QUAD_MAIN_CODE.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "QUAD_MAIN_CODE.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oz8Ot5n1IPHt"
      },
      "source": [
        "#install important python packages\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tT4WTZ-NJDdc"
      },
      "source": [
        "#install some modules form sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5au63iQ_Gij",
        "outputId": "427b075d-ad89-499f-d455-58c05e4fd0bd"
      },
      "source": [
        "!pip install git+https://github.com/laxmimerit/preprocess_kgptalkie.git\n",
        "import preprocess_kgptalkie as pp\n",
        "from textblob import TextBlob\n",
        "import pickle"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/laxmimerit/preprocess_kgptalkie.git\n",
            "  Cloning https://github.com/laxmimerit/preprocess_kgptalkie.git to /tmp/pip-req-build-zosldqnv\n",
            "  Running command git clone -q https://github.com/laxmimerit/preprocess_kgptalkie.git /tmp/pip-req-build-zosldqnv\n",
            "Requirement already satisfied (use --upgrade to upgrade): preprocess-kgptalkie==0.1.3 from git+https://github.com/laxmimerit/preprocess_kgptalkie.git in /usr/local/lib/python3.6/dist-packages\n",
            "Building wheels for collected packages: preprocess-kgptalkie\n",
            "  Building wheel for preprocess-kgptalkie (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for preprocess-kgptalkie: filename=preprocess_kgptalkie-0.1.3-cp36-none-any.whl size=11742 sha256=31b8a60051c3363f4528426834bf7e35c0fddc9d01aac2e657a4f5e7fd22e684\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-_vn8vijk/wheels/a8/18/22/90afa4bd43247fb9a75b710a4a3fcd94966c022ce9e3c7d0a6\n",
            "Successfully built preprocess-kgptalkie\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e8pLfjd05dTl"
      },
      "source": [
        "url1='https://raw.githubusercontent.com/divyansh0608/QUAD/main/21nov/en.csv'\n",
        "url2='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22nov/en.csv'\n",
        "url3='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22nov/en2.csv'\n",
        "url4='https://raw.githubusercontent.com/divyansh0608/QUAD/main/23nov/en.csv'\n",
        "url5='https://raw.githubusercontent.com/divyansh0608/QUAD/main/23nov/en2.csv'\n",
        "url6='https://raw.githubusercontent.com/divyansh0608/QUAD/main/26nov/en.csv'\n",
        "url7='https://raw.githubusercontent.com/divyansh0608/QUAD/main/26nov/en3.csv'\n",
        "url8='https://raw.githubusercontent.com/divyansh0608/QUAD/main/26nov/en4.csv'\n",
        "url9='https://raw.githubusercontent.com/divyansh0608/QUAD/main/5dec/en.csv'\n",
        "url10='https://raw.githubusercontent.com/divyansh0608/QUAD/main/5dec/en2.csv'\n",
        "url11='https://raw.githubusercontent.com/divyansh0608/QUAD/main/6dec/en.csv'\n",
        "url12='https://raw.githubusercontent.com/divyansh0608/QUAD/main/7dec/en.csv'\n",
        "url13='https://raw.githubusercontent.com/divyansh0608/QUAD/main/7dec/en3.csv'\n",
        "url14='https://raw.githubusercontent.com/divyansh0608/QUAD/main/15dec/en1%23quad.csv'\n",
        "url15='https://raw.githubusercontent.com/divyansh0608/QUAD/main/15dec/en2%23indo-pacific.csv'\n",
        "url16='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22dec/en.csv'\n",
        "url17='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22dec/en_indo_pacific.csv'\n",
        "url18='https://raw.githubusercontent.com/divyansh0608/QUAD/main/10jan/en_indo_pacific.csv'\n",
        "url19='https://raw.githubusercontent.com/divyansh0608/QUAD/main/10jan/en_quad.csv'\n",
        "\n",
        "url20='https://raw.githubusercontent.com/divyansh0608/QUAD/main/21nov/hi.csv'\n",
        "url21='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22nov/hi.csv'\n",
        "url22='https://raw.githubusercontent.com/divyansh0608/QUAD/main/23nov/hi.csv'\n",
        "url23='https://raw.githubusercontent.com/divyansh0608/QUAD/main/26nov/hi.csv'\n",
        "url24='https://raw.githubusercontent.com/divyansh0608/QUAD/main/26nov/hi2.csv'\n",
        "url25='https://raw.githubusercontent.com/divyansh0608/QUAD/main/7dec/hi.csv'\n",
        "url26='https://raw.githubusercontent.com/divyansh0608/QUAD/main/15dec/hi1%23quad.csv'\n",
        "url27='https://raw.githubusercontent.com/divyansh0608/QUAD/main/15dec/hi_indopacific.csv'\n",
        "url28='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22dec/hi_indo_pacific.csv'\n",
        "url29='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22dec/hiquad.csv'\n",
        "\n",
        "url30='https://raw.githubusercontent.com/divyansh0608/QUAD/main/7dec/ja.csv'\n",
        "url31='https://raw.githubusercontent.com/divyansh0608/QUAD/main/15dec/ja_indopacific.csv'\n",
        "url32='https://raw.githubusercontent.com/divyansh0608/QUAD/main/22dec/ja_indo_pacific.csv'\n",
        "url33='https://raw.githubusercontent.com/divyansh0608/QUAD/main/10jan/ja_indo_pacific.csv'\n",
        "\n",
        "\n",
        "url35='https://raw.githubusercontent.com/taranjeet/hindi-tokenizer/master/stopwords.txt'\n",
        "stopword=pd.read_csv(url35,index_col=0)\n",
        "\n",
        "en1 = pd.read_csv(url1,index_col=0)\n",
        "en2= pd.read_csv(url2,index_col=0)\n",
        "en3= pd.read_csv(url3,index_col=0)\n",
        "en4= pd.read_csv(url4,index_col=0)\n",
        "en5= pd.read_csv(url5,index_col=0)\n",
        "en6= pd.read_csv(url6,index_col=0)\n",
        "en7= pd.read_csv(url7,index_col=0)\n",
        "en8= pd.read_csv(url8,index_col=0)\n",
        "en9= pd.read_csv(url9,index_col=0)\n",
        "en10= pd.read_csv(url10,index_col=0)\n",
        "en11= pd.read_csv(url11,index_col=0)\n",
        "en12= pd.read_csv(url12,index_col=0)\n",
        "en13= pd.read_csv(url13,index_col=0)\n",
        "en14= pd.read_csv(url14,index_col=0)\n",
        "en15= pd.read_csv(url15,index_col=0)\n",
        "en16= pd.read_csv(url16,index_col=0)\n",
        "en17= pd.read_csv(url17,index_col=0)\n",
        "en18= pd.read_csv(url18,index_col=0)\n",
        "en19= pd.read_csv(url19,index_col=0)\n",
        "\n",
        "en20= pd.read_csv(url20,index_col=0)\n",
        "en21= pd.read_csv(url21,index_col=0)\n",
        "en22= pd.read_csv(url22,index_col=0)\n",
        "en23= pd.read_csv(url23,index_col=0)\n",
        "en24= pd.read_csv(url24,index_col=0)\n",
        "en25= pd.read_csv(url25,index_col=0)\n",
        "en26= pd.read_csv(url26,index_col=0)\n",
        "en27= pd.read_csv(url27,index_col=0)\n",
        "en28= pd.read_csv(url28,index_col=0)\n",
        "en29= pd.read_csv(url29,index_col=0)\n",
        "\n",
        "en30= pd.read_csv(url30,index_col=0)\n",
        "en31= pd.read_csv(url31,index_col=0)\n",
        "en32= pd.read_csv(url32,index_col=0)\n",
        "en33= pd.read_csv(url33,index_col=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLQn3e0M2X9S"
      },
      "source": [
        "eni= pd.concat([en1,en2,en3,en4,en5,en6,en7,en8,en9,en10,en11,en12,en13,en14,en15,en16,en17,en18,en19])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UV7xdbCfiXHf"
      },
      "source": [
        "hi= pd.concat([en19,en20,en21,en22,en23,en24,en25,en26,en27,en28,en29])\r\n",
        "ja= pd.concat([en30,en31,en32,en33])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6C4iEqo2_aId"
      },
      "source": [
        "eni2=eni.sort_values(by=['Date'])\r\n",
        "hi2=hi.sort_values(by=['Date'])\r\n",
        "ja2=ja.sort_values(by=['Date'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "El9hJWa-B3nb"
      },
      "source": [
        "eni2['index_col'] = range(1, len(eni2) + 1)\r\n",
        "hi2['index_col'] = range(1, len(hi2) + 1)\r\n",
        "ja2['index_col'] = range(1, len(ja2) + 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F_x08eA_BRAA"
      },
      "source": [
        "df= eni2\r\n",
        "dfhi= hi2\r\n",
        "dfja= ja2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "SGUXgOUv-8R4",
        "outputId": "23e5fc41-9993-4282-f903-7a673a7cbd02"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>index_col</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>RT @RRRameshRRR: 50% of the #Quad (Japan &amp;amp;...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>NaN</td>\n",
              "      <td>Cool.\\n\\n#Quad (riplegic) military grouping 🙄 ...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>NaN</td>\n",
              "      <td>RT @hellfire_81: Cool.\\n\\n#Quad (riplegic) mil...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>India</td>\n",
              "      <td>The creation of the #Quad — with implications ...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>NaN</td>\n",
              "      <td>RT @asthana_shashi: #Jaishankar attends East A...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>India, France discuss counter-terrorism, coope...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>New Delhi</td>\n",
              "      <td>As #Quad prepares to meet the Chinese challeng...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29496</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>Every team is different. Let's create a perfec...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>This here is very tricky.. \\nYes It's true Aus...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>RT @CPR_India: As #Quad prepares to meet the C...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29499</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 5 columns</p>\n",
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            ],
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              "                           location  ... index_col\n",
              "user                                 ...          \n",
              "jagadishrjain             Hyderabad  ...         1\n",
              "hellfire_81                     NaN  ...         2\n",
              "Savitri4Ever                    NaN  ...         3\n",
              "orfonline                     India  ...         4\n",
              "InsightGL                       NaN  ...         5\n",
              "...                             ...  ...       ...\n",
              "RamKuma19784093          Puducherry  ...     29495\n",
              "CPR_India                 New Delhi  ...     29496\n",
              "Resort_Skelin              Hrvatska  ...     29497\n",
              "KBhairava1                      NaN  ...     29498\n",
              "Deepakk79951168  Deoghar, Jharkhand  ...     29499\n",
              "\n",
              "[29499 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "VXDvc3AdjGl7",
        "outputId": "b1278665-612f-4baf-d99d-d433e2dd56f9"
      },
      "source": [
        "dfhi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>index_col</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>Noida, India</td>\n",
              "      <td>अरब सागर में मालाबार युद्धाभ्यास का दूसरा फेज ...</td>\n",
              "      <td>2020-11-17 03:06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>Noida, India</td>\n",
              "      <td>अरब सागर में मालाबार युद्धाभ्यास का दूसरा फेज ...</td>\n",
              "      <td>2020-11-17 03:06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>Noida, India</td>\n",
              "      <td>अरब सागर में मालाबार युद्धाभ्यास का दूसरा फेज ...</td>\n",
              "      <td>2020-11-17 03:06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>Noida, India</td>\n",
              "      <td>देखिए देश-दुनिया की हर बड़ी खबर, लोकल To ग्लोब...</td>\n",
              "      <td>2020-11-17 03:19:32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>Noida, India</td>\n",
              "      <td>देखिए देश-दुनिया की हर बड़ी खबर, लोकल To ग्लोब...</td>\n",
              "      <td>2020-11-17 03:19:32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Harry_Robots</th>\n",
              "      <td>Renton, WA</td>\n",
              "      <td>RT @r_frojd: It's all about the details! 😃\\nTe...</td>\n",
              "      <td>2021-01-10 05:38:30</td>\n",
              "      <td>NaN</td>\n",
              "      <td>670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>New Delhi</td>\n",
              "      <td>As #Quad prepares to meet the Chinese challeng...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>671</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>Every team is different. Let's create a perfec...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>This here is very tricky.. \\nYes It's true Aus...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>RT @CPR_India: As #Quad prepares to meet the C...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>674</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>674 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                           location  ... index_col\n",
              "user                                 ...          \n",
              "TV9Bharatvarsh         Noida, India  ...         1\n",
              "TV9Bharatvarsh         Noida, India  ...         2\n",
              "TV9Bharatvarsh         Noida, India  ...         3\n",
              "TV9Bharatvarsh         Noida, India  ...         4\n",
              "TV9Bharatvarsh         Noida, India  ...         5\n",
              "...                             ...  ...       ...\n",
              "Harry_Robots             Renton, WA  ...       670\n",
              "CPR_India                 New Delhi  ...       671\n",
              "Resort_Skelin              Hrvatska  ...       672\n",
              "KBhairava1                      NaN  ...       673\n",
              "Deepakk79951168  Deoghar, Jharkhand  ...       674\n",
              "\n",
              "[674 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "1pLTPLN4nVXr",
        "outputId": "fbbcd34c-8d3a-4074-ebe1-b8ba9c260013"
      },
      "source": [
        "dfja"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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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>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>index_col</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ln__o0</th>\n",
              "      <td>ゲームしてる人だけᔦꙬᔨ</td>\n",
              "      <td>@yuk1dqma_quad 私の2リグか4リグ更新してもろて🥺</td>\n",
              "      <td>2020-11-30 15:03:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>RT @samemizu: ゲーム開発初めて6か月、ついにアプリをリリース出来ました🎉\\n『...</td>\n",
              "      <td>2020-11-30 15:03:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>今日からペンパアドベントか</td>\n",
              "      <td>2020-11-30 15:04:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hisakdan4</th>\n",
              "      <td>.gif</td>\n",
              "      <td>キーボード足で踏んでSEGAをQuadする動画めっちゃ見たいですね</td>\n",
              "      <td>2020-11-30 15:07:34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuk1dqma_quad</th>\n",
              "      <td>クアホしか勝たん♡</td>\n",
              "      <td>@Ln__o0 任せてください💪</td>\n",
              "      <td>2020-11-30 15:08:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riekohayakawa</th>\n",
              "      <td>Pacific-NZ-Kyoto</td>\n",
              "      <td>＜インドとオセアニア　可能性と機会 ＞クレオ・パスカルとファカファヌア卿 - インド太平洋研...</td>\n",
              "      <td>2021-01-09 21:49:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LesFW7SuraWxotB</th>\n",
              "      <td>［呪い返し］</td>\n",
              "      <td>RT @riekohayakawa: ＜インドとオセアニア　可能性と機会 ＞クレオ・パスカル...</td>\n",
              "      <td>2021-01-09 21:56:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riuemonn</th>\n",
              "      <td>２４時間ついった監視業(ﾟσ ﾟ) ﾎｼﾞﾎｼﾞ</td>\n",
              "      <td>RT @riekohayakawa: ＜インドとオセアニア　可能性と機会 ＞クレオ・パスカル...</td>\n",
              "      <td>2021-01-09 22:28:59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FumiNozoe</th>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよと。たしかに民主主義や...</td>\n",
              "      <td>2021-01-10 02:59:11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sharkisamazing</th>\n",
              "      <td>NaN</td>\n",
              "      <td>・レモンザメ（全長:3.8mまで 生息地:インド太平洋の熱帯域）（Sicklefin Lem...</td>\n",
              "      <td>2021-01-10 04:54:34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4628</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4628 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    location  ... index_col\n",
              "user                                          ...          \n",
              "Ln__o0                          ゲームしてる人だけᔦꙬᔨ  ...         1\n",
              "QUad_R_ANgle     ///りがくぶ・くりかえす・ふじまめ ←一言メッセージ  ...         2\n",
              "QUad_R_ANgle     ///りがくぶ・くりかえす・ふじまめ ←一言メッセージ  ...         3\n",
              "hisakdan4                               .gif  ...         4\n",
              "yuk1dqma_quad                      クアホしか勝たん♡  ...         5\n",
              "...                                      ...  ...       ...\n",
              "riekohayakawa               Pacific-NZ-Kyoto  ...      4624\n",
              "LesFW7SuraWxotB                       ［呪い返し］  ...      4625\n",
              "riuemonn            ２４時間ついった監視業(ﾟσ ﾟ) ﾎｼﾞﾎｼﾞ  ...      4626\n",
              "FumiNozoe                                NaN  ...      4627\n",
              "sharkisamazing                           NaN  ...      4628\n",
              "\n",
              "[4628 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r44GoOem6c4T",
        "outputId": "beb7e66f-a00a-479e-ab06-40453b19770a"
      },
      "source": [
        "cols = list(df.columns.values)\n",
        "cols"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['location', 'tweet', 'Date', 'Tweet Coordinates', 'index_col']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "pXz8-5uG6kpz",
        "outputId": "d57f3327-6006-4e2c-9c51-c2d8ef639703"
      },
      "source": [
        "df=df[[ 'index_col','location', 'tweet', 'Date', 'Tweet Coordinates']]\n",
        "dfhi=dfhi[[ 'index_col','location', 'tweet', 'Date', 'Tweet Coordinates']]\n",
        "dfja=dfja[[ 'index_col','location', 'tweet', 'Date', 'Tweet Coordinates']]\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>RT @RRRameshRRR: 50% of the #Quad (Japan &amp;amp;...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cool.\\n\\n#Quad (riplegic) military grouping 🙄 ...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RT @hellfire_81: Cool.\\n\\n#Quad (riplegic) mil...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>The creation of the #Quad — with implications ...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RT @asthana_shashi: #Jaishankar attends East A...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>29495</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>India, France discuss counter-terrorism, coope...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
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              "      <td>As #Quad prepares to meet the Chinese challeng...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>Every team is different. Let's create a perfec...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>This here is very tricky.. \\nYes It's true Aus...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>RT @CPR_India: As #Quad prepares to meet the C...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Tweet Coordinates\n",
              "user                        ...                  \n",
              "jagadishrjain            1  ...               NaN\n",
              "hellfire_81              2  ...               NaN\n",
              "Savitri4Ever             3  ...               NaN\n",
              "orfonline                4  ...               NaN\n",
              "InsightGL                5  ...               NaN\n",
              "...                    ...  ...               ...\n",
              "RamKuma19784093      29495  ...               NaN\n",
              "CPR_India            29496  ...               NaN\n",
              "Resort_Skelin        29497  ...               NaN\n",
              "KBhairava1           29498  ...               NaN\n",
              "Deepakk79951168      29499  ...               NaN\n",
              "\n",
              "[29499 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1K68PDLC8ayq"
      },
      "source": [
        "df=df.drop_duplicates(['tweet'])\r\n",
        "dfhi=dfhi.drop_duplicates(['tweet'])\r\n",
        "dfja=dfja.drop_duplicates(['tweet'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "vLXVDzsUIe40",
        "outputId": "620c4c12-64e0-4044-88ba-f48f3f78716c"
      },
      "source": [
        "df['index_col'] = range(1, len(df) + 1)\n",
        "dfhi['index_col'] = range(1, len(dfhi) + 1)\n",
        "dfja['index_col'] = range(1, len(dfja) + 1)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>orfonline</th>\n",
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              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Puducherry</td>\n",
              "      <td>India, France discuss counter-terrorism, coope...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>As #Quad prepares to meet the Chinese challeng...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>Every team is different. Let's create a perfec...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>This here is very tricky.. \\nYes It's true Aus...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>RT @CPR_India: As #Quad prepares to meet the C...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Tweet Coordinates\n",
              "user                        ...                  \n",
              "jagadishrjain            1  ...               NaN\n",
              "hellfire_81              2  ...               NaN\n",
              "Savitri4Ever             3  ...               NaN\n",
              "orfonline                4  ...               NaN\n",
              "InsightGL                5  ...               NaN\n",
              "...                    ...  ...               ...\n",
              "RamKuma19784093      29495  ...               NaN\n",
              "CPR_India            29496  ...               NaN\n",
              "Resort_Skelin        29497  ...               NaN\n",
              "KBhairava1           29498  ...               NaN\n",
              "Deepakk79951168      29499  ...               NaN\n",
              "\n",
              "[29499 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "c7l2-VjrjhdF",
        "outputId": "953ddfd5-231f-4221-fc5a-5b192c921210"
      },
      "source": [
        "dfhi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>1</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>अरब सागर में मालाबार युद्धाभ्यास का दूसरा फेज ...</td>\n",
              "      <td>2020-11-17 03:06:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>2</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>देखिए देश-दुनिया की हर बड़ी खबर, लोकल To ग्लोब...</td>\n",
              "      <td>2020-11-17 03:19:32</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>3</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>समंदर में चीन के खिलाफ #QUAD देशों की तैयारी.....</td>\n",
              "      <td>2020-11-17 07:00:39</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SDeviputra</th>\n",
              "      <td>4</td>\n",
              "      <td>Jodhpur, India</td>\n",
              "      <td>RT @TV9Bharatvarsh: अरब सागर में मालाबार युद्ध...</td>\n",
              "      <td>2020-11-17 10:06:59</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>5</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>मालाबार एक्सरसाइज से चीन की हालात खराब...देखिए...</td>\n",
              "      <td>2020-11-17 11:24:37</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darking2jarlie</th>\n",
              "      <td>341</td>\n",
              "      <td>Space</td>\n",
              "      <td>@SecPompeo for 2024🇺🇲🎃\\nThis man is great🙏🏽 th...</td>\n",
              "      <td>2021-01-10 04:04:02</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>342</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>As #Quad prepares to meet the Chinese challeng...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>343</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>Every team is different. Let's create a perfec...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>This here is very tricky.. \\nYes It's true Aus...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>345</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>RT @CPR_India: As #Quad prepares to meet the C...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>345 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Tweet Coordinates\n",
              "user                        ...                  \n",
              "TV9Bharatvarsh           1  ...               NaN\n",
              "TV9Bharatvarsh           2  ...               NaN\n",
              "TV9Bharatvarsh           3  ...               NaN\n",
              "SDeviputra               4  ...               NaN\n",
              "TV9Bharatvarsh           5  ...               NaN\n",
              "...                    ...  ...               ...\n",
              "darking2jarlie         341  ...               NaN\n",
              "CPR_India              342  ...               NaN\n",
              "Resort_Skelin          343  ...               NaN\n",
              "KBhairava1             344  ...               NaN\n",
              "Deepakk79951168        345  ...               NaN\n",
              "\n",
              "[345 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "id": "9tro4M6ynmbr",
        "outputId": "68a238c8-0df6-4d2d-c02c-ac7423f45fd7"
      },
      "source": [
        "dfja"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ln__o0</th>\n",
              "      <td>1</td>\n",
              "      <td>ゲームしてる人だけᔦꙬᔨ</td>\n",
              "      <td>@yuk1dqma_quad 私の2リグか4リグ更新してもろて🥺</td>\n",
              "      <td>2020-11-30 15:03:14</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>2</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>RT @samemizu: ゲーム開発初めて6か月、ついにアプリをリリース出来ました🎉\\n『...</td>\n",
              "      <td>2020-11-30 15:03:40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>3</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>今日からペンパアドベントか</td>\n",
              "      <td>2020-11-30 15:04:40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hisakdan4</th>\n",
              "      <td>4</td>\n",
              "      <td>.gif</td>\n",
              "      <td>キーボード足で踏んでSEGAをQuadする動画めっちゃ見たいですね</td>\n",
              "      <td>2020-11-30 15:07:34</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuk1dqma_quad</th>\n",
              "      <td>5</td>\n",
              "      <td>クアホしか勝たん♡</td>\n",
              "      <td>@Ln__o0 任せてください💪</td>\n",
              "      <td>2020-11-30 15:08:46</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hiromi_HIROSE</th>\n",
