Untitled1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Quandl in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (3.5.0)\n",
      "Requirement already satisfied: python-dateutil in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from Quandl) (2.8.1)\n",
      "Requirement already satisfied: six in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from Quandl) (1.14.0)\n",
      "Requirement already satisfied: numpy>=1.8 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from Quandl) (1.18.1)\n",
      "Requirement already satisfied: pandas>=0.14 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from Quandl) (1.0.1)\n",
      "Requirement already satisfied: requests>=2.7.0 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from Quandl) (2.22.0)\n",
      "Requirement already satisfied: inflection>=0.3.1 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from Quandl) (0.5.0)\n",
      "Requirement already satisfied: more-itertools in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from Quandl) (8.2.0)\n",
      "Requirement already satisfied: pytz>=2017.2 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from pandas>=0.14->Quandl) (2019.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests>=2.7.0->Quandl) (2019.11.28)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests>=2.7.0->Quandl) (2.8)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests>=2.7.0->Quandl) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\lenovo\\anaconda3\\lib\\site-packages (from requests>=2.7.0->Quandl) (1.25.8)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install Quandl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import quandl,math, datetime\n",
    "import numpy as np\n",
    "from sklearn import preprocessing, svm\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "import pickle\n",
    "\n",
    "\n",
    "style.use('ggplot')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = quandl.get(\"wiki/googl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>100.01</td>\n",
       "      <td>104.06</td>\n",
       "      <td>95.96</td>\n",
       "      <td>100.335</td>\n",
       "      <td>44659000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.159839</td>\n",
       "      <td>52.191109</td>\n",
       "      <td>48.128568</td>\n",
       "      <td>50.322842</td>\n",
       "      <td>44659000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>101.01</td>\n",
       "      <td>109.08</td>\n",
       "      <td>100.50</td>\n",
       "      <td>108.310</td>\n",
       "      <td>22834300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.661387</td>\n",
       "      <td>54.708881</td>\n",
       "      <td>50.405597</td>\n",
       "      <td>54.322689</td>\n",
       "      <td>22834300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>110.76</td>\n",
       "      <td>113.48</td>\n",
       "      <td>109.05</td>\n",
       "      <td>109.400</td>\n",
       "      <td>18256100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.551482</td>\n",
       "      <td>56.915693</td>\n",
       "      <td>54.693835</td>\n",
       "      <td>54.869377</td>\n",
       "      <td>18256100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>111.24</td>\n",
       "      <td>111.60</td>\n",
       "      <td>103.57</td>\n",
       "      <td>104.870</td>\n",
       "      <td>15247300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>55.792225</td>\n",
       "      <td>55.972783</td>\n",
       "      <td>51.945350</td>\n",
       "      <td>52.597363</td>\n",
       "      <td>15247300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>104.76</td>\n",
       "      <td>108.00</td>\n",
       "      <td>103.88</td>\n",
       "      <td>106.000</td>\n",
       "      <td>9188600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>52.542193</td>\n",
       "      <td>54.167209</td>\n",
       "      <td>52.100830</td>\n",
       "      <td>53.164113</td>\n",
       "      <td>9188600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low    Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                   \n",
       "2004-08-19  100.01  104.06   95.96  100.335  44659000.0          0.0   \n",
       "2004-08-20  101.01  109.08  100.50  108.310  22834300.0          0.0   \n",
       "2004-08-23  110.76  113.48  109.05  109.400  18256100.0          0.0   \n",
       "2004-08-24  111.24  111.60  103.57  104.870  15247300.0          0.0   \n",
       "2004-08-25  104.76  108.00  103.88  106.000   9188600.0          0.0   \n",
       "\n",
       "            Split Ratio  Adj. Open  Adj. High   Adj. Low  Adj. Close  \\\n",
       "Date                                                                   \n",
       "2004-08-19          1.0  50.159839  52.191109  48.128568   50.322842   \n",
       "2004-08-20          1.0  50.661387  54.708881  50.405597   54.322689   \n",
       "2004-08-23          1.0  55.551482  56.915693  54.693835   54.869377   \n",
       "2004-08-24          1.0  55.792225  55.972783  51.945350   52.597363   \n",
       "2004-08-25          1.0  52.542193  54.167209  52.100830   53.164113   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
       "2004-08-19   44659000.0  \n",
       "2004-08-20   22834300.0  \n",
       "2004-08-23   18256100.0  \n",
       "2004-08-24   15247300.0  \n",
       "2004-08-25    9188600.0  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['Adj. Open','Adj. High','Adj. Low','Adj. Close','Adj. Volume',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>50.159839</td>\n",
       "      <td>52.191109</td>\n",
       "      <td>48.128568</td>\n",
       "      <td>50.322842</td>\n",
       "      <td>44659000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>50.661387</td>\n",
       "      <td>54.708881</td>\n",
       "      <td>50.405597</td>\n",
       "      <td>54.322689</td>\n",
       "      <td>22834300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>55.551482</td>\n",
       "      <td>56.915693</td>\n",
       "      <td>54.693835</td>\n",
       "      <td>54.869377</td>\n",
       "      <td>18256100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>55.792225</td>\n",
