other_algos.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['trainpredict', 'testpredict']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import os\n",
    "print(os.listdir(\"../input\"))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "8405c12bf6745054bd75d9c9f19eb86c9139e477"
   },
   "outputs": [],
   "source": [
    "X = pd.read_csv(\"../input/trainpredict/train.csv\")\n",
    "X_1 = pd.read_csv(\"../input/testpredict/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "e7e1ab41c9c257d192650c280ba55b006f56a647"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID                  0\n",
       "datetime            0\n",
       "siteid         370776\n",
       "offerid             0\n",
       "category            0\n",
       "merchant            0\n",
       "countrycode         0\n",
       "browserid      221906\n",
       "devid          704619\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = X\n",
    "test = X_1\n",
    "train.isnull().sum(axis=0)\n",
    "test.isnull().sum(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "894733397692f969117d157ba88462cc0db1cd95"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>datetime</th>\n",
       "      <th>siteid</th>\n",
       "      <th>offerid</th>\n",
       "      <th>category</th>\n",
       "      <th>merchant</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>browserid</th>\n",
       "      <th>devid</th>\n",
       "      <th>click</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IDsrk7SoW</td>\n",
       "      <td>2017-01-14 09:42:09</td>\n",
       "      <td>4709696.0</td>\n",
       "      <td>887235</td>\n",
       "      <td>17714</td>\n",
       "      <td>20301556</td>\n",
       "      <td>e</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>xx</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IDmMSxHur</td>\n",
       "      <td>2017-01-18 17:50:53</td>\n",
       "      <td>5189467.0</td>\n",
       "      <td>178235</td>\n",
       "      <td>21407</td>\n",
       "      <td>9434818</td>\n",
       "      <td>b</td>\n",
       "      <td>Mozilla Firefox</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IDVLNN0Ut</td>\n",
       "      <td>2017-01-11 12:46:49</td>\n",
       "      <td>98480.0</td>\n",
       "      <td>518539</td>\n",
       "      <td>25085</td>\n",
       "      <td>2050923</td>\n",
       "      <td>a</td>\n",
       "      <td>Edge</td>\n",
       "      <td>xx</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ID32T6wwQ</td>\n",
       "      <td>2017-01-17 10:18:43</td>\n",
       "      <td>8896401.0</td>\n",
       "      <td>390352</td>\n",
       "      <td>40339</td>\n",
       "      <td>72089744</td>\n",
       "      <td>c</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IDqUShzMg</td>\n",
       "      <td>2017-01-14 16:02:33</td>\n",
       "      <td>5635120.0</td>\n",
       "      <td>472937</td>\n",
       "      <td>12052</td>\n",
       "      <td>39507200</td>\n",
       "      <td>d</td>\n",
       "      <td>Mozilla Firefox</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID             datetime  ...      devid  click\n",
       "0  IDsrk7SoW  2017-01-14 09:42:09  ...         xx      0\n",
       "1  IDmMSxHur  2017-01-18 17:50:53  ...    Desktop      0\n",
       "2  IDVLNN0Ut  2017-01-11 12:46:49  ...         xx      0\n",
       "3  ID32T6wwQ  2017-01-17 10:18:43  ...     Mobile      0\n",
       "4  IDqUShzMg  2017-01-14 16:02:33  ...    Desktop      0\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = train.replace('NaN', np.NAN)\n",
    "test = test.replace('NaN',np.NAN)\n",
    "train['siteid'].fillna(-111, inplace = True)\n",
    "test['siteid'].fillna(-111, inplace = True)\n",
    "train['browserid'].fillna(\"x\", inplace = True)\n",
    "test['browserid'].fillna(\"x\", inplace = True)\n",
    "train['devid'].fillna(\"xx\", inplace = True)\n",
    "test['devid'].fillna(\"xx\", inplace = True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "569dde48060f0901d214f499cfae76aba701684a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>siteid</th>\n",
       "      <th>offerid</th>\n",
       "      <th>category</th>\n",
       "      <th>merchant</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>browserid</th>\n",
       "      <th>devid</th>\n",
       "      <th>click</th>\n",
       "      <th>day</th>\n",
       "      <th>hour1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4709696.0</td>\n",
       "      <td>887235</td>\n",
       "      <td>17714</td>\n",
       "      <td>20301556</td>\n",