              "      <td>2404</td>\n",
              "      <td>NaN</td>\n",
              "      <td>アメリカがインドの新しい大使館の建物の工事を開始。2027年秋に完成する予定。チャナキャプリ...</td>\n",
              "      <td>2021-01-09 12:25:39</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QJapq2v5xFfgq0h</th>\n",
              "      <td>2405</td>\n",
              "      <td>NaN</td>\n",
              "      <td>RT @Hiromi_HIROSE: アメリカがインドの新しい大使館の建物の工事を開始。20...</td>\n",
              "      <td>2021-01-09 12:30:13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riekohayakawa</th>\n",
              "      <td>2406</td>\n",
              "      <td>Pacific-NZ-Kyoto</td>\n",
              "      <td>＜インドとオセアニア　可能性と機会 ＞クレオ・パスカルとファカファヌア卿 - インド太平洋研...</td>\n",
              "      <td>2021-01-09 21:49:14</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LesFW7SuraWxotB</th>\n",
              "      <td>2407</td>\n",
              "      <td>［呪い返し］</td>\n",
              "      <td>RT @riekohayakawa: ＜インドとオセアニア　可能性と機会 ＞クレオ・パスカル...</td>\n",
              "      <td>2021-01-09 21:56:10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FumiNozoe</th>\n",
              "      <td>2408</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよと。たしかに民主主義や...</td>\n",
              "      <td>2021-01-10 02:59:11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2408 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Tweet Coordinates\n",
              "user                        ...                  \n",
              "Ln__o0                   1  ...               NaN\n",
              "QUad_R_ANgle             2  ...               NaN\n",
              "QUad_R_ANgle             3  ...               NaN\n",
              "hisakdan4                4  ...               NaN\n",
              "yuk1dqma_quad            5  ...               NaN\n",
              "...                    ...  ...               ...\n",
              "Hiromi_HIROSE         2404  ...               NaN\n",
              "QJapq2v5xFfgq0h       2405  ...               NaN\n",
              "riekohayakawa         2406  ...               NaN\n",
              "LesFW7SuraWxotB       2407  ...               NaN\n",
              "FumiNozoe             2408  ...               NaN\n",
              "\n",
              "[2408 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "4T2G58iA9QiW",
        "outputId": "23451ff7-4f08-4c9c-d83e-857c9b0af53e"
      },
      "source": [
        "#PREPROCESSING OF OBTAINED DATA\n",
        "import re\n",
        "df['tweet']=df['tweet'].apply(lambda x : x.lower())\n",
        "df['tweet']=df['tweet'].apply(lambda x : pp.remove_emails(x))\n",
        "df['tweet']=df['tweet'].apply(lambda x : pp.remove_html_tags(x))\n",
        "df['tweet']=df['tweet'].apply(lambda x : pp.remove_rt(x))\n",
        "df['tweet']=df['tweet'].apply(lambda x : pp.remove_special_chars(x))\n",
        "df['tweet']=df['tweet'].apply(lambda x : pp.remove_urls(x))\n",
        "\n",
        "dfhi['tweet']=dfhi['tweet'].apply(lambda x : x.lower())\n",
        "dfhi['tweet']=dfhi['tweet'].apply(lambda x : pp.remove_emails(x))\n",
        "dfhi['tweet']=dfhi['tweet'].apply(lambda x : pp.remove_html_tags(x))\n",
        "dfhi['tweet']=dfhi['tweet'].apply(lambda x : pp.remove_rt(x))\n",
        "dfhi['tweet']=dfhi['tweet'].apply(lambda x : pp.remove_special_chars(x))\n",
        "dfhi['tweet']=dfhi['tweet'].apply(lambda x : pp.remove_urls(x))\n",
        "\n",
        "dfja['tweet']=dfja['tweet'].apply(lambda x : x.lower())\n",
        "dfja['tweet']=dfja['tweet'].apply(lambda x : pp.remove_emails(x))\n",
        "dfja['tweet']=dfja['tweet'].apply(lambda x : pp.remove_html_tags(x))\n",
        "dfja['tweet']=dfja['tweet'].apply(lambda x : pp.remove_rt(x))\n",
        "dfja['tweet']=dfja['tweet'].apply(lambda x : pp.remove_special_chars(x))\n",
        "dfja['tweet']=dfja['tweet'].apply(lambda x : pp.remove_urls(x))\n",
        "\n",
        "df   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>29495</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Tweet Coordinates\n",
              "user                        ...                  \n",
              "jagadishrjain            1  ...               NaN\n",
              "hellfire_81              2  ...               NaN\n",
              "Savitri4Ever             3  ...               NaN\n",
              "orfonline                4  ...               NaN\n",
              "InsightGL                5  ...               NaN\n",
              "...                    ...  ...               ...\n",
              "RamKuma19784093      29495  ...               NaN\n",
              "CPR_India            29496  ...               NaN\n",
              "Resort_Skelin        29497  ...               NaN\n",
              "KBhairava1           29498  ...               NaN\n",
              "Deepakk79951168      29499  ...               NaN\n",
              "\n",
              "[29499 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1oOiNg3NJRE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "outputId": "b5ee26b8-3133-400b-86f9-292a6ac982e0"
      },
      "source": [
        "dfhi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>1</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>अरब सगर म मलबर यदधभयस क दसर फज आज स शर quad दश...</td>\n",
              "      <td>2020-11-17 03:06:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>2</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>दखए दशदनय क हर बड खबर लकल to गलबल top 100 मloc...</td>\n",
              "      <td>2020-11-17 03:19:32</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>3</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>समदर म चन क खलफ quad दश क तयरthesamirabbas gen...</td>\n",
              "      <td>2020-11-17 07:00:39</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SDeviputra</th>\n",
              "      <td>4</td>\n",
              "      <td>Jodhpur, India</td>\n",
              "      <td>tv9bharatvarsh अरब सगर म मलबर यदधभयस क दसर फज ...</td>\n",
              "      <td>2020-11-17 10:06:59</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>5</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>मलबर एकसरसइज स चन क हलत खरबदखए फल फइनलmalabarw...</td>\n",
              "      <td>2020-11-17 11:24:37</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darking2jarlie</th>\n",
              "      <td>341</td>\n",
              "      <td>Space</td>\n",
              "      <td>secpompeo for 2024this man is great thxelectio...</td>\n",
              "      <td>2021-01-10 04:04:02</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>342</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>343</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>345</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>345 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Tweet Coordinates\n",
              "user                        ...                  \n",
              "TV9Bharatvarsh           1  ...               NaN\n",
              "TV9Bharatvarsh           2  ...               NaN\n",
              "TV9Bharatvarsh           3  ...               NaN\n",
              "SDeviputra               4  ...               NaN\n",
              "TV9Bharatvarsh           5  ...               NaN\n",
              "...                    ...  ...               ...\n",
              "darking2jarlie         341  ...               NaN\n",
              "CPR_India              342  ...               NaN\n",
              "Resort_Skelin          343  ...               NaN\n",
              "KBhairava1             344  ...               NaN\n",
              "Deepakk79951168        345  ...               NaN\n",
              "\n",
              "[345 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 969
        },
        "id": "3YvQGdwhOVk_",
        "outputId": "a0c72a2e-70eb-40cf-8916-e815207bec68"
      },
      "source": [
        "dfja"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ln__o0</th>\n",
              "      <td>1</td>\n",
              "      <td>ゲームしてる人だけᔦꙬᔨ</td>\n",
              "      <td>yuk1dqma_quad 私の2リグか4リグ更新してもろて</td>\n",
              "      <td>2020-11-30 15:03:14</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>2</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>samemizu ゲーム開発初めて6か月ついにアプリをリリース出来ましたくっつきパズル のび...</td>\n",
              "      <td>2020-11-30 15:03:40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>3</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>今日からペンパアドベントか</td>\n",
              "      <td>2020-11-30 15:04:40</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hisakdan4</th>\n",
              "      <td>4</td>\n",
              "      <td>.gif</td>\n",
              "      <td>キーボード足で踏んでsegaをquadする動画めっちゃ見たいですね</td>\n",
              "      <td>2020-11-30 15:07:34</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuk1dqma_quad</th>\n",
              "      <td>5</td>\n",
              "      <td>クアホしか勝たん♡</td>\n",
              "      <td>ln__o0 任せてください</td>\n",
              "      <td>2020-11-30 15:08:46</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hiromi_HIROSE</th>\n",
              "      <td>2404</td>\n",
              "      <td>NaN</td>\n",
              "      <td>アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...</td>\n",
              "      <td>2021-01-09 12:25:39</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QJapq2v5xFfgq0h</th>\n",
              "      <td>2405</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...</td>\n",
              "      <td>2021-01-09 12:30:13</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riekohayakawa</th>\n",
              "      <td>2406</td>\n",
              "      <td>Pacific-NZ-Kyoto</td>\n",
              "      <td>インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...</td>\n",
              "      <td>2021-01-09 21:49:14</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LesFW7SuraWxotB</th>\n",
              "      <td>2407</td>\n",
              "      <td>［呪い返し］</td>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "      <td>2021-01-09 21:56:10</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FumiNozoe</th>\n",
              "      <td>2408</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...</td>\n",
              "      <td>2021-01-10 02:59:11</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2408 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Tweet Coordinates\n",
              "user                        ...                  \n",
              "Ln__o0                   1  ...               NaN\n",
              "QUad_R_ANgle             2  ...               NaN\n",
              "QUad_R_ANgle             3  ...               NaN\n",
              "hisakdan4                4  ...               NaN\n",
              "yuk1dqma_quad            5  ...               NaN\n",
              "...                    ...  ...               ...\n",
              "Hiromi_HIROSE         2404  ...               NaN\n",
              "QJapq2v5xFfgq0h       2405  ...               NaN\n",
              "riekohayakawa         2406  ...               NaN\n",
              "LesFW7SuraWxotB       2407  ...               NaN\n",
              "FumiNozoe             2408  ...               NaN\n",
              "\n",
              "[2408 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JxX7pER6JlcY",
        "outputId": "6ff6b4ea-fc7e-47eb-ea12-484f05a5c7eb"
      },
      "source": [
        "!pip install googletrans==3.1.0a0"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: googletrans==3.1.0a0 in /usr/local/lib/python3.6/dist-packages (3.1.0a0)\n",
            "Requirement already satisfied: httpx==0.13.3 in /usr/local/lib/python3.6/dist-packages (from googletrans==3.1.0a0) (0.13.3)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (2020.12.5)\n",
            "Requirement already satisfied: chardet==3.* in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (3.0.4)\n",
            "Requirement already satisfied: rfc3986<2,>=1.3 in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (1.4.0)\n",
            "Requirement already satisfied: hstspreload in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (2020.12.22)\n",
            "Requirement already satisfied: httpcore==0.9.* in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (0.9.1)\n",
            "Requirement already satisfied: idna==2.* in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (2.10)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (1.2.0)\n",
            "Requirement already satisfied: h2==3.* in /usr/local/lib/python3.6/dist-packages (from httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (3.2.0)\n",
            "Requirement already satisfied: h11<0.10,>=0.8 in /usr/local/lib/python3.6/dist-packages (from httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (0.9.0)\n",
            "Requirement already satisfied: contextvars>=2.1; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from sniffio->httpx==0.13.3->googletrans==3.1.0a0) (2.4)\n",
            "Requirement already satisfied: hpack<4,>=3.0 in /usr/local/lib/python3.6/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (3.0.0)\n",
            "Requirement already satisfied: hyperframe<6,>=5.2.0 in /usr/local/lib/python3.6/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (5.2.0)\n",
            "Requirement already satisfied: immutables>=0.9 in /usr/local/lib/python3.6/dist-packages (from contextvars>=2.1; python_version < \"3.7\"->sniffio->httpx==0.13.3->googletrans==3.1.0a0) (0.14)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j9fpT-VkKMEb",
        "outputId": "dcb40fc7-9144-4f05-d592-4103ce526a7f"
      },
      "source": [
        "!pip install google-trans-new\r\n",
        "from google_trans_new import google_translator  \r\n",
        "\r\n",
        "translator = google_translator()  "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: google-trans-new in /usr/local/lib/python3.6/dist-packages (1.1.9)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 532
        },
        "id": "LF0N8l8YG2dz",
        "outputId": "4da097a2-c117-4c5a-e5c5-1f047d274a87"
      },
      "source": [
        "dfja2 = dfja.copy()\r\n",
        "dfja3=dfja2[['tweet']]\r\n",
        "dfja3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tweet</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ln__o0</th>\n",
              "      <td>yuk1dqma_quad 私の2リグか4リグ更新してもろて</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>samemizu ゲーム開発初めて6か月ついにアプリをリリース出来ましたくっつきパズル のび...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>今日からペンパアドベントか</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hisakdan4</th>\n",
              "      <td>キーボード足で踏んでsegaをquadする動画めっちゃ見たいですね</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuk1dqma_quad</th>\n",
              "      <td>ln__o0 任せてください</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hiromi_HIROSE</th>\n",
              "      <td>アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QJapq2v5xFfgq0h</th>\n",
              "      <td>hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riekohayakawa</th>\n",
              "      <td>インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LesFW7SuraWxotB</th>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FumiNozoe</th>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2408 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                             tweet\n",
              "user                                                              \n",
              "Ln__o0                              yuk1dqma_quad 私の2リグか4リグ更新してもろて\n",
              "QUad_R_ANgle     samemizu ゲーム開発初めて6か月ついにアプリをリリース出来ましたくっつきパズル のび...\n",
              "QUad_R_ANgle                                         今日からペンパアドベントか\n",
              "hisakdan4                        キーボード足で踏んでsegaをquadする動画めっちゃ見たいですね\n",
              "yuk1dqma_quad                                       ln__o0 任せてください\n",
              "...                                                            ...\n",
              "Hiromi_HIROSE    アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...\n",
              "QJapq2v5xFfgq0h  hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...\n",
              "riekohayakawa    インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...\n",
              "LesFW7SuraWxotB  riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...\n",
              "FumiNozoe        ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...\n",
              "\n",
              "[2408 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtWIqq_UJoNV"
      },
      "source": [
        "# create a translator object\r\n",
        "translator = Translator()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "yKeXxGNMP8x9",
        "outputId": "dc13ffc5-dc1c-4da6-ad03-b0ab7ad1dfd5"
      },
      "source": [
        "# install googletrans using pip\r\n",
        "!pip install googletrans==3.1.0a0\r\n",
        "\r\n",
        "# import the library\r\n",
        "import googletrans\r\n",
        "from googletrans import Translator\r\n",
        "# create a translator object\r\n",
        "translator = Translator()\r\n",
        "\r\n",
        "# use translate method to translate a string - by default, the destination language is english\r\n",
        "translated = translator.translate('Hola Mundo')\r\n",
        "\r\n",
        "# the translate method returns an object\r\n",
        "print(translated)\r\n",
        "# Translated(src=es, dest=en, text=Hello World, pronunciation=Hello World, extra_data=\"{'translat...\")\r\n",
        "\r\n",
        "# obtain translated string by using attribute .text\r\n",
        "translated.text\r\n",
        "# 'Hello World'"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: googletrans==3.1.0a0 in /usr/local/lib/python3.6/dist-packages (3.1.0a0)\n",
            "Requirement already satisfied: httpx==0.13.3 in /usr/local/lib/python3.6/dist-packages (from googletrans==3.1.0a0) (0.13.3)\n",
            "Requirement already satisfied: rfc3986<2,>=1.3 in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (1.4.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (2020.12.5)\n",
            "Requirement already satisfied: sniffio in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (1.2.0)\n",
            "Requirement already satisfied: hstspreload in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (2020.12.22)\n",
            "Requirement already satisfied: chardet==3.* in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (3.0.4)\n",
            "Requirement already satisfied: httpcore==0.9.* in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (0.9.1)\n",
            "Requirement already satisfied: idna==2.* in /usr/local/lib/python3.6/dist-packages (from httpx==0.13.3->googletrans==3.1.0a0) (2.10)\n",
            "Requirement already satisfied: contextvars>=2.1; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from sniffio->httpx==0.13.3->googletrans==3.1.0a0) (2.4)\n",
            "Requirement already satisfied: h11<0.10,>=0.8 in /usr/local/lib/python3.6/dist-packages (from httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (0.9.0)\n",
            "Requirement already satisfied: h2==3.* in /usr/local/lib/python3.6/dist-packages (from httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (3.2.0)\n",
            "Requirement already satisfied: immutables>=0.9 in /usr/local/lib/python3.6/dist-packages (from contextvars>=2.1; python_version < \"3.7\"->sniffio->httpx==0.13.3->googletrans==3.1.0a0) (0.14)\n",
            "Requirement already satisfied: hyperframe<6,>=5.2.0 in /usr/local/lib/python3.6/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (5.2.0)\n",
            "Requirement already satisfied: hpack<4,>=3.0 in /usr/local/lib/python3.6/dist-packages (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==3.1.0a0) (3.0.0)\n",
            "Translated(src=es, dest=en, text=Hello World, pronunciation=Hello World, extra_data=\"{'translat...\")\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Hello World'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rb6CPrhODDMC",
        "outputId": "76dd54c9-9d73-4707-c73e-17488085ac0a"
      },
      "source": [
        "dfja2 = dfja.copy()\r\n",
        "dfja2.rename(columns=lambda x: translator.translate(x).text, inplace=True)\r\n",
        "\r\n",
        "# translated column names\r\n",
        "dfja2.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['index_col', 'location', 'tweet', 'Date', 'Tweet Coordinates'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 700
        },
        "id": "99WRBIc_LW4I",
        "outputId": "7fa9c0eb-94d0-4ee8-e23d-efc745985afc"
      },
      "source": [
        "dfja['English'] = dfja['tweet'].apply(translator.translate, src='ja', dest='en').apply(getattr, args=('text',))\r\n",
        "dfja"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>English</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ln__o0</th>\n",
              "      <td>1</td>\n",
              "      <td>ゲームしてる人だけᔦꙬᔨ</td>\n",
              "      <td>yuk1dqma_quad 私の2リグか4リグ更新してもろて</td>\n",
              "      <td>2020-11-30 15:03:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yuk1dqma_quad Update my 2 rig or 4 rig</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>2</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>samemizu ゲーム開発初めて6か月ついにアプリをリリース出来ましたくっつきパズル のび...</td>\n",
              "      <td>2020-11-30 15:03:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>samemizu Game development First 6 months I fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>3</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>今日からペンパアドベントか</td>\n",
              "      <td>2020-11-30 15:04:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Is it Penpa Advent from today?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hisakdan4</th>\n",
              "      <td>4</td>\n",
              "      <td>.gif</td>\n",
              "      <td>キーボード足で踏んでsegaをquadする動画めっちゃ見たいですね</td>\n",
              "      <td>2020-11-30 15:07:34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I really want to see a video that quads sega b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuk1dqma_quad</th>\n",
              "      <td>5</td>\n",
              "      <td>クアホしか勝たん♡</td>\n",
              "      <td>ln__o0 任せてください</td>\n",
              "      <td>2020-11-30 15:08:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ln__o0 leave it to me</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hiromi_HIROSE</th>\n",
              "      <td>2404</td>\n",
              "      <td>NaN</td>\n",
              "      <td>アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...</td>\n",
              "      <td>2021-01-09 12:25:39</td>\n",
              "      <td>NaN</td>\n",
              "      <td>アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QJapq2v5xFfgq0h</th>\n",
              "      <td>2405</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...</td>\n",
              "      <td>2021-01-09 12:30:13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riekohayakawa</th>\n",
              "      <td>2406</td>\n",
              "      <td>Pacific-NZ-Kyoto</td>\n",
              "      <td>インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...</td>\n",
              "      <td>2021-01-09 21:49:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LesFW7SuraWxotB</th>\n",
              "      <td>2407</td>\n",
              "      <td>［呪い返し］</td>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "      <td>2021-01-09 21:56:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FumiNozoe</th>\n",
              "      <td>2408</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...</td>\n",
              "      <td>2021-01-10 02:59:11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2408 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ...                                            English\n",
              "user                        ...                                                   \n",
              "Ln__o0                   1  ...             yuk1dqma_quad Update my 2 rig or 4 rig\n",
              "QUad_R_ANgle             2  ...  samemizu Game development First 6 months I fin...\n",
              "QUad_R_ANgle             3  ...                     Is it Penpa Advent from today?\n",
              "hisakdan4                4  ...  I really want to see a video that quads sega b...\n",
              "yuk1dqma_quad            5  ...                              ln__o0 leave it to me\n",
              "...                    ...  ...                                                ...\n",
              "Hiromi_HIROSE         2404  ...  アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...\n",
              "QJapq2v5xFfgq0h       2405  ...  hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...\n",
              "riekohayakawa         2406  ...  インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...\n",
              "LesFW7SuraWxotB       2407  ...  riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...\n",
              "FumiNozoe             2408  ...  ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...\n",
              "\n",
              "[2408 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "8UYzJCqhaIFy",
        "outputId": "86b2fa81-2a8c-4c33-cdfc-7f49c57eeb57"
      },
      "source": [
        "dfhi['English'] = dfhi['tweet'].apply(translator.translate, src='hi', dest='en').apply(getattr, args=('text',))\r\n",
        "dfhi"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>1</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>अरब सगर म मलबर यदधभयस क दसर फज आज स शर quad दश...</td>\n",
              "      <td>2020-11-17 03:06:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>अरब सगर म मलबर यदधभयस क दसर फज आज स शर quad दश...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>2</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>दखए दशदनय क हर बड खबर लकल to गलबल top 100 मloc...</td>\n",
              "      <td>2020-11-17 03:19:32</td>\n",
              "      <td>NaN</td>\n",
              "      <td>दखए दशदनय क हर बड खबर लकल to गलबल top 100 मloc...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>3</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>समदर म चन क खलफ quad दश क तयरthesamirabbas gen...</td>\n",
              "      <td>2020-11-17 07:00:39</td>\n",
              "      <td>NaN</td>\n",
              "      <td>समदर म चन क खलफ quad दश क तयरthesamirabbas gen...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SDeviputra</th>\n",
              "      <td>4</td>\n",
              "      <td>Jodhpur, India</td>\n",
              "      <td>tv9bharatvarsh अरब सगर म मलबर यदधभयस क दसर फज ...</td>\n",
              "      <td>2020-11-17 10:06:59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>tv9bharatvarsh अरब सगर म मलबर यदधभयस क दसर फज ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TV9Bharatvarsh</th>\n",