       "      <td>55.972783</td>\n",
       "      <td>51.945350</td>\n",
       "      <td>52.597363</td>\n",
       "      <td>15247300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>52.542193</td>\n",
       "      <td>54.167209</td>\n",
       "      <td>52.100830</td>\n",
       "      <td>53.164113</td>\n",
       "      <td>9188600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "            Adj. Open  Adj. High   Adj. Low  Adj. Close  Adj. Volume\n",
       "Date                                                                \n",
       "2004-08-19  50.159839  52.191109  48.128568   50.322842   44659000.0\n",
       "2004-08-20  50.661387  54.708881  50.405597   54.322689   22834300.0\n",
       "2004-08-23  55.551482  56.915693  54.693835   54.869377   18256100.0\n",
       "2004-08-24  55.792225  55.972783  51.945350   52.597363   15247300.0\n",
       "2004-08-25  52.542193  54.167209  52.100830   53.164113    9188600.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['HL_PCT'] = (df['Adj. High'] - df['Adj. Close'])/ df['Adj. Close'] * 100.0 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PCT_change'] = (df['Adj. High'] - df['Adj. Open']) / df['Adj. Open'] * 100.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df =df[['Adj. Close','HL_PCT','PCT_change','Adj. Volume',]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>HL_PCT</th>\n",
       "      <th>PCT_change</th>\n",
       "      <th>Adj. Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>50.322842</td>\n",
       "      <td>3.712563</td>\n",
       "      <td>4.049595</td>\n",
       "      <td>44659000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>54.322689</td>\n",
       "      <td>0.710922</td>\n",
       "      <td>7.989308</td>\n",
       "      <td>22834300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>54.869377</td>\n",
       "      <td>3.729433</td>\n",
       "      <td>2.455760</td>\n",
       "      <td>18256100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>52.597363</td>\n",
       "      <td>6.417469</td>\n",
       "      <td>0.323625</td>\n",
       "      <td>15247300.0</td>\n",
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       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>53.164113</td>\n",
       "      <td>1.886792</td>\n",
       "      <td>3.092784</td>\n",
       "      <td>9188600.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj. Close    HL_PCT  PCT_change  Adj. Volume\n",
       "Date                                                     \n",
       "2004-08-19   50.322842  3.712563    4.049595   44659000.0\n",
       "2004-08-20   54.322689  0.710922    7.989308   22834300.0\n",
       "2004-08-23   54.869377  3.729433    2.455760   18256100.0\n",
       "2004-08-24   52.597363  6.417469    0.323625   15247300.0\n",
       "2004-08-25   53.164113  1.886792    3.092784    9188600.0"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_col = 'Adj. Close'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.fillna(-99999, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35\n"
     ]
    }
   ],
   "source": [
    "forecast_out = int(math.ceil(0.01 * len(df)))\n",
    "print(forecast_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = df[forecast_col].shift(-forecast_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.array(df.drop(['label'],1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = preprocessing.scale(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Lately = X[-forecast_out:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X[:-forecast_out]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['label'] = df[forecast_col].shift(-forecast_out)\n",
    "\n",
    "df.dropna(inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array(df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3389 3389\n"
     ]
    }
   ],
   "source": [
    "y = np.array(df['label'])\n",
    "\n",
    "print(len(X),len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clf = LinearRegression(n_jobs = 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=2, normalize=False)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "#with open('linearregression.pickle','wb') as f:\n",
    "    #pickle.dump(clf,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_in = open('linearregression.pickle','rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = pickle.load(pickle_in)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy = clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9782162502087837\n"
     ]
    }
   ],
   "source": [
    "print(accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "forecast_set = clf.predict(X_Lately)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1105.05757169 1072.96015304 1020.65306767 1062.25822584 1073.47646713\n",
      " 1073.85208851 1092.95951789 1111.58238918 1115.1993882  1123.74156702\n",
      " 1133.35858456 1129.57213337 1148.97858089 1164.70782032 1136.16055105\n",
      " 1122.60758309 1088.5126469  1104.53684244 1115.28422934 1121.14789705\n",
      " 1136.03338959 1150.20817294 1182.03545192 1186.23455394 1158.26888598\n",
      " 1169.16905803 1170.98175577 1153.3087688  1118.62099512 1115.18238795\n",
      " 1113.59518914 1070.41568    1043.63168899 1073.17541262 1022.01260266] 0.9782162502087837 35\n"
     ]
    }
   ],
   "source": [
    "print(forecast_set, accuracy, forecast_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['forecast'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_date = df.iloc[-1].name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_unix = last_date.timestamp()\n",
    "one_day = 86400\n",
    "next_unix = last_unix + one_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in forecast_set:\n",
    "    next_date = datetime.datetime.fromtimestamp(next_unix)\n",
    "    next_unix += one_day\n",
    "    df.loc[next_date] = [np.nan for _ in range(len(df.columns) - 1)] + [i]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Adj. Close'].plot()\n",
    "df['forecast'].plot()\n",
    "plt.legend(loc = 4)\n",
    "plt.xlabel = 'Date'\n",
    "plt.ylabel = 'Price'\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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}