Lily_Data_144_Final_Proj_v3_(Model_Improvements).ipynb

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    "<a href=\"https://colab.research.google.com/github/PoojaDoctor/Data144-FinalProject/blob/master/Lily_Data_144_Final_Proj_v3_(Model_Improvements).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    "### **Data 144 Final Project - Lily Li - Version 3**"
   ]
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    "Recommend Methods:\n",
    "1. Random forest\n",
    "2. Decision tree\n",
    "3. Neural network\n",
    "4. Linear Regression\n",
    "\n",
    "Try different models and throw the data into them!"
   ]
  },
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   "source": [
    "Use Mean square error;\n",
    "Mean absolute error;\n",
    "Train the data of the log of views\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "id": "1_SXrZu3FxyN"
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   "source": [
    "### **Ted Main Dataset**"
   ]
  },
  {
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    "colab_type": "code",
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   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
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   "source": [
    "# Upload the \"Ted Main\" dataset\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/PoojaDoctor/Data144-FinalProject/master/datasets/ted_main.csv'\n",
    "df_ted = pd.read_csv(url)\n",
    "# Dataset is now stored in a Pandas Dataframe"
   ]
  },
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       "   comments  ...     views\n",
       "0      4553  ...  47227110\n",
       "1       265  ...   3200520\n",
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       "3       200  ...   1697550\n",
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       "      <td>2550.000000</td>\n",
       "      <td>2550.000000</td>\n",
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       "      <td>2.550000e+03</td>\n",
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       "      <td>826.510196</td>\n",
       "      <td>1.321928e+09</td>\n",
       "      <td>27.326275</td>\n",
       "      <td>1.028235</td>\n",
       "      <td>1.343525e+09</td>\n",
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       "      <td>7.464960e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.151367e+09</td>\n",
       "      <td>5.044300e+04</td>\n",
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       "      <th>25%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>1.257466e+09</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.268463e+09</td>\n",
       "      <td>7.557928e+05</td>\n",
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       "      <th>50%</th>\n",
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       "      <td>848.000000</td>\n",
       "      <td>1.333238e+09</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.340935e+09</td>\n",
       "      <td>1.124524e+06</td>\n",
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       "      <td>221.750000</td>\n",
       "      <td>1046.750000</td>\n",
       "      <td>1.412964e+09</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.423432e+09</td>\n",
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       "      <td>5256.000000</td>\n",
       "      <td>1.503792e+09</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.506092e+09</td>\n",
       "      <td>4.722711e+07</td>\n",
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       "          comments     duration  ...  published_date         views\n",
       "count  2550.000000  2550.000000  ...    2.550000e+03  2.550000e+03\n",
       "mean    191.562353   826.510196  ...    1.343525e+09  1.698297e+06\n",
       "std     282.315223   374.009138  ...    9.464009e+07  2.498479e+06\n",
       "min       2.000000   135.000000  ...    1.151367e+09  5.044300e+04\n",
       "25%      63.000000   577.000000  ...    1.268463e+09  7.557928e+05\n",
       "50%     118.000000   848.000000  ...    1.340935e+09  1.124524e+06\n",
       "75%     221.750000  1046.750000  ...    1.423432e+09  1.700760e+06\n",
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       "         views  comments\n",
       "0     47227110      4553\n",
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       "...        ...       ...\n",
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5P5ObYJw1aXTR/oz9cYNoSCduy0APyu5eCQew33N4mUeCKLVSj2Y5v5TFLzOA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\nFsYYY0r5F8IoHLYQON9eAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Because comments and views have strong correlation:\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "data = df_ted[['views','comments']]  # load data set\n",
    "X = data.iloc[:, 0].values.reshape(-1, 1)  # values converts it into a numpy array\n",
    "Y = data.iloc[:, 1].values.reshape(-1, 1)  # -1 means that calculate the dimension of rows, but have 1 column\n",
    "linear_regressor = LinearRegression()  # create object for the class\n",
    "linear_regressor.fit(X, Y)  # perform linear regression\n",
    "Y_pred = linear_regressor.predict(X)  # make predictions\n",
    "data.plot.scatter(x='views', y='comments', xlim=(1,7.722711e+06), ylim=(0,1004))\n",
    "plt.plot(X, Y_pred, color='red')\n",
    "plt.show()\n",
    "\n",
    "#var = 'views'\n",
    "#data = pd.concat([df_ted['comments'], df_ted[var]], axis=1)\n",
    "#data.plot.scatter(x=var, y='comments', xlim=(1,8.722711e+06), ylim=(0,1004));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "E2ETi4slMvsP"
   },
   "outputs": [],
   "source": [
    "# How to use number of comments to predict views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FwKQiCdrKKyU"
   },
   "outputs": [],
   "source": [
    "# Now, the next goal is to use the number of comments to predict views."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "RFOrZ-RwUH3q",
    "outputId": "24531052-bac2-4d70-a7ca-ee064c042aab"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2550, 17)"
      ]
     },
     "execution_count": 21,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "4YxKkrjoUNOK",
    "outputId": "69769015-eb33-4e69-d45f-890f2907c791"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['comments', 'description', 'duration', 'event', 'film_date',\n",
       "       'languages', 'main_speaker', 'name', 'num_speaker', 'published_date',\n",
       "       'ratings', 'related_talks', 'speaker_occupation', 'tags', 'title',\n",
       "       'url', 'views'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 297
    },
    "colab_type": "code",
    "id": "HlyAN6T3URBd",
    "outputId": "8d63c2ff-8d9c-4539-bb97-439ffe8d2295"
   },
   "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>comments</th>\n",
       "      <th>duration</th>\n",
       "      <th>film_date</th>\n",
       "      <th>languages</th>\n",
       "      <th>num_speaker</th>\n",
       "      <th>published_date</th>\n",
       "      <th>views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2550.000000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>2.550000e+03</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>2550.000000</td>\n",
       "      <td>2.550000e+03</td>\n",
       "      <td>2.550000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>191.562353</td>\n",
       "      <td>826.510196</td>\n",
       "      <td>1.321928e+09</td>\n",
       "      <td>27.326275</td>\n",
       "      <td>1.028235</td>\n",
       "      <td>1.343525e+09</td>\n",
       "      <td>1.698297e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>282.315223</td>\n",
       "      <td>374.009138</td>\n",
       "      <td>1.197391e+08</td>\n",
       "      <td>9.563452</td>\n",
       "      <td>0.207705</td>\n",
       "      <td>9.464009e+07</td>\n",
       "      <td>2.498479e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>7.464960e+07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.151367e+09</td>\n",
       "      <td>5.044300e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>63.000000</td>\n",
       "      <td>577.000000</td>\n",
       "      <td>1.257466e+09</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.268463e+09</td>\n",
       "      <td>7.557928e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>118.000000</td>\n",
       "      <td>848.000000</td>\n",
       "      <td>1.333238e+09</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.340935e+09</td>\n",
       "      <td>1.124524e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>221.750000</td>\n",
       "      <td>1046.750000</td>\n",
       "      <td>1.412964e+09</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.423432e+09</td>\n",
       "      <td>1.700760e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>6404.000000</td>\n",
       "      <td>5256.000000</td>\n",
       "      <td>1.503792e+09</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.506092e+09</td>\n",
       "      <td>4.722711e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          comments     duration  ...  published_date         views\n",
       "count  2550.000000  2550.000000  ...    2.550000e+03  2.550000e+03\n",
       "mean    191.562353   826.510196  ...    1.343525e+09  1.698297e+06\n",
       "std     282.315223   374.009138  ...    9.464009e+07  2.498479e+06\n",
       "min       2.000000   135.000000  ...    1.151367e+09  5.044300e+04\n",
       "25%      63.000000   577.000000  ...    1.268463e+09  7.557928e+05\n",
       "50%     118.000000   848.000000  ...    1.340935e+09  1.124524e+06\n",
       "75%     221.750000  1046.750000  ...    1.423432e+09  1.700760e+06\n",
       "max    6404.000000  5256.000000  ...    1.506092e+09  4.722711e+07\n",
       "\n",
       "[8 rows x 7 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 323
    },
    "colab_type": "code",
    "id": "UPPIY0Nv6upP",
    "outputId": "8993372b-9df2-4962-80f6-b4df0cc07dec"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comments              0\n",
      "description           0\n",
      "duration              0\n",
      "event                 0\n",
      "film_date             0\n",
      "languages             0\n",
      "main_speaker          0\n",
      "name                  0\n",
      "num_speaker           0\n",
      "published_date        0\n",
      "ratings               0\n",
      "related_talks         0\n",
      "speaker_occupation    6\n",
      "tags                  0\n",
      "title                 0\n",
      "url                   0\n",
      "views                 0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Finding the null values\n",
    "print(df_ted.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 697
    },
    "colab_type": "code",
    "id": "ryDatc4U7aEJ",
    "outputId": "9bb2a532-b5b0-43fe-e683-f52e6fdd3df5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comments</th>\n",
       "      <th>description</th>\n",
       "      <th>duration</th>\n",
       "      <th>event</th>\n",
       "      <th>film_date</th>\n",
       "      <th>languages</th>\n",
       "      <th>main_speaker</th>\n",
       "      <th>name</th>\n",
       "      <th>num_speaker</th>\n",
       "      <th>published_date</th>\n",
       "      <th>ratings</th>\n",
       "      <th>related_talks</th>\n",
       "      <th>speaker_occupation</th>\n",
       "      <th>tags</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>145</td>\n",
       "      <td>After a crisis, how can we tell if water is sa...</td>\n",
       "      <td>217</td>\n",
       "      <td>TEDGlobal 2011</td>\n",
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       "      <td>Sonaar Luthra</td>\n",
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       "      <td>[{'id': 10, 'name': 'Inspiring', 'count': 73},...</td>\n",
       "      <td>[{'id': 523, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['TED Fellows', 'design', 'global development'...</td>\n",
       "      <td>Meet the Water Canary</td>\n",
       "      <td>https://www.ted.com/talks/sonaar_luthra_meet_t...</td>\n",
       "      <td>353749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1192</th>\n",
       "      <td>122</td>\n",
       "      <td>The Pirate Party fights for transparency, anon...</td>\n",
       "      <td>1097</td>\n",
       "      <td>TEDxObserver</td>\n",
       "      <td>1331424000</td>\n",
       "      <td>10</td>\n",
       "      <td>Rick Falkvinge</td>\n",
       "      <td>Rick Falkvinge: I am a pirate</td>\n",
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       "      <td>1333289675</td>\n",
       "      <td>[{'id': 8, 'name': 'Informative', 'count': 156...</td>\n",
       "      <td>[{'id': 1329, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Internet', 'TEDx', 'global issues', 'politic...</td>\n",
       "      <td>I am a pirate</td>\n",
       "      <td>https://www.ted.com/talks/rick_falkvinge_i_am_...</td>\n",
       "      <td>181010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1220</th>\n",
       "      <td>257</td>\n",
       "      <td>As you surf the Web, information is being coll...</td>\n",
       "      <td>399</td>\n",
       "      <td>TED2012</td>\n",
       "      <td>1330473600</td>\n",
       "      <td>32</td>\n",
       "      <td>Gary Kovacs</td>\n",
       "      <td>Gary Kovacs: Tracking our online trackers</td>\n",
       "      <td>1</td>\n",
       "      <td>1336057219</td>\n",
       "      <td>[{'id': 23, 'name': 'Jaw-dropping', 'count': 9...</td>\n",
       "      <td>[{'id': 1370, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['Internet', 'advertising', 'business', 'priva...</td>\n",
       "      <td>Tracking our online trackers</td>\n",
       "      <td>https://www.ted.com/talks/gary_kovacs_tracking...</td>\n",
       "      <td>2098639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>140</td>\n",
       "      <td>In this lovely talk, TED Fellow Ryan Holladay ...</td>\n",
       "      <td>389</td>\n",
       "      <td>TED@BCG San Francisco</td>\n",
       "      <td>1383091200</td>\n",
       "      <td>33</td>\n",
       "      <td>Ryan Holladay</td>\n",
       "      <td>Ryan Holladay: To hear this music you have to ...</td>\n",
       "      <td>1</td>\n",
       "      <td>1389369735</td>\n",
       "      <td>[{'id': 1, 'name': 'Beautiful', 'count': 211},...</td>\n",
       "      <td>[{'id': 1152, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['TED Fellows', 'entertainment', 'music', 'tec...</td>\n",
       "      <td>To hear this music you have to be there. Liter...</td>\n",
       "      <td>https://www.ted.com/talks/ryan_holladay_to_hea...</td>\n",
       "      <td>1284510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>48</td>\n",
       "      <td>What do you do with an outdated encyclopedia i...</td>\n",
       "      <td>366</td>\n",
       "      <td>TEDYouth 2014</td>\n",
       "      <td>1415059200</td>\n",
       "      <td>34</td>\n",
       "      <td>Brian Dettmer</td>\n",
       "      <td>Brian Dettmer: Old books reborn as art</td>\n",
       "      <td>1</td>\n",
       "      <td>1423238442</td>\n",
       "      <td>[{'id': 1, 'name': 'Beautiful', 'count': 361},...</td>\n",
       "      <td>[{'id': 610, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['TEDYouth', 'art', 'books', 'creativity']</td>\n",
       "      <td>Old books reborn as art</td>\n",
       "      <td>https://www.ted.com/talks/brian_dettmer_old_bo...</td>\n",
       "      <td>1159937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1949</th>\n",
       "      <td>70</td>\n",
       "      <td>Photographer Boniface Mwangi wanted to protest...</td>\n",
       "      <td>440</td>\n",
       "      <td>TEDGlobal 2014</td>\n",
       "      <td>1413763200</td>\n",
       "      <td>33</td>\n",
       "      <td>Boniface Mwangi</td>\n",
       "      <td>Boniface Mwangi: The day I stood up alone</td>\n",
       "      <td>1</td>\n",
