZww46kKsVLSP/k+5LmBPq4\npAsj4usF8jiXVIW0H+mseQolZ5qNiOn5BKdxQlJ2QZcfkKpg5tLXNlRKPoCfTpp6PXIZyo6JmJF7\nNA2sUmtXd7/cBj5VsGpEzMzVYfcCx0j6LSkolLF2pOkxPgqcGRHT1L/rcxF3kma5vVrSoRHxByoE\no36ig4mEhutG6prXNq1NHvsDj5O6qZWa5Crv/z3S/DedfI6OJkHrwve4AnBOF/KZ0urWw8/RlXnS\nSWdYazQ9X4M0zmJV4LaCedyY7+c0pZVdD+AtwL2kdQ5+Q5oC4k0l9i81ydjyKEPO425So3i/W69+\nF136bf2edLL5M9IJ57tIwbBsPnNJYyl+1ThuUGLCxLz9Tfl+PGmQ4KcbaVVvo/UKYAqpv3azf26R\n1lI+s9mKNBjjlaRplb8bTb0eCngr8AlJ95DObkqv8UnFUbiSLoiIAzXIwLiiZYhUfTNW0srR2UR4\nF9FiWckO8itrl+hbU/jLkr5FtbPEzYHm7+E5YItIU2wUbZvpdIpvgG+RZs28E0DSK0l94YtecV4l\naSqpKq/KYuzdKAOkevNPkaYICVK36VPb7aTlOPCpgiNJg9o+Q5rTf3f6Foov41jSCcbvIuIGpUFu\n80rmIXiho8YbSdVTHXW1HlUBIHej+gCwpfov1bcmaW7som4BPhopnN6du5WW/VHtU3L7VqqOwj0i\n3+/XhTLcQ+r9cgn9L9PLfB+dTBzWDd2aJ/3HwLWSLs7P3wH8RGlcw20F8/iK0gC3z5O6Kq9FqqYs\nY6VoWpM4Iv6kNCirqA/k+6Ob0sp2A+20DJAmPXuMNOgS0kDH6cCBbfbreD6lbomIG/LDJ0j1/1Xz\nuZCm2QEidf1+T8k8tm96/CRwoCosN9psVPUCUhqwtCUtRo6SLqcKD75RFxb/VudT7nY8CrcqSWdH\nxIclPUKLvu9RYnShpNkRsV27tOVF0n+QDraT6Juz5YcRUXq9VKU5bxqT2v0uIrre9a5AGc4gfYZG\nX/QPAWOiwCjz5ViGDwIrRon5aCT9MSK2bZc2kkmaAPwbyw62LHXmLelMWl+tF/6bKk2xcSjLtnVV\n/l2MqiuASI0w95KWQqtMafHvb5L6NW8paTvg2AJn3s15dDzlbqcH+txL4gTSamiiXFfUHXNAvY90\n8OxExxOHdSIijssPfyrpMjqYJz1S/+/Ck30NpL75a95AaoAtPX8NaW3onUjVDiLVwc8vWY7XsmzX\nxTJTfH+SNEakuQyFRwFnN0uaGH297XamwBxPWo4Dnyo4F/gCHTSoZ81TtK9CaktYNMi2gzmbtE7y\nXqQqpQ9Scq6qZSzvRpRu3khnZJDO+B9ruj0OPFYin24s/j2b9I/RnEehRp0ufo75wKsrfpeNic6e\npn8j3d2UbKgjzbPyZ1Id729zuXbs4e9iFdLCOD8Dfkqqcim1klcXy3ItqZfaivn2IcovyH4TTStP\nkapOyiymPo20etYDpJOTvwIXldi/o84BpIPlnPz7+gepmvHu/PiWAvs/lO+PZBg7F+Qy/G455TuG\nkqubNY41jeMM6aSz9AppzbfRdgXQrTnXl0bEo+o/7W/ZurDKMzZ28XM8EBGVzgAi4iTgJEmnRMQn\nOylEpEatVzHIxGGS3hbLd2TwWaTg2biSeT/pbOmA5fieg1FEnN30/JzcH76M9wIXKa1P/EbSSPc9\nS+6/LemAcYikjSjXh7/TzgGdtk09kK9ODyF1thhO0yT9iNTO1dxO12lX1PGkTgdlNP6nHslXeH8l\njSGqbFQFgC7qxuLfwzb/TdMAmVmSzietTlTpx9npwb8pn+dIjeutnEDFeW0K2jr61ytfJemPy/H9\nhtLx/DURcZekg0h/1wWk3jhlqtT+Hqkf/1KlCeEepFwDMHTQOSAKzso7hFNI3W9fTv/pD6rMadSp\nQ0gj9Vei/3xbhf/HlM40n6evkwSkg3fZucROUxpF/O+ksSprkKaqrqyuAeBwUsPOM6SeH1eQungV\nFsM7/01jgEyQRgM3nx32eqBMEZ0NVmmvUl3zctI8f03zlBJt569p0a13PVJ1zHVKI8SLNjzOUlq7\n9oek6s4nSFMhl7Eo38bQ4145kWYQPbkbV6ddsG1UXFu6IdcUzI4O1ghXmlrjsYh4mNQe05UgOKp6\nAXVLU8v+OPqCYJT4BxsRJE0nDYBrnrL3W9HD3iJFKE+Fuxzzv50UhBtTc2xOX/1zT/+ukg4kLeD+\nWO6dtANwXBSYr0VdWu9iQJ7jgLWi5LwzTfuvmd66+nxTo5mkHwInRkTRbsCD5fNdYHr0dSutkkep\nqeIL5VnTAHAnacbEW2hq2S/zD9ZhD5yuUIv5xFulDbceBICuHzg7KMucSIPSdgO+RhpQ9aUouYhJ\nF8rR0ToRuY75bNJVCMDfgIMj4tZulnOkyycXW5EasZ+h2oBPJN1GOkm5h4oDR/MJxd8pP7XGoOpa\nBbQ48rq8Hfg68I6qjbBdMkbSuvmykFzPPBL/pvcsz8wbB3ilaaybuz12Y7K+shrz5ewLnBoRF0s6\nppcFUN86EbfRf46pMgsFnQZ8LiKuynm+hVSl1KvBfSPF3l3KpxsDRxvViJ8akF65OmgkHix6oRst\n+5V74HTRt0izPl5E+mEcCHx1OAqitJbuOPqfcZ6V78tM1Fflvd9J+i5eRmrw3IJUBfSa5fm+g/hL\n7hywB3CC0nTfY3pchm6sE7F64+APEBFXl+np9mIx2MlF1Xw6VGlqjaHUtQroHFLLfr+VtIrUnTf1\nwHkzaR6fyj1wukHSNqT5SQTM7LSusmIZWs5qGj0asJN7/OwO/DrSmgZvJS1fOLUX7z+gLKuRzhrn\nRpqzZWPgdRHxqx6WoRvrRPycNB6heTTyhIjYvwtFHDUGO7mIiJ6fXCitE/EYaXAapO7O60REu6k1\nBs+zpgFgbtWW/TykezCFgsiLTa4n3SaG6cekvgWC/ghsn7tAXh8ROw1HeYabpJ+SxgFUXicidyj4\nMmlke2Mk8DGNDgd1McJOLro+tUZdq4CulbRNlbPlyHOhSNo1Ivp1NZRUeBqIF5lKs5p20SOS1iBd\nEp8r6UGgo0XZR7lW60SUDc5bkdbZGEM6TkwiHQhHVU+5LnguIh6SNEbSmIi4KrexDIeud3euawDY\nDZgiqZOW/ZNZdiWeVml1UHVW0255J2lKiyNIVRVrkc5e62qdiOg3NbqkIwbbeBDn0qKnXA0N+8lF\n0/iQlYCDJd2Xn29B8VlqW+dd0yqglt0GizTUSHoDqSfEkfSfRXMt4F2dXI6NVhqmWU3VetHvxqCz\nfwBLgG9ERNlJzEa1Vt1uy3YPbny33S/d6JLbdJ4m/a4aJxfndtL1skIZlls351oGgE7kg91bSMv9\nNbfAPw5cGhFlF3mw5UTS+qTVsbYe7rL0gvrWy9iNdMbasCbwfETsUSKvSaRGxm7PgTMq1OXkwgGg\nIklb9HKA0Ug24J9kZdKl6pO9HBQ3GEkbR8RwtU30lLq7XkblnnJ18GI5uXAAqEjSVbSeq7zw4vIv\nVpL2B3aKiC8Nd1nqKgeD8RHxa6XFj1aMiMdL7F+5p1xdvBhOLuraCNwN/9L0eBXS8m517nnygoj4\nnzwjpg0Dpdlpp5KmcdiKtCbxqaSePEVV7ilXF6P94A8OAJVFWjmq2e8lLfelHEeipsFxkLoNTqB8\nt0PrnsNIK4pdBy8sIr5hyTy60VPORjgHgIryvDsNY4AdSX3h6+gdTY+Xkub+mTw8RTHgmYh4VnnB\nI0krUj4gd2sOHBvBHACqu5H0TyXSQe9u0oLNtRMlFgq3nrhG0peAVZXWrPgUUGryQ3dwqAc3Altl\nGnrx7iB1lTsnIv7c+9LVV1485FDSQkEiLXj0o+GaqsNGLgeAiiStBHwSaCzQcDXwg2haC/fFTtI7\nIuJSSVMG2WR90iLetRscZzYaOABUlKeTXgmYnpM+TBps89HhK9XII+njEfGD4S5HHUi6ICIO1LJL\nSwKUWVLSasIBoKLlMTPfaCVpLGmB623ovyBL7cdE9FKjX7qkz5HWAF7Q/Lrr9W2gXi9U8WLyvKSt\nGk8kvZy+ufDr5lzSAixbkiZhuweovPapVdPUL31N4AfAOcB+wNM++FsrvgKoSNLuwH8Dd+WkccAh\nzaso1YWkGyNix8Z6uDntmohoOUmc9Yak15OWhnwPsLDMXEBWD+4GWt36wGtJB/7JpBlCHx3OAg2j\nRsP3/ZL2BRaRRp/a8HoQ+CvwEFB2IJjVgKuAqvuPiHiMND3s20hD7U8Z3iINm69IWhv4PGmKjB+R\npsu2YSDpk5KuJs3kuQHwMTcAWysOANU16vv3BU6NiItJM2HW0QGk6sRbIuKtpID4rmEuU51tARwZ\nEa+JiGmez8cG4zaAiiRdBvwF2IM0DcTfgetr2gtomcVGyi5AYma95yuA6g4kjbDcOy+UvR7wheEt\n0rAZkxcRB16YJ8ntS2YjnK8ArGOSDgaOBi4iDUA6EPhqRJw9rAUzsyE5AFhXSNoG2J0098xM1zub\njXwOAGZmNeU2ADOzmnIAMDOrKQcAM7OacgAwM6spBwAzs5r6/8iT6cNvhKlLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f92623acf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train['cuisine'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def arraytotext(records):\n",