              "      <td>5</td>\n",
              "      <td>Noida, India</td>\n",
              "      <td>मलबर एकसरसइज स चन क हलत खरबदखए फल फइनलmalabarw...</td>\n",
              "      <td>2020-11-17 11:24:37</td>\n",
              "      <td>NaN</td>\n",
              "      <td>मलबर एकसरसइज स चन क हलत खरबदखए फल फइनलmalabarw...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>darking2jarlie</th>\n",
              "      <td>341</td>\n",
              "      <td>Space</td>\n",
              "      <td>secpompeo for 2024this man is great thxelectio...</td>\n",
              "      <td>2021-01-10 04:04:02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>secpompeo for 2024this man is great thxelectio...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>342</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>343</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>344</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>345</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>345 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ...                                            English\n",
              "user                        ...                                                   \n",
              "TV9Bharatvarsh           1  ...  अरब सगर म मलबर यदधभयस क दसर फज आज स शर quad दश...\n",
              "TV9Bharatvarsh           2  ...  दखए दशदनय क हर बड खबर लकल to गलबल top 100 मloc...\n",
              "TV9Bharatvarsh           3  ...  समदर म चन क खलफ quad दश क तयरthesamirabbas gen...\n",
              "SDeviputra               4  ...  tv9bharatvarsh अरब सगर म मलबर यदधभयस क दसर फज ...\n",
              "TV9Bharatvarsh           5  ...  मलबर एकसरसइज स चन क हलत खरबदखए फल फइनलmalabarw...\n",
              "...                    ...  ...                                                ...\n",
              "darking2jarlie         341  ...  secpompeo for 2024this man is great thxelectio...\n",
              "CPR_India              342  ...  as quad prepares to meet the chinese challenge...\n",
              "Resort_Skelin          343  ...  every team is different lets create a perfect ...\n",
              "KBhairava1             344  ...  this here is very tricky yes its true aussies ...\n",
              "Deepakk79951168        345  ...  cpr_india as quad prepares to meet the chinese...\n",
              "\n",
              "[345 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xGgzMVH6OBdS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "64a71b60-c813-435a-e05b-e98ad70b7add"
      },
      "source": [
        " df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(29499, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1ogX58vONkV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "f274ad02-85b6-4e46-9dbf-135a224f6f9c"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>29499.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8515.772132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7375.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>14750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>22124.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>29499.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          index_col\n",
              "count  29499.000000\n",
              "mean   14750.000000\n",
              "std     8515.772132\n",
              "min        1.000000\n",
              "25%     7375.500000\n",
              "50%    14750.000000\n",
              "75%    22124.500000\n",
              "max    29499.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXjtlLywbihC"
      },
      "source": [
        "WORD CLOUD VISUALIZATION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R4CyFTxLbgz_",
        "outputId": "1074bead-4b19-409f-acb0-f6e08f84a9a6"
      },
      "source": [
        "!pip install wordcloud"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: wordcloud in /usr/local/lib/python3.6/dist-packages (1.5.0)\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from wordcloud) (1.19.5)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.6/dist-packages (from wordcloud) (7.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3bNRRIRbs7C"
      },
      "source": [
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dpniNqp_cCHt"
      },
      "source": [
        "text1= ' '.join(df['tweet'])\r\n",
        "text2= ' '.join(dfhi['tweet'])\r\n",
        "text3= ' '.join(dfja['tweet'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0LPBlhpcOwu",
        "outputId": "76836b3d-d002-40d0-ed38-90a6a9b74aa5"
      },
      "source": [
        "len(text1), len(text2),len(text3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(977891, 35067, 126011)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "UmuJIl2WcTDm",
        "outputId": "ecdf708d-fba3-4c71-ebdc-9d8bbae2d7c7"
      },
      "source": [
        "wc= WordCloud(width=800,height=400,max_font_size = 100, min_font_size=10).generate(text1)\n",
        "plt.imshow(wc)\n",
        "plt.axis('off')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 232
        },
        "id": "Yqjyu7I4OvMB",
        "outputId": "8af9fc95-a019-4f65-cbc8-ecc32e9f59e2"
      },
      "source": [
        "wc= WordCloud(width=500,height=300,random_state=21,max_font_size= 156, min_font_size=30).generate(text1)\r\n",
        "plt.imshow(wc)\r\n",
        "plt.axis('off')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m/v2GkypCpdtlrNrCZk9hTQRxlqvfVFZtoeV037UifRfXLiOlDLPTOoCqRYjGskQiPRiRFIaRQNPj6EYURTWW+qUpio6iqAhFQ1HU+mva0r+3m9JVhEZaH1w2a4mAwKnW8FIWWxmFSncbIXHs9mcHdRK9ScSDY1c6GmHguhZIH8TWc8hsN1TNxDCTZHpGyfSOEk8MomoRVM1EVfVl/fjubyyvzO9mfrjm90n8tuzqQqXbZdxt5I0XQm1aOjLo+dY5pet5VrDS7dgI9z+6kSDbf4S+gWNksvtRtds1Oh4EJXs3Ep+iu3lmvlDpdhm5mamta7y/bteQXf2NQcHrzuH73qYVJtnuqKpJ3+Bxdo48Syzev8aGlCGdIlS6XWZTizqvUXctKt1G+J1uGrmiYyOkGbHEIHsPvFwvk/lgmA22C6HSfZDowH3X6Vs5zEhbK0Ha9uhil+kWlO1iNwnfc/B9F99zcJwKllXAdaq4bg3ftfA8G89z8Dwb37PxPJtUZg+793511ZKfIfcSKt2QVWmle8dGykW2RLhAWxOJ1DCHTvztegeOxh+e73tUyzMU8zcpFScplyapVXNYtULLxYtURa83k9qeqEInqibrkQuNr0JKvy0FckKl+yCxxkXjktKVclWB7HRuvSK2X0jSZqHpMfYd/GZDhSulxPcdigs3Gb/5LsX8GLZVWHctkO26DxEo9EdG2Bt/jIgar8tZYxzf4rczPwjjdEM6R7DltBuuZIJuFRuPXVyN0PnTGkIoDO08SbpJxw3bKnD98q+ZHv9kS3Z06BZ95h5OpF9EVyKAxJUOqtARgCc9lEUnMgJPuuTtKYruXFukPFS6IQ1x7MbNLBebYnbK9qppZudNGPcBupFgYPjRVYsaSSlx7BKXzr3K7PTZuoNy42zH56EqdIajh9CVCHlniqulT6h6BY6nvw4ILhbfw/UtYlqG3fHjJLVeJqoXGa9+STsWF6HSDWmIZRWgwVrXjGSCO68TOlcoaHps1bFDbpNM7ybRoEaF9F1uXn2DuTYqXACxBUs3NkMVOhljEEdanMu/tWSntf0ammJQcueoeSUWnEnmrBscS3+dfYnHKbqzLNgbr70QLiEeJNahuxrV6IWgCpnokBipio5hJkLzQgv09K3emRegWs0xeeujpo7RtaLp0aAuwzZCQcFQYjh+jYJzu9uKJ11Uod0hz5Zf4Wr5EyJqnKFI48+49fFDHhzWsRqtlKYbvjES670jw6mdqJqxJRsbbj0EydSuhkfMTX/RkQaVkWjP9rMxCFCEgi+9O9J6XVlDEyaquNMAUHJyWH6FrLmzLQ+YUOk+SKxnpVudrxeqXhlF0UimVy9yvhG0ekGWkMZoehRdjzaMWOhE6yNF0YlGt9/3I6XE9W00xUAVt80jNa+MqcQwlLv7ukl86WEoi07jjREq3QeJdax0Pd+hmB9b9fdCCHp627PtuptYYgDd2HrdZLcammYilNVvZSl9rFq+7eNGY72BeWmbrXR96VL2FtCETlLvXXq94MygCJXh6KE7TAwRNYmpxPGlRzucF6HSDWmI9D3y89ca2HWDDCjDTLd97Gzf4W13Q28OiyUHV2Yp1rrNJNO7AvPCNsOVDnl7ClXoZI3bZpm8PU3NLzMcPcDB5FNkjZ0MRkY5mn4OVWgUnNkwZCxkjaxLfwVbU8cuYqzQTVgIQTwxRCa7n+mJTzY8xUXMSIaevkNtO9/9jO97DZ2di7Vv24mqRRjadart5+0GEp9Z6wYZYwhPOiio+HjYfpWb5c8ZTT7BvsTj7Es8Vn+HwParjFW+IFzphqyNdcpLpTRFuTS96o2taibDu06hqu1qty3oH364XkA9pBmuW22YtiuEaPOKVNA/9FDLtR22IvP2OKfnX+NG+XN8giJUEp+b5TNcK32G41fxpIsnXSpenvOFd8hZq5vZ1sL2e0yFdB3HKTMzeZp0zz7EKumS6Z69DO0+xfj13244LCmR2sHwrqdWHSvkTnzPxrIKROP9qyrBbN9BZiY/a8t4md5R9ux/YVt/P4vK9G4caXGx+B43yp8T1VL40qPiLjTsnbZWwpVuSEvMTn3RuNWQUNg18lzdJLD+1Y9uxNl74Bvb0kGzmZQKjVdhmewo0VjfBkcRJJI7GD38HSLR7H38/Ugsv8yCPUHBmW6rwoVQ6T5YbOAecewSY9feXrVouRACM5Lh0PG/Tf/QQ+tq955I7eLwQ39Etv/IfXxDd4a5mQsNdxhmNMPI6Ev1DL+1oygagzse5+ijf494cjj8fjbAtjYvNP/i29NI7r5hQx+DZHbqDH2Dx8msUlRFCIFhJjlw7PtksvsZu/Y2Vm0B318t1CboPmsYcfqHHmFo1xN3VMgK6rz6TbtXdJ/G0QLBId2VvUppmlJxnGRqZTurEAp9Qw8hpce1S69hW8UWzEACRdGIJQfZuftpegePo9+ltH3fC7oGb9P6GAIFQ4mSMYaIaxl0JRLUqZBVik6OvDOF61tt7V6yZZSuEErQdVRd7D6qrf53RUfVTGKJgYbnjCcH2bX3eTzPwnOteoFmF9+vF2te6d++g++5hIr6XmyrwNULv+D4Y/8VZuTeSAaoK14jwfDup+kffoT8/DXyuStYtQU81w76nSkKqmYSiWZJpnaSye5H06Mstfeu43kWN6+8wfDup4hEO5eZttgLbqnzraotdby943VFQ1UNVM0k0zva6Ixkeg8gpcTzLHzXvlO+luRupX+vT/Ycu8TUrY+CZpOqseIxqqozuPMk6Z59zEyeJj9/FatWCIqU1x1xiqKh6VEMI0k8OUim9wCZ7OiKOxfXtbh1/W0SqZ309h9Z85w3m6iaYiT+CMPRg+hKpP4YXZQ/iURS88qMVb7gZuUMjt+e/oabrnQNM8Xeg69gmMm6cNdbPYu6ohW3Wz+LxdbPLa58EqmdQREQGXyA0vfqgh38lLL+c+n1238vFsYYu/YWnmt14VPYPhTzN7lx5XX2HXwFVYus+j0IIdD1GH0Dx+jtP4qULp7nQl3pKore8HuU0md64lNuXX+bnt4DHVG68eQORg68hFpXqKLeXvzeVuP1vwsVWpA9IQS9/UcCRSRlUMNWBnInl8vfkhzefs33XXIz55gc+3CNDknJzMRp+gcfIp3d3+B7UYjG+9i9/wWG3adxrFLQCcJ3CVa2KqpmousxND226nl832Vy7APGrr3F8K4n2xZTraUy9L/4HRRz9UgYv1Yj9+7fYE2Nr3uciJrkaPqr9JsjyHrGmSttXN8CBIYSQRUaUTXBgeQpImqci8X32qJ4N13palqE3v4jnQ0PEgKBQKhK0+62EGxrVdVg/Ma7eNxHSrctO3TJ5Nj7KIrKyIFvoLVQdyHYfuotFzz3fZeZyc+5euEXeJ5NqTjeZGW5PkwzSd/Asc5ujUWwelcxUFtw9kspsWtFhPh4zVEgjlPm8vmfcvSRv0s01tdQCS4+FO82F7SC51pM3PqQqxd+ju87lIoTeG6tvlvZGIoZITF6BDUWX/UYt1wi//mH6x5DoDASe4g+cw+WX2asco6J6gWq3m2TiyJUklqWnbGjDEUPsit2jIIzU4/V3Rjb0xDTYbaW/bCNtMli4vsu4zd+x/VLv8a2Sm2rpRt0NfCYHv+EK+f/eqlAS7m08RYp2wUhxIYejqXCBJfPvUqt2rg63HqQUuK5FjevvsH1i6/h+4FT1aoudCTNuFPoiklfZARfelwufsDl4geU3Xl86SLxkfh40mHBmeLLwtvcLH+OQGEocoCw9kLIpuH7LmPX3uLcZ/+OwsL1dbd7WURKSbUyx9XzP+PiuZ9gW4Wl35WLU5vbun5bIcnNnueLT/6M+bmL67YR33NW6VPI3+Dc6b/gxpXXcd3bRZBq1XmqTUqAbiUUVGJqCsevMl69cEelsbvxpMvNyll8POJaT1s2i5tuXgjZzkgWclc4d/oH9A+eYMfupzGjmZZbfgc3qcR1qsxNn2P85ruUCrfu2Va7TgXLLhGJtL++w/1KqTjO+c//kv6hh9mx52ki0Z41tWJfVKBSeli1AlPjHzF16xNq1Rx3K3HfdygXJ+gdOMJ2KTgv6y16PLlyCORyXN+pl4Fsz0Nl05Wu69aYmz5X77W1dSgXp9a+epOQz13Fc1c2tgfe7M71papV55mdOoOyQjtsKWXHtulWdZ6xa28zeesjMtlRevuPEI33YZhJND2GqgZOMykDZ6br1nCcMrXKPIX8dWYnz9S3wyuvOBynzNStj4nFe+/5XaW0/kr+tlVkZvLzLWdOKuXvffCsB9sqcOv6O0yNf0RP70Gy/YeJxha/lyiqaizZs6X08X0Pz7Vw3SqOVaJSnmEhd5n52Yu4bo1GK+a5mS+DhJa7qp1VK7MN37cZeNKl5M4TUePoIoIjGzvHTDWOKjTmnLG2XIlotCUQQmytTytkeyAUDCOBYSbR9VgQdiXUwEvsu7hOoHTtWv6Bbo7YbYRQ0I0EpplC0yMoqnmH0pW+i+tZuE4Vxy417Y/XCYz+IUb+5J82daSN//jPqFy5sK4xBIK9icfYl3icC4XfcatyblUTgyI0DiWfZmfsKGcWfsNU7XJLY0gpV32Sb/pKN+Q+RPrYVuEOu2zI5iMfsO9FoBBVV4onl8zVbpLWBzmUfJq4lma2dgPbry0Vv1GFRkRJMBw9SF9khGulT5mxrrdlXqHSDQkJuS8x1TiPZr9116uLtmqJIlR0JcLe+GPsih3H9qv1QuVB80pTiaIIDYlPSu9nT+wE18qfbnheodINCQm5L1FQSWq9Lfn2NGGgKStn8gkUBiL7yBhDXC+fbhjt0Aqh0g0JCbkvsfwypxdea9v57m5kuV5CpRsSEnJf4kmHier6nG2dJEyOCAkJCekiodINCQkJ6SKheSEkJOSBxVCiJLRsvbRjY4+bj8907cqGxwyVbkhIyAOHKnR2x06wN/EImjBbykp0/BoztWth9EJISEjIWhmI7GM0+QSaMPClh+NbGEpQisD1HRShoAgNgcCXLlO1q+SdyTB6ISQkJGStaMJgOHoIVehM1S5zqfg+Na/M49lvI4TCmYXXsb0KMS3NnvhD9Ef2UnTn2lJLF0KlGxIS8oChCo2k1ovrW1wqvk/JzQFBiJkmTDxp48gaeafGufw8Ep+98UcoONPMWTc3PH4YvRASEvJAIVAw1Ci2rFFy55ded6WLKnQU1GWv2VwrfYqmGOyIHiYsYh4SEhKyVkRQaWyxnvMirl9DFyaKuNMAUPNKWF6ZtD6wfYqYJ1KCg0cNrJrk8nkHq3ZvxchURmHvAR3pw/kzFvYKFf+EAn39Kj19CtGoghBg25Jy0WduxqNUklutdGdISMgWQ0of26+hKwa6MHFk0Aex6hUx1RhRLUnJnVv2DgEINMWs/31jSqYrSvfwcYN/9YMhblxx+Gf/aJrrV+4tDn7iMZN/8S/7cBzJP/zeBBO37mzPYkYE3/2jBN/4boyR/TqpTKB0qxXJ3LTH1YsOP/0PJd76VfWec3efu/pcyaX/hdz3rNLj7L6Xge0j8570qLgLJPU+kno/OXsMgLwzjUBhZ/QIc7WbS2UeE1oPphqj5pVoxzVtC0eabsA/+Ccp/u4/TmFbkmuXHKYnPVQV+gZUsv0qTz4f4dd/3f2iy2o8gZZIYQ7swBwYwuwbRE2kUE0Toen4roNvWbjFPPbcNLWpceyZSdzCAl610toYsQRGtg9WiCWU0seem8Fv8Vwrnj8aw+gdWPH8ywaiOn4D/I2HzLSCYkYw+gbv6URwx5R8D3tmCt/ubsdmxTBR4wnUaAw904uR7cfI9qEmkmjxJEI3UHQNEEjPRboufq2KWynjlgo4CznsuWmc/AJepYRXLiG9jfWY6yZqIomWSBEZ3InRP4jZN4iWSKKYEYSq3SnzuRmsyXGs2WUy36iXWhd64XnSJmffImMM0RfZw7x9C4mk4MxQcnMMRPbxSM83mbVuYChRhmOHUFDJWeNteYxsC6Wb7VX55vfjSAn/+v/O8+oPS9SqweUbJuzeqzN6WOf9tzfek75V9EyWxKETJA4exRwYRo0nmwRY7wYCJemVilQnxqhcOU/h7Kd4lVLDsZJHH2bgle+jaPe2MPdqVSb+6s8pXTi77muJjoyy401pIbAAACAASURBVHt/jGKu3jLJty0u/cv/Hb/WnZ2EOTDMrr/z3zTuIFAqMPYX/y+18Rsdn4/QDSJDO4mNjGIO7sDo7UfP9KIYrQXW342UEr9aCZTS9CTlaxepXL3YVBY2Ez3bR/LwQ8RHj2AODKHGEi3KvMQrl6hNjlG5dpHC2U9xCwsrvsN3nI43uJRI5qybpLR+CvYMiyYD17e4UT7NodQzDET2MRjdvzT/mlfkVvUcD9BKV5DpVbEtycVz9pLCBbAtuHze4fL55g3m2jKXTJbksUfpeeLZQNGqrTf7g6BlipZMk0ikiO8/RM+pZ5n/8LeB8i0XV3iDgt7Ti1C3xVd1X6FG4xj9g8T3HyZ55CG0RArFMEFRNtxXTQiBGosTjcWJ7BwhdeIx3FKRwucfUjj7KXZupvGKsIvoPX2kH3qczONfCR6CylplXqAlksRHjxDbe5DMyWfJn/6A/Gcf4Obn7zjWt2rQhv5wzViwJ/nM+SWevN0tWSK5VfkST3rsiZ9AVyJIJFU3z5XSR+Tt9ffjW862uJOrFcnYdYeDRw3++B+lqFXynD9rd1cmhSA2coD+F36PyI49Dbe9rZ1OIDQdo3eQgW98j9jeg8y99Rq1iTvjABXDQE9nt1zzxPsZLZEiefQR4qOHie7Zj9pgB9AOhBAIw8TImvQ+/wrJY4+Se+8NCp9/hHQ30ewgBIkDR+n72rcwB3e0SeY1jJ5e+r76TeJ7DzL3zq8oX7mw9IDxbRvpdd7EIJErdgL28Rivfsl07QqmGseXHpZXXrLvtoNtoXTn5zz+/F8X+Cf/vIevvhzlkSdMPn3f4hd/VeaLzyxmpr2OmoKErtPzxLNkn/56C2aEdZxfVUkcOo45uIOZX71K8fyZJduWYhgYPdm2jheyMloqQ+bxr5A6/ihaKoNQta4/7ISiYPQNMvjKHxDduZeZv/kZXqn7Pc0Uw6Tnqa/Rc+o51Fi8/TIvBNE9+xnu//vMvfUaC5+8i3RskBKvUkZP97R1vLXiShvX7UzT1G2hdH0ffvlqhdlpjz/44yTPvRTlhd+L8vzLUc6fsXn9ZxV+8sMS+fn2b0uEbtBz6jn6vvoKir5yO4+2jCMEerqHwW/9IUJVKXzxKfg+im6ipUOl2w2iO0foffalDa/oNsriyjf96JMgYOZXr7bsdG0HimGS/coLZJ95YUU/QrsQQqDF4vR9/VsokQi5d36D9Fy8avcd4t1kyyhdXQ/icFdD+vDR7yy++Mxm54jGN78X5ytfjzJ6ROfQsQxPfS3C//m/5rh+uY3bMVWl58nn6XvuG4gOCt8iQgjUeIKBb/4B0vMonjuNlkqjRmMdHzsEKtcuYk1PEBnaudlTWSJ14iRetcLs3/wc6XbebyE0newzL5J9+mtd8yOoZoTeZ15Eeh7z772JW1rBt7GpCLLGDmJqirHquQ2frSufqpTc9kiusktJphU0LXCMNaJakVw653Dp3AI//P+KvPB7Mf7kn6Q49UyE//Ifpvg//pdc2+adPHSC3mdeDJwnLbDc6+pVSrjFPL5lgfQRmo4SjaEl03esmO/etgkhUKNx+l/4Ns7CHGb/UGjP7RJerUr+8w8x+wdbUjh3e9mlbeGWS3jVCtJ1kL6PUFUUM4KWSN0RidHKd7poA808/gyVqxcpX/5y7Re1RlLHHyP71Fdb3tXdIfPlIm6xgG/XQAYKXI3G0FLpOxYtK127ohtkn/4abn6+q6v6VlBQ2BU7Rl9khFvV89ujtKNjg+sGUQix+L3LWU2D0UM6himolFv3js1MefyHf1vEjAj+9H/KcOrZSDsSRgCIDO+m96uvNAyjWkRKifRcnIUcpfNnKV8+hz0/h3RdpPQDARQCVAVFNzB6B+qe3AMYPb33CLgQAj3bS/83vrdlPNgPBFJSvvgF1sOnMAeGV1GMEt918SrlIAxq4ibViTGsqXG8UgHf88D3AmUkJQiBUBSEqqEl08T3HSI+ejgIMzQjjWOj6yiGQfaZF6neut7RkL3o7n30PvcSogWFK6VEug7O/BzF82coX/4SJ59Dut69Mm+YmH2DxA8cJbZnP3pP74pmCzUaZ+Dl72+9mGUhUEX7drpdUbqVkk9uxiOTVTj6kMGXZ+w7okJ279P5ytdjq8qfYQZ23ZV2V74Hjh0oJsuSbdG5QtXIPvNCS6vMxRjE+fffJH/m43tCYO7GA5z5OcqXzqEmkiSPPkLP489g9A/eMZYQCvG9BzZ4JSFrxZ6boXTxLObA8NJri6s5r1ykfPUi1bFr1MZvYM1MBc6fFnELC9RuXWf+/TdIHDxO5olnie7a29SGLIQgMrST+L6DFM+dXt+FNUEYJtlnX0Lv6WtJ5t1intx7b1A8+yluMd/weI8iTm6W0oWzaMk0yeOPkXn8aYxs/10yH4SWbTUEoCnbTOlOTbh88r7FK9+L8V//0zS1muTcaRshYGRU5+//d0nSWYXVHnAnn47wD/80zTu/qfLZRxb5eR/fkxgRwWNPRvjjf5TCc+H1n1fasjCMHzxG4uDxloSvNnGTmd/8ZyrXL685W8srFVn48B3Kl87R/8K3SRx+CEXbMmb2B5b8p++Teexp1FgCr1LCnpki//mHVK5fxi0V16RoV8K3bQpnP6F8/RIDL/4+yeOPNf3eFTNC4uBxSpe+3PD4K5E88jCJ/Ydakvnq2DVmfv1Tqreur1nm3WKe+ffepHTxLAMvfZfEgSMdsx0LBKa6enJNq6hCRxftCxvsyh1eKkr+/f9T4MCRIHPsf/u/eikXfRCCeEJw9YLDv/wXOf70f+5B01aw96iCh0+aPHLKREoolySOLYnFBWZE4Djwsx+X+E9/tnEDvBpP0PPEM4gmN4GUEntumsn//JdYk7fWP6CUOPNzTP3sP+HbNumHn0CoavP3hXQMp7DA/AdvoyVSFM99RnXsekecWF6pyPRrPwFVJXXs0YYrXiEE0ZFR1EgUt81KV0umyZz8CiiN5U5KiTV1i8mf/hB7dmr9A0ofZ26Gyb/+Dwx847ukjj/WkYgRU03wePbbGz6PQCGmpeuJFBuna8uqc2ds/sf/foYXfy/Gya9ESGcU8gs+pz+y+MWPy0yOu7z0nTjxpIJzl3x//G6Nf/bfznDq2Qh7RzXSPSqqKigVfS6ft3nn9SqnP7KolDa+zI3vO0h050jDJ76UErdUYObXP92Ywl2GVy0z8+ufBtlqo4dbsvWFdAjPI/fu34CUHU9O8KplZl//a6LDuzF6+xseq6cyGNn+ptv5tSFIHDxGZHBHc5nPzzP9q1c3pnCX4ZUKTL/2E/R0D7Hd+9ou8woqSa0PYMPOL4GCxzZTukgYu+byb/5VgX/zr1YO9v7n/3hmxderFck7v6nyzm86m/cvNC3Y6jWLVpCS/GcfULrUXm+yVy0z++YvMAeH0ZPptp47ZG3Iu5/8HcRZyDH/0TsMvPz9xtt7IYgM76Jy/VLbxlZMg+SxR5pHK/g+8x//LjCjtRGvXGT2zV+y4w/+XsfsuQVnmlvV8+t+vypU9sQfbpszLTQgLsPI9hPfe7DpcU4+x8KH73SkIpI1eYvi2U/oefKrmx6kH9I9ypfP45ycbbraNfqH2jqu0T9EdPe+psdZc9MsfPxuR6rMVW9epXT+DOnHnuqIzOftKW6U1++AVIVOv7mPhN6eJKXwrl5G/ODxpkkQUvrkP/sQt0OpmdJzKZz5+L7Pygm5E6ewQG1yrOlxejrT1nGTh040dWRJ3yf/6Xv4HZJJ6TrkT3/QsRKdtr/R6oMST7bPjh4q3TqKYRIfGW1qV3LyC5SvnO9o/GxtcrxttuKQ7YG0LazZaWSTlaQajTdO3VwDihklNjLa9Dh7fpbK1YttGXM1ahM3sWfaU8VrER+PkjtH2Vu5jGSrSClxVyiOs15CpVtHz/YHJRSbOBPsmYm2C8e9A/lB0ZuQBwpnYa5hYoBYTLRoU1ih2T8UFPZpIvPWxBj2/Gxbxlx1HM+jeKG9Ml/zirwz8wMmqhc2dB6JJG9P1jsBPyD1dLuB0ZNFS6QaHyQl1ZvXutKpoHrrOr5ttZyCHLL98cqlpitdhECoKu1YeBm9/cHKueGkPKo3r3XFsVgdu7ElZV7ic7NylrHKl8g2KN1wpQsgFIz+YYTexJ7r+1RuXOnKlLxyCWt2uitjhWwNpOs0N1sJ0Z7QKkXFGBhqumr2PZfq2NWNj9cCbjGPvTDX/MBNwJMurmzPYitUuoBQlaDISRNh9h0Lq9OmhcWxbAtniwpgSGcIVrndqbWhaBpm70BzmbdqWHPdefj7tQpufmP21+1AqHQhWOn29DU9zJmb6Up5PVhUuvMd7xcV8mAiVA090zwEyp6d7lr3Cq9Wwynk73uZD226BJ0bWhLA3CzS75JASBn0TPN9CNOCQ9qM0LSWujPYuZUTljqC9PHKhaXqbJ1GFRpxrYeUPoChRBGr1Z2t40mXa+VP2ehuJFS6BC3IFaN5OTunsNDVUotuqYj03LAWwzZD6AZqJBrUUNZ1hKYjNC1oYqpqCEVFaOpd/9aCqltq54vlQyDzrZRwdPLz3ZX5YgHpex1PDNKVCAeST7IrdgyF1u4vR9a4Xv5sw860UOkCaizBqtXVl+GVCl3pVLo0Xq3SlSZ9IWtEKCiGgaIbaKk0Ru8ARt8gRqYXLZUO2rJrgTJFUYIwL6GAIkDUOwkvdhRe9m+E6FrJDTXeWspte+s8NMerVjqS9XYngh3Rw+yKHQOC0DLLrxDXehAIql4RgUBTDAwlii89bpQ/Z84e23ANBwiVLlBXus2EXUq8DhaQXgnftoKC0CFbAi2RIrJzD5GhXUR27sHsG0RL3Zshth06fajxRPODpOxo0fSV8Kxqx226mjAYjOxHILhR/pzLxQ9xZI1T2e8jhMJn87/E8stE1AS7osfYHT+BEAp5uz2FfkKlC6imSTOtK30fvwN1TBvh23YXnvohjRC6gdk3SOqhk8RGRtHTPShmZNvXxVBbiIWVvtd1mZe23XFzRmDLzeD4Na6VP8ORQZqwh4shosGuBKh5Ja6UPkIIhZH4Q+SdaSarG8/MC5UutNYPy/eg21t93wvb9WwSQlWJ7hkl8/jTxEePLAXsb4dVbCu00mhVel7zZI02Iz234zIvhEBXIpTdBWre7RrcnnRQRQplWVCXj8dY5Swj8YcZjhxksnqJ0JHWBlpKq5Sy66Es0vO6FLUZshwtmab3uW+QPPIQajx53yja5QitufNI+n7XH/rdUvISubSiXcTxa+hK5J4Sjo5vYfkVEnq2Le3AQqULLYWnLDUa7CKbMeYDjRDERg7Q/+K3iezYU3+pnQpXBp2xPTdoWrrsp++6KLqBnu7piuniboWzynQ3R+l2eExf+theFV0YmEoMyw+6D1fcPIYSJa73UHRv15oQCBShoSBoR+fbUOlCS3ZT0a70yzXxACvcNlXSan08QWzvAYZ+/++gp7NrUraL3aCl6+I7Nl65hJ2bxS3mcUsFvGoFv1bBtyx8xw4Uix9s3YO/Bz/j+w8x8I3vIVoIX9woLa0olc2Q+c7jS5eSm6PHGCZtDDJdC9KcF5wg825P7ATz1i1sv4ZAIWvuxFSilN0FwoI3baKljBshWlsdtJHgxr//hL4VRD2EqlvERkYZ+s7aFK70PKyZCWqTt6iN38SaGseancKv1VjPzemVh9f1vvXQisxvxkIjGLOzY7jSIWeN0Wvuotfcw0ztBhKPkjNH3p6kx9jByex3mbfH0ZUIfZE9CBRmatfbUvAmVLoERT2aEQSydzdJQajafWlPbAWhql173qiJJP0vfgc901zhSimRjkN17CoLn75P7dYN3GK+YUnGrYjfQjq7ULVNkfnOf/GSWesmkXKCierFpdhbV9pcK3/KETVJUu8jZQRdPKT0yTtTjG+g5c9yQqVL0JuseXUnBWF2t+Sc0PRgi/cAouhGd0wMikL2ya8S2bGnJYVrzUww/7s3KJz9eFsnrnjVSvODFKXrZRaFrndldV10ZzlXeOue16dqV7D9GrtiRzHVBFJ6lJwcNytnqXjtSRQJlS7gVZq3IRFCoEZigUB0ybmgmGbXTRpbhW7FwpoDwySPPNz0uKD9+DgTr/4Aa2p82zs4vUqp6TFCCNRorAuzuY1qRjZ9dzdvj5O3p9AUA4nE9a22mBUWCZUuQUdSKWXTTY2eynRV6arR2ANb7EaNxYM02k4iFBIHj7XWMWRumsmf/rCzbZQ6fb3LcEvFlgrL6KnmRXHaiRqLBynRm4yPh+13JhsvVLoEmV9epYySatz2fLG1SbfWOGos8WAWuxEiaMfd4ZtPMQzi+w83X1F7HgsfvEOtw33rhKZ1zXHl2zW8agWtSTqwlu7p7kIjnuz8wxYAgalESRuDxNUMmmIgmlS69aTL5dKHhCFjbUD6Hs78LHoTpWv2D9XtjF2w5QmBnsy0lC23YWSLYtQlhaDoBloi3fFtphqLExne1fQ4e36O/OkPO17sSIvFu5ZeLF0XZ2GuqdI1B4ZpR2xqSwgRlJvs8GcgEAxGD3Ao+TRRNUmrjjvHr3Gl9FFYZawt+D72/GzTzqh6KoMajeIWO1/IXOg6Wrpx08C2IVsLSO/WqlsxI2iZzm9rjd6BlrIRS5fO4Tud74unJVJdMzFIz8NZyBHdOdLwOD3dgxKJ4LfieNsgimGiJVIdl3lNMdmfOElUTVHx8uTtKSy/0rS4lCedMGSsXUjPx5ocR0q/oeNKaCrRnSMUv/y843NSzShGb3/Hx4HgBmyaIFJ3JHqlYuPj2oAajWNkO3/twefbvNBR9ebVjm+vFTOC3tPb0TGWI10Ha3oCeezRhkpO0Q2iO3ZTvtyecKlGqLF4Vz6DoOBNmppX4v3ZH9Uz0rrnGN18i/WWQGLnZvCrTQznikJ09/6uzEiNxrqieKCeluo3MZks2lm7gDm4oyte81bG8K1aEFLY6bnEE8HKu1ueeymxZqfxrVrDwxYXGt1AS6QwWujgslE86VJ2F5D4eNKh25mfodKtY+dmgs4QDRFEd42gNbH9toPYyCiKGen4OAB+rda0hJ8QCkbfYFfmkzx8vCvKR2mhc4LvOF3pEWYODHdtZ7OIPTuJV24SOiYUonv2t1z0fCPE9uxHdCEu2PFrXCy8i+3XOJx+ll5zFzEtg6nEMZRYgz/RtowfmhfqOPl57NkpzMEdq97wQgiMvkEiQ7soFTpYUV8oJA51R/EAeLVqULu3EYpCZHBHxz3ZeraPSJdWVq3UHxDdqD8gFFInHu+S1/42zvwcdm4GPdvXUObNwZ2YfYNUyh00LSlqV2U+Z98iXr3EaPIUA+Y+bL+CJz0arXod3+Lj3E83bNcNV7qL+D7FC2eb2jYVM0Lq4VMdjSqI7t6LOdTcq94u3HIBr1puWrrSHNwZxCp3CkUlfeIkWhdWVQC+1dw5pugmit7ZvmWxkVHi+w51dIyVkJ5H8fyZpsep0RjpR5/saIZgbO8oRv9Qx86/HAWV/YmTHEg+iSZ0DCVCVE0R19LEtUzDP+0gXOkuo3L9Em6piJ5e/cMVQhDff4jo7r1Url1q+xyEbpA6/hhqpD1bmVaQjoOzkIMGC8xglT9AZHhX0KywAxjZPpLHH+1alIRbLjQ9Rug6WrJz5iQ1nqDn1HNdMyXdTeXqRbxKqeGDTghB/MBRIjt2U7t1ve1zUAyT9ImTXUs51hSTHbEjKEJhsnaJqdplbK/adAUrpd+W6IVwpbsMr1IOcuqbrPgUw6T3+Vc6YtuN7z1Q32p296upXL/c1GygGCaZk8925OYQukHf8y9j9A60/dyrYc/NNL1moSjE9h7syCpPqCrZJ79K4tCxTUt9dYt5iudON5V5NRqj7/mXO2LbTRw6RuLIQ137DAQCTehUvRJnFn7DZPUSOfsW8/Z4wz8LzmRbxg+V7nJ8n9LFL4KqUQ2EUAhBdNdesk99vaW2J62iZ/vo++orXV3lLlK9eRXZpPKUEILonv2kTjzeVjun0HV6Tj1H4vCJriofZ34W325uYojvP9T2B6xQNdKPPEnmiWe7bstdjvRcShfO4pVLTWU+tvcgPaeebWtqutE3SO9zL6N2caXvSoup6hUEou4c6+4DLzQv3EV17DqlLz8nc+q5hscpmkbPqWfxHYvcu2/i1zYWPG4O72Lw5e9hDu/e0HnWi1ssUL56keThEw2PE6pK7/Ov4Ds2hTMfb9ippsbiZJ9+gZ4nn2vrA6wVPMuiOnadxIEjDY/TEimyT3+d2df/uiUl3Qw1GqPnqa+RffL5rnjrm1G5dpHy5XOkHj7V8DhF18k+/QLScZj/8J2m4WbNiO7ay8DL3+taVMwinvS4Xj6NKjQey/4e09WrlL15POnWTQgrI/GZs25uePxQ6d6N7zH/0Tskjz2KlkjQ6CkoVI3sU19DT/Uw88bPcQsLa1ZCQjeIjx6m77mXMYd2bto2U7oOpQtnie8/3NBxJIRAT6Xpf/E7CE2ncPYT5HoUkaISHd5F9pkXiB84htJKn7o2Ix2byrWLxPcfamjOEYpC+uGTOPOzLHz02/WXdFQUzIFher/yIokjJ1C6/JBZDel55N5/m8ShE6jRxis/RdfJPvsSWrqH2Td/iVcurlnmFcMkfvAYfc+9jNE/2HWZN5QoxzJfw1RiRNUUqVQ/Ukp8vIZZaY5f483pP1uqv7teQqW7AvbcDHNvv0b/i99par9UDJPUwyeJ7tlP/rP3KV/6Mki0aLAKEIaBns4SGdxB+pFTRPfsX7FguVet4NtWkI/eBcqXzlEbvxHMp8mNoCXTDH7rD0kdf5SFj35HbfJW4GBrkGQhDBM9mcYc2EHyyAkSB48hDPPesaTEKeZRdKOzSRJSUr58jvRjT2Fk+xtesxqJ0v/S72P0DpD/9D1qUxMNr/WO90ZjmIM7SRw+TvqhJ1Ai0RXHcstF8CVaMrXuS1ov1vQ4ud/9ht7nX2kav6yaETKPPU1s7wHyn75P+cp5nNwcvr26zCuGiZ7JYg7tJPPok0R2jqwo826ljKBebaxjSHzpUfNK1LzmJS4XcaVN2K6nU0hJ4cwnRPeMkjz6cFMFJISC0dNL39e+RfqRJ7FzM9hz0zgL8/i1CtL3UTQdJRJFT/egZ3rQM73oPb0rrnaklEjXZf6Dt1FMk+xTX+vUld6BWyqw8Mm7RHeNQJOQOCEEQtOI7T1IdOcI9twMdm4We34Wt5APahXIoHKWGo2hp9LomV60VAYj27fqjS2lxK9WmP2bnxEfPUrq+KOduNQlrNlpShfOkn36602OFCiaHrRk33eIyo3LVG5exZoYwyks1BMoJCAQmh5cb7af6M49RIZ2Yg7sQI0nVpQlKSV+rcrcW6+hxpP0PfeN7vcm830WPv2A6J5R4qNHmsu8omD2DtD/wrfJPPoU9vzsbZm3qrdlPhoLZD7dg97Th57JrrirCTpy2My/+zfomSyZx7/SqSvF9mt8Nv+LNb9P1v/bKKHSXQWvWmb6tb9CT6WDp3ILN4EQAqOnFz2TJb7/EPiS20/Geu+nuhe82fmK5z5j7nevM/DS72/sQtZI8dxpYnsPkn74ZEsOHiEEwjCJDO/CHNoZbDXl+q8b32fh49+S//wjFCPScaWL75N79w3i+w7dTv5ogFBU9Gwf6Wwf6YefWOpeK10X33PrLW60elKFstTrrWG9Xsdm7p1fMf/RbwMH7dNf73hs8Ep45SJTv/gRO//2P2iYJLQcoSgYvf3o2T7io4fXL/NSkj/9Ibn33yJ55CEyjz9N5xxcsmO1clshjF5ogFtYYPpXr2JNTzYNqVmOqDexDPqqaUu9poSi1n+3sjBJKZHSp3zlPLNv/BxpWx3eZq0wB9dh7p1fUx27vqZrhvp1K2u/bljsqOtR+OJTcu+9Cb5PbXpizXNYD165yOybv8ApFVoab/FahKqh6AaKYQbFWpJptFgc1TSD1zUt+DwafN++VSP33pvMf/gO+D5uMY89P7vi8d3Ayc0x/dpPcHKz3ZP5etTQ3Nu/Qjp2Paqk85X8NotQ6TahevMqEz/+M2oTN1trW70RpE/p/Bkm//NfBskK0LXsrOU4uRkmf/pDqreud/6a60jPI//5h0z/8q+W2id5pTxusYPp1kuDS0oXzjLzq5+21MamXXiVMjOv/4y5t15DOoGScUtFrKnxrjxsVkZSuXaJ8R//u6AKWYdrCOP7FM5+zNTP/+PSd+1Wyjj5XGfH3URCpdsC1vQEk6/+BcUvTyM9r+03hJQS37ZY+OR9pn7+I9xlGV/d7lG1yGJ7mtKFsx255kWklHi1KvPvvcn0r169Q+l5lrX08Ok4UlL44hOmf/UqzkKuo0pvsf3P9C9/zPxHv72jk7B07KAHW5cedqvMkNr4DSZ+8gPKF88h/c7IvGfVmP/oHaZf+wnuslomfq2Kk29WfGr7Etp0W8SanmDiJz+g+tg10o+cwhwYbkvWmPQ8alPj5N59ndKXZ+64AYVudD12dTn2zCTjP/5zMie/QvrhJzAHhtrWKDNwFjpUb90g99tfU7566Z5oAL9WDYrL79lHVwLYfZ/C5x9hTd6i7+vfIjZyIGiQ2SanlpQSr1yifOkcs2/9Moj2WEGZVceu4dvWpj1wF7Emxxj/0b8j88QzpB86idE/1JbPwnddrMkx5n77G8qXzt0TgufVqjjzgXljs5tUdoJQ6a4B6djMf/AW5ctfkjrxOMljj2D09C21F2lFQJZWDL6HPTdL/rP3KJ4/gzM/d8+ximF0PR34bqRjMf/eG5QvnSN57BFSxx4N6vyu4ZqXzlV3sknPpTZ+k/zpDyhd+hKvtHINBOk6OLlZfMdZMQ03uFnbvCKVMnjAvvoXxPcdInPyGaI79iz1L1vz9dbP6VXLlC9/Sf7zj4PsvwalNGtT43iVEmKFCI9ut3337Rq5371O6dI50iceJ3n0EfRMT+uOUe6UeWtmivyn71G8cPaOHd2dg/rYa/mrRAAAIABJREFUuRmkbSNXyH6TnrutuzGLJql/2/fKOo2ioBgmsb0Hie87gDmwEzUWR41GUXQzKNqiKOD7SM/Ddyy8ahWvXMSanqB06RzVsev4VnVVAdIzWfb8yZ82LMDj1apM/NWfU7pwtlNXehuhoBgG0Z17iO09SGR4F2osgRqNBQ8Ivf6QEEpduXpIz8WvVfFqVdxinuqt65Qvf4k1M9VQ8SwNqWr1ljor3dyBWaaTN6DQdYzeQZKHjxPZsQctma5fr4nQ9KXoBHwf6fvB9drW0jVbM5PUxq5TvnI+qOTWotJUDHPleg/Sb0tW3LpQFFQzQmzfIWJ7DxAZ2IESi6FGYii6sSTz0vfA84KGr7UKXqlIbWqc0sUvqE3cDGLYm9W8UNX6Lm+V792xN9kE0xgp5apPo01RuvuVE0REnHl/igl5bcPnEwj6xA6yYpAZOU5OtqcwxVpQY3G0RAo1nkQ1I4GiUJTgRnRdfKuKVy7hFPMt95vSe/rY8yf/Q8Nyil1VunchNB0tmUKLJ1EiERQjUvdYL7tu18GrlPHKJdxSvisFwTuF0HS0VBotnkSNxoKHjKoCAnwveMi4Dl6tilcp45ZLG04P38qo8cQymTeDcqeKUn/Yevi1Km6lGMRt1zYvRGszaKR0N8W80C92khQ9+MJti9LV0NmrHCMteonI+P/f3pkGyXVe5/n57tK39+nu2QcYADMYrIONBEGClEiREiVwMUGRouhIJSeuKFUpJ1GVHTsV20nFTtlRJVVOKlVO7PxIHFOKpJJoiaQoCRIpUiIpgQQIEiD2IXbMvvT0zPTed/ny4/Y0MJzpngXgYNF9+IMF9O3bXzdun/7uOe95D2l7HJP5d1HXEzuXLVfdB6/fSW/ydJa0TMxUcs7UyO3IdLrDHL9xkq6bCTubmX/yhMcsbgv1goNNRqYwKZGWKWxu3d2Uh4fH7c1tUUizselxDnPGOYqDhXONhhQeHh4eHxe3RdAFd7frsLyVXQ8PD4/FclukFzw8PDxuFa7LTjdCnKiSIEAIFQ0HhxJ50nKSKTmONU9RK0iEhNJMgDACQYEcKTlCRk5UdfWJEKNV6UBhpo5vQo4yLC/XdANqE51ERYJ+5xxpUhgEqBetBEUYBZUSRSbkKFMyWTNVoaJRJxoIizr8BBEILExyMkNKDlPg5qpcC7+f0M4daLEYuSNHKfUP3OglLQijYw2hu+4EoHDmLLkPjt3SOs1bCTURJ7L7bmSpRPrtAzjZ63BNKwp6cxP+jjWo8RiKYbhqh1wOa3ycYm8/1ujYgv6NlWCQ8D13oQQCZN59z33eTc41Bd2oSLBK2UC9aEVFQ0FxK+7StUFzsBmWvZy2D1W99W9X1rFG2YwPA+FaEpWfadPvnOO8c3zOwphP+EmIZnzCj4KCio4iFBRHYcTurRl065UWmsUq8mQIySidajd+wii447allNhYjMlBPrTfp8Rsn9CVyjralXX4CaKgICrvXZZ/dApcdnrodc5es+nx9SK4bQuJJ59A+HT8nR0M/Y//dUsEL72liej997l/sC1yx07AMjcJ/EYiBPHH9hDaeYeriVVUJl/5+TWdUo3VEX/8EQLdm1EMnzv6Z7rBoqxpz584zejff3NB5wvv3kXssUcQmore0szo333jmta3HCw56IaJsVm9mzAxJA5TMsWkTGJhoqERFBGiIsGUHK8acGNKIy2spkCOAec8RQro+GgSK4mIOO3KOnIyTb88N+u5E3KME/YBd4QyBquVTdSJ+kW9h2ZlFX6C2Nj0Oh9SIIuKTkI0kxDNtIh2CkqGc86xWUHch0GQCBkmmHSS5Ejj4BAgRKOygiAROpWtTMkUE3J0Uev6WBACrS6KMHwIIdAaG9yL/RYIuh43BqFpaPG42+SjKGj112amL/x+Yo98ltCunZVrz8nlsTMZhKKgBAMogQDFvr6FXZeKghqrQ+huGNMaGq5pfcvFkoKujkGXupUwMUoU6LHfJykHsbGRSASivPvUsKsVtwSEZB2jso8e+zAlpkcgC4a4RLd6DzGlkWZlFSN27yzdrY1FmhRI9za/hTWLfh9REqTkCD32e2SZqgTWfs6ySd1Fk9JOg2hjgAvkSM94br9zjqQcIiunZigmBIJ+5xx3aA8SIESLsooJ+yYIulJiDo9gp9MogQCFD894AdejJtI0Kfb24Vu9yjXiuXDxms7nW9FGcPtWhKJg5/OkXvox+RMnkWb5TlZV0KIR7Kl07RNN4ziYA4M4uTxC19xr+hZgSUE3KuLERTMSh0vOaYZlL1f3wEtkWUtQWy9bosAl5zTFGblPSZ4Mo3KAOtlAUERQ0T+WZgcHm8tODxlm2gealBhyLtMg2jBEAJ8IkJMzL4QieYpydpeNRJIjTcoZIaB2EJTLb81YjfzpHpLfeR4lGKRw5qwXdD3mZfK1X1AaGEAWS+RPnr6mc/naWlFDrj90/ugJsgcPzepQLKUX12yRPXwUJ19A6Dr50z3XtL7lYklBt040oAqNrJxizBlgSaYjEvIyy5Sc27qvKHM42Gho5Vzv9cekRFIOV3msiIWJioZK9QkKbjbZh4Ze3t+7ptWqUBHlPb9AXJcxH9eKLJnkT5y60cvwuIVw0hmyB9+79hMJgRYvt7NLSfFy73VpCZfFoltYvYVYdNAVCILCVRmYskiRpfdUF8jUCEZXRn58XEG3IHPIKumP6XlI1V5bRaNetNKotBEkgiEC5WKieqWwVl6/h4cHCN8Vm9KFmB3drixpp6vifnhOWWWwVCx5Y9t1bcwl7T91fGxQd9IoVgBQwN2xF2QOkxI2Fo1iBQnRfH0XvBiEQKtPoIbnHvdj5/JYIwvLNSuRMHp9Ase0sEZHkeVRKko4hBoOI3yuBaG0LLcwMjW1KAcooeuo0QhKwA+KijRNnGwOO10e773YfyQhXPercMj9ogulvLYcdjozr/JB6Dp6WysgsUaTODk3/SX8frcYOf1+TRM7ncbJVXeKq3Z+JRxC8fvLRSDXpcwplXCy2aXLsjQNNRRECQYR2rTLnXQ/z0IBJ5urTKiYc12ahtbUiOKbe1iqNZ7CWeDtvxLwIwz3/QmfD8XwoSUSV5ZaX4+xZtWs59m5fHW5mKKgNdSjBgNzvqadzmAlr8H0Xgi3mBd0XfMo20pKy0IWSzj5fOVauBaWFHSnc7VK+T97yZKoG3/LvRTalE6axSokknPOBww5l2ft+INKmAQ3LugKXSP22B7CZX3rR8kePc7o/3luQcEitH0r9c9+ASs1wcjffQNzcJDg1i2Ed+1Eb21BjYQBcAoFrLFx8idPkX7nIPbE/KN29JZmIvffh39tB1o8jtB1nHwec3iU3NFjZA68iywtfFekhIKE7ryDYPdG9JYWlFAIoSrltSUpnD1P5uC7mEMjVc+h1Sdo/f1/CVKSfP4FMu8cxL+ui/DuXRida9Aibp7ezuUo9faT/eAo2UOH5w3mSihIcMtmjLWd+Npa0WIxlEAAFFEO4BnM4RHyp3vIHDyELCzQwlEIV8t8x3Z87SvQ6usrAV1aNk4uizUxiTk0TP7kaXJHjs55GjUep/F3v4KvZfZ1Ky2L8Rd+SPqt/fMvx6cT/cxDBNavc4NYKOQG4WlvaCGI7XmY2J6HZz03d+wEo9/4FrI4+99cCfhJPLWXYPem2euTksz+AyS/+w/zrm8u1FgdoR3b8W/oQm9sRI2UNxMSnGIBeyrtXj/nL5A5eGjBPz5zseigK5GVopJP+AkQmlWIup3R8JEQroN+0hmizzk3R8FQ4BP+G7K+Co7EGktS6usHTUNoKoo/gBIKLtmNXwmF0OsThHbuILL7boRhgGW51WdVRQmF8IVC+NpX4N+0gbHnvo01Xn3nEdi8kfhTe9Gvkq/JUgnh82F0rsFY3Y6xtnPBeWjfyhXEn96LsbpsOg6uEbZloQQCGKtX4WtvJ3THdsZ/+GNyR4/XDpSKgq+1hdCunSSe/C2UcAhpWkjLRmgqajRKoDuKv2stxooVTOx7BSc/d7pNjcdo+EdfxFjbgdB1hBCu/aVpgiURuobe2IDWUI9/wzr8aztJPv8CTqb2l1sJh6n7zIOE797p/sCUA5uUEhwHoWuodXVosRjG6lVY41WMwym7qA0Ouc9TVdDUGXcyC0YoaPEYaqzOPa9pYpsmSsCPYhhlU/d85Y7papxsrupeTNoO5ugopf46UFWEplVkZktGVQls3ED8ycfRG+rd85a1+q42uXzXFAqhtzSjhEJk3z+y9NdjiTvdSZnEkiYBQjQobWSdqZuiULQcuFI4t0hWJDdnx5qBn+giNcPXG2lZTOx7hcnXfuHuevwGoW1bqNvz2cV/icooPp3IA5/EWNWONEtk3j5AsbcPJ5dH8fkw1nYQ3LYVJeDHWLOa6MMPMf4PL8yZatAaG0g8tRe9uQkpJebgENn3j2CNJcua0AShHdsIbtvifhnmQWtsoP7Zp/GtXgUSzMEhckePY46MguOgRiIYXZ0EuzehJuLUf+Hz4Dg1izBCCAJbNxO8YxtSStK/fpvS5V6cQhE1FMTo7CB0x3aE4SN8327sySkmf/HL8hjymTiZDNb4OP71XVjJJIUz5zGHhrCn0kjbRo2E8a/rIrhlM4quE9zSTbG3j6nX36iaqhG6TvzxRwjfe3elwaA0MEjh7Dms8RTSNBE+A72