       "      <td>1427989423</td>\n",
       "      <td>[{'id': 3, 'name': 'Courageous', 'count': 614}...</td>\n",
       "      <td>[{'id': 1757, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>['TED Fellows', 'activism', 'art', 'corruption...</td>\n",
       "      <td>The day I stood up alone</td>\n",
       "      <td>https://www.ted.com/talks/boniface_mwangi_boni...</td>\n",
       "      <td>1342431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      comments  ...    views\n",
       "1113       145  ...   353749\n",
       "1192       122  ...   181010\n",
       "1220       257  ...  2098639\n",
       "1656       140  ...  1284510\n",
       "1911        48  ...  1159937\n",
       "1949        70  ...  1342431\n",
       "\n",
       "[6 rows x 17 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identify the null values rolls\n",
    "no_speaker_occ = df_ted[df_ted[\"speaker_occupation\"].isnull()]\n",
    "no_speaker_occ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "BZvZoO2Y-9py",
    "outputId": "a7c7de35-d6fb-40d6-a2ff-d0cec8523780"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.550000e+03\n",
       "mean     1.698297e+06\n",
       "std      2.498479e+06\n",
       "min      5.044300e+04\n",
       "25%      7.557928e+05\n",
       "50%      1.124524e+06\n",
       "75%      1.700760e+06\n",
       "max      4.722711e+07\n",
       "Name: views, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted['views'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "FpB3i6IfUsa1",
    "outputId": "8f5990e1-ec7e-42fe-9407-c8ad7c5c22a1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.000000e+00\n",
       "mean     1.070046e+06\n",
       "std      7.056541e+05\n",
       "min      1.810100e+05\n",
       "25%      5.552960e+05\n",
       "50%      1.222224e+06\n",
       "75%      1.327951e+06\n",
       "max      2.098639e+06\n",
       "Name: views, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_speaker_occ['views'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6OOoPyt0UuP_"
   },
   "outputs": [],
   "source": [
    "# Finding:\n",
    "# Becuase the average views for ted talks that speakers' have no occupations is samller than the average views.\n",
    "# I want to learn more about the relationship between occupations & views."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "GC6_r5fBVTBE",
    "outputId": "2dc50a04-525a-469c-af41-0e0289b5eb24"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       2544\n",
       "unique      1458\n",
       "top       Writer\n",
       "freq          45\n",
       "Name: speaker_occupation, dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted['speaker_occupation'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "V861uUzpVVb4",
    "outputId": "0f5463fd-0071-45a0-f0fc-5c52babf09e4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most popular occupation for speakers is Writer.\n"
     ]
    }
   ],
   "source": [
    "print('The most popular occupation for speakers is Writer.')\n",
    "# later, in the graphing area, I will explore more about how different occupations are related to views."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "1__MAnhbV--q",
    "outputId": "b2a0587f-9abe-414b-805d-f43440840f11"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF UNIQUE OCCUPATIONS\n",
      "1459\n"
     ]
    }
   ],
   "source": [
    "# How many unique occupations?\n",
    "print('NUMBER OF UNIQUE OCCUPATIONS')\n",
    "print(len(df_ted['speaker_occupation'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "fHlFrEY0WHIC",
    "outputId": "8b260d4f-be4e-4022-aedb-d37d215140c6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF UNIQUE SPEAKERS\n",
      "2156\n"
     ]
    }
   ],
   "source": [
    "# How many unique speakers?\n",
    "print('NUMBER OF UNIQUE SPEAKERS')\n",
    "print(len(df_ted['main_speaker'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "X-ZsAzvxWZhi",
    "outputId": "006b1b61-aa1f-4376-ddc7-638d810d0d0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NUMBER OF UNIQUE EVENT\n",
      "355\n"
     ]
    }
   ],
   "source": [
    "# How many unique event?\n",
    "print('NUMBER OF UNIQUE EVENT')\n",
    "print(len(df_ted['event'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "id": "Iq3-Al7W_jth",
    "outputId": "02a4cb5a-618f-4fd6-9008-aa6a29138cde"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MDHW8LhGRXkn3jsjIyVnjSxAARoeLALx6cLYbZYmI9EyqQ7scxU27bICRoSS0Dym0RaS/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\n0BYRyZD/D22dXVPbufSDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram\n",
    "# to learn about how do views distribute\n",
    "sns.distplot(df_ted['views']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J60t_e5IBK1i"
   },
   "source": [
    "Relationship with numerical variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P19ZCDeVBa_g"
   },
   "source": [
    "scatter plot comments/views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "-N-_e6yH_4L_",
    "outputId": "946d1661-1ad5-4363-96de-d342a29304f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uwdsuZYhit9xqn1+7TAgqoV5aX1A4H/ntfTXlwkYKJ7epIMAwYCFO0defA/dJ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\nmrJDOyeNRqPRlB3/DwqXRMdh2EToAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# is the the number of comments related to the popularity?\n",
    "\n",
    "var = 'comments'\n",
    "data = pd.concat([df_ted['views'], df_ted[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='views', xlim=(1,400), ylim=(0,7000000));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nyn8SreZBWKl"
   },
   "source": [
    "scatter plot languages/views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 283
    },
    "colab_type": "code",
    "id": "pYcvkQSDAmCH",
    "outputId": "947128cd-1248-498d-8f32-9a087c9c7ff3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4c9cVi3gHC1EXkC83meOj03EcH5pE0OdeVCO45aIYLcN0PIUqrwuzifm/ZZXXhel4Cu3N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\nM0ZPKIlCet9odC4kLcPXbtsKn9vQYvO5XfjabVsRrvGzxc1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+Jd7svJeFmX3OzHrMbA5R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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# is the the number of language available related to the popularity?\n",
    "\n",
    "var = 'languages'\n",
    "data = pd.concat([df_ted['views'], df_ted[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='views', xlim=(1,51), ylim=(0,7000000));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Hy-Fw2EPBQxQ"
   },
   "source": [
    "scatter plot num_speaker/views"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 284
    },
    "colab_type": "code",
    "id": "0cL0oETfA2Y_",
    "outputId": "c117ca11-15a7-4f6e-b99f-3353a5497623"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5MFnhLviZuaPOYHvBz8ytTUJmVtdcmKxwczrauPVXFzOlWbQ2iynN4tZfXeyb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\nf4g94UVPOw5fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# is the the number of speakers related to the popularity?\n",
    "\n",
    "var = 'num_speaker'\n",
    "data = pd.concat([df_ted['views'], df_ted[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='views', xlim=(1,5), ylim=(0,7000000));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GbTCTIdpBHlQ"
   },
   "source": [
    "Relationship with categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "pcfq60lQAa3W",
    "outputId": "eccfc419-f12d-4e26-8ac3-df2c72127b6c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PTAI+CdzYrs4Jqb/+wGOSfg9cD5wp6bR03+7PA19Kx/9N4CKyLykbKLvf9wHA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\n/wEqDa/fsuDpXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot views/speaker_occupation\n",
    "# is the the speaker occupation related to the popularity?\n",
    "\n",
    "var = 'views'\n",
    "data = pd.concat([df_ted['speaker_occupation'], df_ted[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=var, y=\"speaker_occupation\", data=data)\n",
    "fig.axis(ymin=0, xmax=2e+07, ymax = 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 170
    },
    "colab_type": "code",
    "id": "Y625DJ8c5YK-",
    "outputId": "e7d79391-0625-41bf-e014-1d3666d67cc1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.000000e+00\n",
       "mean     1.070046e+06\n",
       "std      7.056541e+05\n",
       "min      1.810100e+05\n",
       "25%      5.552960e+05\n",
       "50%      1.222224e+06\n",
       "75%      1.327951e+06\n",
       "max      2.098639e+06\n",
       "Name: views, dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_speaker_occ['views'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "c4a3VCxY54XP",
    "outputId": "78ce30bd-5b53-422f-f140-f66d39096878"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comments</th>\n",
       "      <th>description</th>\n",
       "      <th>duration</th>\n",
       "      <th>event</th>\n",
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       "      <th>languages</th>\n",
       "      <th>main_speaker</th>\n",
       "      <th>name</th>\n",
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       "      <td>Global health expert; data visionary</td>\n",
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       "      <td>Insights on HIV, in stunning data visuals</td>\n",
       "      <td>https://www.ted.com/talks/hans_rosling_the_tru...</td>\n",
       "      <td>904813</td>\n",
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       "      <td>Asia's rise -- how and when</td>\n",
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       "      <td>The world's population will grow to 9 billion ...</td>\n",
       "      <td>604</td>\n",
       "      <td>TED@Cannes</td>\n",
       "      <td>1277078400</td>\n",
       "      <td>46</td>\n",
       "      <td>Hans Rosling</td>\n",
       "      <td>Hans Rosling: Global population growth, box by...</td>\n",
       "      <td>1</td>\n",
       "      <td>1278663300</td>\n",
       "      <td>[{'id': 22, 'name': 'Fascinating', 'count': 76...</td>\n",
       "      <td>[{'id': 695, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Global health expert; data visionary</td>\n",
       "      <td>['culture', 'data', 'demo', 'global issues', '...</td>\n",
       "      <td>Global population growth, box by box</td>\n",
       "      <td>https://www.ted.com/talks/hans_rosling_on_glob...</td>\n",
       "      <td>2934262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>784</th>\n",
       "      <td>342</td>\n",
       "      <td>Hans Rosling reframes 10 years of UN data with...</td>\n",
       "      <td>934</td>\n",
       "      <td>TEDxChange</td>\n",
       "      <td>1284940800</td>\n",
       "      <td>33</td>\n",
       "      <td>Hans Rosling</td>\n",
       "      <td>Hans Rosling: The good news of the decade? We'...</td>\n",
       "      <td>1</td>\n",
       "      <td>1286442720</td>\n",
       "      <td>[{'id': 8, 'name': 'Informative', 'count': 554...</td>\n",
       "      <td>[{'id': 540, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Global health expert; data visionary</td>\n",
       "      <td>['Africa', 'TEDx', 'data', 'global development...</td>\n",
       "      <td>The good news of the decade? We're winning the...</td>\n",
       "      <td>https://www.ted.com/talks/hans_rosling_the_goo...</td>\n",
       "      <td>738895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>893</th>\n",
       "      <td>268</td>\n",
       "      <td>What was the greatest invention of the industr...</td>\n",
       "      <td>555</td>\n",
       "      <td>TEDWomen 2010</td>\n",
       "      <td>1291420800</td>\n",
       "      <td>46</td>\n",
       "      <td>Hans Rosling</td>\n",
       "      <td>Hans Rosling: The magic washing machine</td>\n",
       "      <td>1</td>\n",
       "      <td>1300714380</td>\n",
       "      <td>[{'id': 9, 'name': 'Ingenious', 'count': 457},...</td>\n",
       "      <td>[{'id': 620, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Global health expert; data visionary</td>\n",
       "      <td>['culture', 'data', 'economics', 'women']</td>\n",
       "      <td>The magic washing machine</td>\n",
       "      <td>https://www.ted.com/talks/hans_rosling_and_the...</td>\n",
       "      <td>2391977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1239</th>\n",
       "      <td>491</td>\n",
       "      <td>Hans Rosling had a question: Do some religions...</td>\n",
       "      <td>800</td>\n",
       "      <td>TEDxSummit</td>\n",
       "      <td>1334534400</td>\n",
       "      <td>36</td>\n",
       "      <td>Hans Rosling</td>\n",
       "      <td>Hans Rosling: Religions and babies</td>\n",
       "      <td>1</td>\n",
       "      <td>1337698856</td>\n",
       "      <td>[{'id': 22, 'name': 'Fascinating', 'count': 86...</td>\n",
       "      <td>[{'id': 1418, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>Global health expert; data visionary</td>\n",
       "      <td>['TEDx', 'data', 'global issues', 'population'...</td>\n",
       "      <td>Religions and babies</td>\n",
       "      <td>https://www.ted.com/talks/hans_rosling_religio...</td>\n",
       "      <td>2138419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      comments  ...     views\n",
       "4          593  ...  12005869\n",
       "117        261  ...   3243784\n",
       "447        125  ...    904813\n",
       "502        122  ...   1471039\n",
       "558        276  ...   1738069\n",
       "726        607  ...   2934262\n",
       "784        342  ...    738895\n",
       "893        268  ...   2391977\n",
       "1239       491  ...   2138419\n",
       "\n",
       "[9 rows x 17 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted[df_ted['main_speaker'] == 'Hans Rosling']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PsTgMDQUXyhT"
   },
   "outputs": [],
   "source": [
    "# discovering patterns from the most popular speaker, Hans Rosling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "colab_type": "code",
    "id": "SUrPES0mDkT0",
    "outputId": "61b9b06b-578f-4d05-8d82-525b675e0dd1"
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>comments</th>\n",
       "      <th>duration</th>\n",
       "      <th>film_date</th>\n",
       "      <th>languages</th>\n",
       "      <th>num_speaker</th>\n",
       "      <th>published_date</th>\n",
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       "      <th>event</th>\n",
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       "      <th>AORN Congress</th>\n",
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       "      <th>BBC TV</th>\n",
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       "      <th>Bowery Poetry Club</th>\n",
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       "      <td>182</td>\n",
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       "      <th>The Do Lectures</th>\n",
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       "      <th>Toronto Youth Corps</th>\n",
       "      <td>373</td>\n",
       "      <td>262</td>\n",
       "      <td>74649600</td>\n",
       "      <td>0</td>\n",
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       "      <td>1273847820</td>\n",
       "      <td>1028630</td>\n",
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       "    <tr>\n",
       "      <th>University of California</th>\n",
       "      <td>121</td>\n",
       "      <td>8829</td>\n",
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       "    <tr>\n",
       "      <th>Web 2.0 Expo 2008</th>\n",
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       "      <td>927</td>\n",
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       "<p>355 rows × 7 columns</p>\n",
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      ],
      "text/plain": [
       "                             comments  duration  ...  published_date    views\n",
       "event                                            ...                         \n",