    "    return [\" \".join(record).lower() for record in records]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['romaine lettuce', 'black olives', 'grape tomatoes', 'garlic', 'pepper', 'purple onion', 'seasoning', 'garbanzo beans', 'feta cheese crumbles']\n",
      "['r o m a i n e   l e t t u c e', 'b l a c k   o l i v e s', 'g r a p e   t o m a t o e s', 'g a r l i c', 'p e p p e r', 'p u r p l e   o n i o n', 's e a s o n i n g', 'g a r b a n z o   b e a n s', 'f e t a   c h e e s e   c r u m b l e s']\n"
     ]
    }
   ],
   "source": [
    "train2 = train\n",
    "print((train2['ingredients'][0]))\n",
    "print(arraytotext(train2['ingredients'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_features = tfidf.fit_transform(arraytotext(train['ingredients']))\n",
    "test_features = tfidf.transform(arraytotext(test['ingredients']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "classifier = SVC(C=200, kernel='rbf', degree=3,gamma=1, \\\n",
    "                 coef0=1, shrinking=True,tol=0.001, probability=False,\\\n",
    "                 cache_size=200,class_weight=None, verbose=False,\\\n",
    "                 max_iter=-1,decision_function_shape=None,\\\n",
    "                 random_state=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.79 (+/- 0.00)\n"
     ]
    }
   ],
   "source": [
    "model = OneVsRestClassifier(classifier)\n",
    "scores = cross_val_score(classifier,train_features, ytrain, cv=2)\n",
    "print (\"Accuracy: %0.2f (+/- %0.2f)\" % \\\n",
    "       (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OneVsRestClassifier(estimator=SVC(C=200, cache_size=200, class_weight=None, coef0=1,\n",
       "  decision_function_shape=None, degree=3, gamma=1, kernel='rbf',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False),\n",
       "          n_jobs=1)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_features, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['irish' 'southern_us' 'italian' ... 'italian' 'southern_us' 'mexican']\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(test_features)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "submission = pd.DataFrame()\n",
    "submission['id'] = Id\n",
    "submission['cuisine'] = predictions\n",
    "submission.to_csv('submissionSVM2.csv', index=False)\n",
    "#Score: 0.821"
   ]
  }
 ],
 "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}