       "      <td>e</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>xx</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5189467.0</td>\n",
       "      <td>178235</td>\n",
       "      <td>21407</td>\n",
       "      <td>9434818</td>\n",
       "      <td>b</td>\n",
       "      <td>Mozilla Firefox</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>98480.0</td>\n",
       "      <td>518539</td>\n",
       "      <td>25085</td>\n",
       "      <td>2050923</td>\n",
       "      <td>a</td>\n",
       "      <td>Edge</td>\n",
       "      <td>xx</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8896401.0</td>\n",
       "      <td>390352</td>\n",
       "      <td>40339</td>\n",
       "      <td>72089744</td>\n",
       "      <td>c</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5635120.0</td>\n",
       "      <td>472937</td>\n",
       "      <td>12052</td>\n",
       "      <td>39507200</td>\n",
       "      <td>d</td>\n",
       "      <td>Mozilla Firefox</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      siteid  offerid  category  merchant  ...      devid click day  hour1\n",
       "0  4709696.0   887235     17714  20301556  ...         xx     0   5      1\n",
       "1  5189467.0   178235     21407   9434818  ...    Desktop     0   2      1\n",
       "2    98480.0   518539     25085   2050923  ...         xx     0   2      0\n",
       "3  8896401.0   390352     40339  72089744  ...     Mobile     0   1      2\n",
       "4  5635120.0   472937     12052  39507200  ...    Desktop     0   5      0\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['datetime'] = pd.to_datetime(train['datetime'])\n",
    "train['day'] = train['datetime'].dt.weekday\n",
    "train['hour'] = train['datetime'].dt.hour\n",
    "\n",
    "test['datetime'] = pd.to_datetime(test['datetime'])\n",
    "test['day'] = test['datetime'].dt.weekday\n",
    "test['hour'] = test['datetime'].dt.hour\n",
    "\n",
    "train['hour1'] = train['hour']%4\n",
    "test['hour1'] = test['hour']%4\n",
    "\n",
    "train = train.drop(columns=['ID', 'datetime', 'hour',]) #'siteid' ,]) #,'offerid'])\n",
    "test = test.drop(columns=['datetime', 'hour', ])#'siteid', ]) #'offerid'])\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "8e94059a4ab53c88859769c721bbea904561c6a5"
   },
   "outputs": [],
   "source": [
    "\n",
    "train = train.astype('object')\n",
    "test = test.astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "4bc02b94aae7bb8c0662a530a03af601c8e3e33d"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "#cols = ['siteid','offerid','category','merchant', 'countrycode','browserid','devid']\n",
    "for col in test.columns[1:]:\n",
    "    lbl = LabelEncoder()\n",
    "    lbl.fit(list(train[col].values) + list(test[col].values))\n",
    "    train[col] = lbl.transform(list(train[col].values))\n",
    "    test[col] = lbl.transform(list(test[col].values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "f1fb1ee672d4d570bb3454d73415eb3d544d1c86"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>siteid</th>\n",
       "      <th>offerid</th>\n",
       "      <th>category</th>\n",
       "      <th>merchant</th>\n",
       "      <th>countrycode</th>\n",
       "      <th>browserid</th>\n",
       "      <th>devid</th>\n",
       "      <th>click</th>\n",
       "      <th>day</th>\n",
       "      <th>hour1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>128865</td>\n",
       "      <td>784773</td>\n",
       "      <td>48</td>\n",
       "      <td>127</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>142053</td>\n",
       "      <td>157563</td>\n",
       "      <td>59</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2618</td>\n",
       "      <td>458279</td>\n",
       "      <td>69</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>243406</td>\n",
       "      <td>345067</td>\n",
       "      <td>117</td>\n",
       "      <td>507</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>154278</td>\n",
       "      <td>417948</td>\n",
       "      <td>36</td>\n",
       "      <td>276</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   siteid  offerid  category  merchant  ...   devid  click  day hour1\n",
       "0  128865   784773        48       127  ...       3      0    5     1\n",
       "1  142053   157563        59        65  ...       0      0    2     1\n",
       "2    2618   458279        69        15  ...       3      0    2     0\n",
       "3  243406   345067       117       507  ...       1      0    1     2\n",
       "4  154278   417948        36       276  ...       0      0    5     0\n",
       "\n",