       "AORN Congress                      87       510  ...      1263027540   149818\n",
       "Arbejdsglaede Live                258      1092  ...      1267802040   971594\n",
       "BBC TV                            146      3955  ...      1267631820   521974\n",
       "Bowery Poetry Club                177       182  ...      1264252560   676741\n",
       "Business Innovation Factory       264      3317  ...      3832642740   912258\n",
       "...                               ...       ...  ...             ...      ...\n",
       "The Do Lectures                    40      1690  ...      1285488000   112321\n",
       "Toronto Youth Corps               373       262  ...      1273847820  1028630\n",
       "University of California          121      8829  ...      2532243480   523663\n",
       "Web 2.0 Expo 2008                 556       927  ...      1268412360   757791\n",
       "World Science Festival            321       184  ...      1267232400  3302312\n",
       "\n",
       "[355 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted.groupby('event').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 102
    },
    "colab_type": "code",
    "id": "OSGBI9ayRKqc",
    "outputId": "bd239ac0-2083-4a57-aa01-982a6fa9be7c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        2550\n",
       "unique        355\n",
       "top       TED2014\n",
       "freq           84\n",
       "Name: event, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ted['event'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 392
    },
    "colab_type": "code",
    "id": "vvWz4O7qQbKy",
    "outputId": "2064ea08-b5bb-49d2-c033-ae052e4d520a"
   },
   "outputs": [
    {
     "data": {
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0Z4B9U/lBwDBgdT49GZ3Smp0MPBQRz5fbPzMzM+saDgb7GEkjgauAK6PMZZeS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\na2P/C7dHrkXKn4eGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# box plot views/speaker_occupation\n",
    "var = 'views'\n",
    "data = pd.concat([df_ted['event'], df_ted[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = sns.boxplot(x=var, y=\"event\", data=data)\n",
    "fig.axis(ymin=0, xmax=8000000, ymax = 20);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 568
    },
    "colab_type": "code",
    "id": "OvtT3pbSEn9X",
    "outputId": "8366a970-1bfb-476d-c907-bbe4ec011e96"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Yt9/2+bUi98/Iuj7Yx/elXFPgz6QZ1CRjxHsJ6wkTJrBq1aoiIy/0urba3Wf3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\nkSqnoBYRqXIKahGRKqegFhGpcv8ftr0HGPtimhQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ut75cb3253vrwEI2ZWU454M3McsoBX96yZhdQJddbX663vlxvHXgM3swsp9yD\nNzPLKQe8mVlOOeArkPQPkn4t6R5Jt0o6pNk1VSLpk5IeSmv+lqSWvkO4pDdLul/SXkktOeVM0hmS\nfiupW9LfNbuewUi6WtImSfc1u5YsJM2UdIekB9K/C4ubXVMlksZL+oWke9N6P9LsmirxGHwFkl4S\nEU+nj98DzIuIy5pcVlmSXgvcHhG7Jf1fgIj42yaXVZakPwT2Av8GvD8iVjS5pCKS2oGHgdOB9cAv\ngQsj4oGmFlaBpJOB7cB1EXFks+sZjKTpwPSIuFvS/sBK4I2t+jOWJGC/iNguaSzwY2BxRPysyaWV\n5B58BT3hntoPaOn/DSPi1ojYnW7+DJjRzHoGExEPRsRvm11HBccC3RHxWES8AHwdOKfJNVUUET8E\ntja7jqwi4omIuDt9/AzwIHBoc6sqLxLb082x6VfL5oIDfhCSlkpaBywE/r7Z9VThHcAtzS5ihDsU\nWNdnez0tHD4jnaRO4JXAz5tbSWWS2iXdA2wCvh8RLVvvqA94ST+QdF+Jr3MAImJJRMwECsBfNrfa\nwetN2ywBdpPU3FRZ6jWTNBG4CXhvv9+cW05E7ImIo0l+Qz5WUssOhY1pdgHNFhELMjYtAMuBD9Wx\nnEENVq+kS4GzgNOiBS6wVPHzbUUbgJl9tmek+6yG0rHsm4BCRNzc7HqyioinJN0BnAG05EXtUd+D\nr0TSYX02zwEealYtWUg6A/gb4OyI2NHsenLgl8BhkmZLGgdcAHynyTXlSnrR8ivAgxHxqWbXMxhJ\n03pmp0nal+QCfMvmgmfRVCDpJuBwkpkea4DLIqJle3CSuoF9gCfTXT9r8Vk/5wKfA6YBTwH3RMTr\nmltVMUlnAp8G2oGrI2Jpk0uqSNLXgNeQLGe7EfhQRHylqUVVIOmPgR8BvyH5dwbwvyNiefOqKk/S\nUcC1JH8f2oBvRMRHm1tVeQ54M7Oc8hCNmVlOOeDNzHLKAW9mllMOeDOznHLAm5nllAPeak7SlHQF\nznsk/U7ShvTxU5IauoiUpKPTqY4922cPdVVISaslTa1ddVWd+9K+q5lK+rKkec2uy1qbA95qLiKe\njIij049zfwn4l/Tx0fTOda4ZSZU+kX008GLAR8R3IuIfa11DA1wKvBjwEfGuVl1x0VqHA94arV3S\nVela2remnwZE0lxJ35W0UtKPJB2R7u+UdHu6xv1tkjrS/ddI+pKknwOfkLRfuhb6LyT9StI56adP\nPwqcn/4GcX7aE/58+hoHK1k3/97068R0/7fTOu6XtGiwNyTp7ZIeTs99VZ/Xv0bSeX3abU+/T0zf\ny92SftOzLk/6Xh/s//NJX0bYtSsAAAMHSURBVGM+UEjfx76S7lSJNfQlXZTWcY+kf1OyMFZ7Wst9\n6fn+ahh/fjaCOOCt0Q4D/jUiXkby6dU/S/cvA66MiGOA9wNfSPd/Drg2Io4iWQ/os31eawZwYkS8\nD1hCshb+scApwCdJlnL9e+CG9DeKG/rV8lngfyLiFcCrgPvT/e9I65gPvEfSlHJvRsl65h8BTgL+\nGJiX4WewEzg3Il6V1vrP6Uf2S/58IuKbwApgYfo+nitTyx8C5wMnpb8x7SFZBfVo4NCIODIiXg78\ne4YaLQdG/WJj1nCrIuKe9PFKoDNdSfBE4MbenGOf9PsJwJvSx18FPtHntW6MiD3p49cCZ0t6f7o9\nHugYpJZTgUsgWSEQ+H26/z3pMgqQLDZ2GL3LP/R3HHBnRGwGkHQD8AeDnFfAx5TcnGMvyRLEB6fH\nBvx8Bnmtvk4DjgF+mf4c9yVZ0va/gDmSPgf8P+DWKl7TRjAHvDXa830e7yEJoTbgqbTXWY1n+zwW\nSW+36AYiko6r5gUlvQZYAJwQETsk3Unyn8VQ7Cb9LVlSGzAu3b+QZP2dYyJil6TVfc5R6ueTuXyS\n33Y+MOCA9ArgdcBlwFtI7hdgOechGmu6dP3vVZLeDMkKg2kgAfyEZBVHSILxR2Ve5nvAlT1DHZJe\nme5/Bti/zHNuAy5P27dLmgRMAral4X4EcPwg5f8c+JN05tBY4M19jq0m6VEDnE0yZER6jk1puJ8C\nzBrkHIO9j77v5zxJB6XvabKkWekMm7aIuAn4IMlwlI0CDnhrFQuBd0q6l2QsvOeGIFcCb5f0a+Bi\noNxNmf+BJEB/Len+dBvgDmBez0XWfs9ZDJwi6TckwyHzgO8CYyQ9CPwjya0Py4qIJ4APAz8F7iK5\n5VyPq0jC/16Soaae3zgKwPz0vJeQbbnZa4Av9VxkLVPLAyQBfmv68/o+MJ1kCOhOJXchuh4Y0MO3\nfPJqkmY1pOSGK/Mjoul3/zJzD97MLKfcgzczyyn34M3McsoBb2aWUw54M7OccsCbmeWUA97MLKf+\nP+CpG1ldUJAWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram and normal probability plot\n",
    "sns.distplot(df_ted['views'], fit=norm);\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(df_ted['views'], plot=plt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "colab_type": "code",
    "id": "CeULk7w2LiN1",
    "outputId": "48b3737d-91d9-4d4f-b5f6-7fc945230447"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>comments</th>\n",
       "      <th>description</th>\n",
       "      <th>duration</th>\n",
       "      <th>languages</th>\n",
       "      <th>main_speaker</th>\n",
       "      <th>name</th>\n",
       "      <th>num_speaker</th>\n",
       "      <th>related_talks</th>\n",
       "      <th>speaker_occupation</th>\n",
       "      <th>tags</th>\n",
       "      <th>title</th>\n",
       "      <th>url</th>\n",
       "      <th>views</th>\n",
       "      <th>transcript</th>\n",
       "      <th>Funny</th>\n",
       "      <th>Beautiful</th>\n",
       "      <th>Ingenious</th>\n",
       "      <th>Courageous</th>\n",
       "      <th>Longwinded</th>\n",
       "      <th>Confusing</th>\n",
       "      <th>Informative</th>\n",
       "      <th>Fascinating</th>\n",
       "      <th>Unconvincing</th>\n",
       "      <th>Persuasive</th>\n",
       "      <th>Jaw-dropping</th>\n",
       "      <th>OK</th>\n",
       "      <th>Obnoxious</th>\n",
       "      <th>Inspiring</th>\n",
       "      <th>max_rating</th>\n",
       "      <th>yr</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4553</td>\n",
       "      <td>Sir Ken Robinson makes an entertaining and pro...</td>\n",
       "      <td>1164</td>\n",
       "      <td>60</td>\n",
       "      <td>Ken Robinson</td>\n",
       "      <td>Ken Robinson: Do schools kill creativity?</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 865, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Author/educator</td>\n",
       "      <td>['children', 'creativity', 'culture', 'dance',...</td>\n",
       "      <td>Do schools kill creativity?</td>\n",
       "      <td>https://www.ted.com/talks/ken_robinson_says_sc...</td>\n",
       "      <td>47227110</td>\n",
       "      <td>Good morning. How are you?(Laughter)It's been ...</td>\n",
       "      <td>19645</td>\n",
       "      <td>4573</td>\n",
       "      <td>6073</td>\n",
       "      <td>3253</td>\n",
       "      <td>387</td>\n",
       "      <td>242</td>\n",
       "      <td>7346</td>\n",
       "      <td>10581</td>\n",
       "      <td>300</td>\n",
       "      <td>10704</td>\n",
       "      <td>4439</td>\n",
       "      <td>1174</td>\n",
       "      <td>209</td>\n",
       "      <td>24924</td>\n",
       "      <td>Inspiring</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>265</td>\n",
       "      <td>With the same humor and humanity he exuded in ...</td>\n",
       "      <td>977</td>\n",
       "      <td>43</td>\n",
       "      <td>Al Gore</td>\n",
       "      <td>Al Gore: Averting the climate crisis</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 243, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Climate advocate</td>\n",
       "      <td>['alternative energy', 'cars', 'climate change...</td>\n",
       "      <td>Averting the climate crisis</td>\n",
       "      <td>https://www.ted.com/talks/al_gore_on_averting_...</td>\n",
       "      <td>3200520</td>\n",
       "      <td>Thank you so much, Chris. And it's truly a gre...</td>\n",
       "      <td>544</td>\n",
       "      <td>58</td>\n",
       "      <td>56</td>\n",
       "      <td>139</td>\n",
       "      <td>113</td>\n",
       "      <td>62</td>\n",
       "      <td>443</td>\n",
       "      <td>132</td>\n",
       "      <td>258</td>\n",
       "      <td>268</td>\n",
       "      <td>116</td>\n",
       "      <td>203</td>\n",
       "      <td>131</td>\n",
       "      <td>413</td>\n",
       "      <td>Funny</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>New York Times columnist David Pogue takes aim...</td>\n",
       "      <td>1286</td>\n",
       "      <td>26</td>\n",
       "      <td>David Pogue</td>\n",
       "      <td>David Pogue: Simplicity sells</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 1725, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>Technology columnist</td>\n",
       "      <td>['computers', 'entertainment', 'interface desi...</td>\n",
       "      <td>Simplicity sells</td>\n",
       "      <td>https://www.ted.com/talks/david_pogue_says_sim...</td>\n",
       "      <td>1636292</td>\n",
       "      <td>(Music: \"The Sound of Silence,\" Simon &amp; Garfun...</td>\n",
       "      <td>964</td>\n",
       "      <td>60</td>\n",
       "      <td>183</td>\n",
       "      <td>45</td>\n",
       "      <td>78</td>\n",
       "      <td>27</td>\n",
       "      <td>395</td>\n",
       "      <td>166</td>\n",
       "      <td>104</td>\n",
       "      <td>230</td>\n",
       "      <td>54</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>230</td>\n",
       "      <td>Funny</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  comments  ...    yr  month\n",
       "0           0      4553  ...  2006      6\n",
       "1           1       265  ...  2006      6\n",
       "2           2       124  ...  2006      6\n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upload the \"Cleanned\" dataset\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/PoojaDoctor/Data144-FinalProject/master/datasets/cleaned.csv'\n",
    "df_cleanned = pd.read_csv(url)\n",
    "# Dataset is now stored in a Pandas Dataframe\n",
    "df_cleanned.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "11wWfeYJMJac"
   },
   "source": [
    "# **RANDOM FOREST:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g4PU09JDMQQ4"
   },
   "outputs": [],
   "source": [
    "# Import necessary library\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "colab_type": "code",
    "id": "9iUwk7SMzuwB",
    "outputId": "4eb3362a-4b03-411a-d211-60c4b4f1fc4c"
   },
   "outputs": [
    {
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       "   Unnamed: 0  comments  ...    yr  month\n",
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     },
     "execution_count": 48,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/PoojaDoctor/Data144-FinalProject/master/datasets/cleaned.csv'\n",
    "df_cleanned = pd.read_csv(url)\n",
    "df_cleanned.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fg2iAudoMLHt"
   },
   "outputs": [],
   "source": [
    "#Import scikit-learn dataset library\n",
    "from sklearn import datasets\n",
    "data = df_cleanned\n",
    "\n",
    "# Import train_test_split function\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X=data[['comments','duration','languages','num_speaker','month']]  # Features\n",
    "# 'Funny','Beautiful','Ingenious','Courageous','Longwinded',\n",
    "#        'Confusing','Informative','Fascinating','Unconvincing','Persuasive','Jaw-dropping','OK','Obnoxious','Inspiring',\n",
    "#        'max_rating','yr',\n",
    "y=data['views']  # Labels\n",
    "\n",
    "# Split dataset into training set and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0PdS_Q_9NfO0"
   },
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xf1E33snORCk"
   },
   "outputs": [],
   "source": [
    "# Feature Scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HUfqptXjOUil"
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "regressor = RandomForestRegressor(n_estimators=20, random_state=0)\n",
    "regressor.fit(X_train, y_train)\n",
    "y_pred = regressor.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "tDAmEo4kOXgu",
    "outputId": "afdb7dd3-f2dc-4e97-a8d9-1bcadf9f8cc5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 866042.0115384617\n",
      "Mean Squared Error: 3402861345723.3545\n",
      "Root Mean Squared Error: 1844684.619582262\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DT7QtatKSmM3"
   },
   "outputs": [],
   "source": [
    "# The following is what I did wrong. Because we are training a regressive model,\n",
    "# accuracy should not be the metric to test the model performance.\n",
    "\n",
    "\n",
    "\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "# from sklearn import metrics\n",
    "\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "# print(\"Accuracy:\", metrics.accuracy_score(y_test, y_pred))\n",
    "\n",
    "# print('Accuracy with descriptive labels is Accuracy: 0.001349527665317139')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Qji3Q4eYL1PW"
   },
   "outputs": [],
   "source": [
    "# helpful link: \n",
    "# 1 | https://www.datacamp.com/community/tutorials/random-forests-classifier-python\n",
    "# 2 | https://stackabuse.com/random-forest-algorithm-with-python-and-scikit-learn/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "POs_RqopQvTc"
   },
   "source": [
    "Conclude Random Forest.\n",
    "\n",
    "Explore: on the slide:\n",
    "\n",
    "1) Use a flow chart to visualize what I did with the random forest\n",
    "\n",
    "2) Talk about my \"failure\"\n",
    "\n",
    "3) Talk about the results & interpret it (with a table of other models' performances) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zeF_8mjHQ4kT"
   },
   "source": [
    "# **DECISION TREE**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gmA2p5UjPxa0"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "colab_type": "code",
    "id": "m6lzlg8kQCzR",
    "outputId": "4f6799a4-d01a-418f-e466-1d6dfce20a29"
   },
   "outputs": [
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       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>New York Times columnist David Pogue takes aim...</td>\n",
       "      <td>1286</td>\n",
       "      <td>26</td>\n",
       "      <td>David Pogue</td>\n",
       "      <td>David Pogue: Simplicity sells</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 1725, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>Technology columnist</td>\n",
       "      <td>['computers', 'entertainment', 'interface desi...</td>\n",
       "      <td>Simplicity sells</td>\n",
       "      <td>https://www.ted.com/talks/david_pogue_says_sim...</td>\n",
       "      <td>1636292</td>\n",
       "      <td>(Music: \"The Sound of Silence,\" Simon &amp; Garfun...</td>\n",
       "      <td>964</td>\n",
       "      <td>60</td>\n",
       "      <td>183</td>\n",
       "      <td>45</td>\n",
       "      <td>78</td>\n",
       "      <td>27</td>\n",
       "      <td>395</td>\n",
       "      <td>166</td>\n",
       "      <td>104</td>\n",
       "      <td>230</td>\n",
       "      <td>54</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>230</td>\n",
       "      <td>Funny</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  comments  ...    yr  month\n",