hHt+qlah10ZoKBDs1wehz30IxDITfQPH7iT32OYLbts77+V+NLJWYfOW1mbtiIYg+9AChHdsASP9qP/njs39MnVz1FIgsFEi98DIThs/1b/YbhHfdRfShByqpgEUhBMEtm0k88xRq1L17cbI5CmfPUerrx8kX3GsxGsG3cgW+9hUUzp1f0B1cLZYUdKdkkqQcpEm0s1rZhInJmDOARRGnXHxS0fBhAIIsU9yqqYSPYmNhUkRKSZ2ox8BfafcVCAKE6VC7MbjBO11wd47FEnaxBJNgjY3PGRAWg9GxGms8ReqFH5I/1TPjC5I9fITipcsknnoSoWsENnTha22Z1W4sdJ3opz6J1tiAlJLC2XMkv/v9mbk8Ici+f4TEU3sJbN5Yc03C8FH32U/jW9UOQO74Cca//yL25MzcsnLwXYp330Xdo3tQImHiTzxGqW8AK1nFD1gItEQCe2qKsee+RfHCxRkV98yhw5T6B4k9+lkUwyD60ANkjx6bsxVVmhZTb+0nf6qH4qXeOYNL5t33qHv409R95iGErhHc2k1m/4G584hCEN59N6G77kAoCk4+z+Qv3iTzzkHsTBasq2ftaQjDjxIMYKeq73QBN7ecz0M+jw046Wzt4+eirAn/6Hqv1t9aY0mKFy8t6dyyUMQuFLEBKzWxZOmjlogT3/u4G3ClpHDmHKkXX8YcS7oprenzKgqKYaAEA+5d3TVKLZcUdE1KnLePE9QihImxUbmTjFhLhklsLFRUDAKERB1JOVSzDXgpqOhERbws59LQhY8AbsEoRJRWpQNLmthY2JhMXUePXRuLEaePerWNMDG61d2k5Ag2Jj4CJEQzARFiVPbTRPt1ec2bCsch/eav3Vbcj1x8smSSPXiIyH27MdpXokajaA31s4KulogT2LjBDRamyeS+V2YX9aTEGh1j8tXXMdasQg2Hqy5Jb2oitH0rQgjMsSQTP/opdmr2NFknXyD963fQV7QRvmcXWn2C8N07mfjZz2sW/jL7D1A4e272+y0WybxzAP/aDgJbNqOEQ4R2bGPy1dfnPI85MIg5UN3kXhaKZA4eIrRjG3pTI76WZrcQOEfMVSMRN6VgGEgpmXprP5OvvDbn+5CmhTQz86YqftMI7dqJVr6LKvX2kfze9+f2bij/EFVLHS2WJbcBZ5jkmLWfVcp66pVWgsINwNP5ELc1okRBZq976iEs6timfrIsJptWw7q3F3WinoiIVyRfEskR+00m5fUzwhiWvQScMG1KJ3WigZhoxHVccMiR5pT9LiWKJNQbqF74mHByObKH3q/6ay8tm9LFyxjtK92qdWj2RFu9ufHKxX65l1J/9UBUvHQZc3ikZtANdG9C+P1IIH/yNOZodVWGtCzS+98hfNdOUBX869ehvn2g6i2jk8mS76nevefk8uSOn3R344ri5mzf0OfMVy4EJ5fDTCbRmxoRfn8lN/1R9OYmfCtd9Yw5PELm7QM39cywmw3hNyoFOWlapN8+iDW2PGY512R4k2WKU84hQrKOMHUYwo+CioNNSRbJyMlye+3Mi6HXOYNP+ElXaYwASMsJzjvHAUmJmVXcosxxyTm9YAVsUc7cKgw5l0iLCQoyQ7W0R15mueicQkGZ1Y3mYHPBOcGI7KNO1KPhw8GmKHNMyiRF8ugYnHWOYknrtsp3l/oHseeRzUw/LoSYM3+st7VVCj6lvgGcWuoEx6HU149/bWfVQ/ydHYC70y719s2rIrBGx7BSKfTGBvTmRtRIpGrQtSenXOlaDYqXLiNtG0V1i2tqNOrmppeC44B5VWqgyjRoo2M1QlWRUlI8f2HhrbMeAGWFQgQhBE6xQOF0z7JlQK+LtWNWTpJlcsGLHpDn5z02yxRZZ+7R3AVyXHROLnKVVxiV/YzK/prHFMnR63xY9XGJJCMnyMjZt7HgdrT1OWeXvMabFWt8fP6c1lWPCyFmGeto5ao2UrrBbr4gOUeqoHJ+TUOti7qnsyysBRQ5pG1Xgq4SDNasfjvFwrySNXviSl5RMdwCVNX1Gj7UaNQtzLS2oCXiKKEgiuF3VQ2G70rnVg20+nJxUboqlVr6W4/ZqJFIZUPg5PILum6uF7fN5AiP5cEpXuP4b0ElKElwR2nPg6zxmsLnjn0X02PQF6LpdWTlnEJR3KaMaq9t2Ui79m27NK3Krb0oS5nmwrdqJZH7P0Fg43rUaHT+ddZAmW4QcBychep5PSoIw+c2kICbq13G1IwXdG9ipG1TSo7WHF/tlIo4pWX80jmSa7oPk8wMYgvQDNfaWEvHdjWV4HZcV7kdn4EQV46TElljpy0UBaHMs8bp3TyuPlbK2V/gwOZNJJ7e61pqOg7WeAqzf4Bibx9WasLtFiuVQFWJ7XkY/9qO2q959Ycy3/o8ZnN1p+NCrpnriBd0b2KsyRS9/+9vb/QyrjvTPyJCCFcsP4+vb63bdVksIU0TKSVCVa/sAGsgyoJ3AOk4OIXZJvWVY3161Z1rZX2BAIjyF3e6WWTG435iv/UIelMj0rbJHvmAiX2vztmGrYRCOMXq65mm0iqsqpX34rFwZLGItMudteW2aWktjzG+NyPNY9m5WpajNdRXbvOqoTUkqj8oZUWGJXR9QY0UwuerHGdNTNb0OnBbWGsHcr2psbIbtnM5nMxMbavRsQa9qbGiX534yRwSuem1aar7QzQPpaEh93ghXGnZAp7jcQUrNVFJyygBP3rz8imNvKDrseyU+gdxyoUfo2N1zYAhNA1jVW29c/6U66MqVBWjswNRY2cMYHR2oITDbhAcGnYNeqqgxerclEAN/Ou7oKwksJLj2B/Rw2rxOEJRkLhf9lpzvJRweEETEIoXLlVUH0ZXZ0WC57EwzLEkViqFlBLF7yewtXvZ0gxe0PVYdsyxMUp9/Ugp0RsaCG7dMnduVwgCmzeizxOECucvUOofQAKBjesJrO+qmitWY3VE7/8EAI5lkTt2vOZOV2ga4Xt2VQ3kekuzq9EVAmyb3PETs4oyjmVW0idC16BKukLoOpHduyotqbWwxpIUPjyLlBI1EiG252GUKo5yM1jm/OVNi2WRfbesN1cUIvfswr9u7fw1huvw+Xn/Ah7Ljj0xSe7wB2DZoCiVuVlXa3qFphHc0k3ssT2IGuoCcLW06V++hSwUEIZB4um9hO7cMTNQKgq+9pXUP/OUu7sWguL5i+QOz+24dTWBjeuJP/7IzB2vEOgtzcT3PoZv5Qr3fH39FE7Nnl5g9g8ibRtRfk5wa/esL7eaSBB75GHC994z73rAbaJIv/VrnEwWIQTBrd00fuVLBLZ2o4SuCr5CoIbDGB1riH7qkwQ2bVjQ+X8TyB09RuGMO39RTcRp+PKzRB74hJtquCq4Cp8PvbWZ4B3biXzy3pmf7xLwCmm3IxUv3bBrdhPwo/j9+Nd2VPKnemMDkfvvw8kXkPk8TqHo2h+Oj7v+sB8nUpJ5513869cR2LwRNVZH/ReeJHr/fZijYwhFQUvE0RoaEJrK1BtvEb3/E67zVZXzZQ8fQWtsIPrgA6jxOPXPPk10PIU1Moq0bNRYFL2hwdX0CkHxci+pl38yb2tnsa8fHIfIJ3YT6N6EOTKKk8mghMPozU0VTa01Ps7kT191TVI+QmlomPzpDwlu24IaiVD/zOcJbd/q+hOUzX2MVe2o8Ril3j6s8QlCd26fd9eV7znDxM9eJfboHpRgAP+mDfhWr8LJZHAKrr5YGIZrYBPwowaDTP789Tld2ypeun6/6wbm96MEAvhWtLoHKG73nnQcZL7g+vOW/2+Ojro/oB8nioLW0IAaCqD4A1eu6fVdlQCpr2itXNNXr89KJue0yXRyeVI//DGJLz7lfv6xGPG9j+M85HokO4WCW6gsa6+VQABzeJj88ZM42SV4UpTxgu5tiBIK0vTPfhe9taXqMXpLM4kvfH7W36defJmpX7z5cS4PcL13k8//gPhjewjeucP9gq9qr5jWgLsjnvjxPgrnLxLavq1m04A0LSZ/9nPsyUnXTKepCV9bK7621soxAnBKJfInTjGx71XM4blHNV2NPZ5i8vU3iD36OfxdnWj1s4t65uAQqR/tI3+qioOXZZF6+ScIVSXQvQk1EiF4x/aPvJBNvudDUi++jN7SQnDr5vnd4ByH9K/exkpNUPfgAxida1BDQdRQ8IqMDipWntI0sav8oOor2mj9g381Z6AXQiCB4LYtBLdtmfGYNE2G/vv/dC1EP0bUSJiWr/3zqvpmIQTG6lUYq2cbo49949tk3zs85/NKvX2MPfdt6j73aYJbt6CEgmiJBDI+U00zbW9g53LXrHLwgu7tiCOxUhMzxqMs+Klz7NScfAGz3JfuZLPzyHSl6x8wfXy++lQFOzVB8vsvkT1ylODWbnxtbYiAHyedoXD+ArnDH1AaGkYJBtyZWraFncnV8H2wSO8/QL7nDIGNG/Cv70JLxBGqip3JUurtI3/ylNt6vEDzEqHrmMMjjH3z2/jXrSW4bauruFAUrMlJ8idOkz/d47b91vhcrNExxr7zPQKbNhLs3oTW1Ijw6ch8wTVsP36SwpmzONks0nFVDkogUFND7H7ADvljJyhd6nV9jNd34VvRhhoKITQdp1jEnpioWD5WC47Sssq2movT/Lr+wgszk3KyWfe6kCy6oUOWtc1L6bybr6HHSiYZ/8FLpH/1NoH1XfjWrEaL16EYfqRtY2eymMMjFC9donj2/KxC6WIRsoY+Ughx+5gGeHgsEL2lmbZ/+68Rqkq+5wyjz33Lc+jyWBRSyqq/Xjd1Ie3zj4Y4sK+d+3f7aWlS+dpX63jxuVbefGklz//vVr765Sixurnfgq7B5g0+/vJP6vnxt9r45Ysree6vm3nykRDRyJXnPPLpIG+8uJJ//OyVirGuw9f/tJ539rXz5/8mUbnjUgR89ctRfvXySu6+4ybwy/Xw8LjluKnTCw31Knft8PPMExG6N/i4c5vBhUsWQsD2boMn9oT41L0B/uA/jJJMXZHp6Br8zrNR/vyP6knEFS5cssgXHB79dIgv7o3w3RfT/OGfjTI+4VAyJV0dOvfeFeCbz6eREnRNsHdPmI3rdDJZh4aEymjSRlHhU/cF2LbZYHDkxg7V9PDwuDW5qYPuNL/zTIRzF02+8ntDnPzQFYTv3O7nr/6sgS88EeaFn2R4Yd+VauLunX6+/if1FEqSf/r7wxw6UsSyJCtbNf7Tv6vn2SfDnLtk8l/+epz+AYuxcZsVrRrRiMLklENLk8aKVo3TZ0q0NGm0NrtBV1UEm9b7uNxvMZ5anpZBDw+P24ubOr0wjWXDv//PSX7yWo6LvRYXey2+/6MM330pjeETfOaBK73nhiH4w9+LE4up/MV/G+d7L2U4f8nkcr/F/kMF/uK/jpOacPjtJ8OsXaPTN2gxPGrT2qTSkHAlSXftMJicstn3eo6WJpWVre5vUyQs6Fyt88GJIqa30fXw8FgCt0TQ7R0weePt2dXmD066Vcm25isb9q4Onc0bDC73Wew/OLsSf6KnRP+gRdcaH6tX6GRzkrMXTJobNerj7sexc5vBSNLmtbdyGLpgQ5cPRcC6Th+GT3D8lLtz9vDw8Fgst0R64fxFk0JhdpCb/jvdd6VQuKZdJ1anMJV2eOaJMBOTM1syfT6Ix1R0HVqa3J3t4eMF/slvR2hu1AgFS2zdZDA0YnP4WJGBEYud2/2oKmzZ6MM04dRZ05uM4uHhsSRuiaCbzix8VxkMCHRN0N6m8Uf/Il7VMTCTlShlTeKxkyVURdDVoXPmvEZLk8abb+eZmHI4erLEjm4fmibY2OUjmbIZHPJyC7c1tu1OElYVnFz2mqe/enhczS0RdBdzzZdKEtuWnOgx+au/TTExUX1Levy0m5748FyJqbTDpvU+TvSUaEgoHPqggGlKDh0psOfBIKvbNTrX6IyM2vQPekH3dsYcHaPvP37d/YMXcD2uM7dE0F0MA0MW6YyDosCB9wqcuzh/B0uxJOk5V2J9p87KNo1wSOHI8SKOAz1nS5im5IHdAerjKgPDFiNjXtC97fGCrcfHxC1RSFsMJz8s0XO2xIa1Ph7YPf8UAYCSKfngRJGuDh9bNvgYn3A4Ww7Wl/ssRsZsHrwvSDymcKKnyBKna3t4eHjcfkE3m5P8zd9PUixJ/vT3Ezy7N0xTg0o0olAXVWhqUPnUvQG+9FSYgN/N6VqWG6zjMYXdOwMcPlaoqBMGhl1J2c7tBtGIwrFTCxh86OHh4VGF2y69APDT17P88V+O8cdfi/PNv2lhsBw4fbps2F0JAAABaklEQVRgRatGXUThxX0ZfvSK21AhJVy4ZJLLS+7aYfCDr2cqU8FHxmwuXDa5b5efQlFy5Lg3edXDw2Pp3NRBt1CQjCbdHO1cFEuSsaTNVHrm4yUT/u93pjhyvMiXno5w711+WptULNstnh18v8jzP0yTzV/J2/UOWJw9b9LWqnH0ZLGS0nMcOPRBgc89GGR41GZw2Mvnenh4LJ2b2mVMCFAVd+r3XLrY6celBLuKSEER7qDWaSWvlO755nrbquoeZzszH6+8DjCf056Hh4dHLZexmzroenh4eNyK3LLWjh4eHh63G17Q9fDw8FhGvKDr4eHhsYx4QdfDw8NjGfGCroeHh8cy4gVdDw8Pj2XEC7oeHh4ey4gXdD08PDyWES/oenh4eCwjXtD18PDwWEZqtgF7eHh4eFxfvJ2uh4eHxzLiBV0PDw+PZcQLuh4eHh7LiBd0PTw8PJYRL+h6eHh4LCNe0PXw8PBYRv4/kllCjc62nkgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "Dlm3mzG5QNzN",
        "outputId": "3610f0d4-9920-404c-c78f-008e9417fb13"
      },
      "source": [
        "\r\n",
        "#Import Library\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from wordcloud import WordCloud, STOPWORDS\r\n",
        "from nltk.tokenize import word_tokenize\r\n",
        "import re\r\n",
        "#stopword=[open(\"stopwords.txt\",'r')]\r\n",
        "stop_words=[]\r\n",
        "# pre-process stopword\r\n",
        "for i in stopword:\r\n",
        " i = re.sub('[\\n]', '', i)\r\n",
        " stop_words.append(i)\r\n",
        " stopwords = set(stop_words)\r\n",
        "wordcloud = WordCloud(font_path='/content/drive/My Drive/Lohit-Devanagari.ttf',width = 400, height = 400,\r\n",
        "                      #stopwords=stopwords,\r\n",
        "                      min_font_size = 10).generate(text2)\r\n",
        "\r\n",
        "# plot the WordCloud image\r\n",
        "#plt.figure(figsize = (8, 8), facecolor = None)\r\n",
        "#plt.imshow(wordcloud)\r\n",
        "#plt.axis(\"off\")\r\n",
        "#plt.tight_layout(pad = 0)\r\n",
        "\r\n",
        "#plt.show()\r\n",
        "plt.imshow(wc7)\r\n",
        "plt.axis('off')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "Wwi6kUnnO9Z5",
        "outputId": "c96d98c5-9500-4dd6-f4fd-6f00168d77f6"
      },
      "source": [
        "wc8= WordCloud(font_path='/content/drive/My Drive/NotoSans-Condensed.ttf',width = 400, height = 400,\r\n",
        "                      #stopwords=stopwords,\r\n",
        "                      min_font_size = 10).generate(text3)\r\n",
        "\r\n",
        "plt.imshow(wc8)\r\n",
        "plt.axis('off')\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRnRlnvqt6jc"
      },
      "source": [
        "textblob sentiment classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzTxlTbNt5XQ"
      },
      "source": [
        "from textblob import TextBlob\n",
        "from textblob.sentiments import NaiveBayesAnalyzer\n",
        "import nltk\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "6zSf28cquo9H",
        "outputId": "1a6e8fce-22c5-4555-8c97-39f61a8ddc40"
      },
      "source": [
        "df['tweet'].apply(lambda x: TextBlob(x,analyzer=NaiveBayesAnalyzer()))\n",
        "df['TextBlob_sentiment']= df['tweet'].apply(lambda x: TextBlob(x).sentiment)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>TextBlob_sentiment</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.06818181818181818, 0.7272727272727273)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.25, 0.4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.2, 0.5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>29495</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.5, 0.8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.2287878787878788, 0.4681818181818182)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.20000000000000004, 0.45)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ...                         TextBlob_sentiment\n",
              "user                        ...                                           \n",
              "jagadishrjain            1  ...  (0.06818181818181818, 0.7272727272727273)\n",
              "hellfire_81              2  ...                                (-0.1, 0.1)\n",
              "Savitri4Ever             3  ...                                (-0.1, 0.1)\n",
              "orfonline                4  ...                               (-0.25, 0.4)\n",
              "InsightGL                5  ...                                (-0.2, 0.5)\n",
              "...                    ...  ...                                        ...\n",
              "RamKuma19784093      29495  ...                                 (0.0, 0.0)\n",
              "CPR_India            29496  ...                                 (0.0, 0.0)\n",
              "Resort_Skelin        29497  ...                                 (0.5, 0.8)\n",
              "KBhairava1           29498  ...   (0.2287878787878788, 0.4681818181818182)\n",
              "Deepakk79951168      29499  ...                (0.20000000000000004, 0.45)\n",
              "\n",
              "[29499 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "YXT3iLa1vaUi",
        "outputId": "96d6a0a3-f6ce-46fe-daec-a919138499b8"
      },
      "source": [
        "df2=df\n",
        "df2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>TextBlob_sentiment</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.06818181818181818, 0.7272727272727273)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.25, 0.4)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.2, 0.5)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>29495</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.5, 0.8)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.2287878787878788, 0.4681818181818182)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.20000000000000004, 0.45)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ...                         TextBlob_sentiment\n",
              "user                        ...                                           \n",
              "jagadishrjain            1  ...  (0.06818181818181818, 0.7272727272727273)\n",
              "hellfire_81              2  ...                                (-0.1, 0.1)\n",
              "Savitri4Ever             3  ...                                (-0.1, 0.1)\n",
              "orfonline                4  ...                               (-0.25, 0.4)\n",
              "InsightGL                5  ...                                (-0.2, 0.5)\n",
              "...                    ...  ...                                        ...\n",
              "RamKuma19784093      29495  ...                                 (0.0, 0.0)\n",
              "CPR_India            29496  ...                                 (0.0, 0.0)\n",
              "Resort_Skelin        29497  ...                                 (0.5, 0.8)\n",
              "KBhairava1           29498  ...   (0.2287878787878788, 0.4681818181818182)\n",
              "Deepakk79951168      29499  ...                (0.20000000000000004, 0.45)\n",
              "\n",
              "[29499 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "XT6tgr9bYXAt",
        "outputId": "651bb0ef-a0a5-48a6-e36c-3dae88dd3a4f"
      },
      "source": [
        "dfja['tweet'].apply(lambda x: TextBlob(x,analyzer=NaiveBayesAnalyzer()))\r\n",
        "dfja['TextBlob_sentiment']= dfja['tweet'].apply(lambda x: TextBlob(x).sentiment)\r\n",
        "dfja"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>location</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ln__o0</th>\n",
              "      <td>1</td>\n",
              "      <td>ゲームしてる人だけᔦꙬᔨ</td>\n",
              "      <td>yuk1dqma_quad 私の2リグか4リグ更新してもろて</td>\n",
              "      <td>2020-11-30 15:03:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>2</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>samemizu ゲーム開発初めて6か月ついにアプリをリリース出来ましたくっつきパズル のび...</td>\n",
              "      <td>2020-11-30 15:03:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>3</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>今日からペンパアドベントか</td>\n",
              "      <td>2020-11-30 15:04:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hisakdan4</th>\n",
              "      <td>4</td>\n",
              "      <td>.gif</td>\n",
              "      <td>キーボード足で踏んでsegaをquadする動画めっちゃ見たいですね</td>\n",
              "      <td>2020-11-30 15:07:34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuk1dqma_quad</th>\n",
              "      <td>5</td>\n",
              "      <td>クアホしか勝たん♡</td>\n",
              "      <td>ln__o0 任せてください</td>\n",
              "      <td>2020-11-30 15:08:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riekohayakawa</th>\n",
              "      <td>4624</td>\n",
              "      <td>Pacific-NZ-Kyoto</td>\n",
              "      <td>インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...</td>\n",
              "      <td>2021-01-09 21:49:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LesFW7SuraWxotB</th>\n",
              "      <td>4625</td>\n",
              "      <td>［呪い返し］</td>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "      <td>2021-01-09 21:56:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riuemonn</th>\n",
              "      <td>4626</td>\n",
              "      <td>２４時間ついった監視業(ﾟσ ﾟ) ﾎｼﾞﾎｼﾞ</td>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "      <td>2021-01-09 22:28:59</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FumiNozoe</th>\n",
              "      <td>4627</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...</td>\n",
              "      <td>2021-01-10 02:59:11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sharkisamazing</th>\n",
              "      <td>4628</td>\n",
              "      <td>NaN</td>\n",
              "      <td>レモンザメ全長38mまで 生息地インド太平洋の熱帯域sicklefin lemmon sha...</td>\n",
              "      <td>2021-01-10 04:54:34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4628 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... TextBlob_sentiment\n",
              "user                        ...                   \n",
              "Ln__o0                   1  ...         (0.0, 0.0)\n",
              "QUad_R_ANgle             2  ...         (0.0, 0.0)\n",
              "QUad_R_ANgle             3  ...         (0.0, 0.0)\n",
              "hisakdan4                4  ...         (0.0, 0.0)\n",
              "yuk1dqma_quad            5  ...         (0.0, 0.0)\n",
              "...                    ...  ...                ...\n",
              "riekohayakawa         4624  ...         (0.0, 0.0)\n",
              "LesFW7SuraWxotB       4625  ...         (0.0, 0.0)\n",
              "riuemonn              4626  ...         (0.0, 0.0)\n",
              "FumiNozoe             4627  ...         (0.0, 0.0)\n",
              "sharkisamazing        4628  ...         (0.0, 0.0)\n",
              "\n",
              "[4628 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BIp5TmtATTpZ"
      },
      "source": [
        "#Sentiment analyzer function:\n",
        "def analyze_sentiment(tweet):\n",
        " count_total=0\n",
        " count_pos=0\n",
        " count_neg=0\n",
        " count_neut=0 \n",
        "\n",
        " analysis = TextBlob(tweet)\n",
        " \n",
        " if analysis.sentiment.polarity > 0:\n",
        "  count_pos=count_pos+1\n",
        "  count_total=count_total+1\n",
        "  return 'Positive'\n",
        " \n",
        " elif analysis.sentiment.polarity ==0:\n",
        "  count_neut+=1\n",
        "  count_total=count_total+1\n",
        "  return 'Neutral'\n",
        " else:\n",
        "  count_neg=count_neg+1\n",
        "  count_total=count_total+1\n",
        "  return 'Negative'\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2wvTqTSiVfBI"
      },
      "source": [
        "df['Sentiment_new'] = df['tweet'].apply(lambda x: analyze_sentiment(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Dw9qJtKQPA-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "outputId": "a705d8d0-6a7c-4754-c1ef-de1d001edaca"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.06818181818181818, 0.7272727272727273)</td>\n",
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              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.25, 0.4)</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.2, 0.5)</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>29495</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.5, 0.8)</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.2287878787878788, 0.4681818181818182)</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.20000000000000004, 0.45)</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Sentiment_new\n",
              "user                        ...              \n",
              "jagadishrjain            1  ...      Positive\n",
              "hellfire_81              2  ...      Negative\n",
              "Savitri4Ever             3  ...      Negative\n",
              "orfonline                4  ...      Negative\n",
              "InsightGL                5  ...      Negative\n",
              "...                    ...  ...           ...\n",
              "RamKuma19784093      29495  ...       Neutral\n",
              "CPR_India            29496  ...       Neutral\n",
              "Resort_Skelin        29497  ...      Positive\n",
              "KBhairava1           29498  ...      Positive\n",
              "Deepakk79951168      29499  ...      Positive\n",
              "\n",
              "[29499 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 784
        },
        "id": "3mj5daJQYqFI",
        "outputId": "78ba7018-b125-4c0d-c77b-79bbb900f16d"
      },
      "source": [
        "dfja['Sentiment_new'] = dfja['tweet'].apply(lambda x: analyze_sentiment(x))\r\n",
        "dfja"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>English</th>\n",
              "      <th>TextBlob_sentiment</th>\n",
              "      <th>Sentiment_new</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Ln__o0</th>\n",
              "      <td>1</td>\n",
              "      <td>ゲームしてる人だけᔦꙬᔨ</td>\n",
              "      <td>yuk1dqma_quad 私の2リグか4リグ更新してもろて</td>\n",
              "      <td>2020-11-30 15:03:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>yuk1dqma_quad Update my 2 rig or 4 rig</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>2</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>samemizu ゲーム開発初めて6か月ついにアプリをリリース出来ましたくっつきパズル のび...</td>\n",
              "      <td>2020-11-30 15:03:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>samemizu Game development First 6 months I fin...</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUad_R_ANgle</th>\n",
              "      <td>3</td>\n",
              "      <td>///りがくぶ・くりかえす・ふじまめ ←一言メッセージ</td>\n",
              "      <td>今日からペンパアドベントか</td>\n",
              "      <td>2020-11-30 15:04:40</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Is it Penpa Advent from today?</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hisakdan4</th>\n",
              "      <td>4</td>\n",
              "      <td>.gif</td>\n",
              "      <td>キーボード足で踏んでsegaをquadする動画めっちゃ見たいですね</td>\n",
              "      <td>2020-11-30 15:07:34</td>\n",
              "      <td>NaN</td>\n",
              "      <td>I really want to see a video that quads sega b...</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yuk1dqma_quad</th>\n",
              "      <td>5</td>\n",
              "      <td>クアホしか勝たん♡</td>\n",
              "      <td>ln__o0 任せてください</td>\n",
              "      <td>2020-11-30 15:08:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ln__o0 leave it to me</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hiromi_HIROSE</th>\n",
              "      <td>2404</td>\n",
              "      <td>NaN</td>\n",
              "      <td>アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...</td>\n",
              "      <td>2021-01-09 12:25:39</td>\n",
              "      <td>NaN</td>\n",
              "      <td>アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完成する予定チャナキャプリ地区...</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QJapq2v5xFfgq0h</th>\n",
              "      <td>2405</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...</td>\n",
              "      <td>2021-01-09 12:30:13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hiromi_hirose アメリカがインドの新しい大使館の建物の工事を開始2027年秋に完...</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>riekohayakawa</th>\n",
              "      <td>2406</td>\n",
              "      <td>Pacific-NZ-Kyoto</td>\n",
              "      <td>インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...</td>\n",
              "      <td>2021-01-09 21:49:14</td>\n",
              "      <td>NaN</td>\n",
              "      <td>インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿 インド太平洋研究会indo...</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LesFW7SuraWxotB</th>\n",
              "      <td>2407</td>\n",
              "      <td>［呪い返し］</td>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "      <td>2021-01-09 21:56:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>riekohayakawa インドとオセアニア可能性と機会 クレオパスカルとファカファヌア卿...</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FumiNozoe</th>\n",
              "      <td>2408</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...</td>\n",
              "      <td>2021-01-10 02:59:11</td>\n",
              "      <td>NaN</td>\n",
              "      <td>ルールに基づいたインド太平洋戦略というならディエゴガルシアからはじめよとたしかに民主主義や自...</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2408 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col  ... Sentiment_new\n",
              "user                        ...              \n",
              "Ln__o0                   1  ...       Neutral\n",
              "QUad_R_ANgle             2  ...       Neutral\n",
              "QUad_R_ANgle             3  ...       Neutral\n",
              "hisakdan4                4  ...       Neutral\n",
              "yuk1dqma_quad            5  ...       Neutral\n",
              "...                    ...  ...           ...\n",
              "Hiromi_HIROSE         2404  ...       Neutral\n",
              "QJapq2v5xFfgq0h       2405  ...       Neutral\n",
              "riekohayakawa         2406  ...       Neutral\n",
              "LesFW7SuraWxotB       2407  ...       Neutral\n",
              "FumiNozoe             2408  ...       Neutral\n",
              "\n",
              "[2408 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUe3PSgvQPX7"
      },
      "source": [
        "#Sentiment analyzer function:\n",
        "def analyze_sentiment2(tweet):\n",
        " count_total=0\n",
        " count_pos=0\n",
        " count_neg=0\n",
        " count_neut=0 \n",
        "\n",
        " analysis = TextBlob(tweet)\n",
        " if analysis.sentiment.polarity > 0:\n",
        "  count_pos=count_pos+1\n",
        "  count_total=count_total+1\n",
        "  return 1\n",
        " \n",
        " elif analysis.sentiment.polarity ==0:\n",
        "  count_neut+=1\n",
        "  count_total=count_total+1\n",
        "  return 0\n",
        " else:\n",
        "  count_neg=count_neg+1\n",
        "  count_total=count_total+1\n",
        "  return 0\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2fl774ItQPX7"
      },
      "source": [
        "df['Sentiment_value'] = df['tweet'].apply(lambda x: analyze_sentiment2(x))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "6ocbxCPAVhmP",
        "outputId": "6675572e-7eda-44c8-8930-b3e41a6e8651"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>TextBlob_sentiment</th>\n",
              "      <th>Sentiment_new</th>\n",
              "      <th>Sentiment_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.06818181818181818, 0.7272727272727273)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.25, 0.4)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.2, 0.5)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>29495</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.5, 0.8)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.2287878787878788, 0.4681818181818182)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.20000000000000004, 0.45)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col            location  ... Sentiment_new Sentiment_value\n",
              "user                                            ...                              \n",
              "jagadishrjain            1           Hyderabad  ...      Positive               1\n",
              "hellfire_81              2                 NaN  ...      Negative               0\n",
              "Savitri4Ever             3                 NaN  ...      Negative               0\n",
              "orfonline                4               India  ...      Negative               0\n",
              "InsightGL                5                 NaN  ...      Negative               0\n",
              "...                    ...                 ...  ...           ...             ...\n",
              "RamKuma19784093      29495          Puducherry  ...       Neutral               0\n",
