Simple-Linear-Regression-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple Learning Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'from sklearn.preprocessing import StandardScaler\\nsc_X = StandardScaler()\\nX_train = sc_X.fit_transform(X_train)\\nX_test = sc_X.transform(X_test)\\nsc_Y = StandardScaler()\\nY_train = sc_Y.fit_transform(Y_train)'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Preprocessing Template\n",
    "\n",
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Importing the dataset\n",
    "dataset = pd.read_csv('Salary_Data.csv')\n",
    "X = dataset.iloc[:, :-1].values\n",
    "Y = dataset.iloc[:, 1].values\n",
    "\n",
    "# Splitting the dataset into the Training set and Test set\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 = 1/3, random_state = 0)\n",
    "\n",
    "# Feature Scaling,not required for Linear Regression since it is taken care of in Simple Linear Regression\n",
    "\"\"\"from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X_train = sc_X.fit_transform(X_train)\n",
    "X_test = sc_X.transform(X_test)\n",
    "sc_Y = StandardScaler()\n",
    "Y_train = sc_Y.fit_transform(Y_train)\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fitting Simple Linear Regression on Training Set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None,\n",
       "         normalize=False)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()\n",
    "regressor.fit(X_train,Y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predicting the Testing Set results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 40835.10590871, 123079.39940819,  65134.55626083,  63265.36777221,\n",
       "       115602.64545369, 108125.8914992 , 116537.23969801,  64199.96201652,\n",
       "        76349.68719258, 100649.1375447 ])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vector of predicted salaries of all observations\n",
    "Y_pred = regressor.predict(X_test)\n",
    "Y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 37731., 122391.,  57081.,  63218., 116969., 109431., 112635.,\n",
       "        55794.,  83088., 101302.])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Real Salaries\n",