       "0           0      4553  ...  2006      6\n",
       "1           1       265  ...  2006      6\n",
       "2           2       124  ...  2006      6\n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 87,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/PoojaDoctor/Data144-FinalProject/master/datasets/cleaned.csv'\n",
    "dataset = pd.read_csv(url)\n",
    "dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2m3fIjY_Q6TG"
   },
   "outputs": [],
   "source": [
    "X = dataset[['comments','duration','languages','num_speaker','month']]\n",
    "y = dataset['views']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nlRBNN5SRJng"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "5fAWEF9dRMKZ",
    "outputId": "638d130e-0ec1-42c6-f0d1-a16307dcc13c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 90,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier = DecisionTreeClassifier()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nASgUdFbRho9"
   },
   "outputs": [],
   "source": [
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "mApTmIdHRmWB",
    "outputId": "6fa67495-8367-4830-ac4e-0d729699715d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 1229155.7995951418\n",
      "Mean Squared Error: 9709761173378.059\n",
      "Root Mean Squared Error: 3116048.968385776\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "from sklearn import metrics\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7FDL3QaxL09b"
   },
   "outputs": [],
   "source": [
    "# Model Accuracy, how often is the classifier correct?\n",
    "# print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "\n",
    "# print('The accuracy of metrics in decision tree is higher, 0.006747638326585695, is higher than the random forest.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_gipm9enL0v7"
   },
   "outputs": [],
   "source": [
    "# At the beginning, I try to implement Scikit-learn, optimization of decision tree classifier\n",
    "\n",
    "# Create Decision Tree classifer object\n",
    "# clf = DecisionTreeClassifier(criterion=\"entropy\", max_depth=3)\n",
    "\n",
    "# Train Decision Tree Classifer\n",
    "# clf = clf.fit(X_train,y_train)\n",
    "\n",
    "# Predict the response for test dataset\n",
    "# y_pred = clf.predict(X_test)\n",
    "\n",
    "# Model Accuracy, how often is the classifier correct?\n",
    "# print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))\n",
    "\n",
    "# print('The accuracy with scikit-learn does not help to optimize the accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_-77gSXoTKzY"
   },
   "source": [
    "# **To-do: think about how to visualize the decision tree**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9A11k_QLSr2p"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jCjvPo4VSw1_"
   },
   "outputs": [],
   "source": [
    "# remember, xxxx.data = df_cleanned.views\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ic_prvw36_5"
   },
   "source": [
    "# **Neural Network**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "colab_type": "code",
    "id": "HjT6DXZd39o7",
    "outputId": "f8a0722a-4e55-4de1-b24f-11b80458c3fe"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gensim in /usr/local/lib/python3.6/dist-packages (3.6.0)\n",
      "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.9.0)\n",
      "Requirement already satisfied: numpy>=1.11.3 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.17.4)\n",
      "Requirement already satisfied: six>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.12.0)\n",
      "Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.6/dist-packages (from gensim) (1.3.2)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (2.21.0)\n",
      "Requirement already satisfied: boto3 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (1.10.18)\n",
      "Requirement already satisfied: boto>=2.32 in /usr/local/lib/python3.6/dist-packages (from smart-open>=1.2.1->gensim) (2.49.0)\n",
      "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2.8)\n",
      "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (1.24.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (2019.9.11)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->smart-open>=1.2.1->gensim) (3.0.4)\n",
      "Requirement already satisfied: botocore<1.14.0,>=1.13.18 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (1.13.18)\n",
      "Requirement already satisfied: jmespath<1.0.0,>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (0.9.4)\n",
      "Requirement already satisfied: s3transfer<0.3.0,>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from boto3->smart-open>=1.2.1->gensim) (0.2.1)\n",
      "Requirement already satisfied: docutils<0.16,>=0.10 in /usr/local/lib/python3.6/dist-packages (from botocore<1.14.0,>=1.13.18->boto3->smart-open>=1.2.1->gensim) (0.15.2)\n",
      "Requirement already satisfied: python-dateutil<2.8.1,>=2.1; python_version >= \"2.7\" in /usr/local/lib/python3.6/dist-packages (from botocore<1.14.0,>=1.13.18->boto3->smart-open>=1.2.1->gensim) (2.6.1)\n",
      "--2019-11-26 03:03:43--  https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.96.149\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.96.149|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1647046227 (1.5G) [application/x-gzip]\n",
      "Saving to: ‘GoogleNews-vectors-negative300.bin.gz’\n",
      "\n",
      "GoogleNews-vectors- 100%[===================>]   1.53G  29.1MB/s    in 45s     \n",
      "\n",
      "2019-11-26 03:04:28 (34.7 MB/s) - ‘GoogleNews-vectors-negative300.bin.gz’ saved [1647046227/1647046227]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import string\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import pprint\n",
    "from urllib.request import urlopen\n",
    "\n",
    "import gensim\n",
    "from gensim import corpora, models, similarities\n",
    "import nltk\n",
    "from nltk import word_tokenize, tokenize\n",
    "\n",
    "!pip install gensim\n",
    "!wget -nc https://s3.amazonaws.com/dl4j-distribution/GoogleNews-vectors-negative300.bin.gz \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "r7z6cLB25lBq"
   },
   "outputs": [],
   "source": [
    "from gensim.models import KeyedVectors\n",
    "model = KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin.gz', binary=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "axewD9_r5nm6",
    "outputId": "d6cff8a1-45aa-4f1e-d83d-cb30e6e18b19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'</s>': <gensim.models.keyedvectors.Vocab at 0x7fbf38129be0>,\n",
       " 'in': <gensim.models.keyedvectors.Vocab at 0x7fbf38129cc0>,\n",
       " 'for': <gensim.models.keyedvectors.Vocab at 0x7fbf38129d30>,\n",
       " 'that': <gensim.models.keyedvectors.Vocab at 0x7fbf38129da0>,\n",
       " 'is': <gensim.models.keyedvectors.Vocab at 0x7fbf38129e10>,\n",
       " 'on': <gensim.models.keyedvectors.Vocab at 0x7fbf38129e80>,\n",
       " '##': <gensim.models.keyedvectors.Vocab at 0x7fbf38129ef0>,\n",
       " 'The': <gensim.models.keyedvectors.Vocab at 0x7fbf38129f60>,\n",
       " 'with': <gensim.models.keyedvectors.Vocab at 0x7fbf38129fd0>,\n",
       " 'said': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f080>,\n",
       " 'was': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f0f0>,\n",
       " 'the': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f160>,\n",
       " 'at': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f1d0>,\n",
       " 'not': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f240>,\n",
       " 'as': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f2b0>,\n",
       " 'it': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f320>,\n",
       " 'be': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f390>,\n",
       " 'from': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f400>,\n",
       " 'by': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f470>,\n",
       " 'are': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f4e0>,\n",
       " 'I': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f518>,\n",
       " 'have': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f588>,\n",
       " 'he': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f5f8>,\n",
       " 'will': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f668>,\n",
       " 'has': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f6d8>,\n",
       " '####': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f748>,\n",
       " 'his': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f7b8>,\n",
       " 'an': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f828>,\n",
       " 'this': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f898>,\n",
       " 'or': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f908>,\n",
       " 'their': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f978>,\n",
       " 'who': <gensim.models.keyedvectors.Vocab at 0x7fbf3813f9e8>,\n",
       " 'they': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fa58>,\n",
       " 'but': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fac8>,\n",
       " '$': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fb00>,\n",
       " 'had': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fb70>,\n",
       " 'year': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fbe0>,\n",
       " 'were': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fc50>,\n",
       " 'we': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fcc0>,\n",
       " 'more': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fd30>,\n",
       " '###': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fda0>,\n",
       " 'up': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fe10>,\n",
       " 'been': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fe80>,\n",
       " 'you': <gensim.models.keyedvectors.Vocab at 0x7fbf3813fef0>,\n",
       " 'its': <gensim.models.keyedvectors.Vocab at 0x7fbf3813ff60>,\n",
       " 'one': <gensim.models.keyedvectors.Vocab at 0x7fbf3813ffd0>,\n",
       " 'about': <gensim.models.keyedvectors.Vocab at 0x7fbf38142080>,\n",
       " 'would': <gensim.models.keyedvectors.Vocab at 0x7fbf381420f0>,\n",
       " 'which': <gensim.models.keyedvectors.Vocab at 0x7fbf38142160>,\n",
       " 'out': <gensim.models.keyedvectors.Vocab at 0x7fbf381421d0>,\n",
       " 'can': <gensim.models.keyedvectors.Vocab at 0x7fbf38142240>,\n",
       " 'It': <gensim.models.keyedvectors.Vocab at 0x7fbf381422b0>,\n",
       " 'all': <gensim.models.keyedvectors.Vocab at 0x7fbf38142320>,\n",
       " 'also': <gensim.models.keyedvectors.Vocab at 0x7fbf38142390>,\n",
       " 'two': <gensim.models.keyedvectors.Vocab at 0x7fbf38142400>,\n",
       " 'after': <gensim.models.keyedvectors.Vocab at 0x7fbf38142470>,\n",
       " 'first': <gensim.models.keyedvectors.Vocab at 0x7fbf381424e0>,\n",
       " 'He': <gensim.models.keyedvectors.Vocab at 0x7fbf38142550>,\n",
       " 'do': <gensim.models.keyedvectors.Vocab at 0x7fbf381425c0>,\n",
       " 'time': <gensim.models.keyedvectors.Vocab at 0x7fbf38142630>,\n",
       " 'than': <gensim.models.keyedvectors.Vocab at 0x7fbf381426a0>,\n",
       " 'when': <gensim.models.keyedvectors.Vocab at 0x7fbf38142710>,\n",
       " 'We': <gensim.models.keyedvectors.Vocab at 0x7fbf38142780>,\n",
       " 'over': <gensim.models.keyedvectors.Vocab at 0x7fbf381427f0>,\n",
       " 'last': <gensim.models.keyedvectors.Vocab at 0x7fbf38142860>,\n",
       " 'new': <gensim.models.keyedvectors.Vocab at 0x7fbf381428d0>,\n",
       " 'other': <gensim.models.keyedvectors.Vocab at 0x7fbf38142940>,\n",
       " 'her': <gensim.models.keyedvectors.Vocab at 0x7fbf381429b0>,\n",
       " 'people': <gensim.models.keyedvectors.Vocab at 0x7fbf38142a20>,\n",
       " 'into': <gensim.models.keyedvectors.Vocab at 0x7fbf38142a90>,\n",
       " 'In': <gensim.models.keyedvectors.Vocab at 0x7fbf38142b00>,\n",
       " 'our': <gensim.models.keyedvectors.Vocab at 0x7fbf38142b70>,\n",
       " 'there': <gensim.models.keyedvectors.Vocab at 0x7fbf38142be0>,\n",
       " 'A': <gensim.models.keyedvectors.Vocab at 0x7fbf38142c18>,\n",
       " 'she': <gensim.models.keyedvectors.Vocab at 0x7fbf38142c88>,\n",
       " 'could': <gensim.models.keyedvectors.Vocab at 0x7fbf38142cf8>,\n",
       " 'just': <gensim.models.keyedvectors.Vocab at 0x7fbf38142d68>,\n",
       " 'years': <gensim.models.keyedvectors.Vocab at 0x7fbf38142dd8>,\n",
       " 'some': <gensim.models.keyedvectors.Vocab at 0x7fbf38142e48>,\n",
       " 'U.S.': <gensim.models.keyedvectors.Vocab at 0x7fbf38142eb8>,\n",
       " 'three': <gensim.models.keyedvectors.Vocab at 0x7fbf38142f28>,\n",
       " 'million': <gensim.models.keyedvectors.Vocab at 0x7fbf38142f98>,\n",
       " 'them': <gensim.models.keyedvectors.Vocab at 0x7fbf38143048>,\n",
       " 'what': <gensim.models.keyedvectors.Vocab at 0x7fbf381430b8>,\n",
       " 'But': <gensim.models.keyedvectors.Vocab at 0x7fbf38143128>,\n",
       " 'so': <gensim.models.keyedvectors.Vocab at 0x7fbf38143198>,\n",
       " 'no': <gensim.models.keyedvectors.Vocab at 0x7fbf38143208>,\n",
       " 'like': <gensim.models.keyedvectors.Vocab at 0x7fbf38143278>,\n",
       " 'if': <gensim.models.keyedvectors.Vocab at 0x7fbf381432e8>,\n",
       " 'only': <gensim.models.keyedvectors.Vocab at 0x7fbf38143358>,\n",
       " 'percent': <gensim.models.keyedvectors.Vocab at 0x7fbf381433c8>,\n",
       " 'get': <gensim.models.keyedvectors.Vocab at 0x7fbf38143438>,\n",
       " 'did': <gensim.models.keyedvectors.Vocab at 0x7fbf381434a8>,\n",
       " 'him': <gensim.models.keyedvectors.Vocab at 0x7fbf38143518>,\n",
       " 'game': <gensim.models.keyedvectors.Vocab at 0x7fbf38143588>,\n",
       " 'back': <gensim.models.keyedvectors.Vocab at 0x7fbf381435f8>,\n",
       " 'because': <gensim.models.keyedvectors.Vocab at 0x7fbf38143668>,\n",
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       " 'any': <gensim.models.keyedvectors.Vocab at 0x7fbf38143898>,\n",
       " 'team': <gensim.models.keyedvectors.Vocab at 0x7fbf38143908>,\n",
       " 'against': <gensim.models.keyedvectors.Vocab at 0x7fbf38143978>,\n",
       " 'off': <gensim.models.keyedvectors.Vocab at 0x7fbf381439e8>,\n",
       " 'This': <gensim.models.keyedvectors.Vocab at 0x7fbf38143a58>,\n",
       " 'most': <gensim.models.keyedvectors.Vocab at 0x7fbf38143ac8>,\n",
       " 'made': <gensim.models.keyedvectors.Vocab at 0x7fbf38143b38>,\n",
       " 'through': <gensim.models.keyedvectors.Vocab at 0x7fbf38143ba8>,\n",
       " 'make': <gensim.models.keyedvectors.Vocab at 0x7fbf38143c18>,\n",
       " 'second': <gensim.models.keyedvectors.Vocab at 0x7fbf38143c88>,\n",
       " 'state': <gensim.models.keyedvectors.Vocab at 0x7fbf38143cf8>,\n",
       " 'well': <gensim.models.keyedvectors.Vocab at 0x7fbf38143d68>,\n",
       " 'day': <gensim.models.keyedvectors.Vocab at 0x7fbf38143dd8>,\n",
       " 'season': <gensim.models.keyedvectors.Vocab at 0x7fbf38143e48>,\n",
       " 'says': <gensim.models.keyedvectors.Vocab at 0x7fbf38143eb8>,\n",
       " 'week': <gensim.models.keyedvectors.Vocab at 0x7fbf38143f28>,\n",
       " 'where': <gensim.models.keyedvectors.Vocab at 0x7fbf38143f98>,\n",
       " 'while': <gensim.models.keyedvectors.Vocab at 0x7fbf38146048>,\n",
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       " 'being': <gensim.models.keyedvectors.Vocab at 0x7fbf38146128>,\n",
       " 'government': <gensim.models.keyedvectors.Vocab at 0x7fbf38146160>,\n",
       " 'your': <gensim.models.keyedvectors.Vocab at 0x7fbf381461d0>,\n",
       " '#-#': <gensim.models.keyedvectors.Vocab at 0x7fbf38146240>,\n",
       " 'home': <gensim.models.keyedvectors.Vocab at 0x7fbf381462b0>,\n",
       " 'going': <gensim.models.keyedvectors.Vocab at 0x7fbf38146320>,\n",
       " 'my': <gensim.models.keyedvectors.Vocab at 0x7fbf38146390>,\n",
       " 'good': <gensim.models.keyedvectors.Vocab at 0x7fbf38146400>,\n",
       " 'They': <gensim.models.keyedvectors.Vocab at 0x7fbf38146470>,\n",
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       " 'should': <gensim.models.keyedvectors.Vocab at 0x7fbf38146550>,\n",