              "CPR_India            29496           New Delhi  ...       Neutral               0\n",
              "Resort_Skelin        29497            Hrvatska  ...      Positive               1\n",
              "KBhairava1           29498                 NaN  ...      Positive               1\n",
              "Deepakk79951168      29499  Deoghar, Jharkhand  ...      Positive               1\n",
              "\n",
              "[29499 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZvGLvjgkb02",
        "outputId": "82975577-8b52-407b-a50a-b99d4b604235"
      },
      "source": [
        "df.Sentiment_new.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Neutral     13185\n",
              "Positive    12183\n",
              "Negative     4131\n",
              "Name: Sentiment_new, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQEkPkI0Y06A",
        "outputId": "894d5325-a7c8-4781-89d1-e55cbe85303e"
      },
      "source": [
        "dfja.Sentiment_new.value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Neutral     2373\n",
              "Positive      33\n",
              "Negative       2\n",
              "Name: Sentiment_new, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asoD9_OWM8ZK"
      },
      "source": [
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "plt.style.use('seaborn')\r\n",
        "import seaborn as sns\r\n",
        "import nltk\r\n",
        "import re\r\n",
        "import wordcloud\r\n",
        "import warnings"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "2HO6KuUCNyTF",
        "outputId": "2dcdaf70-d0c4-4cd4-abdf-7c8ba60e99f8"
      },
      "source": [
        "\r\n",
        "from textblob import TextBlob\r\n",
        "\r\n",
        "#get the subjectivity\r\n",
        "def subjectivity(txt):\r\n",
        "  return TextBlob(txt).sentiment.subjectivity\r\n",
        "\r\n",
        "#get the polarity\r\n",
        "def polarity(txt):\r\n",
        "  return TextBlob(txt).sentiment.polarity\r\n",
        "\r\n",
        "\r\n",
        "df['subjectivity'] = df['tweet'].apply(subjectivity)\r\n",
        "df['polarity'] = df['tweet'].apply(polarity)\r\n",
        "#dfja['subjectivity'] = dfja['tweet'].apply(subjectivity)\r\n",
        "#dfja['polarity'] = dfja['tweet'].apply(polarity)\r\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>index_col</th>\n",
              "      <th>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>TextBlob_sentiment</th>\n",
              "      <th>Sentiment_new</th>\n",
              "      <th>Sentiment_value</th>\n",
              "      <th>subjectivity</th>\n",
              "      <th>polarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>1</td>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.06818181818181818, 0.7272727272727273)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "      <td>0.727273</td>\n",
              "      <td>0.068182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>-0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>-0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>4</td>\n",
              "      <td>India</td>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.25, 0.4)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>-0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(-0.2, 0.5)</td>\n",
              "      <td>Negative</td>\n",
              "      <td>0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>-0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>29495</td>\n",
              "      <td>Puducherry</td>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>29496</td>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>Neutral</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>29497</td>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.5, 0.8)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>29498</td>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.2287878787878788, 0.4681818181818182)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "      <td>0.468182</td>\n",
              "      <td>0.228788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>29499</td>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>(0.20000000000000004, 0.45)</td>\n",
              "      <td>Positive</td>\n",
              "      <td>1</td>\n",
              "      <td>0.450000</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 index_col            location  ... subjectivity  polarity\n",
              "user                                            ...                       \n",
              "jagadishrjain            1           Hyderabad  ...     0.727273  0.068182\n",
              "hellfire_81              2                 NaN  ...     0.100000 -0.100000\n",
              "Savitri4Ever             3                 NaN  ...     0.100000 -0.100000\n",
              "orfonline                4               India  ...     0.400000 -0.250000\n",
              "InsightGL                5                 NaN  ...     0.500000 -0.200000\n",
              "...                    ...                 ...  ...          ...       ...\n",
              "RamKuma19784093      29495          Puducherry  ...     0.000000  0.000000\n",
              "CPR_India            29496           New Delhi  ...     0.000000  0.000000\n",
              "Resort_Skelin        29497            Hrvatska  ...     0.800000  0.500000\n",
              "KBhairava1           29498                 NaN  ...     0.468182  0.228788\n",
              "Deepakk79951168      29499  Deoghar, Jharkhand  ...     0.450000  0.200000\n",
              "\n",
              "[29499 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "Fk8Lu-EvMwpU",
        "outputId": "45b64b59-e86c-4bc6-9fbd-82d0e0c6ece0"
      },
      "source": [
        "sns.countplot(df.Sentiment_new,palette=['green','red','blue'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1570577b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "upS880XzY81o",
        "outputId": "55ed2e95-7999-4b06-e73a-86615c12425c"
      },
      "source": [
        "sns.countplot(dfja.Sentiment_new)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb874af7780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "OPyjy7VCNd8q",
        "outputId": "54d082ef-018f-4f2a-c631-aec59c4e37ca"
      },
      "source": [
        "sns.scatterplot(x='polarity',y='subjectivity',hue=\"Sentiment_new\",\r\n",
        "                    palette=['green','red','blue'], data=df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa1570c9550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "JcxWuXSjZp3a",
        "outputId": "a3dae159-474e-422d-da4a-ff378b6c3707"
      },
      "source": [
        "sns.scatterplot(x='polarity',y='subjectivity',data=dfja)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb873ba65c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-1qzEFrVO6nB"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZmnKviDbLLy",
        "outputId": "3d8dc6d3-f171-4799-98a9-d69a700d82f5"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NDb_3eFAQr-Z"
      },
      "source": [
        "#install some modules form sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 972
        },
        "id": "tnAfLug5SN-n",
        "outputId": "92b69548-6ac3-45da-bece-d45598c621b9"
      },
      "source": [
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>location</th>\n",
              "      <th>tweet</th>\n",
              "      <th>Date</th>\n",
              "      <th>Tweet Coordinates</th>\n",
              "      <th>index_col</th>\n",
              "      <th>TextBlob_sentiment</th>\n",
              "      <th>Sentiment_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>Hyderabad</td>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>2020-11-15 09:00:05</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>(0.06818181818181818, 0.7272727272727273)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>NaN</td>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>2020-11-15 09:09:46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>NaN</td>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>2020-11-15 09:19:10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>(-0.1, 0.1)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>India</td>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>2020-11-15 09:25:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>(-0.25, 0.4)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>NaN</td>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>2020-11-15 09:36:43</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>(-0.2, 0.5)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>Puducherry</td>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>2021-01-10 06:52:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29495</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>New Delhi</td>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>2021-01-10 07:30:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29496</td>\n",
              "      <td>(0.0, 0.0)</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>Hrvatska</td>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>2021-01-10 07:31:08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29497</td>\n",
              "      <td>(0.5, 0.8)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>NaN</td>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>2021-01-10 07:39:52</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29498</td>\n",
              "      <td>(0.2287878787878788, 0.4681818181818182)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>Deoghar, Jharkhand</td>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>2021-01-10 07:52:47</td>\n",
              "      <td>NaN</td>\n",
              "      <td>29499</td>\n",
              "      <td>(0.20000000000000004, 0.45)</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                           location  ... Sentiment_value\n",
              "user                                 ...                \n",
              "jagadishrjain             Hyderabad  ...               1\n",
              "hellfire_81                     NaN  ...               0\n",
              "Savitri4Ever                    NaN  ...               0\n",
              "orfonline                     India  ...               0\n",
              "InsightGL                       NaN  ...               0\n",
              "...                             ...  ...             ...\n",
              "RamKuma19784093          Puducherry  ...               0\n",
              "CPR_India                 New Delhi  ...               0\n",
              "Resort_Skelin              Hrvatska  ...               1\n",
              "KBhairava1                      NaN  ...               1\n",
              "Deepakk79951168  Deoghar, Jharkhand  ...               1\n",
              "\n",
              "[29499 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sTj0uQsDS0rC"
      },
      "source": [
        "def run_svm(df):\n",
        "    X=df['tweet']\n",
        "    y=df['Sentiment_value']\n",
        "    tfidf= TfidfVectorizer()\n",
        "    X= tfidf.fit_transform(X) # now a sparse matrix is created which can be checked by just writing X\n",
        "    X_train , X_test , y_train , y_test= train_test_split(X,y,test_size=0.2, random_state=0, stratify=y)\n",
        "    print('shape Of X: ', X.shape) #shape of original text data which was converted into tfidf vectorizer\n",
        "    clf= LinearSVC() # a classifier is created with LinearSVC which uses L2 regularization\n",
        "    clf.fit(X_train,y_train) #now we will rain our algorithm in which we pass input data (X_train) and Output target label(y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    # now since we have trained the model we will check the accuracy\n",
        "    print()\n",
        "    print('Printing Report')\n",
        "    print(classification_report(y_test,y_pred))\n",
        "\n",
        "    return tfidf,clf #used for custom data  prediction , therefor tfidf vectorizer is returned and clf is returned for testing on external dataset\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAsmAaLcU2ZV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1185d4f-756c-4ab3-8e70-694cb200fdbb"
      },
      "source": [
        "X_train.shape, X_test.shape,y_train.shape, y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((23599, 300), (5900, 300), (23599,), (5900,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIEbxehfUDS_",
        "outputId": "ebba605c-9a17-46b2-83ca-34825b1d39da"
      },
      "source": [
        "%%time\n",
        "tfidf,clf =run_svm(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape Of X:  (29499, 23057)\n",
            "\n",
            "Printing Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.99      0.99      3463\n",
            "           1       0.99      0.97      0.98      2437\n",
            "\n",
            "    accuracy                           0.98      5900\n",
            "   macro avg       0.98      0.98      0.98      5900\n",
            "weighted avg       0.98      0.98      0.98      5900\n",
            "\n",
            "CPU times: user 817 ms, sys: 13 ms, total: 830 ms\n",
            "Wall time: 859 ms\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PcYcm8IjV_Km"
      },
      "source": [
        "word2vec"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BuRBS_fkWA8D",
        "outputId": "459bc7b6-0296-49c7-8f97-1edcaf4c9fbb"
      },
      "source": [
        "!python -m spacy download en_core_web_lg"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting en_core_web_lg==2.2.5\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.2.5/en_core_web_lg-2.2.5.tar.gz (827.9MB)\n",
            "\u001b[K     |████████████████████████████████| 827.9MB 1.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from en_core_web_lg==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.19.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (51.1.2)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (2.0.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.8.0)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_lg==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_lg==2.2.5) (2020.12.5)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_lg==2.2.5) (3.4.0)\n",
            "Building wheels for collected packages: en-core-web-lg\n",
            "  Building wheel for en-core-web-lg (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for en-core-web-lg: filename=en_core_web_lg-2.2.5-cp36-none-any.whl size=829180945 sha256=55d9a7f0386642eac39b37a86ad07cec917791e3b2df87060265ee623a3e1831\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-zp15n88i/wheels/2a/c1/a6/fc7a877b1efca9bc6a089d6f506f16d3868408f9ff89f8dbfc\n",
            "Successfully built en-core-web-lg\n",
            "Installing collected packages: en-core-web-lg\n",
            "Successfully installed en-core-web-lg-2.2.5\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_lg')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pohls5UpWZhr"
      },
      "source": [
        "import spacy\n",
        "nlp = spacy.load('en_core_web_lg')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eyTAC4dYffz"
      },
      "source": [
        "def get_vec(x):\n",
        "  doc=nlp(x)\n",
        "  vec=doc.vector\n",
        "  return vec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G0dt-aDCZn1D",
        "outputId": "bb1e0bf7-c4d1-4bef-bbb9-c836b7559933"
      },
      "source": [
        "%%time\n",
        "df['vec']=df['tweet'].apply(lambda x: get_vec(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 5min 25s, sys: 659 ms, total: 5min 26s\n",
            "Wall time: 5min 27s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "xf-ZLWpUZ7sg",
        "outputId": "bb915fa5-adee-4de6-9ab2-45fa9b9bb733"
      },
      "source": [
        "df3=df[['tweet','Sentiment_value']]\n",
        "df3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>tweet</th>\n",
              "      <th>Sentiment_value</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                             tweet  Sentiment_value\n",
              "user                                                                               \n",
              "jagadishrjain    rrrameshrrr 50 of the quad japan australia and...                1\n",
              "hellfire_81      coolquad riplegic military grouping httpstcocx...                0\n",
              "Savitri4Ever     hellfire_81 coolquad riplegic military groupin...                0\n",
              "orfonline        the creation of the quad with implications for...                0\n",
              "InsightGL        asthana_shashi jaishankar attends east asia su...                0\n",
              "...                                                            ...              ...\n",
              "RamKuma19784093  india france discuss counterterrorism cooperat...                0\n",
              "CPR_India        as quad prepares to meet the chinese challenge...                0\n",
              "Resort_Skelin    every team is different lets create a perfect ...                1\n",
              "KBhairava1       this here is very tricky yes its true aussies ...                1\n",
              "Deepakk79951168  cpr_india as quad prepares to meet the chinese...                1\n",
              "\n",
              "[29499 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Xkwj-ttbYZK",
        "outputId": "6531664c-7a9b-42c9-823b-1d0074a6a4bd"
      },
      "source": [
        "df3.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(29499, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPch3mGFaNM1"
      },
      "source": [
        "def run_word2vec(df):\n",
        "    X=df['vec'].to_numpy()\n",
        "    X = X.reshape(-1,1)\n",
        "    y=df['Sentiment_value']\n",
        "    X=np.concatenate(np.concatenate(X,axis = 0),axis = 0).reshape(-1,300)\n",
        "    X_train , X_test , y_train , y_test= train_test_split(X,y,test_size=0.2, random_state=0, stratify=y)\n",
        "    print('shape Of X: ', X.shape) #shape of original text data which was converted into tfidf vectorizer\n",
        "    clf= LogisticRegression(solver = 'liblinear') # a classifier is created with LinearSVC which uses L2 regularization\n",
        "    clf.fit(X_train,y_train) #now we will rain our algorithm in which we pass input data (X_train) and Output target label(y_train)\n",
        "    y_pred = clf.predict(X_test)\n",
        "    # now since we have trained the model we will check the accuracy\n",
        "    print()\n",
        "    print('Printing Report')\n",
        "    print(classification_report(y_test,y_pred))\n",
        "\n",
        "    return clf #used for custom data  prediction , therefor tfidf vectorizer is returned and clf is returned for testing on external dataset\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hwH4jzgocCMw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "45acd410-5346-41ed-c103-0f61df90cad4"
      },
      "source": [
        " X_train.shape , X_test.shape "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((23599, 300), (5900, 300))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eO3D5rrMdy_V",
        "outputId": "93e721d6-a9f5-4153-81d0-caba4480abd2"
      },
      "source": [
        "%%time\n",
        "clf =run_word2vec(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "shape Of X:  (29499, 300)\n",
            "\n",
            "Printing Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.88      0.92      0.90      3463\n",
            "           1       0.88      0.82      0.85      2437\n",
            "\n",
            "    accuracy                           0.88      5900\n",
            "   macro avg       0.88      0.87      0.87      5900\n",
            "weighted avg       0.88      0.88      0.88      5900\n",
            "\n",
            "CPU times: user 3.26 s, sys: 56 ms, total: 3.31 s\n",
            "Wall time: 3.29 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "afKMCuX1DIG0"
      },
      "source": [
        "NORMALIZATION AND FEATURE STANDARDIZATION( i.e will bring all the features b/w 0 and 1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1nLBzCdya-lx"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOVs9m_LbrWf"
      },
      "source": [
        "scaler=MinMaxScaler()\n",
        "X_train= scaler.fit_transform(X_train)\n",
        "X_test=scaler.transform(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d9d__lC2ChlH",
        "outputId": "153f3450-b922-47f6-984e-ae088a32e7c5"
      },
      "source": [
        "np.min(X_train), np.max(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0000001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9ZaWijs8elVP"
      },
      "source": [
        "test every model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmx_wP47en4b",
        "outputId": "aa0cf93f-0635-4f38-c90d-77f717ea0984"
      },
      "source": [
        "!pip install lazypredict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: lazypredict in /usr/local/lib/python3.6/dist-packages (0.2.7)\n",
            "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.6/dist-packages (from lazypredict) (7.1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ou_6BoL1esUC",
        "outputId": "e4753869-934e-4850-9f77-8553dd7752c6"
      },
      "source": [
        "from lazypredict.Supervised import LazyClassifier\n",
        "!pip install xgboost\n",
        "!pip install lightgbm"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.6/dist-packages (0.90)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.4.1)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.6/dist-packages (2.2.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from lightgbm) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from lightgbm) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->lightgbm) (1.0.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8IlsFMfe3OP"
      },
      "source": [
        "clf = LazyClassifier(verbose=0,ignore_warnings=True, custom_metric=None)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9gRGDpwfFE9",
        "outputId": "46b5be5d-0e2c-4aa8-e1c4-4bdfd1c914b9"
      },
      "source": [
        "%%time\n",
        "models, predictions = clf.fit(X_train,X_test,y_train,y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 30/30 [18:18<00:00, 36.61s/it]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 18min 31s, sys: 18.2 s, total: 18min 49s\n",
            "Wall time: 18min 18s\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 906
        },
        "id": "DQVuexRtf1YK",
        "outputId": "08ce8034-c116-4e91-a794-3c67cd896215"
      },
      "source": [
        "models"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.97</td>\n",
              "      <td>193.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>17.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>8.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.96</td>\n",
              "      <td>24.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.95</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.95</td>\n",
              "      <td>57.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.94</td>\n",
              "      <td>38.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>11.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.94</td>\n",
              "      <td>60.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.94</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.92</td>\n",
              "      <td>0.94</td>\n",
              "      <td>57.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.92</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.92</td>\n",
              "      <td>1.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.91</td>\n",
              "      <td>29.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.89</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.89</td>\n",
              "      <td>2.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.89</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.89</td>\n",
              "      <td>145.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "      <td>35.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.88</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.88</td>\n",
              "      <td>1.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.88</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.88</td>\n",
              "      <td>2.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.88</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.87</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.87</td>\n",
              "      <td>368.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.86</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.86</td>\n",
              "      <td>3.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "      <td>0.84</td>\n",
              "      <td>34.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.68</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.68</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.66</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.52</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CheckingClassifier</th>\n",
              "      <td>0.59</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.43</td>\n",
              "      <td>0.30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Accuracy  ...  Time Taken\n",
              "Model                                    ...            \n",
              "SVC                                0.97  ...      193.54\n",
              "LGBMClassifier                     0.96  ...       17.28\n",
              "ExtraTreesClassifier               0.96  ...        8.46\n",
              "RandomForestClassifier             0.96  ...       24.85\n",
              "BaggingClassifier                  0.95  ...       57.80\n",
              "KNeighborsClassifier               0.94  ...       38.75\n",
              "DecisionTreeClassifier             0.93  ...       11.99\n",
              "ExtraTreeClassifier                0.93  ...        0.39\n",
              "LabelPropagation                   0.94  ...       60.10\n",
              "LabelSpreading                     0.94  ...       57.09\n",
              "QuadraticDiscriminantAnalysis      0.92  ...        1.62\n",
              "XGBClassifier                      0.91  ...       29.23\n",
              "LogisticRegression                 0.89  ...        2.31\n",
              "CalibratedClassifierCV             0.89  ...      145.72\n",
              "LinearSVC                          0.88  ...       35.25\n",
              "RidgeClassifierCV                  0.88  ...        1.74\n",
              "LinearDiscriminantAnalysis         0.88  ...        2.29\n",