    "Y_test\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising the Training Set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+42D2K98qpmykVLWr2Lj7V8CtwK1mdhhwN3Bd2O1c7u71EeYoIp3wr7c8z4sNS1Ni6mYkLu2+ZgNUAqcBCeC3QB1wKPAIsEtE+YlIB61es5adqx9NiT1bNZjePTaNKSOR9p9GWwDMBK519+eS4pPDTkdE8kD/6kdYtcZTYupmJB+0WWzCruZOd78s0/PuflbWsxKRDvny21XsdcnjKbE3Lj2K72ysuxskP7T5f6K7rzGzI4CMxUZE4pU+AeA7G3fjjUuPiikbkcza+8+e58zsBuAe4OumoLvPjSQrEWnTos+Wc9i1M1Ni7145kq5aOFPyUHuLzcHh1+TuxoHB2U1HRNojvZs5tH9P7jr9gJiyEWlbe6c+HxF1IiLShro6Zt5Qx2k/PCMlrAkAUgjaffXQzCqBgUDzfrAtTRoQkSyrqwt2zkwqNMPrZ3HLv+waY1Ii7dfe+2xuAcqAI4DbgBOAFyPMS0RCt/19IVekbdHccPXRwYPZfbVNsxSELu0cd7C7nwp87u6XAgcBO3T2Q83sHDObZ2ZvmNndZraJmfUzs1lmtsDM7jGz7uHYjcPj+vD5RNL7jA/jb5vZUUnx4WGs3syqOpunSNwSVVO5Yupbzce/enriukIDsGhRDFmJdFx7T6N9E35dbmbbA58B/TrzgWbWGzgLGODu35jZvQTrro0ErnP3SWEndTpwc/j1c3ff2cxGA1cDJ5nZgPB1A4HtgSfMrGklgxuBoUAj8JKZTdEColJIzrnnFR58+YOUWEqRaVJenqOMRDZMezubh82sB3AtMBdoACZtwOd2AzY1s24Ep+c+IpjZNjl8fiJwXPh4VHhM+PwQM7MwPsndV7j7e0A9MCj8U+/uC919ZZjnqA3IVSSnElVTUwrNn36yPw17LoOystSBZWUwYUKOsxPpnPbORrs8fHi/mT0MbOLuX3TmA939AzP7DbCIoGN6HJgDLHP31eGwRqB3+Lg3sDh87Woz+wL4bhh/Iemtk1+zOC2ecU6omY0DxgGU61+IErNDr3mSxUu/SYk1zzQbGF6Xqa4OTp2VlweFRtdrpEC0WmzM7PhWnsPdH+joB5rZVgSdRj9gGXAfMCLD0KYFnjLdoeatxDN1a54hhrvXArUAFRUVGceIRG3NWmenCx5JiT169qHsvt0WqQPHjFFxkYLVVmdzTCvPOdDhYgMcCbzn7p8AmNkDBDeN9jCzbmF30wf4MBzfSDAZoTE87bYlsDQp3iT5NS3FRfJK+s2ZoPtmpDi1Wmzc/bQIPnMRcKCZlRGcRhsCzCZYVfoEgmssY4GHwvFTwuPnw+efdHc3synAX83sdwQTBPoTTMc2oL+Z9QM+IJhEcEoE34dIp3317Sr2TFs4c/avj6TndzaOKSORaOX8pk53nxVuujYXWA28THAqayowycyuCGO3hy+5HbjLzOoJOprR4fvMC2eyvRm+zxnuvibM9ZfANKArcIe7z+toniJRUTcjpcjc275U0dJNne5+erTp5U5FRYXPnj077jSkiGVaOPPtK4azcbeuMWUksuHMbI67V7Q1rt0Lcbr7Xmb2mrtfama/pXPXa0RKUno308Vg4VXqZqR0dPamzqV08qZOkVIya+FnnFT7QkrsvatGEtwqJlI62ltsmm7qvIbgnhgITqeJSAvSu5mDdvwud487MKZsROLV1n023wcWN93UaWbfAV4H5gPXRZ+eSOG5+8VFjH/g9ZSYJgBIqWurs/kTwX0xmNlhQA1wJrAPwQyyEyLNTqTApHczP//hTlSN2C2mbETyR1vFpqu7Lw0fnwTUuvv9BMvWvBJtaiKF48K/vcFdL7yfElM3I7JOm8Um6a7+IYTriLXztSIlIb2b+f1J+3Dcvr1bGC1SmtoqGHcDT5vZpwQz0v4OYGY7A51aiFOkWOx+4WN8s2pNSkzdjEhmbS1XM8HMZgDbAY/7ujtAuxBcuxEpOe5Ov/GpC2f+9WcHcPBOPWPKSCT/tXkqzN1fyBB7J5p0RPKblpoR6RxddxFph+UrVzPgomkpsWd+dQTl3y1r4RUikkzFRqQN6mZENpyKjUgLFi9dzqHXpC6cOe/So9hsY/21Eeko/a0RyUDdjEh2qdiIJHn+3c84+dbUOTELrxxJly5aOFNkQ6jYiITSu5nuXbvwzoQRMWUjUlxUbKTk3fV8Axc+lLqZq06ZiWSXio2UtPRu5ohde/Hn0wbFlI1I8VKxkZJ07r2vcv/cxpSYuhmR6HSJOwGRXEtUTU0pNOcO3aXtQlNXB4kEdOkSfK2rizRHkWKjzkZKxiE1T/LBsm9SYu3qZurqYNw4WL48OH7//eAYYMyYLGcpUpxs3dqapa2iosJnz54ddxoSgUwLZ952agVHDti2fW+QSAQFJl3fvtDQsMH5iRQyM5vj7hVtjVNnI0UtKzdnLlrUsbiIrEfXbKQorVi9Zr1CM/2cwzo3CaC8vPW4rueItEmdjRSdrC81M2FC6jUbgLKyIK7rOSLtomIjRWPJl98y6MoZKbFXLhpKj7LuG/bGTUWjujo4dVZeHhSaMWOCTia5CEFwXF2tYiOSRKfRJHtiPJ2UqJq6XqFpqKnc8ELTZMyYYDLA2rXB16ZCous5Iu2izkayI6bTSa8sXsZxNz6bEqufMIJuXXP076jy8swz1Vq6ziNSotTZSHZUV7d8Oikiiaqp6xWahprK3BUaCE6nlaXt1tl0PUdEmqmzkezI4emkh1/7kF/+9eWUWGxLzbR2PUdEmqnYSHbk6HRS+kyzfct78OB/HJLVz+iwMWNUXETaoGIj2dHa9OAs+M20t7lhZn1KTAtnihQOFRvJjghPJ6V3M2cP6c85Q3fZ4PcVkdxRsZHsyfLppB/fNot/1H+aElM3I1KYNBtN8lKiampKobl5zH7ZKzRaXkYk59TZSF7pN34q6QuRZ7Wb0fIyIrGIpbMxsx5mNtnM5pvZW2Z2kJltbWbTzWxB+HWrcKyZ2fVmVm9mr5nZfknvMzYcv8DMxibF9zez18PXXG9mFsf3WbQi6AxWr1lLoiq10Dxy1qHZP20Ww/1AIhJfZ/MH4DF3P8HMugNlwAXADHevMbMqoAo4HxgB9A//HADcDBxgZlsDFwMVgANzzGyKu38ejhkHvAA8AgwHHs3lN1i0IugMsr5wZmu0vIxILHLe2ZjZFsBhwO0A7r7S3ZcBo4CJ4bCJwHHh41HAXzzwAtDDzLYDjgKmu/vSsMBMB4aHz23h7s97sDPcX5LeSzZUFjuDZctXrldoZv/6yGgnAbS1XYCIRCKOzmZH4BPgz2a2NzAHOBvY1t0/AnD3j8xsm3B8b2Bx0usbw1hr8cYM8fWY2TiCDohy/bJpnyx1BjntZpJFfD+QiGQWxzWbbsB+wM3uvi/wNcEps5Zkut7inYivH3SvdfcKd6/o1atX61lLYAM7g/olX61XaN65YkTupjSPGQO1tcGWzmbB19paTQ4QiVgcnU0j0Ojus8LjyQTF5mMz2y7sarYDliSN3yHp9X2AD8P44Wnxp8J4nwzjJRs2oDOIrZtJp+VlRHIu552Nu/8fsNjMdg1DQ4A3gSlA04yyscBD4eMpwKnhrLQDgS/C023TgGFmtlU4c20YMC187iszOzCchXZq0nvJhupEZzBz/pL1Cs17V43UDZoiJSSumzrPBOrM7DVgH+BKoAYYamYLgKHhMQSzyRYC9cCtwH8AuPtS4HLgpfDPZWEM4BfAbeFr3kUz0bKrpY3EMkhUTeW0O19qPt57hx401FSScTZ6e6dU66ZMkYJjnn4HXYmqqKjw2bNnx51G/qqr69C6Z7c8/S41j85PibXayaRPqYbg9Fx619TecSKSE2Y2x90r2hynYhNQsWlFB3/Bp58y++kh/bjomAGtf0YikXmLgr59g+6po+NEJCdUbDpIxaYV7fwFP/G5Bi6eMi9lSLuvy3Tpwnrr1EBwXWjt2o6PE5GcaG+x0dpo0rZ23FuT3s385sS9OWH/PumvaFl7N1/L0SZtIpJdWvVZ2tbKvTUXPfTGeoWmoaayY4UGgmtAZWWpsUxTqts7TkTyioqNtC3DL3gvKyMx+kb+8vy6LuOpXb6kYdIZnZsl1t4p1bopU6Qg6ZpNSNds2pA0G+3E067jpV47pzzdsOcyzRITKUGaINBBKjZt+3bVGna78LGU2CsXDaVHWXfNEhMpUZogIFm158XT+GrF6ubjHmUb8cpFw9YN0NL9ItIKFRtp1bLlK9nnsukpsXeuGEH3bmmX+zRLTERaoWIjLUqfZXb8vr353Un7ZB6spftFpBUqNrKeRZ8t57BrZ6bE3rtqZOb1zJo0TQLowJI2IlI6VGwkRXo386ujduWMI3ZuYXQaLd0vIi1QsREA5i76nONvei4lpi0ARCRbVGxkvW7mjyfvyzF7bx9TNiJSjFRsStijr3/EL+rmpsTUzYhIFFRsSlR6NzP55wdRkdg6pmxEpNip2JSYPz39Lld1ZFMzEZEsULEpEe5Ov/GPpMRmnnc4/XpuFlNGIlJKVGxKQNX9rzHppcUpMXUzIpJLKjZFbNWatfSvfjQlNvfCoWy9WfeYMhKRUqViU6SOv+lZ5i5a1nzcr+dmzDzv8PgSEpGSpmJTZL76dhV7XvJ4Smz+5cPZZKOuMWUkIqKdOqNTVxfs8dKZXSs76exJL6cUmuEDv0dDTaUKjYjETp1NFOrqUldAfv/94BgiWTts6dcr2e/y1G0AFl45ki5dWlk4U0Qkh7RTZyirO3XmcNfKUTc+y6uL112buf7kfTlWS82ISI5op8445WDXysVLl3PoNanbAGg6s4jkKxWbKES8a2X6Fs1//dkBHLxTz6y8t4hIFDRBIAoTJgS7VCbLwq6Vb3zwBYmqqSmFpqGmUoVGRPKeik0UxoyB2trgGo1Z8LW2doMmBySqpnL0H//RfPzYoxNouOaYnM10ExHZEDqNFpUs7Vr59DufMPaOF5uPt+22llnXjc7ZTDcRkWxQsclj6dsAPFc1mO332X1doWmyfDlUV6vYiEjeUrHJQ5PnNHLefa82Hx+449ZMGndQcJCDmW4iItmmYpNH1q51drwgdRuAVy8expabbrQuEPFMNxGRKGiCQJ644ckFKYVm9Pd3oKGmMrXQQGQz3UREoqTOJmbfrlrDbhc+lhJrdeHMpusy1dXBqbPy8qDQ6HqNiOQxFZsYpW9qdt6wXfjl4P5tvzBLM91ERHIlttNoZtbVzF42s4fD435mNsvMFpjZPWbWPYxvHB7Xh88nkt5jfBh/28yOSooPD2P1ZlaV6++tLV98s4pE1dSUQrPwypHtKzQiIgUozms2ZwNvJR1fDVzn7v2Bz4HTw/jpwOfuvjNwXTgOMxsAjAYGAsOBm8IC1hW4ERgBDABODsfmhZNrX2DvS9dtA3DtCXvRUFOZnRWaY9jWQESkPWIpNmbWB6gEbguPDRgMTA6HTASOCx+PCo8Jnx8Sjh8FTHL3Fe7+HlAPDAr/1Lv7QndfCUwKx2ZfB365f7jsGxJVU3l+4WfNsYaaSk6s2CF7uYwbF8xUc193s6cKjojkgbiu2fwe+G9g8/D4u8Ayd29a9KsR6B0+7g0sBnD31Wb2RTi+N/BC0nsmv2ZxWvyATEmY2ThgHEB5R6cOd2DPmgOufIKPv1zRfDzxp4P44S69OvZ5bamu1s2eIpK3ct7ZmNnRwBJ3n5MczjDU23iuo/H1g+617l7h7hW9enXwl39rv9xD8//vSxJVU1MKTUNNZfYLDehmTxHJa3F0NocAx5rZSGATYAuCTqeHmXULu5s+wIfh+EZgB6DRzLoBWwJLk+JNkl/TUjx72vjlXvPofG55+t3m8MNn/oA9em+Z9TSa6WZPEcljOe9s3H28u/dx9wTBBf4n3X0MMBM4IRw2FngofDwlPCZ8/kkPthedAowOZ6v1A/oDLwIvAf3D2W3dw8+YkvVvpIVf4ot334dE1dTmQjNw+y1oqKmMttCAbvYUkbyWT/fZnA9MMrMrgJeB28P47cBdZlZP0NGMBnD3eWZ2L/AmsBo4w93XAJjZL4FpQFfgDnefl/VsJ0xIvWYDnHfMuUwecETz8asXDWPLso0yvTr7dLOniOQxC5oEqaio8NmzZ3fsRXV1UF3N/OXG8J/e0ByIFCIAAAAF0klEQVS+6vg9OXmQTl+JSPEzsznuXtHWuHzqbArPmDEsOeZHDL9yBgAbd+vCKxcNY9PuLSw1IyJSolRsNtCm3bvyw116Mfr7OzBiz+3iTkdEJC+p2GygzTfZiIk/HRR3GiIieU1bDIiISORUbEREJHIqNiIiEjkVGxERiZyKjYiIRE7FRkREIqdiIyIikVOxERGRyGlttJCZfQJkWKM/r/UEPo07iRiV+vcP+hmU+vcP8f8M+rp7m5t0qdgUMDOb3Z4F8IpVqX//oJ9BqX//UDg/A51GExGRyKnYiIhI5FRsCltt3AnErNS/f9DPoNS/fyiQn4Gu2YiISOTU2YiISORUbAqMme1gZjPN7C0zm2dmZ8edUxzMrKuZvWxmD8edSxzMrIeZTTaz+eH/CwfFnVOumdk54d+BN8zsbjPbJO6comZmd5jZEjN7Iym2tZlNN7MF4det4syxJSo2hWc1cK677w4cCJxhZgNizikOZwNvxZ1EjP4APObuuwF7U2I/CzPrDZwFVLj7HkBXYHS8WeXEncDwtFgVMMPd+wMzwuO8o2JTYNz9I3efGz7+iuCXTO94s8otM+sDVAK3xZ1LHMxsC+Aw4HYAd1/p7svizSoW3YBNzawbUAZ8GHM+kXP3Z4ClaeFRwMTw8UTguJwm1U4qNgXMzBLAvsCseDPJud8D/w2sjTuRmOwIfAL8OTyVeJuZbRZ3Urnk7h8AvwEWAR8BX7j74/FmFZtt3f0jCP4xCmwTcz4ZqdgUKDP7DnA/8J/u/mXc+eSKmR0NLHH3OXHnEqNuwH7Aze6+L/A1eXrqJCrhdYlRQD9ge2AzM/txvFlJa1RsCpCZbURQaOrc/YG488mxQ4BjzawBmAQMNrP/iTelnGsEGt29qaOdTFB8SsmRwHvu/om7rwIeAA6OOae4fGxm2wGEX5fEnE9GKjYFxsyM4Fz9W+7+u7jzyTV3H+/ufdw9QXBB+El3L6l/0br7/wGLzWzXMDQEeDPGlOKwCDjQzMrCvxNDKLFJEkmmAGPDx2OBh2LMpUXd4k5AOuwQ4CfA62b2Shi7wN0fiTEnyb0zgToz6w4sBE6LOZ+ccvdZZjYZmEswQ/NlCuRO+g1hZncDhwM9zawRuBioAe41s9MJivCJ8WXYMq0gICIikdNpNBERiZyKjYiIRE7FRkREIqdiIyIikVOxERGRyKnYiHSSma0xs1eS/kR6F7+ZHRv1Z4hERVOfRTrJzP7p7t/J0Wd1c/fVufgskSiosxHJIjPb0szebrq7P9xn5Wfh43+a2W/NbK6ZzTCzXmF8JzN7zMzmmNnfzWy3MH6nmf3OzGYCV5vZv5nZDeFzvczsfjN7KfxzSBi/JNzz5CkzW2hmZyXldqqZvWZmr5rZXa29j0i2qdiIdN6maafRTnL3L4BfAnea2WhgK3e/NRy/GTDX3fcDnia4+xuCO9/PdPf9gfOAm5I+YxfgSHc/N+2z/wBc5+7fB35E6nYLuwFHAYOAi81sIzMbCFQDg919b4L9gNp6H5Gs0XI1Ip33jbvvkx509+lmdiJwI8HGZk3WAveEj/8HeCBcvftg4L5giS8ANk56zX3uvibDZx8JDEh6zRZmtnn4eKq7rwBWmNkSYFtgMDDZ3T8Nc1za2vuEeyWJZI2KjUiWmVkXYHfgG2BrglWaM3GCswvLMhWt0NctxLsAB7n7N2mfDbAiKbSG4O+5hZ/XrvcRyTadRhPJvnMIViA+Gbgj3BICgr9vJ4SPTwH+Ee5F9F7YCWGBvdPfMIPHCU7XEb6upWLVZAbwr2b23XD81p18H5FOUbER6bz0azY1ZrYL8O/Aue7+d+AZ4Nfh+K+BgWY2h+C01mVhfAxwupm9Cswj2BSsLWcBFeEF/zeBn7c22N3nAROAp8PPadqeokPvI9JZmvoskiO5nCotkm/U2YiISOTU2YiISOTU2YiISORUbEREJHIqNiIiEjkVGxERiZyKjYiIRE7FRkREIvf/s4DMJ7J7cGgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train,Y_train,color = 'red')\n",
    "plt.plot(X_train,regressor.predict(X_train))\n",
    "plt.xlabel('Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualising the Test Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test,Y_test,color = 'red')\n",
    "plt.plot(X_train,regressor.predict(X_train))\n",
    "plt.xlabel('Experience')\n",
    "plt.ylabel('Salary')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}