       " 'many': <gensim.models.keyedvectors.Vocab at 0x7fbf381465c0>,\n",
       " 'way': <gensim.models.keyedvectors.Vocab at 0x7fbf38146630>,\n",
       " 'those': <gensim.models.keyedvectors.Vocab at 0x7fbf381466a0>,\n",
       " 'four': <gensim.models.keyedvectors.Vocab at 0x7fbf38146710>,\n",
       " 'during': <gensim.models.keyedvectors.Vocab at 0x7fbf38146780>,\n",
       " 'such': <gensim.models.keyedvectors.Vocab at 0x7fbf381467f0>,\n",
       " 'may': <gensim.models.keyedvectors.Vocab at 0x7fbf38146860>,\n",
       " 'very': <gensim.models.keyedvectors.Vocab at 0x7fbf381468d0>,\n",
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       " 'since': <gensim.models.keyedvectors.Vocab at 0x7fbf381469b0>,\n",
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       " 'take': <gensim.models.keyedvectors.Vocab at 0x7fbf38146a90>,\n",
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       " 'then': <gensim.models.keyedvectors.Vocab at 0x7fbf38146ba8>,\n",
       " '%': <gensim.models.keyedvectors.Vocab at 0x7fbf38146be0>,\n",
       " 'next': <gensim.models.keyedvectors.Vocab at 0x7fbf38146c50>,\n",
       " '#,###': <gensim.models.keyedvectors.Vocab at 0x7fbf38146cc0>,\n",
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       " 'much': <gensim.models.keyedvectors.Vocab at 0x7fbf38146da0>,\n",
       " 'still': <gensim.models.keyedvectors.Vocab at 0x7fbf38146e10>,\n",
       " 'go': <gensim.models.keyedvectors.Vocab at 0x7fbf38146e80>,\n",
       " 'think': <gensim.models.keyedvectors.Vocab at 0x7fbf38146ef0>,\n",
       " 'old': <gensim.models.keyedvectors.Vocab at 0x7fbf38146f60>,\n",
       " 'even': <gensim.models.keyedvectors.Vocab at 0x7fbf38146fd0>,\n",
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       " 'world': <gensim.models.keyedvectors.Vocab at 0x7fbf381490f0>,\n",
       " 'see': <gensim.models.keyedvectors.Vocab at 0x7fbf38149160>,\n",
       " 'say': <gensim.models.keyedvectors.Vocab at 0x7fbf381491d0>,\n",
       " 'business': <gensim.models.keyedvectors.Vocab at 0x7fbf38149208>,\n",
       " 'five': <gensim.models.keyedvectors.Vocab at 0x7fbf38149278>,\n",
       " 'told': <gensim.models.keyedvectors.Vocab at 0x7fbf381492e8>,\n",
       " 'under': <gensim.models.keyedvectors.Vocab at 0x7fbf38149358>,\n",
       " 'us': <gensim.models.keyedvectors.Vocab at 0x7fbf381493c8>,\n",
       " '1': <gensim.models.keyedvectors.Vocab at 0x7fbf38149400>,\n",
       " 'these': <gensim.models.keyedvectors.Vocab at 0x7fbf38149470>,\n",
       " 'If': <gensim.models.keyedvectors.Vocab at 0x7fbf381494e0>,\n",
       " 'right': <gensim.models.keyedvectors.Vocab at 0x7fbf38149550>,\n",
       " 'And': <gensim.models.keyedvectors.Vocab at 0x7fbf381495c0>,\n",
       " 'me': <gensim.models.keyedvectors.Vocab at 0x7fbf38149630>,\n",
       " 'between': <gensim.models.keyedvectors.Vocab at 0x7fbf381496a0>,\n",
       " 'play': <gensim.models.keyedvectors.Vocab at 0x7fbf38149710>,\n",
       " 'help': <gensim.models.keyedvectors.Vocab at 0x7fbf38149780>,\n",
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       " 'market': <gensim.models.keyedvectors.Vocab at 0x7fbf38149860>,\n",
       " 'That': <gensim.models.keyedvectors.Vocab at 0x7fbf381498d0>,\n",
       " 'know': <gensim.models.keyedvectors.Vocab at 0x7fbf38149940>,\n",
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       " 'long': <gensim.models.keyedvectors.Vocab at 0x7fbf38149a90>,\n",
       " 'information': <gensim.models.keyedvectors.Vocab at 0x7fbf38149ac8>,\n",
       " 'points': <gensim.models.keyedvectors.Vocab at 0x7fbf38149b38>,\n",
       " 'does': <gensim.models.keyedvectors.Vocab at 0x7fbf38149ba8>,\n",
       " 'both': <gensim.models.keyedvectors.Vocab at 0x7fbf38149c18>,\n",
       " 'There': <gensim.models.keyedvectors.Vocab at 0x7fbf38149c88>,\n",
       " 'part': <gensim.models.keyedvectors.Vocab at 0x7fbf38149cf8>,\n",
       " 'around': <gensim.models.keyedvectors.Vocab at 0x7fbf38149d68>,\n",
       " 'police': <gensim.models.keyedvectors.Vocab at 0x7fbf38149dd8>,\n",
       " 'want': <gensim.models.keyedvectors.Vocab at 0x7fbf38149e48>,\n",
       " \"'ve\": <gensim.models.keyedvectors.Vocab at 0x7fbf38149eb8>,\n",
       " 'based': <gensim.models.keyedvectors.Vocab at 0x7fbf38149f28>,\n",
       " 'For': <gensim.models.keyedvectors.Vocab at 0x7fbf38149f98>,\n",
       " 'got': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4048>,\n",
       " 'third': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb40b8>,\n",
       " 'school': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4128>,\n",
       " 'left': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4198>,\n",
       " 'another': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4208>,\n",
       " 'country': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4278>,\n",
       " 'need': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb42e8>,\n",
       " '2': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4320>,\n",
       " 'best': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4390>,\n",
       " 'win': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4400>,\n",
       " 'quarter': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4470>,\n",
       " 'use': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb44e0>,\n",
       " 'today': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4550>,\n",
       " '##.#': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb45c0>,\n",
       " 'same': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4630>,\n",
       " 'public': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb46a0>,\n",
       " 'run': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4710>,\n",
       " 'Friday': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4780>,\n",
       " 'set': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb47f0>,\n",
       " 'month': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4860>,\n",
       " 'top': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb48d0>,\n",
       " 'billion': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4940>,\n",
       " 'Tuesday': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb49b0>,\n",
       " 'come': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4a20>,\n",
       " 'Monday': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4a90>,\n",
       " 'She': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4b00>,\n",
       " 'city': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4b70>,\n",
       " 'place': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4be0>,\n",
       " 'night': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4c50>,\n",
       " 'six': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4cc0>,\n",
       " 'each': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4d30>,\n",
       " 'Thursday': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4d68>,\n",
       " '###,###': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4dd8>,\n",
       " 'Wednesday': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4e10>,\n",
       " 'here': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4e80>,\n",
       " 'You': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4ef0>,\n",
       " 'group': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4f60>,\n",
       " 'really': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb4fd0>,\n",
       " 'found': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8080>,\n",
       " 'As': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb80f0>,\n",
       " 'used': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8160>,\n",
       " '3': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8198>,\n",
       " 'lot': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8208>,\n",
       " \"'m\": <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8278>,\n",
       " 'money': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb82e8>,\n",
       " 'put': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8358>,\n",
       " 'games': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb83c8>,\n",
       " 'support': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8438>,\n",
       " 'program': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb84a8>,\n",
       " 'half': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8518>,\n",
       " 'report': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8588>,\n",
       " 'family': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb85f8>,\n",
       " 'months': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8668>,\n",
       " 'number': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb86d8>,\n",
       " 'officials': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8710>,\n",
       " 'am': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8780>,\n",
       " 'former': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb87f0>,\n",
       " 'own': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8860>,\n",
       " 'man': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb88d0>,\n",
       " 'Saturday': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8908>,\n",
       " 'too': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8978>,\n",
       " 'better': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb89e8>,\n",
       " 'days': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8a58>,\n",
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       " 'lead': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8b38>,\n",
       " 'life': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8ba8>,\n",
       " 'American': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8be0>,\n",
       " '##-##': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8c50>,\n",
       " 'show': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8cc0>,\n",
       " 'past': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8d30>,\n",
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       " 'expected': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8e48>,\n",
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       " 'great': <gensim.models.keyedvectors.Vocab at 0x7fbf37cb8f28>,\n",
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       " 'children': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba048>,\n",
       " 'hit': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba0b8>,\n",
       " 'area': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba128>,\n",
       " 'system': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba198>,\n",
       " 'every': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba208>,\n",
       " 'pm': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba278>,\n",
       " 'big': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba2e8>,\n",
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       " 'few': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba3c8>,\n",
       " 'per': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba438>,\n",
       " 'members': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba4a8>,\n",
       " 'Sunday': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba518>,\n",
       " 'early': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba588>,\n",
       " 'point': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba5f8>,\n",
       " 'start': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba668>,\n",
       " 'companies': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba6a0>,\n",
       " 'little': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba710>,\n",
       " '&': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba748>,\n",
       " 'case': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba7b8>,\n",
       " 'ago': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba828>,\n",
       " 'local': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba898>,\n",
       " 'according': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba8d0>,\n",
       " 'never': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba940>,\n",
       " '5': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba978>,\n",
       " 'without': <gensim.models.keyedvectors.Vocab at 0x7fbf37cba9e8>,\n",
       " 'sales': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbaa58>,\n",
       " 'until': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbaac8>,\n",
       " 'went': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbab38>,\n",
       " 'players': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbaba8>,\n",
       " '##th': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbac18>,\n",
       " 'New_York': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbac50>,\n",
       " 'won': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbacc0>,\n",
       " 'financial': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbacf8>,\n",
       " 'news': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbad68>,\n",
       " '4': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbada0>,\n",
       " 'When': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbae10>,\n",
       " 'share': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbae80>,\n",
       " 'several': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbaef0>,\n",
       " 'free': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbaf60>,\n",
       " 'away': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbafd0>,\n",
       " '##.##': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd080>,\n",
       " 'already': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd0f0>,\n",
       " 'On': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd160>,\n",
       " 'industry': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd198>,\n",
       " \"'ll\": <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd208>,\n",
       " 'call': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd278>,\n",
       " 'With': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd2e8>,\n",
       " 'students': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd320>,\n",
       " 'line': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd390>,\n",
       " 'available': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd3c8>,\n",
       " 'County': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd438>,\n",
       " 'making': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd4a8>,\n",
       " 'held': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd518>,\n",
       " 'final': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd588>,\n",
       " '#:##': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd5f8>,\n",
       " 'power': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd668>,\n",
       " 'plan': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd6d8>,\n",
       " 'might': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd748>,\n",
       " 'least': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd7b8>,\n",
       " 'look': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd828>,\n",
       " 'forward': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd898>,\n",
       " 'give': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd908>,\n",
       " 'At': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd978>,\n",
       " 'again': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbd9e8>,\n",
       " 'later': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbda58>,\n",
       " 'full': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdac8>,\n",
       " 'must': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdb38>,\n",
       " 'things': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdba8>,\n",
       " 'major': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdc18>,\n",
       " 'community': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdc50>,\n",