              "RidgeClassifier                    0.88  ...        0.56\n",
              "NuSVC                              0.87  ...      368.04\n",
              "SGDClassifier                      0.86  ...        3.48\n",
              "AdaBoostClassifier                 0.84  ...       34.07\n",
              "Perceptron                         0.83  ...        0.59\n",
              "PassiveAggressiveClassifier        0.83  ...        0.71\n",
              "GaussianNB                         0.68  ...        0.40\n",
              "BernoulliNB                        0.69  ...        0.53\n",
              "NearestCentroid                    0.66  ...        0.44\n",
              "DummyClassifier                    0.52  ...        0.30\n",
              "CheckingClassifier                 0.59  ...        0.30\n",
              "\n",
              "[28 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1M-WfRWDm4av"
      },
      "source": [
        "import en_core_web_lg"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwnOoNXgl9lz"
      },
      "source": [
        "nlp= en_core_web_lg.load()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "zRMc2qOGoi0a",
        "outputId": "d61d6929-d94b-4d08-faf7-b5029673f217"
      },
      "source": [
        "df3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tweet</th>\n",
              "      <th>Sentiment_value</th>\n",
              "      <th>vec</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>1</td>\n",
              "      <td>[0.09004612, 0.034690373, -0.05848376, -0.0992...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.048197, -0.0028900013, 0.158472, -0.000815...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.04016417, -0.0024083343, 0.13206, -0.00067...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.0034065263, 0.052838676, -0.04663989, -0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>0</td>\n",
              "      <td>[0.005938239, 0.046918474, 0.17063224, -0.0793...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.2111061, 0.14078437, 0.19124909, -0.041424...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>0</td>\n",
              "      <td>[0.07365824, 0.047069248, -0.04199921, -0.0479...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>1</td>\n",
              "      <td>[-0.04819747, 0.14340034, -0.1358337, 0.003916...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>1</td>\n",
              "      <td>[-0.057174828, 0.18994899, -0.1468728, -0.1116...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>1</td>\n",
              "      <td>[-0.009540685, 0.053027954, -0.05255686, -0.04...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                             tweet  ...                                                vec\n",
              "user                                                                ...                                                   \n",
              "jagadishrjain    rrrameshrrr 50 of the quad japan australia and...  ...  [0.09004612, 0.034690373, -0.05848376, -0.0992...\n",
              "hellfire_81      coolquad riplegic military grouping httpstcocx...  ...  [-0.048197, -0.0028900013, 0.158472, -0.000815...\n",
              "Savitri4Ever     hellfire_81 coolquad riplegic military groupin...  ...  [-0.04016417, -0.0024083343, 0.13206, -0.00067...\n",
              "orfonline        the creation of the quad with implications for...  ...  [-0.0034065263, 0.052838676, -0.04663989, -0.0...\n",
              "InsightGL        asthana_shashi jaishankar attends east asia su...  ...  [0.005938239, 0.046918474, 0.17063224, -0.0793...\n",
              "...                                                            ...  ...                                                ...\n",
              "RamKuma19784093  india france discuss counterterrorism cooperat...  ...  [-0.2111061, 0.14078437, 0.19124909, -0.041424...\n",
              "CPR_India        as quad prepares to meet the chinese challenge...  ...  [0.07365824, 0.047069248, -0.04199921, -0.0479...\n",
              "Resort_Skelin    every team is different lets create a perfect ...  ...  [-0.04819747, 0.14340034, -0.1358337, 0.003916...\n",
              "KBhairava1       this here is very tricky yes its true aussies ...  ...  [-0.057174828, 0.18994899, -0.1468728, -0.1116...\n",
              "Deepakk79951168  cpr_india as quad prepares to meet the chinese...  ...  [-0.009540685, 0.053027954, -0.05255686, -0.04...\n",
              "\n",
              "[29499 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zqt9Rghfm8du"
      },
      "source": [
        "def get_vec(x):\n",
        "  doc = nlp(x)\n",
        "  vec = doc.vector\n",
        "  return vec"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6To72Z2nOTH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "2b50fb5b-2efe-45d1-fbc0-8f6fabc2bf0e"
      },
      "source": [
        "df3['vec']=df3['tweet'].apply(lambda x: get_vec(x) )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-74-c3564a73e025>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'vec'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tweet'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   4211\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4212\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4213\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4215\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-74-c3564a73e025>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'vec'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf3\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tweet'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mget_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-63-257b00ec89cd>\u001b[0m in \u001b[0;36mget_vec\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_vec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnlp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0mvec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvector\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mvec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/spacy/language.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, text, disable, component_cfg)\u001b[0m\n\u001b[1;32m    437\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"__call__\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    438\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mErrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mE003\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcomponent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mproc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 439\u001b[0;31m             \u001b[0mdoc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mproc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcomponent_cfg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    440\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    441\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mErrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mE005\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32mpipes.pyx\u001b[0m in \u001b[0;36mspacy.pipeline.pipes.Tagger.__call__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpipes.pyx\u001b[0m in \u001b[0;36mspacy.pipeline.pipes.Tagger.predict\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/model.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mMust\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \"\"\"\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/feed_forward.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_layers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/model.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mMust\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \"\"\"\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/api.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(seqs_in)\u001b[0m\n\u001b[1;32m    308\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseqs_in\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m         \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseq\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mseq\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mseqs_in\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 310\u001b[0;31m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseqs_in\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    311\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlengths\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpad\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/model.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mMust\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \"\"\"\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/feed_forward.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_layers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     41\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/model.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    165\u001b[0m             \u001b[0mMust\u001b[0m \u001b[0mmatch\u001b[0m \u001b[0mexpected\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m         \"\"\"\n\u001b[0;32m--> 167\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/model.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    130\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m         \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    132\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/api.py\u001b[0m in \u001b[0;36muniqued_fwd\u001b[0;34m(X, drop)\u001b[0m\n\u001b[1;32m    377\u001b[0m         )\n\u001b[1;32m    378\u001b[0m         \u001b[0mX_uniq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mascontiguousarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mind\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 379\u001b[0;31m         \u001b[0mY_uniq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbp_Y_uniq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_uniq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    380\u001b[0m         \u001b[0mY\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mY_uniq\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0minv\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mY_uniq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/feed_forward.py\u001b[0m in \u001b[0;36mbegin_update\u001b[0;34m(self, X, drop)\u001b[0m\n\u001b[1;32m     44\u001b[0m         \u001b[0mcallbacks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mlayer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_layers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minc_layer_grad\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minc_layer_grad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/layernorm.py\u001b[0m in \u001b[0;36mbegin_update\u001b[0;34m(self, X, drop)\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mbegin_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 62\u001b[0;31m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbackprop_child\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin_update\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     63\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m             \u001b[0mbackprop_child\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/thinc/neural/_classes/maxout.py\u001b[0m in \u001b[0;36mbegin_update\u001b[0;34m(self, X__bi, drop)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0moutput__boc\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnO\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnP\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m         \u001b[0moutput__boc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput__boc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput__boc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnO\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnP\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m         \u001b[0mbest__bo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhich__bo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaxout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput__boc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     80\u001b[0m         \u001b[0mbest__bo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbp_dropout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbest__bo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdrop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "nW10xHpSpDd0",
        "outputId": "b82060cc-e72d-4815-f160-ef6d8f34d64a"
      },
      "source": [
        "df3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>tweet</th>\n",
              "      <th>Sentiment_value</th>\n",
              "      <th>vec</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>user</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>jagadishrjain</th>\n",
              "      <td>rrrameshrrr 50 of the quad japan australia and...</td>\n",
              "      <td>1</td>\n",
              "      <td>[0.09004612, 0.034690373, -0.05848376, -0.0992...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hellfire_81</th>\n",
              "      <td>coolquad riplegic military grouping httpstcocx...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.048197, -0.0028900013, 0.158472, -0.000815...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Savitri4Ever</th>\n",
              "      <td>hellfire_81 coolquad riplegic military groupin...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.04016417, -0.0024083343, 0.13206, -0.00067...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>orfonline</th>\n",
              "      <td>the creation of the quad with implications for...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.0034065263, 0.052838676, -0.04663989, -0.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>InsightGL</th>\n",
              "      <td>asthana_shashi jaishankar attends east asia su...</td>\n",
              "      <td>0</td>\n",
              "      <td>[0.005938239, 0.046918474, 0.17063224, -0.0793...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RamKuma19784093</th>\n",
              "      <td>india france discuss counterterrorism cooperat...</td>\n",
              "      <td>0</td>\n",
              "      <td>[-0.2111061, 0.14078437, 0.19124909, -0.041424...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CPR_India</th>\n",
              "      <td>as quad prepares to meet the chinese challenge...</td>\n",
              "      <td>0</td>\n",
              "      <td>[0.07365824, 0.047069248, -0.04199921, -0.0479...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resort_Skelin</th>\n",
              "      <td>every team is different lets create a perfect ...</td>\n",
              "      <td>1</td>\n",
              "      <td>[-0.04819747, 0.14340034, -0.1358337, 0.003916...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KBhairava1</th>\n",
              "      <td>this here is very tricky yes its true aussies ...</td>\n",
              "      <td>1</td>\n",
              "      <td>[-0.057174828, 0.18994899, -0.1468728, -0.1116...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Deepakk79951168</th>\n",
              "      <td>cpr_india as quad prepares to meet the chinese...</td>\n",
              "      <td>1</td>\n",
              "      <td>[-0.009540685, 0.053027954, -0.05255686, -0.04...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>29499 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                             tweet  ...                                                vec\n",
              "user                                                                ...                                                   \n",
              "jagadishrjain    rrrameshrrr 50 of the quad japan australia and...  ...  [0.09004612, 0.034690373, -0.05848376, -0.0992...\n",
              "hellfire_81      coolquad riplegic military grouping httpstcocx...  ...  [-0.048197, -0.0028900013, 0.158472, -0.000815...\n",
              "Savitri4Ever     hellfire_81 coolquad riplegic military groupin...  ...  [-0.04016417, -0.0024083343, 0.13206, -0.00067...\n",
              "orfonline        the creation of the quad with implications for...  ...  [-0.0034065263, 0.052838676, -0.04663989, -0.0...\n",
              "InsightGL        asthana_shashi jaishankar attends east asia su...  ...  [0.005938239, 0.046918474, 0.17063224, -0.0793...\n",
              "...                                                            ...  ...                                                ...\n",
              "RamKuma19784093  india france discuss counterterrorism cooperat...  ...  [-0.2111061, 0.14078437, 0.19124909, -0.041424...\n",
              "CPR_India        as quad prepares to meet the chinese challenge...  ...  [0.07365824, 0.047069248, -0.04199921, -0.0479...\n",
              "Resort_Skelin    every team is different lets create a perfect ...  ...  [-0.04819747, 0.14340034, -0.1358337, 0.003916...\n",
              "KBhairava1       this here is very tricky yes its true aussies ...  ...  [-0.057174828, 0.18994899, -0.1468728, -0.1116...\n",
              "Deepakk79951168  cpr_india as quad prepares to meet the chinese...  ...  [-0.009540685, 0.053027954, -0.05255686, -0.04...\n",
              "\n",
              "[29499 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WX8MWw_TpMjt"
      },
      "source": [
        "y=df3['Sentiment_value']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pKDD13j2pMnY"
      },
      "source": [
        "X = df3['vec'].to_numpy()\n",
        "X = X.reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1gsCvWFpxa2",
        "outputId": "b8c788ae-a71d-4b3e-f914-9e9f10a50b53"
      },
      "source": [
        "df3.shape, X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((29499, 3), (29499, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tc4R-50LqHuu"
      },
      "source": [
        "X=np.concatenate(np.concatenate(X,axis = 0),axis = 0).reshape(-1,300)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ic8Hgzf1qpT-"
      },
      "source": [
        "X_train , X_test , y_train , y_test= train_test_split(X,y,test_size=0.2, random_state=0, stratify=y) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NE8GNEr1queq",
        "outputId": "c3524bc6-f6d8-42ff-ceb8-a8bc329f46df"
      },
      "source": [
        "X_test.shape, X_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5900, 300), array([[ 0.17811893, -0.03915342, -0.06457193, ...,  0.18942353,\n",
              "         -0.08440045, -0.02429383],\n",
              "        [ 0.03708239,  0.14212947,  0.02149151, ..., -0.01655268,\n",
              "         -0.07725332,  0.09759342],\n",
              "        [-0.08912952,  0.10476758, -0.02319557, ...,  0.00277692,\n",
              "          0.01585867,  0.07990281],\n",
              "        ...,\n",
              "        [-0.04857615,  0.05300444, -0.1038273 , ...,  0.00338753,\n",
              "         -0.0289232 ,  0.09222812],\n",
              "        [-0.00651754, -0.00507522,  0.02190279, ..., -0.04392143,\n",
              "         -0.03321127,  0.04657936],\n",
              "        [ 0.05335439,  0.06389482,  0.09701247, ...,  0.07037558,\n",
              "         -0.1497407 ,  0.05509168]], dtype=float32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xihy3R7wrCpC"
      },
      "source": [
        "NORMALIZATION AND FEATURE STANDARDIZATION( i.e will bring all the features b/w 0 and 1)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FtMk8ELMrCpC"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZkKl-kKwrCpC"
      },
      "source": [
        "scaler=MinMaxScaler()\n",
        "X_train= scaler.fit_transform(X_train)\n",
        "X_test=scaler.transform(X_test)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2yPjsWaPrCpC",
        "outputId": "fae8e4a7-585e-4c17-9814-753f4fe9c95e"
      },
      "source": [
        "np.min(X_train), np.max(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0000001)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 277
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BOzGM1oTCNDa"
      },
      "source": [
        "MODEL BUILDING & TRAINING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvUt-EtRCRFG",
        "outputId": "429e6e4d-3a55-4f27-8850-faa68fec7b15"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import Sequential\n",
        "from tensorflow.keras.layers import Flatten,Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s82RRef0EIA2",
        "outputId": "2fb75a78-7253-4c66-c94e-2c381a0fa456"
      },
      "source": [
        "X.shape[1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "300"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVfqXzCJEMRl"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(units = 128,activation='relu',input_dim = X.shape[1]))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0EigahsE6AW"
      },
      "source": [
        "model.compile(optimizer=Adam(learning_rate = 0.0005 ),loss = 'binary_crossentropy',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cpfY0FxjR8Tr",
        "outputId": "94bfe4d8-a14f-4008-9cee-29bfad3ecc5c"
      },
      "source": [
        "df3.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tweet              object\n",
              "Sentiment_value     int64\n",
              "vec                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nI4UMIFzFEDt",
        "outputId": "1b3e23b0-4417-4568-e033-74a0c0390b7c"
      },
      "source": [
        "model.fit(X_train, y_train, batch_size=4, epochs=20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "5900/5900 [==============================] - 11s 2ms/step - loss: 0.5649 - accuracy: 0.7027\n",
            "Epoch 2/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.4085 - accuracy: 0.8122\n",
            "Epoch 3/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.3669 - accuracy: 0.8359\n",
            "Epoch 4/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.3408 - accuracy: 0.8506\n",
            "Epoch 5/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.3330 - accuracy: 0.8513\n",
            "Epoch 6/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.3141 - accuracy: 0.8611\n",
            "Epoch 7/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.3048 - accuracy: 0.8692\n",
            "Epoch 8/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2948 - accuracy: 0.8710\n",
            "Epoch 9/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2900 - accuracy: 0.8710\n",
            "Epoch 10/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2718 - accuracy: 0.8828\n",
            "Epoch 11/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2611 - accuracy: 0.8890\n",
            "Epoch 12/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2542 - accuracy: 0.8923\n",
            "Epoch 13/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2488 - accuracy: 0.8933\n",
            "Epoch 14/20\n",
            "5900/5900 [==============================] - 11s 2ms/step - loss: 0.2464 - accuracy: 0.8952\n",
            "Epoch 15/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2433 - accuracy: 0.8948\n",
            "Epoch 16/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2336 - accuracy: 0.9029\n",
            "Epoch 17/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2295 - accuracy: 0.9016\n",
            "Epoch 18/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2190 - accuracy: 0.9076\n",
            "Epoch 19/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2272 - accuracy: 0.9029\n",
            "Epoch 20/20\n",
            "5900/5900 [==============================] - 10s 2ms/step - loss: 0.2113 - accuracy: 0.9106\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fa1506e8780>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Neww_VS0_T12"
      },
      "source": [
        "CONFUSION MATRIX PLOT"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wiozVfVH_W8P",
        "outputId": "caf635c0-5628-49aa-cdfb-cc79e3843612"
      },
      "source": [
        "!pip install mlxtend"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.6/dist-packages (0.14.0)\n",
            "Requirement already satisfied: matplotlib>=1.5.1 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (3.2.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from mlxtend) (51.1.2)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (0.22.2.post1)\n",
            "Requirement already satisfied: pandas>=0.17.1 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (1.1.5)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (1.19.5)\n",
            "Requirement already satisfied: scipy>=0.17 in /usr/local/lib/python3.6/dist-packages (from mlxtend) (1.4.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (2.8.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.5.1->mlxtend) (1.3.1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->mlxtend) (1.0.0)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.1->mlxtend) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib>=1.5.1->mlxtend) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aU8wuzxU_m90"
      },
      "source": [
        "y_pred = model.predict_classes(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggJqtujU_zon",
        "outputId": "5e7e4e1b-82d4-4ca4-d7e0-668c97fec6a9"
      },
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3294,  169],\n",
              "       [ 380, 2057]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBKATdrVr5N1"
      },
      "source": [
        "from mlxtend.plotting import plot_confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "ioBdLK4nsGq5",
        "outputId": "71103336-cf1d-45d1-983b-b4d0ba2387d9"
      },
      "source": [
        "plot_confusion_matrix(confusion_matrix(y_test,y_pred))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXFiWifQsSTg",
        "outputId": "4dd9cad2-67bf-412e-e829-af88b1e82a45"
      },
      "source": [
        "print(classification_report(y_test,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.90      0.95      0.92      3463\n",
            "           1       0.92      0.84      0.88      2437\n",
            "\n",
            "    accuracy                           0.91      5900\n",
            "   macro avg       0.91      0.90      0.90      5900\n",
            "weighted avg       0.91      0.91      0.91      5900\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lcnvwO4nsl1a"
      },
      "source": [
        "Setting custom Threshold"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K49AQbb0spwv"
      },
      "source": [
        "from sklearn.metrics import precision_score, recall_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FG9tClMFs5GU"
      },
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KLgVfKXtC0E",
        "outputId": "3fd4128f-0909-4832-94a9-0b020c297275"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.9931259e-02],\n",