       "[5 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "adf3b7c9f3a3160077b9660f0d1e1455a434f615"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "Y_t = train['click'].astype('float')\n",
    "X_t = train.drop(columns=['click'])\n",
    "#Y_t = Y_t.as_type('float')\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_t, Y_t, test_size=0.1, random_state=69)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Naive Bayesian Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "a447aff860092dcae34198f47958d1d09e11cdb1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[600257 569672]\n",
      " [ 22222  21630]]\n",
      "0.5031606644202486\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "model = MultinomialNB().fit(X_train, y_train)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "predicted = model.predict(X_test)\n",
    "print(confusion_matrix(y_test, np.round(predicted)))\n",
    "print(roc_auc_score(y_test, np.round(predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "0722345e63871229a9b49d90f0784cab2008ce5a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1169929       0]\n",
      " [  43852       0]]\n",
      "0.5\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "model = GaussianNB().fit(X_train, y_train)\n",
    "predicted = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "\n",
    "print(confusion_matrix(y_test, np.round(predicted)))\n",
    "print(roc_auc_score(y_test, np.round(predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "5a5d9231ba5cfe4a0e3c90845e5b09e6d08ddf4d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1169929       0]\n",
      " [  43838      14]]\n",
      "0.5001596278390952\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import BernoulliNB\n",
    "\n",
    "model = BernoulliNB().fit(X_train, y_train)\n",
    "predicted = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "\n",
    "print(confusion_matrix(y_test, np.round(predicted)))\n",
    "print(roc_auc_score(y_test, np.round(predicted)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Decision Tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "80989350e7a4b65de24affe800a1180c37fedbc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1147782   22147]\n",
      " [  20119   23733]]\n",
      "0.7611382889391259\n"
     ]
    }
   ],
   "source": [
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf_gini = DecisionTreeClassifier()\n",
    "clf_gini.fit(X_train, y_train)\n",
    "predicted = clf_gini.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "\n",
    "print(confusion_matrix(y_test, np.round(predicted)))\n",
    "print(roc_auc_score(y_test, np.round(predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "4210f6058c07469f770cd537e89131753087db7c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1148461   21468]\n",
      " [  20075   23777]]\n",
      "0.7619301649833284\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf_ent = DecisionTreeClassifier(criterion = \"entropy\")\n",
    "clf_ent.fit(X_train, y_train)\n",
    "predicted = clf_ent.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "\n",
    "print(confusion_matrix(y_test, np.round(predicted)))\n",
    "print(roc_auc_score(y_test, np.round(predicted)))\n",
    "\n",
    "preds = clf_ent.predict(test[test.columns[1:]])\n",
    "sub2 = pd.DataFrame({'ID':test['ID'], 'click':preds})\n",
    "sub2.to_csv('submission_dt.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Random Forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "f2f0e7e1faa55e98571b0a42c65eb245f75aa063"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1158744   11185]\n",
      " [  17116   26736]]\n",
      "0.8000633601069796\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 50, max_depth = 12, random_state = 69)\n",
    "rf.fit(X_train, y_train)\n",
    "predicted = rf.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score\n",
    "\n",
    "print(confusion_matrix(y_test, np.round(predicted)))\n",
    "print(roc_auc_score(y_test, np.round(predicted)))\n",
    "\n",
    "preds = rf.predict(test[test.columns[1:]])\n",
    "sub1 = pd.DataFrame({'ID':test['ID'], 'click':preds})\n",
    "sub1.to_csv('submission_random.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "95065ce05cc5890688cf3df5b717a9a26e890a74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__notebook_source__.ipynb  submission_dt.csv  submission_random.csv\r\n"
     ]
    }
   ],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "_uuid": "83abb5923c5ce858d70a7fff71297181d013783d"
   },
   "outputs": [],
   "source": []
  }
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