       " 'announced': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdc88>,\n",
       " 'open': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdcf8>,\n",
       " 'record': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdd68>,\n",
       " 'reported': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdda0>,\n",
       " 'court': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbde10>,\n",
       " 'working': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbde80>,\n",
       " 'able': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdef0>,\n",
       " 'something': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdf28>,\n",
       " 'president': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdf60>,\n",
       " 'meeting': <gensim.models.keyedvectors.Vocab at 0x7fbf37cbdfd0>,\n",
       " 'keep': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0080>,\n",
       " 'March': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc00f0>,\n",
       " 'future': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0160>,\n",
       " 'far': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc01d0>,\n",
       " 'deal': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0240>,\n",
       " 'City': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc02b0>,\n",
       " 'May': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0320>,\n",
       " 'development': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0358>,\n",
       " 'University': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0390>,\n",
       " 'find': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0400>,\n",
       " 'times': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0470>,\n",
       " 'After': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc04e0>,\n",
       " 'office': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0550>,\n",
       " 'led': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc05c0>,\n",
       " 'among': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0630>,\n",
       " 'June': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc06a0>,\n",
       " 'increase': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc06d8>,\n",
       " 'China': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0748>,\n",
       " 'John': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc07b8>,\n",
       " 'whether': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0828>,\n",
       " 'cost': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0898>,\n",
       " 'security': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc08d0>,\n",
       " 'job': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0940>,\n",
       " 'less': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc09b0>,\n",
       " 'head': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0a20>,\n",
       " 'seven': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0a90>,\n",
       " 'growth': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0b00>,\n",
       " 'lost': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0b70>,\n",
       " 'pay': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0be0>,\n",
       " 'looking': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0c50>,\n",
       " 'provide': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0cc0>,\n",
       " '6': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0cf8>,\n",
       " 'To': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0d68>,\n",
       " 'plans': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0dd8>,\n",
       " 'products': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0e10>,\n",
       " 'car': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0e80>,\n",
       " 'recent': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0ef0>,\n",
       " 'hard': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0f60>,\n",
       " 'always': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc0fd0>,\n",
       " 'include': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3080>,\n",
       " 'women': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc30f0>,\n",
       " 'across': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3160>,\n",
       " 'tax': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc31d0>,\n",
       " 'water': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3240>,\n",
       " 'April': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc32b0>,\n",
       " 'continue': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc32e8>,\n",
       " 'important': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3320>,\n",
       " 'different': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3358>,\n",
       " 'close': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc33c8>,\n",
       " '7': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3400>,\n",
       " 'One': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3470>,\n",
       " 'late': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc34e0>,\n",
       " 'decision': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3518>,\n",
       " 'current': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3588>,\n",
       " 'law': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc35f8>,\n",
       " 'within': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3668>,\n",
       " 'along': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc36d8>,\n",
       " 'played': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3748>,\n",
       " 'move': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc37b8>,\n",
       " 'United_States': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc37f0>,\n",
       " 'enough': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3860>,\n",
       " 'become': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc38d0>,\n",
       " 'side': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3940>,\n",
       " 'national': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3978>,\n",
       " 'Inc.': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc39e8>,\n",
       " 'results': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3a58>,\n",
       " 'level': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3ac8>,\n",
       " 'loss': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3b38>,\n",
       " 'economic': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3b70>,\n",
       " 'coach': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3be0>,\n",
       " 'near': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3c50>,\n",
       " 'getting': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3cc0>,\n",
       " 'price': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3d30>,\n",
       " 'Department': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3d68>,\n",
       " 'event': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3dd8>,\n",
       " 'fourth': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3e48>,\n",
       " 'change': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3eb8>,\n",
       " 'All': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3f28>,\n",
       " 'small': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc3f98>,\n",
       " 'board': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7048>,\n",
       " 'National': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7080>,\n",
       " 'So': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc70f0>,\n",
       " 'goal': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7160>,\n",
       " 'taken': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc71d0>,\n",
       " 'field': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7240>,\n",
       " 'prices': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc72b0>,\n",
       " 'weeks': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7320>,\n",
       " 'men': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7390>,\n",
       " 'asked': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7400>,\n",
       " 'eight': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7470>,\n",
       " 'data': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc74e0>,\n",
       " 'shot': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7550>,\n",
       " 'New': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc75c0>,\n",
       " 'started': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7630>,\n",
       " 'July': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc76a0>,\n",
       " 'director': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc76d8>,\n",
       " 'President': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7710>,\n",
       " 'party': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7780>,\n",
       " 'federal': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc77f0>,\n",
       " 'done': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7860>,\n",
       " 'political': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7898>,\n",
       " 'minutes': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7908>,\n",
       " 'taking': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7978>,\n",
       " 'Company': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc79e8>,\n",
       " 'technology': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7a20>,\n",
       " 'project': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7a90>,\n",
       " 'center': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7b00>,\n",
       " 'leading': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7b70>,\n",
       " 'issue': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7be0>,\n",
       " 'though': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7c50>,\n",
       " 'having': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7cc0>,\n",
       " 'period': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7d30>,\n",
       " 'likely': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7da0>,\n",
       " 'scored': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7e10>,\n",
       " '8': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7e48>,\n",
       " 'strong': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7eb8>,\n",
       " 'series': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7f28>,\n",
       " 'military': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7f60>,\n",
       " 'seen': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc7fd0>,\n",
       " 'trying': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8080>,\n",
       " 'What': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc80f0>,\n",
       " 'coming': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8160>,\n",
       " 'process': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc81d0>,\n",
       " 'building': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8208>,\n",
       " 'behind': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8278>,\n",
       " 'performance': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc82b0>,\n",
       " 'management': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc82e8>,\n",
       " 'Iraq': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8358>,\n",
       " 'saying': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc83c8>,\n",
       " 'earlier': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8438>,\n",
       " 'believe': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc84a8>,\n",
       " 'oil': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8518>,\n",
       " 'given': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8588>,\n",
       " 'Police': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc85f8>,\n",
       " 'customers': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8630>,\n",
       " 'due': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc86a0>,\n",
       " 'following': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc86d8>,\n",
       " 'term': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8748>,\n",
       " 'others': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc87b8>,\n",
       " 'statement': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc87f0>,\n",
       " 'international': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8828>,\n",
       " 'economy': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8898>,\n",
       " 'health': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8908>,\n",
       " 'thing': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8978>,\n",
       " 'Obama': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc89e8>,\n",
       " 'return': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8a58>,\n",
       " 'killed': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8ac8>,\n",
       " 'Washington': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8b00>,\n",
       " 'further': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8b70>,\n",
       " 'However': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8be0>,\n",
       " 'doing': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8c50>,\n",
       " 'face': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8cc0>,\n",
       " 'low': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8d30>,\n",
       " 'higher': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8da0>,\n",
       " 'site': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8e10>,\n",
       " 'once': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8e80>,\n",
       " 'yet': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8ef0>,\n",
       " 'hours': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8f60>,\n",
       " 'America': <gensim.models.keyedvectors.Vocab at 0x7fbf37cc8fd0>,\n",
       " 'control': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb080>,\n",
       " 'received': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb0b8>,\n",
       " 'rate': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb128>,\n",
       " 'career': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb198>,\n",
       " 'Bush': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb208>,\n",
       " 'teams': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb278>,\n",
       " 'known': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb2e8>,\n",
       " 'offer': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb358>,\n",
       " 'race': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb3c8>,\n",
       " 'ever': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb438>,\n",
       " 'experience': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb470>,\n",
       " 'playing': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb4e0>,\n",
       " 'name': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb550>,\n",
       " 'possible': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb588>,\n",
       " 'countries': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb5c0>,\n",
       " 'Mr.': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb630>,\n",
       " 'average': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb6a0>,\n",
       " 'together': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb6d8>,\n",
       " 'using': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb748>,\n",
       " '9': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb780>,\n",
       " 'cut': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb7f0>,\n",
       " 'While': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb860>,\n",
       " 'total': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb8d0>,\n",
       " 'round': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb940>,\n",
       " 'young': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccb9b0>,\n",
       " 'nearly': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccba20>,\n",
       " 'shares': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccba90>,\n",
       " 'member': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbb00>,\n",
       " 'campaign': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbb38>,\n",
       " 'media': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbba8>,\n",
       " 'needs': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbc18>,\n",
       " 'why': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbc88>,\n",
       " 'house': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbcf8>,\n",
       " 'issues': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbd68>,\n",
       " 'costs': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbdd8>,\n",
       " 'fire': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbe48>,\n",
       " '##-#': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbeb8>,\n",
       " 'victory': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbf28>,\n",
       " 'player': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccbf98>,\n",
       " 'began': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf048>,\n",
       " 'sure': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf0b8>,\n",
       " 'story': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf128>,\n",
       " 'per_cent': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf160>,\n",
       " 'North': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf1d0>,\n",
       " 'His': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf240>,\n",
       " 'staff': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf2b0>,\n",
       " 'order': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf320>,\n",
       " 'war': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf390>,\n",
       " 'large': <gensim.models.keyedvectors.Vocab at 0x7fbf37ccf400>,\n",
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       " 'Do': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce36a0>,\n",
       " 'ready': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3710>,\n",
       " 'arrested': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3748>,\n",
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       " 'rest': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3b38>,\n",
       " 'Johnson': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3ba8>,\n",
       " 'terms': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3c18>,\n",
       " 'expect': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3c88>,\n",
       " 'England': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3cf8>,\n",
       " 'Israel': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3d68>,\n",
       " 'despite': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3dd8>,\n",
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       " 'pressure': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3ef0>,\n",
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       " 'step': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce3fd0>,\n",
       " 'remain': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6080>,\n",
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       " 'date': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce61d0>,\n",
       " 'whole': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6240>,\n",
       " 'land': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce62b0>,\n",
       " 'growing': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6320>,\n",
       " 'James': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6390>,\n",
       " 'Internet': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce63c8>,\n",
       " 'projects': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6400>,\n",
       " 'British': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6470>,\n",
       " 'cases': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce64e0>,\n",
       " 'ground': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6550>,\n",
       " 'legal': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce65c0>,\n",
       " 'International': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce65f8>,\n",
       " 'agreed': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6668>,\n",
       " 'tell': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce66d8>,\n",
       " 'test': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6748>,\n",
       " 'everyone': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6780>,\n",
       " 'pretty': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce67f0>,\n",
       " 'authorities': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6828>,\n",
       " 'Two': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6898>,\n",
       " 'above': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6908>,\n",
       " 'moved': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6978>,\n",
       " 'profit': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce69e8>,\n",
       " 'throughout': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6a20>,\n",
       " 'inside': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6a90>,\n",
       " 'ability': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6b00>,\n",
       " 'overall': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6b70>,\n",
       " 'pass': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6be0>,\n",
       " 'officers': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6c18>,\n",
       " 'rather': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6c88>,\n",
       " 'Australia': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6cc0>,\n",
       " 'actually': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6cf8>,\n",
       " 'county': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6d68>,\n",
       " 'amount': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6dd8>,\n",
       " 'scheduled': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6e10>,\n",
       " 'themselves': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6e48>,\n",
       " 'organization': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6e80>,\n",
       " 'giving': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6ef0>,\n",
       " 'credit': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6f60>,\n",
       " 'father': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce6fd0>,\n",
       " 'drug': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7080>,\n",
       " 'investigation': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce70b8>,\n",
       " 'families': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce70f0>,\n",
       " 'Republican': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7128>,\n",
       " 'funds': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7198>,\n",
       " 'patients': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce71d0>,\n",
       " 'takes': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7240>,\n",
       " 'systems': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce72b0>,\n",
       " 'Japan': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7320>,\n",
       " 'complete': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7358>,\n",
       " 'sold': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce73c8>,\n",
       " 'practice': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7400>,\n",
       " 'calls': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7470>,\n",
       " '•': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce74a8>,\n",
       " 'UK': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7518>,\n",
       " 'force': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7588>,\n",
       " 'student': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce75f8>,\n",
       " 'idea': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7668>,\n",
       " 'reached': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce76d8>,\n",
       " 'reason': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7748>,\n",
       " 'levels': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce77b8>,\n",
       " 'space': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7828>,\n",
       " 'competition': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7860>,\n",
       " 'forces': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce78d0>,\n",
       " 'sector': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7940>,\n",
       " 'Last': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce79b0>,\n",
       " 'tried': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7a20>,\n",
       " 'common': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7a90>,\n",
       " 'homes': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7b00>,\n",
       " 'stage': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7b70>,\n",
       " 'department': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7ba8>,\n",
       " 'named': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7c18>,\n",
       " 'earnings': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7c50>,\n",
       " 'offers': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7cc0>,\n",
       " 'star': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7d30>,\n",
       " 'certain': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7da0>,\n",
       " 'double': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7e10>,\n",
       " 'longer': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7e80>,\n",
       " 'followed': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7eb8>,\n",
       " 'cause': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7f28>,\n",
       " 'Association': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7f60>,\n",
       " 'signed': <gensim.models.keyedvectors.Vocab at 0x7fbf37ce7fd0>,\n",
       " 'committee': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec048>,\n",
       " 'hour': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec0b8>,\n",
       " 'college': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec128>,\n",
       " 'Pakistan': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec160>,\n",
       " 'users': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec1d0>,\n",
       " 'Iran': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec240>,\n",
       " 'sign': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec2b0>,\n",
       " 'living': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec320>,\n",
       " 'failed': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec390>,\n",
       " 'reach': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec400>,\n",
       " 'quickly': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec470>,\n",
       " 'receive': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec4e0>,\n",
       " 'debt': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec550>,\n",
       " 'sale': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec5c0>,\n",
       " 'Board': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec630>,\n",
       " 'Americans': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec668>,\n",
       " 'Road': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec6d8>,\n",
       " 'Brown': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec748>,\n",
       " 'insurance': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec780>,\n",
       " '##:##': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec7f0>,\n",
       " 'anyone': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec860>,\n",
       " 'tournament': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec898>,\n",
       " 'More': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec908>,\n",
       " 'gas': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec978>,\n",
       " 'talks': <gensim.models.keyedvectors.Vocab at 0x7fbf37cec9e8>,\n",
       " 'serious': <gensim.models.keyedvectors.Vocab at 0x7fbf37ceca58>,\n",
       " 'required': <gensim.models.keyedvectors.Vocab at 0x7fbf37ceca90>,\n",
       " 'sell': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecb00>,\n",
       " 'construction': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecb38>,\n",
       " 'evidence': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecb70>,\n",
       " 'remains': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecbe0>,\n",
       " 'black': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecc50>,\n",
       " 'below': <gensim.models.keyedvectors.Vocab at 0x7fbf37ceccc0>,\n",
       " 'improve': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecd30>,\n",
       " 'crisis': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecda0>,\n",
       " 'address': <gensim.models.keyedvectors.Vocab at 0x7fbf37cece10>,\n",
       " 'questions': <gensim.models.keyedvectors.Vocab at 0x7fbf37cece48>,\n",
       " 'easy': <gensim.models.keyedvectors.Vocab at 0x7fbf37ceceb8>,\n",
       " 'begin': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecf28>,\n",
       " 'view': <gensim.models.keyedvectors.Vocab at 0x7fbf37cecf98>,\n",
       " 'School': <gensim.models.keyedvectors.Vocab at 0x7fbf37cef048>,\n",
       " 'heard': <gensim.models.keyedvectors.Vocab at 0x7fbf37cef0b8>,\n",
       " 'executive': <gensim.models.keyedvectors.Vocab at 0x7fbf37cef0f0>,\n",
       " 'raised': <gensim.models.keyedvectors.Vocab at 0x7fbf37cef160>,\n",
       " ...}"
      ]
     },
     "execution_count": 67,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_vectors = model.wv\n",
    "word_vectors.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 400
    },
    "colab_type": "code",
    "id": "xgcXkmou8pXZ",
    "outputId": "e9a6ab2e-1062-451d-ea05-74cb3b640e77"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Jaw-dropping</th>\n",
       "      <th>OK</th>\n",
       "      <th>Obnoxious</th>\n",
       "      <th>Inspiring</th>\n",
       "      <th>max_rating</th>\n",
       "      <th>yr</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>4553</td>\n",
       "      <td>Sir Ken Robinson makes an entertaining and pro...</td>\n",
       "      <td>1164</td>\n",
       "      <td>60</td>\n",
       "      <td>Ken Robinson</td>\n",
       "      <td>Ken Robinson: Do schools kill creativity?</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 865, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Author/educator</td>\n",
       "      <td>['children', 'creativity', 'culture', 'dance',...</td>\n",
       "      <td>Do schools kill creativity?</td>\n",
       "      <td>https://www.ted.com/talks/ken_robinson_says_sc...</td>\n",
       "      <td>47227110</td>\n",
       "      <td>Good morning. How are you?(Laughter)It's been ...</td>\n",
       "      <td>19645</td>\n",
       "      <td>4573</td>\n",
       "      <td>6073</td>\n",
       "      <td>3253</td>\n",
       "      <td>387</td>\n",
       "      <td>242</td>\n",
       "      <td>7346</td>\n",
       "      <td>10581</td>\n",
       "      <td>300</td>\n",
       "      <td>10704</td>\n",
       "      <td>4439</td>\n",
       "      <td>1174</td>\n",
       "      <td>209</td>\n",
       "      <td>24924</td>\n",
       "      <td>Inspiring</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>265</td>\n",
       "      <td>With the same humor and humanity he exuded in ...</td>\n",
       "      <td>977</td>\n",
       "      <td>43</td>\n",
       "      <td>Al Gore</td>\n",
       "      <td>Al Gore: Averting the climate crisis</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 243, 'hero': 'https://pe.tedcdn.com/im...</td>\n",
       "      <td>Climate advocate</td>\n",
       "      <td>['alternative energy', 'cars', 'climate change...</td>\n",
       "      <td>Averting the climate crisis</td>\n",
       "      <td>https://www.ted.com/talks/al_gore_on_averting_...</td>\n",
       "      <td>3200520</td>\n",
       "      <td>Thank you so much, Chris. And it's truly a gre...</td>\n",
       "      <td>544</td>\n",
       "      <td>58</td>\n",
       "      <td>56</td>\n",
       "      <td>139</td>\n",
       "      <td>113</td>\n",
       "      <td>62</td>\n",
       "      <td>443</td>\n",
       "      <td>132</td>\n",
       "      <td>258</td>\n",
       "      <td>268</td>\n",
       "      <td>116</td>\n",
       "      <td>203</td>\n",
       "      <td>131</td>\n",
       "      <td>413</td>\n",
       "      <td>Funny</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>124</td>\n",
       "      <td>New York Times columnist David Pogue takes aim...</td>\n",
       "      <td>1286</td>\n",
       "      <td>26</td>\n",
       "      <td>David Pogue</td>\n",
       "      <td>David Pogue: Simplicity sells</td>\n",
       "      <td>1</td>\n",
       "      <td>[{'id': 1725, 'hero': 'https://pe.tedcdn.com/i...</td>\n",
       "      <td>Technology columnist</td>\n",
       "      <td>['computers', 'entertainment', 'interface desi...</td>\n",
       "      <td>Simplicity sells</td>\n",
       "      <td>https://www.ted.com/talks/david_pogue_says_sim...</td>\n",
       "      <td>1636292</td>\n",
       "      <td>(Music: \"The Sound of Silence,\" Simon &amp; Garfun...</td>\n",
       "      <td>964</td>\n",
       "      <td>60</td>\n",
       "      <td>183</td>\n",
       "      <td>45</td>\n",
       "      <td>78</td>\n",
       "      <td>27</td>\n",
       "      <td>395</td>\n",
       "      <td>166</td>\n",
       "      <td>104</td>\n",
       "      <td>230</td>\n",
       "      <td>54</td>\n",
       "      <td>146</td>\n",
       "      <td>142</td>\n",
       "      <td>230</td>\n",
       "      <td>Funny</td>\n",
       "      <td>2006</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  comments  ...    yr  month\n",
       "0           0      4553  ...  2006      6\n",
       "1           1       265  ...  2006      6\n",
       "2           2       124  ...  2006      6\n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upload the \"Cleanned\" dataset\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/PoojaDoctor/Data144-FinalProject/master/datasets/cleaned.csv'\n",
    "df_cleanned = pd.read_csv(url)\n",
    "# Dataset is now stored in a Pandas Dataframe\n",
    "df_cleanned.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zgqiFS-a94bl"
   },
   "source": [
    "# **LINEAR REGRESSION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A2klOIav98Cp"
   },
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt  \n",
    "import seaborn as seabornInstance \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Ks15zAmQ-R8C",
    "outputId": "e296dcac-d599-4cdc-88be-8e36095462b0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2467, 32)"
      ]
     },
     "execution_count": 96,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Upload the \"Cleanned\" dataset\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/PoojaDoctor/Data144-FinalProject/master/datasets/cleaned.csv'\n",
    "dataset = pd.read_csv(url)\n",
    "# Dataset is now stored in a Pandas Dataframe\n",
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "colab_type": "code",
    "id": "h-rhoZ7o-bdA",
    "outputId": "69015309-e7d6-4d83-d600-f8dc031d48ea"
   },
   "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>Unnamed: 0</th>\n",
       "      <th>comments</th>\n",
       "      <th>duration</th>\n",
       "      <th>languages</th>\n",
       "      <th>num_speaker</th>\n",
       "      <th>views</th>\n",
       "      <th>Funny</th>\n",
       "      <th>Beautiful</th>\n",
       "      <th>Ingenious</th>\n",
       "      <th>Courageous</th>\n",
       "      <th>Longwinded</th>\n",
       "      <th>Confusing</th>\n",
       "      <th>Informative</th>\n",
       "      <th>Fascinating</th>\n",
       "      <th>Unconvincing</th>\n",
       "      <th>Persuasive</th>\n",
       "      <th>Jaw-dropping</th>\n",
       "      <th>OK</th>\n",
       "      <th>Obnoxious</th>\n",
       "      <th>Inspiring</th>\n",
       "      <th>yr</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2.467000e+03</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "      <td>2467.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1290.604783</td>\n",
       "      <td>193.425213</td>\n",
       "      <td>822.725578</td>\n",
       "      <td>28.310093</td>\n",
       "      <td>1.027969</td>\n",
       "      <td>1.747192e+06</td>\n",
       "      <td>157.035671</td>\n",
       "      <td>192.576814</td>\n",
       "      <td>152.175517</td>\n",
       "      <td>168.999595</td>\n",
       "      <td>32.922173</td>\n",
       "      <td>21.098500</td>\n",
       "      <td>354.209566</td>\n",
       "      <td>317.208756</td>\n",
       "      <td>53.531415</td>\n",
       "      <td>223.223754</td>\n",
       "      <td>148.400486</td>\n",
       "      <td>81.757195</td>\n",
       "      <td>25.818808</td>\n",
       "      <td>534.901094</td>\n",
       "      <td>2012.132550</td>\n",
       "      <td>6.337657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>745.473521</td>\n",
       "      <td>285.304705</td>\n",
       "      <td>334.002254</td>\n",
       "      <td>8.234100</td>\n",
       "      <td>0.208371</td>\n",
       "      <td>2.533097e+06</td>\n",
       "      <td>598.778813</td>\n",
       "      <td>478.431012</td>\n",
       "      <td>286.539868</td>\n",
       "      <td>445.398923</td>\n",
       "      <td>41.922335</td>\n",
       "      <td>31.827247</td>\n",
       "      <td>546.221176</td>\n",
       "      <td>629.911244</td>\n",
       "      <td>92.849048</td>\n",
       "      <td>470.826345</td>\n",
       "      <td>559.881195</td>\n",
       "      <td>90.024732</td>\n",
       "      <td>53.270378</td>\n",
       "      <td>1298.578684</td>\n",
       "      <td>3.035853</td>\n",
       "      <td>3.350184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>135.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.558950e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>641.500000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>584.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.891885e+05</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>102.500000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1315.000000</td>\n",
       "      <td>118.000000</td>\n",
       "      <td>853.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.149090e+06</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>217.000000</td>\n",
       "      <td>163.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>236.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1938.500000</td>\n",
       "      <td>224.000000</td>\n",
       "      <td>1048.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.739450e+06</td>\n",
       "      <td>91.000000</td>\n",
       "      <td>187.500000</td>\n",
       "      <td>172.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>346.500000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>231.500000</td>\n",
       "      <td>110.500000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>502.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2558.000000</td>\n",
       "      <td>6404.000000</td>\n",
       "      <td>3608.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.722711e+07</td>\n",
       "      <td>19645.000000</td>\n",
       "      <td>9437.000000</td>\n",
       "      <td>6073.000000</td>\n",
       "      <td>8668.000000</td>\n",
       "      <td>447.000000</td>\n",
       "      <td>531.000000</td>\n",
       "      <td>9787.000000</td>\n",
       "      <td>14447.000000</td>\n",
       "      <td>2194.000000</td>\n",
       "      <td>10704.000000</td>\n",
       "      <td>14728.000000</td>\n",
       "      <td>1341.000000</td>\n",
       "      <td>1361.000000</td>\n",
       "      <td>24924.000000</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0     comments  ...           yr        month\n",
       "count  2467.000000  2467.000000  ...  2467.000000  2467.000000\n",
       "mean   1290.604783   193.425213  ...  2012.132550     6.337657\n",
       "std     745.473521   285.304705  ...     3.035853     3.350184\n",
       "min       0.000000     2.000000  ...  2006.000000     1.000000\n",
       "25%     641.500000    63.000000  ...  2010.000000     4.000000\n",
       "50%    1315.000000   118.000000  ...  2012.000000     6.000000\n",
       "75%    1938.500000   224.000000  ...  2015.000000     9.000000\n",
       "max    2558.000000  6404.000000  ...  2017.000000    12.000000\n",
       "\n",
       "[8 rows x 22 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "yr39uzYR-pWj",
    "outputId": "94245232-3e02-4641-a576-ae858bfd815a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ONUG5SHEU4MsgVwohXze73hrvqrHBMVOue/vScY1dcu29re9eO1ejpkjhFOBj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\nSUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset.plot(x='comments', y='views', style='o')  \n",
    "plt.title('comments vs views')  \n",
    "plt.xlabel('comments')  \n",
    "plt.ylabel('views')  \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 623
    },
    "colab_type": "code",
    "id": "JrL3KxllCQUD",
    "outputId": "c2f6aead-1193-4087-e6ba-b4678b8c8414"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbf086186a0>"
      ]
     },
     "execution_count": 99,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "plt.tight_layout()\n",
    "seabornInstance.distplot(dataset['comments'])\n",
    "seabornInstance.distplot(dataset['views'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tknw9wBYCbQL"
   },
   "outputs": [],
   "source": [
    "X = dataset['comments'].values.reshape(-1,1)\n",
    "y = dataset['views'].values.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "naDuyAz3ChDy"
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "GQkPe2oDCjfv",
    "outputId": "9ad71b7e-2606-4d19-da5f-7113e8db928c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 102,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = LinearRegression()  \n",
    "regressor.fit(X_train, y_train) #training the algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "XB86eFYlCl0T",
    "outputId": "ef64dd5c-cdac-46c2-d2b4-0353a0151953"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[811099.47110646]\n",
      "[[4921.41552533]]\n"
     ]
    }
   ],
   "source": [
    "#To retrieve the intercept:\n",
    "print(regressor.intercept_)\n",
    "#For retrieving the slope:\n",
    "print(regressor.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 419
    },
    "colab_type": "code",
    "id": "yy1PGjEtCpf6",
    "outputId": "08dec6bd-fd6d-4cb8-9257-d88282ae7647"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>415566</td>\n",
       "      <td>1.268791e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1174189</td>\n",
       "      <td>4.679332e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2649262</td>\n",
       "      <td>1.219577e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>816019</td>\n",
       "      <td>2.262917e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1778739</td>\n",
       "      <td>1.362298e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>862238</td>\n",
       "      <td>9.981133e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>3404977</td>\n",
       "      <td>1.258948e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>711977</td>\n",
       "      <td>1.726483e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1405468</td>\n",
       "      <td>1.027642e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>2291444</td>\n",
       "      <td>1.632976e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>494 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Actual     Predicted\n",
       "0     415566  1.268791e+06\n",
       "1    1174189  4.679332e+06\n",
       "2    2649262  1.219577e+06\n",
       "3     816019  2.262917e+06\n",
       "4    1778739  1.362298e+06\n",
       "..       ...           ...\n",
       "489   862238  9.981133e+05\n",
       "490  3404977  1.258948e+06\n",
       "491   711977  1.726483e+06\n",
       "492  1405468  1.027642e+06\n",
       "493  2291444  1.632976e+06\n",
       "\n",
       "[494 rows x 2 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = regressor.predict(X_test)\n",
    "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 594
    },
    "colab_type": "code",
    "id": "CRvLVz1rCvWd",
    "outputId": "e733a5a4-ea33-4967-aacf-628274aeb784"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AAEDTFF8AAACapvgCAADQNMUXAACApim+AAAANE3xBQAAoGmKLwAAAE1TfAEAAGia4gsA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\nRnJHsQ8aQMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1 = df.head(25)\n",
    "df1.plot(kind='bar',figsize=(16,10))\n",
    "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
    "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 276
    },
    "colab_type": "code",
    "id": "uGhtNZVoCzVk",
    "outputId": "e8fb9773-79c2-4259-ec79-e93069c5b27f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZMHvH2wFszdQG05drAbANwF8AnKRH93Df57yOJID6AGtdBPOVuQum3/apYuoE\n8EmYnUbbAfx7BWp+1KnpeefNfmrW+K87Nb8MYHYl3j8ApsO0U54HsMX5+UgE1nW+ukO7vgGcB2Cz\nU9sLAO5xlp8JE8jbAfwOwGBn+RDn79ud+8/0ei0B1vy0s55fAPBLHJ0JU7L3Bw/9JyKyRFhaLkRE\n1E8MdCIiSzDQiYgswUAnIrIEA52IyBIMdCIiSzDQiYgs8f9xFZy5+aAwMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_test, y_test,  color='gray')\n",
    "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "BXQsevLvs_BS",
    "outputId": "31abf3fd-de4a-4378-df90-8d21173189cd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 1003063.9425852079\n",
      "Mean Squared Error: 5230758177844.003\n",
      "Root Mean Squared Error: 2287085.083210505\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))\n",
    "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))\n",
    "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l1umqBoQC3HI"
   },
   "outputs": [],
   "source": [
    "# https://towardsdatascience.com/a-beginners-guide-to-linear-regression-in-python-with-scikit-learn-83a8f7ae2b4f"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uvjuDtTb5cRc"
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   "source": [
    "Transcript:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
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   "source": [
    "# Upload the \"Transcript\" dataset\n",
    "\n",
    "url = 'https://raw.githubusercontent.com/PoojaDoctor/Data144-FinalProject/master/datasets/transcripts.csv'\n",
    "df_trans = pd.read_csv(url)\n",
    "# Dataset is now stored in a Pandas Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "id": "V0cFezWyCozo",
    "outputId": "caa6ef9e-c95b-40f1-eef0-203ad0c956d0"
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   "outputs": [
    {
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       "      <th>transcript</th>\n",
       "      <th>url</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Good morning. How are you?(Laughter)It's been ...</td>\n",
       "      <td>https://www.ted.com/talks/ken_robinson_says_sc...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thank you so much, Chris. And it's truly a gre...</td>\n",
       "      <td>https://www.ted.com/talks/al_gore_on_averting_...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(Music: \"The Sound of Silence,\" Simon &amp; Garfun...</td>\n",
       "      <td>https://www.ted.com/talks/david_pogue_says_sim...</td>\n",
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      "text/plain": [
       "                                          transcript                                                url\n",
       "0  Good morning. How are you?(Laughter)It's been ...  https://www.ted.com/talks/ken_robinson_says_sc...\n",
       "1  Thank you so much, Chris. And it's truly a gre...  https://www.ted.com/talks/al_gore_on_averting_...\n",
       "2  (Music: \"The Sound of Silence,\" Simon & Garfun...  https://www.ted.com/talks/david_pogue_says_sim..."
      ]
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    "df_trans.head(3)"
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