              "       [7.7418435e-01],\n",
              "       [1.8772483e-04],\n",
              "       ...,\n",
              "       [9.4908381e-01],\n",
              "       [6.6570938e-02],\n",
              "       [2.6479512e-02]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajYmW7-ttH0O",
        "outputId": "41e6fea6-aa01-46e9-f1a8-6d14bc0a1eef"
      },
      "source": [
        "for i in [0.30,0.35, 0.40, 0.45, 0.50,0.55, 0.60,0.65, 0.70, 0.75]:\n",
        "  classes = [1 if val>i else 0 for val in y_pred]\n",
        "  print(i,\"-->>\", precision_score(y_test,classes),recall_score(y_test, classes))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.3 -->> 0.8615205585725368 0.9113664341403365\n",
            "0.35 -->> 0.8803866290777286 0.8970045137464095\n",
            "0.4 -->> 0.8982909545644019 0.884283955683217\n",
            "0.45 -->> 0.9077458659704091 0.8559704554780467\n",
            "0.5 -->> 0.9240790655884995 0.8440705785802216\n",
            "0.55 -->> 0.9325688073394496 0.8342224045958145\n",
            "0.6 -->> 0.9400468384074941 0.8235535494460402\n",
            "0.65 -->> 0.9484088717454194 0.8071399261386951\n",
            "0.7 -->> 0.9576271186440678 0.7882642593352482\n",
            "0.75 -->> 0.9670442842430484 0.7706196142798523\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DObkC7Klu0g0"
      },
      "source": [
        " classes = [1 if val>0.75 else 0 for val in y_pred]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        },
        "id": "Y5mB4y2Du4Yq",
        "outputId": "ac17a581-cf3a-488c-dd3e-357ae2206be9"
      },
      "source": [
        "plot_confusion_matrix(confusion_matrix(y_test,classes))\n",
        "plt.show"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function matplotlib.pyplot.show>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ytXxSkcfvRs3"
      },
      "source": [
        "ann is done now CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GRwjSy_LvQwo"
      },
      "source": [
        "from tensorflow.keras import Sequential \n",
        "from tensorflow.keras.layers import Flatten, Dense, Conv1D, BatchNormalization,Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Rev4NIYv3Nl",
        "outputId": "233ef391-0c62-4b14-b2e9-3b26444b19ca"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23599, 300)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTQNA1Fuv7YZ"
      },
      "source": [
        "X_train = X_train.reshape(X_train.shape[0], X_train.shape[1],1)\n",
        "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1],1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHsvzYFpwghz",
        "outputId": "519915b9-5879-400b-b9a1-6e2b091a4bd8"
      },
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((23599, 300, 1), (5900, 300, 1))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7I3fodgwkPu"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv1D(32,2, activation= 'relu', input_shape = X_train[0].shape))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(64,activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1L3U0eh1xm2Y",
        "outputId": "f6541201-6f38-40e0-a074-e959350190bf"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d (Conv1D)              (None, 299, 32)           96        \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 299, 32)           128       \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 299, 32)           0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 9568)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 64)                612416    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 612,705\n",
            "Trainable params: 612,641\n",
            "Non-trainable params: 64\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gqobJm8xrU_",
        "outputId": "a7467752-0f91-4296-b983-51e70e3dd2dc"
      },
      "source": [
        "epochs = 20\n",
        "model.compile(optimizer=Adam(0.00001),loss= 'binary_crossentropy',metrics=['accuracy'])\n",
        "history = model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test,y_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/20\n",
            "738/738 [==============================] - 16s 20ms/step - loss: 0.5956 - accuracy: 0.6766 - val_loss: 0.4055 - val_accuracy: 0.8198\n",
            "Epoch 2/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.4062 - accuracy: 0.8127 - val_loss: 0.3260 - val_accuracy: 0.8605\n",
            "Epoch 3/20\n",
            "738/738 [==============================] - 14s 20ms/step - loss: 0.3562 - accuracy: 0.8448 - val_loss: 0.2943 - val_accuracy: 0.8781\n",
            "Epoch 4/20\n",
            "738/738 [==============================] - 14s 20ms/step - loss: 0.3223 - accuracy: 0.8652 - val_loss: 0.2740 - val_accuracy: 0.8875\n",
            "Epoch 5/20\n",
            "738/738 [==============================] - 14s 20ms/step - loss: 0.2970 - accuracy: 0.8783 - val_loss: 0.2596 - val_accuracy: 0.8966\n",
            "Epoch 6/20\n",
            "738/738 [==============================] - 14s 20ms/step - loss: 0.2815 - accuracy: 0.8854 - val_loss: 0.2473 - val_accuracy: 0.8998\n",
            "Epoch 7/20\n",
            "738/738 [==============================] - 14s 20ms/step - loss: 0.2646 - accuracy: 0.8974 - val_loss: 0.2354 - val_accuracy: 0.9061\n",
            "Epoch 8/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.2591 - accuracy: 0.8930 - val_loss: 0.2266 - val_accuracy: 0.9122\n",
            "Epoch 9/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.2435 - accuracy: 0.9040 - val_loss: 0.2186 - val_accuracy: 0.9144\n",
            "Epoch 10/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.2283 - accuracy: 0.9145 - val_loss: 0.2115 - val_accuracy: 0.9169\n",
            "Epoch 11/20\n",
            "738/738 [==============================] - 16s 22ms/step - loss: 0.2281 - accuracy: 0.9108 - val_loss: 0.2027 - val_accuracy: 0.9232\n",
            "Epoch 12/20\n",
            "738/738 [==============================] - 16s 21ms/step - loss: 0.2174 - accuracy: 0.9167 - val_loss: 0.1979 - val_accuracy: 0.9247\n",
            "Epoch 13/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.2135 - accuracy: 0.9161 - val_loss: 0.1919 - val_accuracy: 0.9290\n",
            "Epoch 14/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.1987 - accuracy: 0.9214 - val_loss: 0.1873 - val_accuracy: 0.9303\n",
            "Epoch 15/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.1981 - accuracy: 0.9225 - val_loss: 0.1812 - val_accuracy: 0.9317\n",
            "Epoch 16/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.1949 - accuracy: 0.9237 - val_loss: 0.1770 - val_accuracy: 0.9342\n",
            "Epoch 17/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.1907 - accuracy: 0.9276 - val_loss: 0.1728 - val_accuracy: 0.9373\n",
            "Epoch 18/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.1843 - accuracy: 0.9284 - val_loss: 0.1688 - val_accuracy: 0.9381\n",
            "Epoch 19/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.1792 - accuracy: 0.9329 - val_loss: 0.1656 - val_accuracy: 0.9403\n",
            "Epoch 20/20\n",
            "738/738 [==============================] - 15s 20ms/step - loss: 0.1738 - accuracy: 0.9368 - val_loss: 0.1621 - val_accuracy: 0.9420\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q6Sf-9sxzDqO"
      },
      "source": [
        "PLOT LEARNING CURVE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S_dO1RSXy5Hf",
        "outputId": "6bf35c5f-4b8c-4bfe-a89a-a57c0b427e0c"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EFvQAKtvzTrd"
      },
      "source": [
        "def plot_learningCurve(history,epoch):\n",
        "  #PLOT TRAINING AND VALIDATION ACCURACY VALUES\n",
        "  epoch_range = range(1, epoch+1)\n",
        "  plt.plot(epoch_range,history.history['accuracy'])\n",
        "  plt.plot(epoch_range,history.history['val_accuracy'])\n",
        "  plt.title('Model accuracy')\n",
        "  plt.ylabel('Accuracy')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Val'], loc = 'upper left')\n",
        "  plt.show()\n",
        "  #PLOT TRAINING & VALIDATION LOSS VALUES\n",
        "  plt.plot(epoch_range,history.history['loss'])\n",
        "  plt.plot(epoch_range,history.history['val_loss'])\n",
        "  plt.title('Model loss')\n",
        "  plt.ylabel('Loss')\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.legend(['Train', 'Val'], loc = 'upper left')\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "id": "VKkPMa1w1O9P",
        "outputId": "2994248e-2439-41fa-e6e6-b7cffdc20c33"
      },
      "source": [
        "plot_learningCurve(history, epochs)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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yzXrSkuOJjmrr5W8yxmK1SthHlOuvv4rf/vYOCgqGBZ/7+9+fIiEhkUsuubzDe+fPP5t33/0w1EUUQvQTHl8LNa7aDmFe5arB5q4noHQMLK1aQ4Y+nWxDJtntrfUMXTraKE0vlT50/IEAVXUuymuOhHrb90Znxysa0VFqMs26o0JdR5ZFjyFe20slP5aEfYjMmjWH9es/6BD2H3+8nief/HsvlkoIEcmc3maqm9vCvNpVS1V7a93e0nDMe+Oj4xhgzCEt3kKazkKaLpW0eAtJsQkhnemtt/gDAapsLg5VOyitdnCoponyGietvo4ffpKNMYzOS24L9fYvS1IcUerw/jeSsA+Rs8+ezY03Luamm24FoLh4D2azmf/+73sB8Pl8/P73D5CZmdWbxRRC9BH+gB+n14XT68TReuTLQVNpI4fqKqhursXhdR6zX4LWyNCkwW2BHt8e6joLBo2+34w59/kDVNqa20PdQVm1g/LajsGuVqnISGm7t56bZiDboifLrCM+tm9e0eiXYb/qwDt8XbuzW485xnIKvxh8bqevJyWZyMjI5JtvdjF8+EjWr/+AKVOmUVAwnLFjx/POO/9h1arXueWW33ZruYQQfUPb6mkeHO3h7Wx1Bh87WptxeNufa3++2evq9FgqVCTHJpFrLCBVZyE9vi3QU+Mt/W7c+ZFgD7bY24P96PvrUWoVmSk6ctMM7eFuJMusQ6vp+8P8juiXYd9bZs2ay4cffsDw4SP5/PNPePTRp3j88f9h+fJ/4HA0MXTosK4PIoToM7x+71GB7cThbe4Q2N+FejOOVudxp3T9Pp0mHoNGT4YuDb1Wj0Gjx6DVYWh/nJ+RjaZF1y/uqX+f13ck2JuCwX7Y6sTn/25W+Ci1ikyzLhjqA9IMZJl1aKIjJ9iPp1+G/S8Gn3vCVnhPOfPM6fz7388xa9YcsrNzWL78H0yaNJkLLljARx+tY+PGz0JeJiHEyQsoAZq9rraQ9jppav1+K7w5GOTOVicef0uXx9RGaTFo9O3D1HQYNPq2EA8GeduXXqNHr4nvclIZc1JkzY3v8weoa3RTWu2gsbmFxuZWmppbg9+Pftzs6bhSaJRaRZZFT26qIXg5PsusRxMd3vfXe0K/DPveEh+vIy8vn3//+3lmzZpLYeH7ZGZmoSgKn322Af/3hm0IIXqPzV3Hnvr97K3fT43L2h7wzSiceO0wtUqNQaMjOc7UIay/C3FdMLwNWj0xUeHTYztUFEWhyeWl0XlseH8/xJ3uroem6eM0JOhjyLboSTPFt1+ON5Jp1gWHufV3EvYhNmvWXP7wh6UsXfogsbFxPPbYn0lLy2DBgl/yyCMPsXnzF71dRCH6pWavi732AxS3B/zRc7fHRcdi1BqwxJuPCu/20P5eCzwuOrZf9F4/WUfumZcfGZde66SsxnFMK/z7dLHRGHVassw6zCYdMdEqEnRajDrtUd9jMMRrJNBPgixx20f0t2Ur+7JIrFck1inRFMvmkl0U17cFfJnjcLDVHhcdy5CkwRQkDabAlI85LqXP9FTvzZ+V0+1tC/Satk5wZbVOKm3N+AMdY8aSFEeWWU+SISYY3EdCPEGnxRCv7XCpPRJ//yCClrhdtmwZ27dvR6VSsWTJEkaNGhV8bd26dTzzzDNotVrmz5/P5Ze3TSzzyCOP8OWXX+Lz+bj++uuZPXs2d911F7t37yYxMRGAxYsXc9ZZZ/Vk0YUQEUZRFCqbqymu309x/X5KGr+lpX26V7VKzaCEAQwz5VNgyifHkBURC670lICiYG1wU17TFujlNQ7KrU7qvzc1rDZaHRy2lmPRk20xkGnWERcjF5VDrcf+xTdv3kxpaSkrV66kpKSEJUuWsHLlSgACgQAPPvggb731FomJiVx77bXMnDmTQ4cOsX//flauXIndbufnP/85s2fPBuB3v/sd06dP76niCiEiUENLYzDci+37cbR+N+48y5hOvjGPAlM+gxMHEhsd24slDU+KouBu8VFV72pvsX83RWxLa8eRA4l6LacMSiYn9bvJZlKT4lGr+8YVkUjXY2G/adMmZs6cCUBeXh6NjY04nU70ej12ux2j0YjJZAJg8uTJbNy4kfPPPz/Y+jcajbjdbvz+roeiCCEEgMfnYX/DQfbWH2CPfT/VzTXB1wxaPRNSx1DQ3nrPz8qKyEvDP4TXF8Du8FDX1EJ9k4f6pvbHDg/1TS3UNXmOCXW1SkV6SnywpZ7dHu7GMJoaVhyrx8LeZrMxYsSI4LbJZMJqtaLX6zGZTDQ3N3Po0CEyMzMpKipi4sSJREVFER8fD8Abb7zBGWecQVRU26W0l156ieeff57k5GTuvffe4AeF40lKiic6AsdMnuh+TF8ViXWCyKxXb9VJURRcXjd2TyMN7kbs7iYaPE3fbXsaaXA30eBppNn73fKq2igNp6YNZ1TaMEalDiM7IeOY++6R+HOCtnoFAgoNzhasdhe2Bg/WBhdWuxtrQ9uXrcFNg6PzoYH6OA3pyTrMSXGkJesYmG5kYGYCOamGXplsJpJ/VqEQshsnR/cDVKlU/PGPf2TJkiUYDAaysjpOEbtu3TreeOMNnnvuOQDOP/98EhMTGTZsGP/7v//LU089xX333dfpuez2zmeW6qsisYNKJNYJIrNePVGngBLA0dpMU2sTjS1NNLU62x87OnxvanXgDXTRc1sTT4I2gWx9FjnGLIaZ8hmYMACNuv1PnBdsto5Tx0bKz8nd4qO81klptYOyWgeNLi/VtmbsjpZjOsYdER2lxmSMYVhuEiZDDCZjLCZjDMnG2ODjWO3x46GxIfR/XyPlZ/V9EdFBz2KxYLPZgtu1tbWYzebg9sSJE3n55ZcB+Mtf/kJmZiYAn376KX//+9/55z//icHQVvApU6YE95sxYwb3339/TxVbCNGD/AE/O+v28HlFEcX2/cessHY0tUqNUWsgXZdGQowBo9aAUWtsf2w86jkD0er+0eGrydVKWY2Dspr2cK9xUGN3d3iPSgUJOi25aQZMxliSjTGYDLEdAt0Qr+kzowtE9+ix/yHTpk3jySefZOHChezevRuLxYJerw++fs011/CnP/2JuLg4PvroI6666iocDgePPPII//rXv4I97wFuueUW7rjjDrKzsykqKiI/P7+nii2E6AE2dz0bKzezqWoLTa1tLZlsfQYp8SkktAe2McZIgtZAQowRo9aAThPfb8erK4qC3dFCaY2jPdSdlNY4sH/vsnt8TDTDcpPISW2bJS4n1cDwfAsN9uZeKrkIVz0W9mPHjmXEiBEsXLgQlUrF0qVLWbVqFQaDgVmzZnHxxRdz9dVXo1KpuO666zCZTMFe+LfddlvwOH/605+47LLLuO2224iLiyM+Pp6HH364p4othOgmvoCPHbZvgq14gLjoOM7Kmsa0jElk6NN6uYThIaAoWO3utmBvX4GttMZ5zMxxCXoto/KSyUk1kNse7skJsce00PvjVLCiazKpTh8RifesIrFOEJn1+iF1qnXZ2Fi5mS+qtgaXWM1LGMC0jEmMsYwKmwVaeuPndGShlrJaR9sY9RoHZbVOPN/r8Z6SENvWUk8zkNse7gn6mJM6R3///etLIuKevRCi//AFfGy37ubzyiL22g8AoIuOZ0b26UzNmEi6LrWXSxh6Ta7Wo8amt80oV1Xn6tBpTqUiOJd7jqVtoZacVD26PrpmughfEvZCiB+txmXl88oiiqq+xOltu0+cnziIaRmTONU8Ek2YtOJ7UiCgUGN3HTP3e4OztcP7tBo1A9pnk8u26MlONZBt1hOjjbxhwiL8SNgLIX4Qb8DH9tqdfFZZxP6Gg0Db0Lezs89gWsZEUnWWXi5hz/G0+jhsbe4w9/thq5NWb8dRBUmGGEblJQeDPSfVgCUxTmaTE71Gwl4IcVKqm2vbWvHVX9LsbRtrPSRpMKdlTGSUeeR3Y9rDlM8foNUbwOvz0+IL4PIp1FgdtHr9tLQ/3+oN0OLz4/UGaPV993xdo4fyWie1dneHBW6j1CrSk+PbZpKz6INTxRpkNjkRZsL7f6cQolcoikJTq5MaVw2VzTXs2vkNe6xtPer1Gh2zcs5iasYELPHmLo4UGo3OFr7cZ2X7gTocrlZafQFavf7gd68v0OkEMydLFxvN0JzEYLBnW/RkpOik97voEyTshejHAkoAu6eBalctVc011DTXUu2qpbq5Fpev42QtBUn5TMucxKiU4WExic2RgN9aXMvesoZgi1urUaONjkKrURMXE02iTotWE4UmWk1M+3etRk2CIRa/zx987zHfNVFoo9u+G+O1mIwxMhGN6LN6/3+sEKLH+QN+rG4b1UeFeXVzDTUuK62BjuO51So15rhk8hMHkaZLJU1nYcLAEajcJzf0qyd1FvCDsxKYMNTCuKFmTMaTW70uUodzCXE8EvZCRJBWfys1LutRoV5DdXMttW7bMVPTatTRpMZbSNNZSIu3kKqzkK5LxRyXfEzL3aw3YHX3TjB2Z8AL0V9J2AvRx7h9HmzuOqzuOmyuOqxuG9b27YaWxmPeHxcdS64hi9T2UE9vb62bYpPCdjpaCXghupeEvRBhqNnragvxo8Lc5q7D6qoLzkp3NBUqEmMSGJKY1xbq7cGeprOQoDX2iXvNEvBC9BwJeyF6wZHe7la3LdhKt7q+a6G7v9c5DtrupSfHJpFlyMAcl4I5PhlzXNtXcqypT05gIwEvRGhI2AsRQs7WZjbXfMWmyi1UNlcf83q0OpqUWBODEwdgjkshJe5IoKdgik0kSt23Z1trbG7lUFUTpdUO9pTa2VcuAS9EKEjYC9HDAkqAvfUH2Fi1mR3W3fgUP1GqKEYmF5CuS2sL8/i2QE+IMYbtffQfqqm5lUPVDkqrmzhU7eBQ9bFLtErACxEaEvZC9JA6t50vqrawqWor9pYGANJ0qUxLn8CEtLEYtPpeLmH3cbhaKW0P9LavJuqbOgZ7gk7L6LxkBqQbGZBmYEC6kQSdzDQnRChI2AvRjbwBHxvLtrKm+BP22g+goBATpWVq+kSmZkxggDGnT3SWOxGn29se7E3tLXcHtkZPh/cY4zWMyktuC/U0I7lpBpIMvT9OX4j+SsJeiG5Q4axiY+VmtlR/TbOvbd74QQkDmJo+gTGWUcRG982g8/oCHKxs5JOd1ewqsVFa3YS1oWOwG+I1jBxkYkCakYFphmCw9/UPNUJEEgl7IX4kt8/N1prtbKrcQqmjHACDRs95BbMYnTCatD64+pvPH+BQe+e54lI7Byoa8fq+m4xHH6dhxEBTsMU+IM0g08gK0QdI2AvxAyiKwoGGb9lUtYWvanfgDXhRoWJkcgFTMyYyMnkYaamJfWYa1kBAobTGQXGZnT2ldvYfbqSl1R98PcuspyA3kfHD0zHFR5OcECvBLkQfJGEvxElobGmiqOpLNlVtodZtAyAlLpkp6ROYnD6OxJiEXi7hyQkoChXW5mDLfW95A+4WX/D19OR4CnKSGJabxJCcRIztS7XKPPJC9G0S9kJ0IqAE+KZuL59VFrG7rpiAEkCjjmZC6limZkxgcOLAsB8mpygKVXUuisvawr24rAGn+7uFbyyJcUwoMFOQk0RBbhKJ+r7Zt0AIcWIS9kJ8j93TwMbKzWys2hKcaz7bkMnU9ImMTz2VeE1cL5ewc4qiYG1wt7XcyxooLrXT2NwafN1kjGFaXhoFuUkU5CSRnCBj24XoDyTshaBtCdjddcV8XlnE7rq9KCjERsVwWuZkpmVMJMeQ1dtF7JSiKJTXOvnimxq27Kmlrum73vIJOi2ThqcyLDeJgpxEzIlxcs9diH5Iwl70a3XuejZWbWFT5RYaW5sAyDVmc1rGJMZaRof1kDlbg5svvqnhi29qqLQ1AxAXE834oeZgyz09OV7CXQghYS/6H3/Az866PXxeUcSe+n3trfhYzsicyrSMiWQZMnq7iJ1yuFrZUlzLF7trOFDRdoshOkrNuKFmJg9PY1ReMpro8O5HIIQIPQl70W/Y3PVsrNzMpqotNLW29SwfaMxlWuYkxlpGERMVnlO3trT6+fqAlS9217D723r8AQUVMCw3icnDUxk31EJ8rPxXFkJ0rkf/Qixbtozt27ejUqlYsmQJo0aNCr62bojf54cAACAASURBVN06nnnmGbRaLfPnz+fyyy/vdJ+qqiruuOMO/H4/ZrOZP//5z2i14fmHWYQXX8DHDts3fF5RRLF9PwBx0XGcmTWNaRkTydSn93IJj88fCLD7WztffFPN1/tstHjbxr7nphqYPCKVicNSZfpZIcRJ67Gw37x5M6WlpaxcuZKSkhKWLFnCypUrAQgEAjz44IO89dZbJCYmcu211zJz5kzKysqOu88TTzzBpZdeyrx583j00Ud54403uPTSS3uq6CIC1LpsbKzczBdVW3F4nQDkJQxgWsYkxlhGoQ3Dtd8VRaGksomi3TVsLq7B4WobImdOjGXS8GymjEglPVnXy6UUQvRFPRb2mzZtYubMmQDk5eXR2NiI0+lEr9djt9sxGo2YTCYAJk+ezMaNGykvLz/uPkVFRTzwwAMATJ8+neeee07CXhyjscXBfvsBNlZtYa/9AAC66HimZ5/GtIxJpOtSe7mEx1dV18ym3TUUfVMdnHfeEK/h7LFZTB6RyqAMo3SyE0L8JD0W9jabjREjRgS3TSYTVqsVvV6PyWSiubmZQ4cOkZmZSVFRERMnTux0H7fbHbxsn5ycjNVqPeG5k5LiiY6O6pmK9SKz2dDbReh2P7ZOjhYnJfVlHLSXUlJfysH6Murc9uDrw835zMw7jYlZY3qlFX+iegUCChVWJ18W1/DxV4cpOdzW0S5WG8VZY7M4c2wWpw4xEx0VXh3t5Pev74jEekVinSB09QpZrx5FUYKPVSoVf/zjH1myZAkGg4GsrOOPYT56nxM99312u+vHFzRMReJ0pSdbJ7fPTbmjgtKmw5Q6DlPWdJg6T32H9xi1Bk5JGUauIZsxllHBRWga6z2A5zhH7Tnfr5e7xcfBqiZKKho5UNHIt5VNNHvapqiNUqsYlZfM5BGpjBlsJkbb9iHVXt8c0jJ3pT///vU1kVivSKwTdH+9TvTBocfC3mKxYLPZgtu1tbWYzebg9sSJE3n55ZcB+Mtf/kJmZiYtLS3H3Sc+Ph6Px0NsbCw1NTVYLH1vNTFxclr8rZQ7KihrD/Uyx2FqXB2v5Og08Qw3DSXHmEWOIYtcY1bYzE2vKAqHax1s2VlFSWUjJRWNVFibOfojqiUxjlF5yeRnJzJ2iDk4/7wQQvSUHgv7adOm8eSTT7Jw4UJ2796NxWJBr9cHX7/mmmv405/+RFxcHB999BFXXXUV6enpx91n6tSpFBYWcv7557N27VpOP/30niq2CKFWv5dDTWWUNR2mtD3Yq5prUI6KxtioWIYkDSbXkEWOMYtcQxam2KSwuYftafXxbWUTByrbWu4lFY3BVjuANlpNfnYigzMTyMs0kpeRgFEn4S6ECK0eC/uxY8cyYsQIFi5ciEqlYunSpaxatQqDwcCsWbO4+OKLufrqq1GpVFx33XWYTCZMJtMx+wDccsst3HnnnaxcuZKMjAwuuOCCniq2CIE6t523S95lu3UXfuW7tdK1ag2DEnLJNWaT0x7u5rjksFlsRlEUau1uDlQ0UtIe7oetTo6+s5SSEMv4YWlkpcSTl2kky6wPu3vvQoj+R6WczE3wPkbu7YSnFn8rH5R+xLqyDXgDPrKN6Qw0DAy22NN0lrAJ9iN8/gCfbq9k58F6DlQ0dlgxThOtZkCagbzMBPIyEhicaSRBHxMRP6vvkzr1HZFYr0isE0TIPXshjlAUhS9rt/PWgXdpaGkkQWvkgsHnMG/k6dTZwqsj2tF2lNh45cMD1NS3dfhMNsYyfEASeZkJDM5MINsirXYhRN8gYS96VJnjMG/sW01J4yGiVVHMyZ3B7NzpxEbHhF0r/oiqumZe+XA/uw7Wo1apOHtsFvMm52AyynKwQoi+ScJe9AhHq5PVJWvYVLUFBYXRKSP4Rf65pMQl93bROuXyePnPZ4dY/9Vh/AGFYblJXDIznyyzvuudhRAijEnYi27lD/jZcPhz3ju0DrfPQ7oulQX551Fgyu/tonUqEFD4ZEclqzYcxOn2Yk6MZeGMfE7NTwmbXv9CCPFTSNiLbrO7bi9v7v8/aly1xEfHcdGQ8zk9YzJR6vCdzXBvmZ2X1+2nvNZJjDaKBWflMWt8tiwTK4SIKBL24ierdVl5c/877KrbgwoVp2dO4dyBs9Frw3fRFluDm9c+LmFrcS0A005J48Iz80jUy0pyQojII2EvfjS3z8OaQx/yUfln+BU/+YmDuGjI+WG7bCy0rQ3/7helFG4uw+sLkJdh5NJZQxiYbuztogkhRI+RsBc/WEAJUFT1Jf85+D6OViem2CR+MfhcTjWPDNt73Iqi8MU3NbzxcQl2RwuJei0XTR/M5OGpYVtmIYToLhL24gf5trGU1/etptRRjkat4dyBszk758ywXB/+iG+rmnh53T5KKpqIjlJz7tQBnDM5h1it/PoLIfoH+WsnTkpDSyNvH3ifLTVfATA+9VQuyDuHpNjEXi5Z5xqcLby5oYTPd1YDMH6omYunDyYlMa6XSyaEEKElYS9OyBvwsb7sE9aUrqfV30q2IZMF+ecxOHFgbxetU15fgA+2lvN/Gw/R0uon26Ln0pn5DM1J6u2iCSFEr5CwF53aZdvD6/tXY3PXodfoWJD/M6akTwjbme8URWHbfhsr1x+gtsGNPk7DL+cO5oxRGajVcl9eCNF/SdiLY9S6bLy5fzW76opRq9RMzz6NcwbMIl4Tnpe//YEAW4utvF9USlmNkyi1itkTsjlv2gDiY8O3L4EQQoSKhL0IavG3UnhoPR+WbcCn+BmSmMdFQ84nQ5/W20U7rhavn892VFG4uQxboweVCsYXWPj56QNJTw7fMf5CCBFqEvYCRVH4qnY7q9pXpUuKSeQX+ecyxnxKWA5Lc7q9rP/yMOu+PIzT7UUTrWb6mExmT8wmNSm+t4snhBBhR8K+n6t0VvPavrfZ33CQaFUUc3NnMHvADGKitL1dtGPYGtys3VLOJzsqafUG0MVG87OpAzh7XBZGXfiVVwghwoWEfT/l8rp579sP2FCxkYAS4JSUYVw4+DzM8eG3Kl1ZjYM1RWVs3lNLQFEwGWOYc0YOp49Ol7HyQghxEuQvZT9zZPa7t0vew+ltxhyXzIL88xiZMqy3i9aBoigUl9p5v6iMXd/WA5Bl1jFvUi4ThlmIjgrPEQFCCBGOJOz7kdKmcl7b9x8ONZWhVWs4b9BcZuScgUYdPr8GgYDCl/usvP9FKYeqHQAU5CQyd1IupwwyhWUfAiGECHfh81de9BhHq5PVJWvYVLUFBYVxltH8fPD8sJr9rtXr5/Nd1RQWlVHb4EYFjBtqZu6kHPIyEnq7eEII0adJ2Ecwf8DPp5Vf8M7Btbh9bjJ0aVw05HyGJOX1dtGCnG4vH33V1rPe4fISHaXmzFMzmDsxh1ST9KwXQojuIGEfofbbD/LavrepbK4mLjqWBfnncUbmFKLUUb1dNADsjhbe3niIwk2ltHj9xMVEM39KLjPHZZEga8oLIUS3krCPMA0tjbx14F221mwDYGr6BM7Lm4dBq+/lkrUJKAoff13BGx+X4Gn1k2SI4YLTB3LG6AziYuTXUQgheoL8dY0gu2x7WL57Ba3+VnIN2Vw89HwGGHN6u1hBFbZmXlhTzIHDjcTFRHPThaM4dZBJetYLIUQPk7CPEN82lvLPXS8BcGnBhWG1YI3XF+C9L0p5d9MhfH6F8UPNXDprCPkDU7BaHb1dPCGEiHg9GvbLli1j+/btqFQqlixZwqhRo4KvrVixgtWrV6NWqxk5ciT33HMPzzzzDBs3bgQgEAhgs9koLCxkxowZpKWlERXVdr/5f/7nf0hNTe3JovcpNc21PLPjefyKn+tPuTKsxswfqGjkX+8XU2lrJlGv5YrZQxkzxNzbxRJCiH6lx8J+8+bNlJaWsnLlSkpKSliyZAkrV64EwOl0snz5ctauXUt0dDRXX30127Zt48Ybb+TGG28E4K233qKuri54vGeffRadThY3+b7Gliae2r6cZq+LywouCpugd7f4WLXhIOu/OowCTB+TyYVn5hEfKxeThBAi1HrsL++mTZuYOXMmAHl5eTQ2NuJ0OtHr9Wg0GjQaDS6Xi/j4eNxuNwkJ342l9vl8vPLKK/z73//uqeJFBLfPw9Pbl1PvsXPuwNlMzZjQ20UCYNsBGy8W7sXuaCE9OZ4r5xYwJDt8xvQLIUR/02Nhb7PZGDFiRHDbZDJhtVrR6/XExMRw8803M3PmTGJiYpg/fz4DBw4Mvnft2rWcdtppxMbGBp9bunQpFRUVjBs3jv/3//7fCWdSS0qKJzo6PIaYdSez2RB87PP7+Puny6lwVjFz0GlcMf6CXp9dzu7w8Ozbu/h0WwXRUSp+OWsIF589BK2m85/F0XWKJJFYL6lT3xGJ9YrEOkHo6hWya6qKogQfO51O/vGPf7BmzRr0ej1XXnklxcXFFBQUAPDmm2/ywAMPBN9/6623cvrpp5OQkMDNN99MYWEhc+fO7fRcdrur5yrSS8xmQ7AzW0AJ8MI3r7KzZi+npAznvJz52GzOXiuboih8vrOalev30+zxMSjDyK/mFZBl1tPY0PnP4ug6RZJIrJfUqe+IxHpFYp2g++t1og8OPRb2FosFm80W3K6trcVsbuuYVVJSQnZ2NiaTCYDx48eza9cuCgoKcLlcVFdXk5WVFdz3ggsuCD4+44wz2Ldv3wnDPtK9feA9ttZsY6Axl6tHXNqrE+XU2l28sGYve0rtxGiiuHRmPjPGZqFWyxz2QggRLnpsbNa0adMoLCwEYPfu3VgsFvT6toldMjMzKSkpwePxALBr1y4GDBgAQHFxMYMGDQoex+FwsHjxYlpbWwHYsmUL+fn5PVXssLe+7BM+LP+E1HgLN4z+FdpeWnfeHwiwpqiM+5ZvZk+pnVF5yfzhmknMHJ8tQS+EEGGmx1r2Y8eOZcSIESxcuBCVSsXSpUtZtWoVBoOBWbNmsXjxYhYtWkRUVBRjxoxh/PjxAFit1mCLH8BgMHDGGWfwy1/+kpiYGIYPH95vW/Vba7bx5oF3SNAauHn0YvSa3hmdUFrt4F/vF1Na48AQr+Gqc4YxcZil1/sMCCGEOD6VcvTN9AgRifd2agKVLNvwJNFqDb8dewNZhoyQl6HF62f1Z99SuLmcgKIwbWQavzw7H32c5kcdT+7D9R1Sp74jEusViXWCCLlnL7rPYUclj2/7Owpw3SmLeiXovzlUzwtrirE2eEhJiOXKuQWMGGjqekchhBC9TsI+zNW57fxt+3LcXg9XjbiUoabBIT2/PxDgpbX72LCtEpUK5k7M4fzTBhKjjbyhjUIIEakk7MOY09vM09uX09jqYNGpCxhvOjWk51cUhVfW7WfDtkqyzHqunl/AgDRjSMsghBDipwuPlVLEMVr9Xv6x41/UuGo5O/sMzh16dsjL8MHWw6z/qoIss467Lx8rQS+EEH2UhH0Y8gf8PL/7ZQ42ljI+9VQuGHxOyMvw1T4rKz/cT4Jey20XjZa15oUQog+TsA8ziqLw2r632WHbzdCkwVwx7OKQL1X7bVUT/7t6NxqNmtsWjMZkjO16JyGEEGFLwj7MrDn0IZ9VFpGlz+DaUxYRrQ5ti9rW6Oavb+zA6wtww3kjyU2LzPmohRCiP5GwDyMbKzfzzrdrSY5N4qbRVxMXHdoWtcvj46+v76CpuZWFM/M5NT8lpOcXQgjRMyTsw8Qu2x5e2bsKnSaem0cvJiEmtJ3hfP4Az7y9kwpbMzPHZTFrfHZIzy+EEKLnSNiHgW8by/jnrpeIUkVx46irSNVZQnp+RVF4ae1edh+yc+rgFBae3X/XHhBCiEgkYd/LalxW/r7jeXwBH4tHXsbAhNyQl+H9ojI+2V5FbqqB684bLgvZCCFEhJGw70WNLQ6e3rYcp7eZSwp+wSkpw0Nehs17anjj4xJMxhhuXTCKWK0MsRNCiEgjYd9LPD4Pz+x4jjpPPecMnMW0jEkhL8OBikb++c4eYrVR/GbBaJIMMSEvgxBCiJ4nYd9LXi5+k3JHBdMyJnLOgJkhP3+t3cUTb+wgEFC46YKRZFv0IS+DEEKI0JCw7wUNLY18VbuDLH0Gvxzy85CvA+90e3n89R043V4unzOEkYOSQ3p+IYQQoSVh3wuKqr5EQeG0zElEqUO7epzXF+DpVTuprncxd1IOZ52aGdLzCyGECD0J+xBTFIVNVVvQqDWMTw39Knb/er+YveUNjBtqZsFZeSE9vxBCiN4hYR9iBxoOYnXXMcZyCnHRcSE99+rPD7FpdzWDMoxce+5w1CG+fSCEEKJ3dBn2JSUloShHv/F55RYApqZPDOl5N+2q5j+ffUtKQiy3XjgKrSa0tw+EEEL0ni7D/tZbb+WSSy7hzTffxO12h6JMEcvldbPNugNLXAqDEweG7Lx7y+w8994e4mOiue2i0Rh12pCdWwghRO/rcgaVd999l3379vH+++9zxRVXMGzYMC666CJGjRoVivJFlK012/AGfExJnxCyHvhVdc08tWonADf/4hQyUnQhOa8QQojwcVL37IcMGcJvfvMb7rrrLkpKSrjpppu47LLLOHToUA8XL7JsqtqMWqVmUvq4kJyvydXK469vp9nj48q5BQzLTQrJeYUQQoSXLlv2FRUVvPXWW7zzzjsMHjyYG264gdNPP52dO3dy++238/rrr4einH1euaOSMkcFp6QMC8mKdl6fnyff3IG1wcO5Uwdw2qj0Hj+nEEKI8NRl2F9xxRUsWLCAF154gdTU1ODzo0aNkkv5P8Cmqs1AaDrmBRSFf76zh5KKJiYPT+Xnp4euf4AQQojw02XYr169mk8++SQY9K+88grnnXceOp2Oe++994T7Llu2jO3bt6NSqViyZEmHDwcrVqxg9erVqNVqRo4cyT333MOqVav461//Sk5ODgBTp07lxhtvpLi4mPvvvx+AoUOH8sADD/zY+vYKr9/LluqvMWoNjEgu6PHzvfXJQbYU15KflcBV5wwL+Qx9QgghwkuX9+zvvvtubDZbcNvj8XDHHXd0eeDNmzdTWlrKypUreeihh3jooYeCrzmdTpYvX86KFSt45ZVXKCkpYdu2bQCcc845vPjii7z44ovceOONADz00EMsWbKEV199FafTyYYNG35wRXvTdusuXD43k9LG9fiMeZ9sr+TdTaWkJsVxy4Wj0ETLVApCCNHfdZkEDQ0NLFq0KLh91VVX0dTU1OWBN23axMyZbQu85OXl0djYiNPpBECj0aDRaHC5XPh8PtxuNwkJCcc9TmtrKxUVFcGrAtOnT2fTpk1d1yyMbKxqG1s/JWNCj55nX3kDLxbuRR+n4baLRqOP0/To+YQQQvQNXYa91+vtMLHOrl278Hq9XR7YZrORlPRd72+TyYTVagUgJiaGm2++mZkzZzJ9+nRGjx7NwIFt95U3b97M4sWLufLKK/nmm2+w2+0Yjd91aEtOTg4epy+wuevZaz9AXsJAUuPNPXYenz/AC2uKCQQUbv75SFJN8T12LiGEEH1Ll/fs7777bm666SYcDgd+vx+TycQjjzzyg0+kKErwsdPp5B//+Adr1qxBr9dz5ZVXUlxczOjRozGZTJx11ll8/fXX3Hnnnfzzn//s9DidSUqKJzo6PGaI+3DnRwDMHXoGZrPhJx3rRPu/tm4fVXUuzpk6gNPG5fyk84TST/03CVeRWC+pU98RifWKxDpB6OrVZdiPHj2awsJC7HY7KpWKxMREvvrqqy4PbLFYOtzrr62txWxua9mWlJSQnZ2NyWQCYPz48ezatYsFCxaQl9e2OMuYMWOor68nKSmJhoaG4HFqamqwWCwnPLfd7uqyfKEQUAKsL9lIbFQsg+PysVodP/pYZrOh0/2tDW5e/WAvRp2WcyZm/6TzhNKJ6tSXRWK9pE59RyTWKxLrBN1frxN9cOjyMr7T6WTFihWsWLGCl156iccee4xbb721y5NOmzaNwsJCAHbv3o3FYkGv1wOQmZlJSUkJHo8HaLs1MGDAAJ599lneeecdAPbt24fJZEKr1TJo0CC2bt0KwNq1azn99NO7PH842FO/j4aWRsanjkYb1TNT1CqKwooP9uH1BVg4YzDxsXKfXgghREddtuxvu+02MjIy+Oyzz5gzZw6ff/55cBjciYwdO5YRI0awcOFCVCoVS5cuZdWqVRgMBmbNmsXixYtZtGgRUVFRjBkzhvHjx5OVlcXtt9/Oq6++is/nC/bgX7JkCffddx+BQIDRo0czderUn1zxUNh4ZNGbjJ4bW//VPhs7SuoYlpvEpOGpXe8ghBCi3+ky7FtaWvjv//5vrrjiCu68804aGhp48MEHgz3tT+S//uu/OmwXFHw3xnzhwoUsXLiww+tpaWm8+OKLxxxn8ODBvPzyy12eL5w4Wp3ssO0mQ5dGjiGrR87hafXx8rp9REepuHz2EBlPL4QQ4rhOqje+y+UiEAhgt9tJTEykvLw8FGXr04qqvySgBJiaMbHHQvg/n32L3dHC3Em5pCfLAjdCCCGOr8uW/fnnn89rr73GRRddxDnnnIPJZCI3NzcUZeuzFEVhU+UWolVRTEgb0yPnKK918sGWw5gTYzl3ivw8hBBCdK7LsD9yzx1gypQp1NXVMWzYsB4vWF/2bVMZ1a5axllGo9d0f4s7oCi8WLiXgKJw+eyhaDXhMcxQCCFEeOryMv7Rs+elpqYyfPhwuTfchU2VbYve9NSMeZ/tqOJARSPjh5o5ZVByj5xDCCFE5OiyZT9s2DD++te/MmbMGDSa74Z1TZkypUcL1ld5fB621m7HFJvE0KTB3X58h6uV1z86QIw2iktmDun24wshhIg8XYb9nj17AILj3AFUKpWEfSe+qt1Bq7+VKTlnolZ1/yI0r39cQrPHx8IZg0kyxHT78YUQQkSeLsP+eEPhROc2Vm5BhYrJ6eO7/dj7yhv4bEcV2RY9Z4/vmeF8QgghIk+XYX/ppZce9x79ihUreqRAfVl1cw3fNpUyzDQEU2xS1zv8AD5/gBfX7kUFLJozlCi1LF0rhBDi5JzUDHpHeL1evvjiC+LjZUW14+nJGfNWf1JChbWZM0/NIC/z+MsBCyGEEMfTZdhPnNgxuKZNm8a1117bYwXqq3wBH0XVX6LTxHNKyvBuPXZdo4eX1+7FEK/hwjPzuvXYQgghIl+XYf/92fKqqqr49ttve6xAfdVO2x6c3mZmZJ+ORt3lP+sP8vK6fbS0+rl81hD0cbLQjRBCiB+my1S68sorg49VKhV6vZ5f//rXPVqovmhjVfvY+vTuHVu/7YCNr/fbGDEomakj07r12EIIIfqHLsN+/fr1BAIB1O0dwrxeb4fx9gLsngb21O1jgDGHDH33BXKL18+KtfuIUqu48cJRMpmREEKIH6XLLt2FhYXcdNNNwe3LLruMNWvW9Gih+povqraioDC1m1v1//f5IeqaPMyZmENumrFbjy2EEKL/6DLsn3/+ef785z8Ht5977jmef/75Hi1UXxJQAmyq2oJWrWFs6uhuO26FrZnCzWUkG2P52bQB3XZcIYQQ/U+XYa8oCgaDIbit1+vlcvJR9tlLqPPYGZs6mrjo2G45pqIovFS4F39A4bJZQ4iRhW6EEEL8BF3esx85ciS33XYbEydORFEUPv30U0aOHBmKsvUJm6rax9and9/Y+o27qtlb3sCY/BROzU/ptuMKIYTon7oM+9///vesXr2aHTt2oFKpOO+885g7d24oyhb2mr0utll3kRpvZlBC96wp73R7ee2jA2g1ai6VhW6EEEJ0gy7D3u12o9FouPfeewF45ZVXcLvd6HTdv057X7Ol+mt8AR9T0id0262NVRtKcLi8XHRWHskJ3XNbQAghRP/W5T37O++8E5vNFtz2eDzccccdPVqovkBRFDZWbUatUjMpfVy3HLOkopGPt1WSmaJj1oTsbjmmEEII0WXYNzQ0sGjRouD2VVddRVNTU48Wqi8od1RQ4azilJThGLWGrnfogj8Q4N+FewG4Ys5QoqNkoRshhBDdo8tE8Xq9lJSUBLd37tyJ1+vt0UL1BRuDHfO6Z2z9h19WUF7r5LRT0hmSndgtxxRCCCHgJO7Z33333dx00004HA4CgQBJSUk88sgjoShb2Gr1t7K15msStEaGmX56Jzq7o4W3Pj2ILjaai6bLQjdCCCG6V5ct+9GjR1NYWMibb77JXXfdhcVi4cYbbwxF2cLW17U7cfs8TE4fT5T6p4+Bf+XD/bS0+rlo+mAM8dpuKKEQQgjxnS5b9tu2bWPVqlW89957BAIBHnzwQWbPnh2KsoWtI2Pru2PRm50H69haXEteppHTRqX/5OMJIYQQ39dp2D/77LO89dZbuN1uzj//fN58801+85vfMH/+/JM++LJly9i+fTsqlYolS5YwatSo4GsrVqxg9erVqNVqRo4cyT333IPP5+Oee+6hrKwMv9/PHXfcwfjx47niiitwuVzEx8cDbSMEemtin1qXjf0NBxmSmIc5PvknHavV6+eltXtRq1QsmlOAWmYmFEII0QM6DfvHH3+cwYMHc9999zF58mSAHzSWfPPmzZSWlrJy5UpKSkpYsmQJK1euBMDpdLJ8+XLWrl1LdHQ0V199Ndu2baOkpIS4uDheeeUV9u/fz913380bb7wBwMMPP8yQIb0/yUywVZ/x01v1724qxdrgYc7EbLIt+p98PCGEEOJ4Og37jz/+mLfeeoulS5cSCAT4+c9//oN64W/atImZM2cCkJeXR2NjI06nE71ej0ajQaPRBFvrbrebhIQEzjvvPM4991wATCYTDQ0NP7F63csf8FNUtZW46FhONZ/yk45VXe/i/aJSkgwxnH/awG4qoRBCCHGsTjvomc1mrrvuOgoLC1m2bBllZWVUVFRwww03sGHDhi4PbLPZSEpKCm6bTCasVisAMTEx3HzzzcycOZPp06czevRoBg4ciEajISYmBoAXXnghBnsjowAAGzxJREFUGPwATzzxBJdddhn33XcfHo/nR1f4p/imfi+NrQ4mpI5BG6X5Scd68+MSfH6FS2fmE6vtsuuEEEII8aOdVMpMmDCBCRMm8Pvf/5533nmHp59+mjPPPPMHnUhRlOBjp9PJP/7xD9asWYNer+fKK6+kuLiYgoICoO1+/u7du/n73/8OwKJFixg6dCg5OTksXbqUFStWsHjx4k7PlZQUT3R0968Ut3Xv1wCcM/wszKYfP5GOoijsO9xIqimeOdMGnfTtEbP5p0/eE24isU4QmfWSOvUdkVivSKwThK5eP6hJqdfrWbhwIQsXLuzyvRaLpcM0u7W1tZjNZgBKSkrIzs7GZDIBMH78eHbt2kVBQQGvv/4669ev529/+xsaTVvredasWcHjzJgxg/fee++E57bbXT+kWielscXBV5U7ydZnYPD///buPTiq+u7j+HuzmwRyAbJhN1EJaiKCpnLJgxcSAaGkXjq2dMqj5BlEO3GsiFAtCpgKCdPhpshTWjutUtpOEYGiMIPUGqatOrbEoEhTiOWhxpYsoHsJBLKEmGxynj9iVkLCJSR7O3xef3H27J79fjm7+e7vd37n90vD62245GMdO9lEQ2Mz12cNxOfzX9RrHI7UXr1nNDJjTmDOvJRT7DBjXmbMCfo+r/P9cAjZnKwFBQWUl5cDUF1djdPpJCWlfRDaVVddRU1NTbA7fv/+/VxzzTW4XC42bdrEiy++GOzONwyDhx56KDhFb2VlJcOGDQtV2Oe0+/M9tBltjLuy90vZ1rrbC/xQDcoTEZEwCNnF4ry8PHJzc5k+fToWi4XS0lK2bt1KamoqhYWFFBcXM3PmTKxWK2PGjGHs2LGsXr2a+vp6HnnkkeBx1q1bx3333cdDDz1E//79ycjIYM6cOaEKu1sdi97Y4mzcnDG618er9bT/khuaYc5uKRERiS4W48yL6SbR1909n9T/m//96BeMzRjN93L/p9fH+/nWfew56OWF2QWkpSZe1GvM2I1lxpzAnHkpp9hhxrzMmBOYpBvfTHYd3Q1A/hW978KH9pZ9Sv94BqVoalwREQk9FfsLaGltYa/nHwzuZ2dYWnavj9fYFMBb38TQjJQeTVIkIiJyqXSD9wVYLBauS8vm1sz/Is7S+99Gro7r9U5drxcRkfBQsb8AW5yN2aPOfU9/T9V6vhyJn6GR+CIiEh7qxg8z15e33WVpJL6IiISJin2Y1XoaiLfFkWnvH+lQRETkMqFiH0aB1jaOeE8xxJGMNU7/9SIiEh6qOGF01HeK1jZDk+mIiEhYqdiHkcujaXJFRCT8VOzDqFaD80REJAJU7MPI5WnAAgxxJEc6FBERuYyo2IeJYRjUuv047Un0S9D0BiIiEj4q9mFSd6KJxi8CXK3JdEREJMxU7MOkY+a8LA3OExGRMFOxD5Nat9awFxGRyFCxD5OOkfi67U5ERMJNxT5MXJ4GBiQnMDAlMdKhiIjIZUbFPgz8p1uoO/mFVroTEZGIULEPg69mztP1ehERCT8V+zBwBQfnqWUvIiLhp2IfBofcuu1OREQiR8U+DFyeBhLjrWSkJUU6FBERuQyp2IdYS6CVz+oaGeJMJi7OEulwRETkMqRiH2JHfY3ta9hrcJ6IiESIin2Idcycl6XBeSIiEiEhXX5t2bJlVFVVYbFYKCkpYeTIkcF9GzZsYPv27cTFxfG1r32NH/3oR7S0tLBw4UKOHj2K1Wpl+fLlZGVlceDAAcrKygAYPnw4S5YsCWXYfeqrmfPUshcRkcgIWct+9+7dHDp0iM2bN7N06VKWLl0a3Of3+1m3bh0bNmxg48aN1NTU8Pe//50dO3YwYMAANm7cyKOPPsoLL7wAwNKlSykpKWHTpk34/X7efffdUIXd52o9DVgsWsNeREQiJ2TFvqKigilTpgCQk5PDiRMn8PvbW7nx8fHEx8fT2NhIIBDg9OnTDBw4kIqKCgoLCwHIz8/no48+orm5mSNHjgR7BSZNmkRFRUWowu5TbYaBy+PnivRkEuKtkQ5HREQuUyEr9j6fj7S0tOC23W7H6/UCkJiYyOzZs5kyZQqTJk1i1KhRXHvttfh8Pux2e3tgcXFYLBZ8Ph8DBgwIHic9PT14nGjnqz9NU3OrFr8REZGICuk1+zMZhhH8t9/v56WXXuKtt94iJSWFBx98kAMHDpz3Ned77GxpaUnYbJFvSR882j4474bsdByO3l+z74tjRBsz5gTmzEs5xQ4z5mXGnCB8eYWs2DudTnw+X3Db4/HgcDgAqKmpISsrK9iKHzt2LPv378fpdOL1ehkxYgQtLS0YhoHD4aC+vj54HLfbjdPpPO97Hz/eGIKMem7/Jx4A7CkJeL0NvTqWw5Ha62NEGzPmBObMSznFDjPmZcacoO/zOt8Ph5B14xcUFFBeXg5AdXU1TqeTlJT27uyrrrqKmpoampqaANi/fz/XXHMNBQUFvPXWWwC8/fbb3HrrrcTHx5Odnc2HH34IwM6dOxk/fnyowu5TtZomV0REokDIWvZ5eXnk5uYyffp0LBYLpaWlbN26ldTUVAoLCykuLmbmzJlYrVbGjBnD2LFjaW1tZdeuXRQVFZGQkMCKFSsAKCkpYfHixbS1tTFq1Cjy8/NDFXafcnn8pKUmMiApIdKhiIjIZcxiXMxF8BgTDd09JxubeeKnf2VkTjpP/PeoXh/PjN1YZswJzJmXcoodZszLjDmBSbrxL3eujsl0NHOeiIhEmIp9iNR6vlzDXjPniYhIhKnYh4ha9iIiEi1U7EOk1uOnX4KVwYP6RzoUERG5zKnYh0BzSyuf1Z0iy5lCnEVr2IuISGSp2IfAYe8pDAOGZuh6vYiIRJ6KfQh8NThP1+tFRCTyVOxD4KvBeWrZi4hI5KnYh0CtpwFrnIUrBydFOhQREREV+77W1taxhn0S8VGw8p6IiIiKfR9zH2+kuaVNXfgiIhI1VOz7mMvz5fV6Dc4TEZEooWLfx4LL2qplLyIiUULFvo913HanNexFRCRaqNj3sVq3n/QBiaT0j490KCIiIoCKfZ864f+Ck6eaNThPRESiiop9H6r9cnCeuvBFRCSaqNj3oVr3l9PkqmUvIiJRRMW+D+m2OxERiUYq9n3okNtPUqKN9IH9Ih2KiIhIkIp9H2lqDuA51sjQjBQsWsNeRESiiIp9HznsPYUBZDl1vV5ERKKLin0fcQUH5+l6vYiIRBcV+z5yyK3b7kREJDqp2PcRl6cBm9XClYOTIx2KiIhIJ7ZQHnzZsmVUVVVhsVgoKSlh5MiRALjdbp566qng81wuF/PmzePw4cPs2rULgLa2Nnw+H+Xl5UyePJnMzEys1vb14VetWkVGRkYoQ++R1rY2DntPceXgZGxW/X4SEZHoErJiv3v3bg4dOsTmzZupqamhpKSEzZs3A5CRkcH69esBCAQCPPDAA0yePJnk5GRmzZoFwLZt26irqwseb+3atSQnR2er+fNjp2kJtDFUg/NERCQKhawZWlFRwZQpUwDIycnhxIkT+P3+Ls/btm0bd955Z6dCHggE2LhxIzNmzAhVeH2qY3BelgbniYhIFApZsff5fKSlpQW37XY7Xq+3y/O2bNnCtGnTOj22c+dObr/9dvr1+2pymtLSUoqKili1ahWGYYQq7EvSsYb91ZomV0REolBIr9mfqbsCvXfvXrKzs0lJ6dwifv3111myZElwe+7cuYwfP56BAwcye/ZsysvLueuuu875XmlpSdhs1r4L/gI+P34agNE3ZJIcwqVtHQ7z/ZgwY05gzryUU+wwY15mzAnCl1fIir3T6cTn8wW3PR4PDoej03Peeecdxo0b1+mxxsZGPv/8c4YMGRJ8bOrUqcF/T5gwgYMHD5632B8/3tjb8C+aYRh8crgex6B+NPqbaPQ3heR9HI5UvN6GkBw7UsyYE5gzL+UUO8yYlxlzgr7P63w/HELWjV9QUEB5eTkA1dXVOJ3OLi34ffv2MWLEiE6PHThwgOzs7OB2Q0MDxcXFNDc3A/DBBx8wbNiwUIXdY/X+ZvynWzQ4T0REolbIWvZ5eXnk5uYyffp0LBYLpaWlbN26ldTUVAoLCwHwer2kp6d3ep3X68Vutwe3U1NTmTBhAvfffz+JiYnceOON523Vh1utBueJiEiUsxjRNtqtD4Szu+eNv/2bbe/9m7nTRjL6usEhex8zdmOZMScwZ17KKXaYMS8z5gQm6ca/XNRqDXsREYlyKva95HL7SekfT1pqYqRDERER6ZaKfS+c/iKAp/40WU6tYS8iItFLxb4XXB5NpiMiItFPxb4XNBJfRERigYp9L2hwnoiIxAIV+15wuf3YrHFkpidFOhQREZFzUrG/RIHWNo74/AxxJGON03+jiIhEL1WpS/RZXSOBVoOhGpwnIiJRTsX+EnUMzhuqwXkiIhLlVOwvkSs4OE8texERiW4q9peo1t2ABbjKkRzpUERERM5Lxf4SGIaBy+PHmdaf/okhWzhQRESkT6jYX4K6k02cagpocJ6IiMQEFftL4HJ/eb1eg/NERCQGqNhfgo6Z87I0OE9ERGKAiv0l0G13IiISS1TsL0Gt28+A5AQGpWgNexERiX4q9j10qqmFupNNWvxGRERihop9D3UMztOytiIiEitU7HuoVjPniYhIjFGx7yGXBueJiEiMUbHvoUNuPwnxcWSkaQ17ERGJDSr2PdASaOOzulNkOVKIi7NEOhwREZGLomLfA0d9p2htM8jSNLkiIhJDQrqKy7Jly6iqqsJisVBSUsLIkSMBcLvdPPXUU8HnuVwu5s2bR0tLC2vWrGHo0KEA5OfnM2vWLA4cOEBZWRkAw4cPZ8mSJaEM+5xqPV9er9dtdyIiEkNCVux3797NoUOH2Lx5MzU1NZSUlLB582YAMjIyWL9+PQCBQIAHHniAyZMnU15ezj333MOCBQs6HWvp0qXBHwvz5s3j3XffZeLEiaEK/Zy+mhNfLXsREYkdIevGr6ioYMqUKQDk5ORw4sQJ/H5/l+dt27aNO++8k+Tk7teFb25u5siRI8FegUmTJlFRURGqsM+r1t2AxaI17EVEJLaErNj7fD7S0tKC23a7Ha/X2+V5W7ZsYdq0acHt3bt3U1xczIMPPsjHH3/M8ePHGTBgQHB/enp6t8cJtTbDoNbjJ9OeRGK8NezvLyIicqlCes3+TIZhdHls7969ZGdnk5LSfg181KhR2O127rjjDvbu3cuCBQv41a9+dcHjnC0tLQmbrW8L8ud1p2hqbmXY0DQcjsh040fqfUPJjDmBOfNSTrHDjHmZMScIX14hK/ZOpxOfzxfc9ng8OByOTs955513GDduXHA7JyeHnJwcAMaMGcOxY8dIS0ujvr4++By3243T6Tzvex8/3tgXKXTy9//zAJAxsB9eb0OfH/9CHI7UiLxvKJkxJzBnXsopdpgxLzPmBH2f1/l+OISsG7+goIDy8nIAqqurcTqdwRZ8h3379jFixIjg9tq1a9mxYwcABw8exG63k5CQQHZ2Nh9++CEAO3fuZPz48aEK+5wOaXCeiIjEqJC17PPy8sjNzWX69OlYLBZKS0vZunUrqampFBYWAuD1eklPTw++5t577+Xpp59m06ZNBAIBli5dCkBJSQmLFy+mra2NUaNGkZ+fH6qwz6ljmtws3XYnIiIxJqTX7M+8lx7o1IoHeOONNzptZ2ZmBm/JO9N1113Hq6++2vcB9kCtx8+glAQGJCdENA4REZGe0gx6F6GhsZnjDV+oC19ERGKSiv1FcHUsa6uV7kREJAap2F+EWrfWsBcRkdilYn8ROubEz1LLXkREYpCK/UVwuf0kJlhxDOof6VBERER6TMX+AppbWvmsrpEsZwpxFq1hLyIisUfF/gIMoH+ilZHZ6Rd8roiISDQK29z4sSox3sr/zrkdm1W/i0REJDapgl0EFXoREYllqmIiIiImp2IvIiJicir2IiIiJqdiLyIiYnIq9iIiIianYi8iImJyKvYiIiImp2IvIiJicir2IiIiJqdiLyIiYnIq9iIiIiZnMQzDiHQQIiIiEjpq2YuIiJicir2IiIjJqdiLiIiYnIq9iIiIyanYi4iImJyKvYiIiMnZIh2AdPbcc8+xZ88eAoEA3//+9/nGN74R3Dd58mQyMzOxWq0ArFq1ioyMjEiFetEqKyv5wQ9+wLBhwwC4/vrrWbRoUXD/rl27WL16NVarlQkTJjB79uxIhXrRtmzZwvbt24Pb+/fvZ+/evcHt3Nxc8vLygtu//e1vg+ctGh08eJDHHnuMhx56iBkzZvDZZ58xf/58WltbcTgcPP/88yQkJHR6zbJly6iqqsJisVBSUsLIkSMjFH33usvpmWeeIRAIYLPZeP7553E4HMHnX+hzGi3OzmvhwoVUV1czaNAgAIqLi7njjjs6vSbWztXcuXM5fvw4APX19YwePZof//jHwedv3bqVNWvWMHToUADy8/OZNWtWRGI/l7P/lt90002R/U4ZEjUqKiqMhx9+2DAMwzh27JgxceLETvsnTZpk+P3+CETWO++//74xZ86cc+6/++67jaNHjxqtra1GUVGR8a9//SuM0fVeZWWlUVZW1umxW265JULR9NypU6eMGTNmGM8++6yxfv16wzAMY+HChcabb75pGIZhvPDCC8aGDRs6vaaystJ45JFHDMMwjE8++cS47777whv0BXSX0/z5840//OEPhmEYxiuvvGKsXLmy02su9DmNBt3ltWDBAuMvf/nLOV8Ti+fqTAsXLjSqqqo6Pfb6668bK1asCFeIPdbd3/JIf6fUjR9Fbr75ZtasWQPAgAEDOH36NK2trRGOKrRcLhcDBw7kiiuuIC4ujokTJ1JRURHpsHrk5z//OY899likw7hkCQkJrF27FqfTGXyssrKSr3/96wBMmjSpyzmpqKhgypQpAOTk5HDixAn8fn/4gr6A7nIqLS3lzjvvBCAtLY36+vpIhXfJusvrQmLxXHX49NNPaWhoiLqeiAvp7m95pL9TKvZRxGq1kpSUBMBrr73GhAkTunT9lpaWUlRUxKpVqzBiaPLDTz75hEcffZSioiL+9re/BR/3er3Y7fbgtt1ux+v1RiLES/KPf/yDK664olN3MEBzczPz5s1j+vTp/OY3v4lQdBfHZrPRr1+/To+dPn062MWYnp7e5Zz4fD7S0tKC29F23rrLKSkpCavVSmtrK6+++ir33ntvl9ed63MaLbrLC+CVV15h5syZPPnkkxw7dqzTvlg8Vx1+97vfMWPGjG737d69m+LiYh588EE+/vjjUIbYY939LY/0d0rX7KPQn/70J1577TV+/etfd3p87ty5jB8/noEDBzJ79mzKy8u56667IhTlxbvmmmt4/PHHufvuu3G5XMycOZOdO3d2uV4Vi1577TW+853vdHl8/vz5fOtb38JisTBjxgzGjh3LTTfdFIEIe+9iflTGyg/P1tZW5s+fz2233ca4ceM67YvVz+m3v/1tBg0axA033MDLL7/Miy++yOLFi8/5/Fg5V83NzezZs4eysrIu+0aNGoXdbueOO+5g7969LFiwgDfeeCP8QV7AmX/Lzxx/FYnvlFr2Uea9997jl7/8JWvXriU1NbXTvqlTp5Keno7NZmPChAkcPHgwQlH2TEZGBvfccw8Wi4WhQ4cyePBg3G43AE6nE5/PF3yu2+3uURdlpFVWVjJmzJgujxcVFZGcnExSUhK33XZbzJyrDklJSTQ1NQHdn5Ozz5vH4+nSuxGNnnnmGa6++moef/zxLvvO9zmNZuPGjeOGG24A2gfxnv1Zi9Vz9cEHH5yz+z4nJyc4CHHMmDEcO3Ys6i55nv23PNLfKRX7KNLQ0MBzzz3HSy+9FBxZe+a+4uJimpubgfYvQseo4Wi3fft21q1bB7R329fV1QXvIhgyZAh+v5/Dhw8TCAR4++23KSgoiGS4F83tdpOcnNyl5ffpp58yb948DMMgEAjw0Ucfxcy56pCfn095eTkAO3fuZPz48Z32FxQUBPdXV1fjdDpJSUkJe5w9sX37duLj45k7d+4595/rcxrN5syZg8vlAtp/fJ79WYvFcwWwb98+RowY0e2+tWvXsmPHDqB9JL/dbo+qu126+1se6e+UuvGjyJtvvsnx48d54okngo/deuutDB8+nMLCQiZMmMD9999PYmIiN954Y0x04UN7a+Opp57iz3/+My0tLZSVlbFjxw5SU1MpLCykrKyMefPmAXDPPfdw7bXXRjjii3P2eIOXX36Zm2++mTFjxpCZmcm0adOIi4tj8uTJUT3AaP/+/axcuZIjR45gs9koLy9n1apVLFy4kM2bN3PllVcydepUAJ588kmWL19OXl4eubm5TJ8+HYvFQmlpaYSz6Ky7nOrq6khMTOSBBx4A2luHZWVlwZy6+5xGWxd+d3nNmDGDJ554gv79+5OUlMTy5cuB2D5XP/vZz/B6vcFb6zrMmjWLX/ziF9x77708/fTTbNq0iUAgwNKlSyMUffe6+1u+YsUKnn322Yh9p7TErYiIiMmpG19ERMTkVOxFRERMTsVeRETE5FTsRURETE7FXkRExOR0652IdHH48GHuuuuuLhMGTZw4kYcffrjXx6+srOQnP/kJGzdu7PWxROTCVOxFpFt2u53169dHOgwR6QMq9iLSIzfeeCOPPfYYlZWVnDp1ihUrVnD99ddTVVXFihUrsNlsWCwWFi9ezHXXXcd//vMfFi1aRFtbG4mJicFJX9ra2igtLeWf//wnCQkJvPTSSyQnJ0c4OxFz0jV7EemR1tZWhg0bxvr16ykqKuKnP/0p0L74zzPPPMP69ev53ve+x5IlS4D2lRqLi4vZsGED3/3ud/njH/8IQE1NDXPmzOH3v/89NpuNv/71rxHLScTs1LIXkW4dO3YsOLVsh6effhqA22+/HYC8vDzWrVvHyZMnqaurC04LfMstt/DDH/4QaF8G+JZbbgHgm9/8JtB+zT47O5vBgwcDkJmZycmTJ0OflMhlSsVeRLp1vmv2Z86ybbFYsFgs59wP7V32Z4umhUtEzE7d+CLSY++//z4Ae/bsYfjw4aSmpuJwOKiqqgKgoqKC0aNHA+2t//feew9oXyBk9erVkQla5DKmlr2IdKu7bvwhQ4YA8PHHH7Nx40ZOnDjBypUrAVi5ciUrVqzAarUSFxdHWVkZAIsWLWLRokW8+uqr2Gw2li1bRm1tbVhzEbncadU7EemR4cOHU11djc2mtoJIrFA3voiIiMmpZS8iImJyatmLiIiYnIq9iIiIyanYi4iImJyKvYiIiMmp2IuIiJicir2IiIjJ/T8ePZqW9bSxKgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TKQCdTuvStp/L3MwoXnrvGLUt3ZjNRqeu29nr8xRqzCWZvIcac6kxE6gz12RlcllRt1gsWK3W4dcNDQ2YzWYAdu/eTXNzMzfddBN9fX1UVlayefNm1q9fz+rVqwGIj48nPDyc+vp64uLizrudlhb3dH8H6hV8dBoKy6w0NrY7bb1ms9Gp6/MUaswlmbyHGnOpMROoM5ezM430A8Fl3e/Z2dls374dgMLCQiwWCwaDAYCVK1fyzjvv8Prrr/P000+TmZnJ+vXrefPNN3n22WcBaGxspKmpiYiICFc1cUK0Gg2JUUHUNHbS3Tvg7uYIIYQQrjtTnzNnDpmZmeTk5KAoChs2bGDLli0YjUZWrFhxzmWWLVvGfffdx4cffkh/fz8bN24csevd3VKigzhW1cqJujamJoa6uzlCCCEuci69pn7fffed8TojI+Os78TGxvLSSy8BYDAY+POf/+zKJjlVcvTQJDTHpagLIYTwADKj3AScHgEvj2EVQgjhCaSoT4DJ6EtYkC/Ha21njO4XQggh3EGK+gQlRwfT1tWP1dbj7qYIIYS4yElRn6CU013wMg+8EEIIN5OiPkHDg+XkuroQQgg3k6I+QfERBrQaRR7DKoQQwu2kqE+Qj15LfISByvp2+gcG3d0cIYQQFzEp6k6QHB3MoN1BRX3Hhb8shBBCuIgUdSc4PVjuuHTBCyGEcCMp6k6QHHNqsJyMgBdCCOFGUtSdwBzshzFALzPLCSGEcCsp6k6gKAop0cE0tfXQ2tHr7uYIIYS4SElRd5Jkua4uhBDCzaSoO0myzCwnhBDCzaSoO0lSVBAKMrOcEEII95Gi7iT+vjqizYGUn2xj0G53d3OEEEJchKSoO1FKdBB9/XZqGjvd3RQhhBAXISnqTjT8cBcZLCeEEMINpKg7kTyGVQghhDtJUXeiqPBA/H21cqYuhBDCLaSoO5FGUUiKCqKuqYvOnn53N0cIIcRFRoq6k52+rl4uZ+tCCCEmmRR1J/vquroUdSGEEJNLirqTJclgOSGEEG4iRd3JggJ8sIT4U17bhsPhcHdzhBBCXESkqLtAckwQnT0D1Ld0u7spQgghLiJS1F0g5dRgubIa6YIXQggxeaSof43dYWfQPjjh9chjWIUQQriDFPWvee3oP/jt7t8zYB+Y0HriLAb0Oo0MlhNCCDGpXFrUN2/ezJo1a8jJySE/P/+c33n88cdZu3btmJZxFR+tHmtPM0eaj01oPTqthoRII9UNnfT2TfzMXwghhBgNlxX1PXv2UFFRQW5uLps2bWLTpk1nfae0tJS9e/eOaRlXmh8xG4C9J/MmvK6U6CDsDgcnTkoXvBBCiMnhsqK+a9culi9fDkBKSgo2m42Ojo4zvvPoo49y7733jmkZV4o3xmL2DyPfWkTPQO+E1pUiT2wTQggxyVxW1K1WKyaTafh1aGgojY2Nw6+3bNnCggULiImJGfUyrqYoCvMjZtNv7yffWjihdSXLzHJCCCEmmW6yNvT1iVhaW1vZsmULzz//PPX19aNa5nxMpgB0Oq1T2ghwlW8275z4gPyWAq6ZsXTc6wkPNxAa5MeJk22EhxtQFGXUy5rNxnFv15OpMZdk8h5qzKXGTKDOXJOVyWVF3WKxYLVah183NDRgNpsB2L17N83Nzdx000309fVRWVnJ5s2bR1zmfFpaupzabj2BxBtjOHTyCMdr6jD6GMa9rqRII/uPNXLsuJXQIL9RLWM2G2lsbB/3Nj2VGnNJJu+hxlxqzATqzOXsTCP9QHBZ93t2djbbt28HoLCwEIvFgsEwVCBXrlzJO++8w+uvv87TTz9NZmYm69evH3GZyTQvYjZ2h528homNvk+OkS54IYQQk8dlZ+pz5swhMzOTnJwcFEVhw4YNbNmyBaPRyIoVK0a9jDvMjZjJP0q3srf+IEtiLx33er4+s9z8DIuzmieEEEKck0uvqd93331nvM7IyDjrO7Gxsbz00kvnXcYdQnyDSTOlcKyllKbuZsL8Q8e1noRIIxpFkRHwQgghJoXMKHce8yNmAbCv/uC41+Gr1xJnMXDiZDsDg3ZnNU0IIYQ4Jynq5zHLPAOdop1QUYeh6+oDg3Yq6yfvfnshhBAXJynq5xGg9yczfCq1nSep6agb93qmxg/dd799T6WzmiaEEEKckxT1Ecw71QU/kWlj56SbSYkJYm9xA4fLm5zVNCGEEOIsUtRHMD1sKn5aX/bVH8TuGN81cY2isPaqdDSKwl/fO0b/gDzgRQghhGtIUR+Bj1bPLPMMWnpbOW6rGPd64iOMLJ8XS0NLN+/slm54IYQQriFF/QLmRZ7qgq+f2JPb/mlxEiEGH7buqqDeybPgCSGEECBF/YKmhKRg9DGQV5/PgH1g3Ovx99Xxg+VTGBi089f3jo1qXnshhBBiLKSoX4BWo2WuZSadA10UN5dMaF3z0s1MTwqlsLyZvcUNTmqhEEIIMUSK+ijMj5wNTLwLXlEUbrpqCjqthlc/LKG7d/xn/kIIIcQ3SVEfhQRjHOH+YeQ3FtIz0DuhdUWYArhmUQK2jj7e+LzcSS0UQgghpKiPiqIozI+YRZ+9nwJr0YTXt3phPBaTPx/sr6KyXl2PGBRCCOE+UtRHaV7EUBf8vgl2wQPodVrWXpWOwwEvbT+KXQbNCSGEcAIp6qMUGWghzhhDUfMxOvo6J7y+zKRQFky1UFbbxueHap3QQiGEEBc7KepjMC9iFnaHnQMN+U5Z35plafj5aPn7J2W0dfU5ZZ1CCCEuXlLUx2BexCwUFKd0wQOYjL5857JkOnsG+NvHpU5ZpxBCiIuXFPUxCPENJi0kmTLbCZq6W5yyzmVzY4i3GNhZcJJjVa1OWacQQoiLkxT1MTo9bez+hok9Z/00rUbD2pXpKAwNmhsYHN+DY4QQQggp6mM02zwDraKd0ONYvyklOpgls6KpsXby5mdlTluvEEKIi4sU9TEK0AeQGZZBbedJajrqnLbe7y1NwRig55X3jtJk63HaeoUQQlw8pKiPw+lpY/fVO6cLHsDgr+eGK1Lp7RvklQ+OOW29QgghLh5S1MdhethUfLU+7Ks/6NSnrV06PZLM5DDySqwcLLU6bb1CCCEuDlLUx8FHq2eWeQbNPS0ct1U4bb2KovCz72Wh1Si88v4xevsHnbZuIYQQ6idFfZzmRQyNgnfWPeunJUQGcdX8OKy2Ht7+4oRT1y2EEELdpKiPU7opFaPewIGGfAbtzj2j/nZ2EmFBvmz7spJa68SnpBVCCHFxkKI+TlqNljkRM+no7+RIs3MHtvn6aLlx+RQG7Q7++t5Rp163F0IIoV5S1Cdg/nAXvPNGwZ82Ky2cmSlhFFe2sruo3unrF0IIoT5S1CcgMSiecL9QDlkL6R107gNZFEXhphVT8NFpyP2whK6efqeuXwghhPpIUZ8ARVGYFzmbvsE+CqxFTl9/eIg/38pOpK2rn//97LjT1y+EEEJdXFrUN2/ezJo1a8jJySE//8zHlb7++uvccMMN5OTksHHjRhwOB19++SULFy5k7dq1rF27locfftiVzXOK013wzpw29uuuXhBPVFgAnxyoobyuzSXbEEIIoQ46V614z549VFRUkJubS1lZGevXryc3NxeA7u5utm7dyssvv4xer2fdunXk5Q0VxQULFvDHP/7RVc1yusjACGIN0RQ1H6WjvxODPtCp69dpNfzwqnR+/2oeL24/ygPr5qHRKE7dhhBCCHUY1Zn64cOH+fjjjwF48sknufnmm9m3b9+Iy+zatYvly5cDkJKSgs1mo6OjAwB/f39eeOEF9Ho93d3ddHR0YDabJ5LDreZHzsbusJPXUOCS9U9NMLEoM4KKk+18nFfjkm0IIYTwfqMq6o888ghJSUns27ePgoICHnjggQueTVutVkwm0/Dr0NBQGhsbz/jOM888w4oVK1i5ciVxcXEAlJaWcscdd/CDH/yAnTt3jjWPW8y1zERBcfpENF93w7I0Anx1bPmsDFtHr8u2I4QQwnuNqvvd19eXxMREcnNzueGGG0hNTUWjGdvl+HPda3377bezbt06brvtNubOnUtiYiJ33XUXq1atoqqqinXr1vHee+/h4+Nz3vWaTAHodNoxtcXZzBiZak6lqLEEJaCf8MDQia3PbDzHe3DztdP4z//N542dFdz3w7kT2oY7nCuXt5NM3kONudSYCdSZa7Iyjaqod3d38+677/LBBx9w55130traSlvbyIO2LBYLVutXDyVpaGgY7mJvbW2lpKSE+fPn4+fnx5IlSzhw4ABz585l9erVAMTHxxMeHk59ff3wWfy5tLR0jSaCy80MnUFRYwnvHdnJioTLx70es9lIY2P7OT+bmxJGYqSRT/OqWZAeztTEif14mEwj5fJWksl7qDGXGjOBOnM5O9NIPxBGdbr9q1/9irfeeot7770Xg8HASy+9xC233DLiMtnZ2Wzfvh2AwsJCLBYLBoMBgIGBAX7zm9/Q2Tk0BWpBQQFJSUm8+eabPPvsswA0NjbS1NRERETEaJrodrMtWWgVLXtd2AWv0SisW5mOosBL7x2jf8Dusm0JIYTwPqM6U1+4cCHTp0/HYDBgtVpZtGgRc+bMGXGZOXPmkJmZSU5ODoqisGHDBrZs2YLRaGTFihXceeedrFu3Dp1OR3p6OldeeSWdnZ3cd999fPjhh/T397Nx48YRu949SaA+gGlh6RRYi6jtOEm0IdIl20mMDGLZ7Fg+PFDNW1+c4LtLkl2yHSGEEN5HcYxiYvGHH36YjIwMVqxYwfXXX8/06dMJCgrit7/97WS0cUSe1E2zv/4gzxW+wtUJy/h2yspxrWM03TRdPQM8+NyXNLf1svbqdK6YHTOubU0m6VLzDmrMBOrMpcZMoM5cHtf9XlRUxPe//33effddvvOd7/CHP/yBigrnPUdcLWaET8NX68O++jyXPoQlwE/Hr9fMIihAz1+3H2XX4ZMu25YQQgjvMaqifrpAffLJJyxbtgyAvj7nznWuBj5aH2aap9PU00J5W6VLtxUVFsiv1szC31fHs1uPsP9o44UXEkIIoWqjKupJSUmsXr2azs5Opk6dyhtvvEFwcLCr2+aV5kXMBnDpPeunxUcYuXfNTPR6DX/+f4c5fLzJ5dsUQgjhuUY9+czjjz/Oc889B0Bqaiq/+93vXNowb5VhSsWgD2R//SEG7YMu315KdDD3fC8LjUbh6S0FHK1scfk2hRBCeKZRFfWenh4++ugj7r77bn72s5+xc+dOrxmVPtm0Gi1zI2bS0d9JcUvppGwzI8HEnd+ZzqDdwb//PV8e/CKEEBepURX1Bx54gI6ODnJycrjhhhuwWq3867/+q6vb5rUmswv+tKyUcH767Ux6+wd5Ivcg1Y0dk7ZtIYQQnmFURd1qtfLP//zPXH755VxxxRXcf//91NfXu7ptXispKJ4wPxOHGg/TNzh5AwrnZVj48eqpdPYM8NhrB6lv9ozZ9oQQQkyOURX17u5uuru7h193dXXR2ysPFTkfRVGYFzGb3sE+CqxFk7rt7BlR3LRiCm2dffz+tTyabD2Tun0hhBDuM6oZ5dasWcOqVauYPn06MDTt6z333OPShnm7+ZGz2V7xEXvrDzI3YtakbvvKubH09A3wv58e5/ev5fEvN80h2OA7qW0QQggx+UZ1pn799dfz6quvct111/Gd73yH1157jdLSyRkE5q2iAiOIMURR1HSUzv7J7wa/ZlEi1yxKoKGlm8dyD9LR3T/pbRBCCDG5Rv381KioKJYvX86VV15JREQE+fn5rmyXKsyPmM2gY5CDDQVu2f53lySzfG4sNY2dPJF7kO7eAbe0QwghxOQY20PRv8aV06CqxdyImQAufXLbSBRFIWd5GotnRHHiZDv//rdD9Pa7/t55IYQQ7jHuoq4oijPboUqhfiZSQ5IoaT1Oc497JoXRKAq3rMpgfoaFY9U2/rSlQB7ZKoQQKjXiQLmlS5ees3g7HA5aWmTmstG4JHIupa3lvFiUy52zfoJeM6qxiU6l0Sjc9q1p9PYPkl/WxH+9WcjPrstEqxn3bzohhBAeaMQK88orr0xWO1RrYdQ8ipqOktdYwItFr/GjzBvRKJNfTHVaDT+/bjp/+NshDhxr5HX3Hh0AACAASURBVLmtxdx67VQ00uMihBCqMWJRj4nx/Od0ezqNouHmaTm0HezgQEM+Ib7BfC/tW25pi49ey93XZ/H4awfZVXgSXx8ta6+aIpdShBBCJaT/dRLotXp+mnUzkQEWPqr6nI+qPndbW/x8dPzyhpnEWwx8klfD3z4pk0GPQgihElLUJ0mgPoCfz7yVYB8jW0re5kCD+24JDPTT86s1s4gKC2Dbl5W89cUJt7VFCCGE80hRn0Rh/iZ+NvNWfLU+vFD0GqWt5W5rS1CgD79eM4vwYD/e+Lyc9/ZWua0tQgghnEOK+iSLM0bzkxlrsTvs/Dn/f6jrdN+DcUKD/LjvB7MJMfjw2oclfHao1m1tEUIIMXFS1N1gaugUfpjxfboHuvnTwWdp7bW5rS2WEH/uy5mNwV/PC+8Ws7vopNvaIoQQYmKkqLvJJVFz+VbySlp6W/mPQ8/RPeC+p6lFhwfy6zWz8PPV8Ze3inhzZzl2GTwnhBBeR4q6G12dcAWLYxZS01HHfxe8xIDdfXOzJ0QauS9nFiajL298Xs6Trx+irWvyngUvhBBi4qSou5GiKKyZch0zwqdR3FLCy8V/d+vtZUlRQWz80QKyUsIoLG/moef3UlLd6rb2CCGEGBsp6m6mUTT8OPNGEoPi2XPyAK8VvOnW9hj89dx9fRbfW5pMa0cv//flPLZ9WSn3sgshhBeQou4BfLQ+3JF1C2b/MP5xZBuf1+xya3s0isI1ixL5Pz+YjTFQz+sfl/LU/xbQ2SPPZBdCCE8mRd1DGH0M3DnzJwT7Gsk9+gaHGgvd3STS401s/NECpiaYOFhq5aHn91Je1+buZgkhhDgPKeoexBwQxj9f9nP0Gh3PF75Cua3C3U0i+NQkNd/OTqTJ1sO//XU/H+6vlu54IYTwQFLUPUxqWCK3Tv8hg45B/pz/PzR0Nbq7SWg0Ctddlsy9a2bi56Pj5feP8V9vFtLd677R+kIIIc4mRd0DTQ+fSk76d+jo7+RPB5+lra/d3U0CYHpSGBt/NJ/U2GD2HGngty/so6qhw93NEkIIcYpLi/rmzZtZs2YNOTk55Oef+QCT119/nRtuuIGcnBw2btw43J070jIXk+zoS1iVuBxrTzP/eeh5egZ63d0kYGhq2f/zg9msvCSe+uYuHnlxH5/ny/SyQgjhCVxW1Pfs2UNFRQW5ubls2rSJTZs2DX/W3d3N1q1befnll3nttdc4fvw4eXl5Iy5zMbomaQULo+ZR2V7Nc4UvM2gfdHeTANBpNdxwRSq/+N4M9FoNz79TzLNbi+jt94z2CSHExcplRX3Xrl0sX74cgJSUFGw2Gx0dQ121/v7+vPDCC+j1erq7u+no6MBsNo+4zMVIURRuTP8e00LTKWwq5rWj//CoAWqz08xs+NF8EiON7Cw4ySMv7qOuqdPdzRJCiIuWzlUrtlqtZGZmDr8ODQ2lsbERg8Ew/N4zzzzDiy++yLp164iLixvVMt9kMgWg02ldE8JNzGbjGa9/c/kdbPj4Cb6o20NsmIXrM69xU8vOZjYbeeLepTz7ZiFbd5bzyIv7uPP6WSydE3vO76qNZPIeasylxkygzlyTlcllRf2bznWGefvtt7Nu3Tpuu+025s6dO6plvqmlpcsp7fMUZrORxsazB8bdNu0WHt//NK8ffhv9gD+XRs93Q+vO73uXJREXHsDz7xbz2Mv72V90kpwrU9Gf+sF1vlzeTDJ5DzXmUmMmUGcuZ2ca6QeCy7rfLRYLVqt1+HVDQwNmsxmA1tZW9u7dC4Cfnx9LlizhwIEDIy5zsQv2NXLnzFsJ1AXw6tH/pbDpqLubdJYFUyPYcMt8Ys2BfJxXw+aXDtDQ2u3uZgkhxEXDZUU9Ozub7du3A1BYWIjFYhnuRh8YGOA3v/kNnZ1D118LCgpISkoacRkBEYEW7ph5C1pFw38ffomKtip3N+kskaEB3L9uHouzoqiob+eh5/dy4Jj777UXQoiLgcu63+fMmUNmZiY5OTkoisKGDRvYsmULRqORFStWcOedd7Ju3Tp0Oh3p6elceeWVKIpy1jLiTMnBidySeSP/XfASTx74T76bei2XxSxCURR3N22Yr17Lj1dPZUpsCH997yhPbyng8IkWrl0YT2iQn7ubJ4QQqqU4PGk49ThcrNdeCqxFvFT0Op0DXWSFZ3LT1Osx6AMnoYVjU93YwbNvH6Givh0fnYaVl8Sz6pIEfH28f3CjXPvzHmrMpcZMoM5cqrimLlxrRvg01l9yL1NCUsi3FvJve/7AsZYydzfrLLFmAw/cMo971szG31fHmztPsP4vu9l1+CR27/49KYQQHke7cePGje5uxER0dfW5uwlOFRjoO+pMfjo/FkTOQa/RUdB0hC/r9jNgHyQ1JAmN4jm/1xRFYcYUC/OnhKMoUHSihX1HGyg43kRMuMFru+THsq+8hRozgTpzqTETqDOXszMFBvqe9zPP+ZdfjItG0XB14jJ+NednhPqZ2F7xEU8e+E+s3c3ubtpZ/Hx0fHdJCptvv4QFUy2U17Wz+a/7+fP/O4zVJqPkhRBiouRM3cOM9xedyS+EhVFzae5ppaj5KLvr9hHmF0K0IcoFrRy7r+cK8NMzL8PCtEQTNY0dFJa38MnBWvoHBkmKCkKn9Y7fmnJG4T3UmEuNmUCdueRMXYyLv86fW6b9gLVTb8COneeLXuWlotc95mEw35QWG8L96+bxk2unEuin4+0vKviXZ3azI79OrrcLIcQ4SFFXGUVRWBg1j3+Zfw/xxhh2n9zH/93771S2Vbu7aeekURQunR7Fv92+iG9nJ9LdM8Bz7xzh4Rf2cayq1d3NE0IIryLd7x7GWd00gfpAFkbNY8A+QEHTEXbX7UOv1ZMYFO+We9ovlEun1ZCRYOLS6ZG0dfVRWN7MjoI6aqydJEYaCfTTT2JrR0e6Cb2HGnOpMROoM5d0vwun0Gl0fCf1Gu6a+RMC9P78o3Qr/3HoOWy9nnsPaGiQH7d/K5P7180lJTqIfcUN3P+XL/n7J2V09w64u3lCCOHRpKhfBKaGTeH+Bb8iMyyDI83H2LznCQ5bj7i7WSNKiQ5m/dq53P7taQQF6nln99D19s8O1WK3y/V2IYQ4FynqFwmjj4GfZf2I69O+Tc9AD/+Z/zx/L3mTfrvnnv0qisLCaZFsum0h112WRE/fAP/zbjG//Z+9FFe0uLt5QgjhcaSoX0QUReGKuMXcN+8XRASY+bhqB4/te5qTnQ3ubtqIfPVavp2dxL/dvojs6ZFUNnTwu1fz+MPfDlFSLYPphBDiNBko52EmY5BIsK+RhVHzae/roLC5mN11ewnyMRJriHbZIDpn5PL31TFnipmslDBONndRdKKFHfl1HDnRTFCgDxaT/6QOApQBPd5DjbnUmAnUmWsyB8pJUfcwk3VA6zRasszTiAqMoLDpKAca8qnraiDDlIZe6/yR5s7MZTL6sjgriqkJJtq6+iiqaGF3UT0Hjlnx99MSFRaAZhKKu/zj4z3UmEuNmUCduSazqLvs0avCO8yxZJFgjON/il4lryGfclsFqxOXszBqHlqNZz9JbUpcCFPiQqhq6ODd3RV8eaSeZ94s4h+fHWflJQksnhGJXufZGYQQwpnkTN3DuONXaoDen0si56BRNBQ3H+OQtZC9J/MI0PkTbYh0Spe2K3MFB/owN93CoumRDA46OFpl42CJlc8ODc1MFxNuQK9z/vAROaPwHmrMpcZMoM5c0v0+BrLznUOjaEgzpZyasGaQY61lHGws4EBDPgZ9IJGBlgkV98nIFeinZ2ZqOEtmRaPVKJTV2sgva+LjvGq6eweJMRvwc+Jz3OUfH++hxlxqzATqzCVFfQxk5zuXn86P6eEZLIicS+9gH8daSznQkE++tZBgnyAsAeZxFffJzOXno2VaYihXzI4lwE/HiZPtHC5v5sP91bR29BIVHuiUGercva9cQY2ZQJ251JgJ1JlrMou64nB495MzGhs9d3a08TCbjR6VqaHLyjvlH7CvPg8HDhKMcVybfBVTQ6eMqbi7M1df/yA7D59k25cVNLb2oChwydQIVi1MIM5iGPd6PW1fOYMaM4E6c6kxE6gzl7Mzmc3G834mRd3DeOoBXddZz9bj75HXWABASnAi30q+mjRTyqiW94Rcg3Y7e4sbeGdXJdWNHQBkpYSxemECU+JCxrw+T8jkbGrMBOrMpcZMoM5ck1nUZfS7GJWowAh+MmMtVe21bC3fToH1CH/I+y8yTGlcm3wVScEJ7m7iBWk1GhZOi+SSqREUHG/mnV0nyC9rIr+sidTYYK5ZmEBWSphbHngjhBDOIEVdjEmcMZo7sn5Eua2St49vp7ilhOL9JUwPm8q1yVcRZ4xxdxMvSFEUslLCyEoJo6S6lXd2VXCorIl//3s+MeZArpwTy8LMCPx85K+HEMK7SPe7h/G2rqeSluO8dXw7ZbZyAGaZZ3BN0gqiDZFnfM/Tc1U3dPDOlxXsPdLAoN2Bn4+W7OlRXDEnhujwwHMu4+mZxkONmUCdudSYCdSZS66pj4HsfPdzOBwUt5Tw1vHtVLRVoaAwL2IWq5OWYwkwA96Tq7Wjl88O1fLpwVpa2nsByIgPYdmcWGalhaPTfnW/u7dkGgs1ZgJ15lJjJlBnLrmmLryKoihMDZ1ChimNw01HeOv4dvbW57G/4RCXRM5lVeKVmDn/QehJQgy+fDs7iWsWJXCwxMpHB2o4UtFCcWUrwQYfls6MZumsGEzG899SIoQQ7iJn6h5GDb9S7Q47BxsPs/X4e5zsakCraMlOmMel5kXEGaPd3bwxq2vq5OO8GnYWnKS7dwCNojB7SjjfuSKNqGBfVQ2sU8Pxdy5qzKXGTKDOXNL9Pgay8z2X3WFnX/1Btp34kPquRgDSQpJZFncZ08OnolG868m/vX2D7C46yUcHaqhqGLolLiosgCtmx3Dp9CgC/Ly/40tNx9/XqTGXGjOBOnNJUR8D2fmez+6wUzNQxRuH36O4pQSAcP8wrohdzMKoefjpvKsr2+FwUFbbxhdF9ew4WMPAoAMfvYZFmZFcMTuG+AjvuNRwLmo8/kCdudSYCdSZS4r6GMjO9w6nc9V01PFJ1Q721OcxYB/AX+fHpdELWBqTTZi/yd3NHBOz2UjZiSY+z6/lk7xamtp6AEiNDWbZ7Bjmpltc8iAZV1L78acmaswE6swlRX0MZOd7h2/mau/r4POaXXxWs4v2vg40ioaZ5uksi7uMZC+YyAbOzGS3O8gva+KjvGoOH28GwBigZ8nMaJbOiiY82N+dTR21i+X4UwM1ZgJ15lJNUd+8eTOHDh1CURTWr19PVlbW8Ge7d+/miSeeQKPRkJSUxKZNm9i7dy/33HMPaWlpAEyZMoUHHnhgxG3IzvcO58vVbx9gf/1BPqr6nJqOOgASg+K5Im4xs80zPPqZ7ufLVN/Sxad5tXyeX0tnzwCKAjOSw1gyM5qslLAzbovzNBfb8efN1JgJ1JlLFbe07dmzh4qKCnJzcykrK2P9+vXk5uYOf/7ggw/y4osvEhkZyd13383nn3+On58fCxYs4I9//KOrmiU8jF6jY2HUPC6JnEtJ63E+qvqcw9YjPF/4Cv/wDeby2GyyoxcQoA9wd1NHLcIUwA3LUrnusiT2HGng47zq4elogw0+LJ4RxWUzo7GEeMfZuxDCe7isqO/atYvly5cDkJKSgs1mo6OjA4Nh6KlYW7ZsGf7/0NBQWlpaiIqKclVzhIdTFIUpphSmmFJo6Grkk+qd7Krbxxtl7/BO+fssjJrH5XGLiTg1mY038NFrWZwVxeKsKCrr2/nsUC27CuvZuquCrbsqmJZoYsnMaGanmb3u2rsQwjO5rPv9gQceYOnSpcOF/cYbb2TTpk0kJSWd8b2GhgZuuukmXn/9dY4dO8ZDDz1EfHw8NpuNu+66i+zs7BG3MzAwiE7nuV20Yvw6+7r48PhOtpV8grVr6Dr1nOgZXDNlGdMt6V55f3hP3wBf5NeyfXcFReVDmYICfVg2L46rFyYQa/HekfNCCPebtBtrz/XboampiTvuuIMNGzZgMplITEzkrrvuYtWqVVRVVbFu3Tree+89fHx8zrvelpYuVzZ70qnxehKMP9eisIUsMM3nkLWQjyo/50BtAQdqC4gOjOSKuMXMi5iNj1bvghZf2HgzzUgwMSPBRK21k88O1fLF4ZO88WkZb3xaxpTYYJbMimZeugUf/eT/WJXjz3uoMROoM5cqrqlbLBasVuvw64aGBszmr7pOOzo6uO222/jlL3/J4sWLAYiIiGD16tUAxMfHEx4eTn19PXFxca5qpvACWo2WOZYs5liyKLdV8nHV5+Q1FvBy8d95o/QdLo1ewGUxi7zulrjo8EByrkzje0tTyCtp5NODtRypaOFYtY1X3i9h0fRIls6MJtZicHdThRBewmVFPTs7m6eeeoqcnBwKCwuxWCzD19ABHn30UW6++WaWLFky/N6bb75JY2Mjt956K42NjTQ1NREREeGqJgovlBQcT1LwTbT0tPJ5zW521n7J+5Wf8EHlp8wIn8bS2EtJN6V6Vde8XqdhwdQIFkyNoKGli8/z69iRX8eH+6v5cH81ydFBLJkZzYKpFnkcrBBiRC69pe2xxx5j3759KIrChg0bKCoqwmg0snjxYubPn8/s2bOHv3vttddyzTXXcN9999HW1kZ/fz933XUXS5cuHXEb0k3jHVyVq3+wnwMN+XxSvZPK9moAIgMsLI29lAWRc106W50r99XAoJ38siY+O1RLQVkTDsDPR8sl0yJYOiuaxMggl2xXjj/vocZMoM5cqrlPfTLIzvcOrs7lcDg40VbFp9U7OdCQz6BjED+tH4ui5rEkdtHwI2CdabL2VZOth8/za9lRUEdz29DjYOMjDGRPj2J2WjjhTrw1To4/76HGTKDOXFLUx0B2vneYzFy23nZ21u5mR81ubH1D25wWms7S2EuZFpbutAfJTPa+stsdHC5v4tODtRwqbcJ+6q9urNnArLRwZqeFkxBpRDOBSw9y/HkPNWYCdeaSoj4GsvO9gztyDdoHOdhYwKfVX1BmOwEMPUhmacwiFkbNJ0A/sTNcd+4rW0cveSVWDpZaKTrRwsCgHYAQgw+zUsOZlWZmakII+jHe7inHn/dQYyZQZy4p6mMgO987uDtXVXsNn1V/wd76PPrtA/ho9CyInMPS2GyiDZHjWqe7M53W0zdAYXkzB0usHCproqO7HwBfvZbpyaHMSg0nKyUMY8D5bw09zVMyOZsac6kxE6gzlxT1MZCd7x08JVdHfye7avfyWc0umntaAJgSksLS2EuZET5tTHPNe0qmrxu02ymraSOvpJG8EisNLd0AKAqkxYYwK3Womz4i9NzT7npiJmdQYy41ZgJ15pKiPgay872Dp+WyO+wUWI/wafVOjraUAmDyDWFxzCXMNs8gItBywXV4WqZvcjgcnGzuGuqmL7FSVmPj9F/2qLCAoevwqWaSo4PQaIauw3t6pvFSYy41ZgJ15pKiPgay872DJ+eq66zns+ov2H1yP32DfQBEBFiYac4kKzyThKDYcw6u8+RM52Lr7CO/1EpeiZWiE830DQxdhw8K0JN16gx+ybx42m3dbm6p83nbvhoNNWYCdeaSoj4GsvO9gzfk6h7o5mBjIQWNhRQ1H6PfPnRtOtjHyIzwaWSZM5liSkWvGZoAxhsynU9v/yBHTrSQV9LIoVIrbV1DWfU6DSnRQaTHm8iIDyE5OlgVD5vx5n11PmrMBOrMJUV9DGTnewdvy9U32EdxcwmHrIUcth6ho78TAD+tL9PC0skKz2Rp+jy6bINubunE2R0Ojte2cbDEypHKFspr24Y/O13kM+JNpHtxkfe242801JgJ1JlLivoYyM73Dt6cy+6wc9xWwaHGw+Q3FmLtGXq6mlbRkBaSQpY5k6zwaZj8Qtzc0okzm42UVzZzrKqV4soWjla2UtXQMfz514t8RoKJpKggryjy3nz8nY8aM4E6c0lRHwPZ+d5BLbkcDgd1nfXkWwspaimmrKVi+LN4YyxZ4ZnMNGcSFRjhVfPPn3au/dTR3T9ikU+NCSY9PoSMeM8t8mo5/r5OjZlAnbmkqI+B7HzvoMZcZrORY1VVFFiLONRYyLHWMuyOocFn4X6hZJkzmWmeTnJwgtNmsXO10eyn4SJf0UJxZSvVjZ5f5NV6/KktE6gzlxT1MZCd7x3UmOubmbr6uylqKuaQtZDCpmJ6T42kN+gDmRaWTmZYBlNDpxCoP/c94p5gPPupo7ufo5WtHK08u8j76DSkxAQzMzWceelmQoP8nN3kUbkYjj+1UGMuKepjIDvfO6gx10iZ+u0DHGspI7/xMAXWouE56BUUkoLjmRaaQWZ4OrGGaI86i3fGfhqpyKfFBjM/w8K8DAshBtc9Qe+bLrbjz5upMZcU9TGQne8d1JhrtJkcDgfVHXUUNhVT2FRMua0Cx6lpYIJ8jEwLTSczPIMMU9qE56OfKFfsJ1tnHweONrDnSAPHqlpxAAowJS6EBVMtzEm3EBx44SlsJ+JiPv68jRpzSVEfA9n53kGNucabqbO/i+LmYxQ2HaWo6Sjt/UNnshpFQ1JQApmnuupjDFGTPtjO1fuptaOX/Ucb2XOknpJqGzA0hW1GvIn5Uy3MnWIe1Rz1YyXHn/dQYy4p6mMgO987qDGXMzLZHXaq2msoajpKYVMxJ9qqhs/ig32Chgt8emga/jrXX4+ezP3U3NbDvqON7C2up6xm6N54jaIwNdHE/AwLc6aYMfjrnbItOf68hxpzSVEfA9n53kGNuVyRqaOvkyPNxyhsKqao+Sid/V3A0Fl8SnAimWEZZIZluOyWOXftJ6utm33FQwW+vG5o+1qNwrTE0FMFPpwAv/EXeDn+vIcac0lRHwPZ+d5BjblcncnusFPRVj1U4JuOUtFeNfyZyTeEqaFppJlSmGJKIcQ32Cnb9IT91Njazd7iBvYeaaCi/qsCPz0plPlTLcxOM+PvqxvTOj0hl7OpMROoM5cU9TGQne8d1JhrsjO19bVzpGnoLP5I8zG6Br568IolIJwpIUMFPs2UQpDP+f/Sj8TT9lN9c9dQgS9uGJ70RqfVMCM5lNlpZlJjg4kw+V+w18LTcjmDGjOBOnNJUR8D2fneQY253Jnp9LX4Yy1lHGsto6y1fPi+eIDIAMtwgZ8SkoLBJ3BU6/Xk/VTX1Dl8Bl9j7Rx+3+CvJzUmmJSYIFJjgkmMCsJXrz1jWU/ONV5qzATqzCVFfQxk53sHNebypEyD9kEq22so+VqR7zv1lDmA6MDI4SKfFpJ83glwPCnTSGqsnRw50UxZbRul1Taa2nqGP9MoCnERBlKjg0mJDSI1OpiMVDNWa8cIa/Q+3rKvxkqNuSazqI/twpQQwiNpNVqSguNJCo7nKq5gwD5ARVs1Ja1lHGsp47jtBLWdJ/mkeicKCjGGKKacuh6fEpzk9vvjxyomPJCY8ECWn3rd0t5LWY2N0hobZbU2Kk62U3GynQ8PDH0eGuRLUlQQKdHBpMYGkxBh9Ijpa4VwNjlT9zBq/JUK6szlTZn67QOcsFVyrLWMkpYyym0VDDiGHhuroBBnjCbNlMKCxBlEKNHotc65lcxd+gcGqajvoLTaRlmNjeN1bbS09w5/rtMqJEQah4p8TDApMcGYjJM3w50zeNPxNxZqzCXd72MgO987qDGXN2fqG+znRFvF0DX5ljJOtFUxeKrI+2h9yAxNJ8ucyfSwDAI8eK760QoPN1Bc2khprY2y6jZKa21U1Xdg/9o/f2FBfqTGDhX5tNhgYs0GNBrPfdKeNx9/I1FjLul+F0K4lI9WzxRTKlNMqQD0DvZRbqugvLuc3ZV55DUWkNdYgEbRMEUFz4xXFIXwEH/CQ/xZOC0SgN6+QU6cbBvqsq8Z+u+XRfV8WVQPgJ+PlpSYYNJihrrsk6OD8PORfzKFZ5MzdQ+jxl+poM5cas3U0NA2/Mz4/MaiM+6P99Znxo9mXzkcDupbuimpbqWk2kZptY2TzV3Dn58egJcWE0xaXAipbu6yV+PxB+rMJWfqQgi3URSFaEMk0YZIViZeSUtP6xnPjK9sr+bt8u2E+4cxMzyTLHOmVz0z/nwURSEyNIDI0AAuy4oGoK2rj7JqGyU1Q0X+xMk2Kk6288H+agDCg/1Iiw0mNTaEtNhgosMD0XjJDx2hTnKm7mHU+CsV1JnrYszU1d9N4alnxhd945nxWeHTyDJnkmFK87iBds7aV/0Dg5TXtVNaY6OkqpXSGhudPQPDnwf46oa67GOH/pzrnnlnUePxB+rMJWfqQgiPFKD3Z37kbOZHzqZ/sJ+jLaXD3fRf1O3li7q9+Gh9mBaazkwVDbQ7Ta/TMiUuhClxIbAwAbvDQV1TF6XVrZSeOqMvON5EwfEmYGh624RIIxnxJmYkh5ISE4xO6909GsKzufRMffPmzRw6dAhFUVi/fj1ZWVnDn+3evZsnnngCjUZDUlISmzZtQqPRjLjMucgvOu+gxlyS6St2h50TbZUcaiwkv7GQhm4rMPQgmtSQZFKCE4g2RBEdGIHZPxytxjVnr+czmfvK1tE7dCZfPfSnsr6dQfvQP7N+PlqmJpiYkRzG9KRQwkPGPz+AGo8/UGcuVZyp79mzh4qKCnJzcykrK2P9+vXk5uYOf/7ggw/y4osvEhkZyd13383nn3+Ov7//iMsIITyTRtGQHJxIcnAi16Wspr6rgUONhRyyFnKspZRjLaXD39UpWiICLUQHRhFtiCA6cOj6vck3xGsG3o0k2ODL3HQLc9MtwNAo++LKFg4fb6agvIm8Eit5JUM/eiJDA5ieHMqM5DDS40LwcVFXvbh4uKyo79q1i+XLh+Z7SklJwWaz0dHRgcFgAGDLli3D/x8aGkpLSwsHDx4ccRkhhOdTFIXIwAgiAyO4OnEZbX3t1LTXUdt58fiZEQAAGGZJREFUktqOk9R2nqSus56ajjqo/2o5P60f0YYIok4V+ejAoT+jnbfeU/n6aJmZGs7M1HAAGlq6OFzezOHjzRypaOGDfdV8sK8avU5DelwI05NCmZ4cRlRYgCp+5IjJ5bKibrVayczMHH4dGhpKY2PjcIE+/d+GhgZ27tzJPffcwxNPPDHiMudiMgWg06nr1+1IXSveTI25JNMo1oeRFKLPeM9ut9PQaaXSVkulrZYqWy2VthpOtFVx3FZxxndD/IKIC44mPjjm1H+jiQ2Owk83ttvJPGVfmc1GMqdEsIahgXdF5c3kHW1gf3HDULEvb4aPSjGb/JmTbmFuhoWsVDOB/mcPPvSUTM6mxlyTlWnSBsqd69J9U1MTd9xxBxs2bMBkMo1qmW9qaem64He8iRqvJ4E6c0mmidHiT5JvCkmWFBjqqabfPkBDVyM1HXXUddZT21FHbWc9BfXFFNQXDy+roGD2DyMhKI7EoHgSguKINUSdd9S9J++r6BA/oi+J55pL4mlp7+VweROHjzdTdKKZ7bsr2L67Ao2ikBoTxPTkMGYkhxEXYSDCEuSxmSbCk/fVeKnimrrFYsFqtQ6/bmhowGw2D7/u6Ojgtttu45e//CWLFy8e1TJCCHXTa3TEGKKIMUSd8X73QA91nfXUdZykpvMktR11VHfUsrc+j731eQBoFS0xhigSg+JJDIojISgOS0C4V90/bzL6cllWNJdlRWO3Oyiva6PgeBOHy5spqbZxrNrGls+OExSgJzMlHIOvDpPRd/hPqNGXEKOvjLC/iLmsqGdnZ/PUU0+Rk5NDYWEhFovljG70Rx99lJtvvpklS5aMehkhxMXJX+dHcnACycEJw+/ZHXYau6ycaKuior2KE21V1LTXUtlezWc1Xy2XYIxjamQKZl0EiUHxBPt6R9euRqOQcuphM9ddlkxHdz9FJ5qHivzxZnYV1J132aAAPSaj3xkF/+tFP9Toh6+Pui5biiEuvaXtscceY9++fSiKwoYNGygqKsJoNLJ48WLmz5/P7Nmzh7977bXXsmbN/9/evQdHWd0NHP/u/ZrsJbubhIQE5BqoLVBFlApKa+tltHbaUTMTbTv4thWBSqkIjkA6nSogdaptp1XGtiNeSkuZd6i1g9PamddWDA7aqIByC7lBkt3sLddNdvO8f+xmyZINFzXZC7/PTGazz9lnc06e3f3tOc95zu/uUfvMnj37vH9DhmlyQz62S9qUfQaHorR2n44H+nAzp8JNdPT6Uh7jMNgTw/bxn8kF5Zd8fj7TFEVBZ9RzvLGTQDhCoDtCoCuCPxwh0NVPoHuAQLifgejQmM9hNmhxFBpwWEcE/UIjLpsRj92Es9CYkYQ2uf4aTEeytF0COfi5IR/bJW3KDb2DvYTUfuqbP+ZUuIlToWa6BruT5SpUlFqKmVI4mYrCyUyylFBq8WT9ojkXOlaKotAbiZ4T9PsJdJ29HwhH6I1E0+6vUatw2Yy47SbcDhMee/zHnfgZr55+Pr4G8+KcuhBCZAOzzkylu5hSTTkQD3aBSJBTiZ58Y7iZpnALp3vaeOvMO8n9bPoCSizFlCYuzytN/FiyPNgPU6lUWIw6LEYd5Z6xT2NGBmLxIB/upzMcwRfqoyPYhzcQv/2wwQ8No/ezWfS4HSbcNhOeRNAfDv4FZp1cjpchEtSFEJcVlUqF0+jAaXSwwBNfsTI2FKOtt4OmcAtnets509NOW08HHweO8/GIhXMACvRWShO9+VJLMSXmYkqtxVh1uXk9vUGvSSaySacvEsUb7MMbTA32HYE+TraGOd4SSvucI4N9RYmVqgoHNmtunebIRRLUhRCXPY1ak3bWfX+0n/Zeb3zmfU87bYnbc1fJAyjQWSmxeFIDvqWYAn1uT/Y1GbRUFBdQUTx6yDcaG8If7k8Ge2+wPxnwvcE+WrzdKY8vLTJTVemgqtLBrAoH1jTX3otPR4K6EEKMwag1Upm4PG6kSGyAtkRvfmTAPx5s4FjwZMpjrToLkwvKmFJYwVRbBVMKK3JmCP9CtBo1HocZj8MMU1PLFEUh3DtIR6CX460hjjQGONYc4o13W3nj3VZUwORiazLIzyi3YzJISPq05D8ohBCXyKDRpw32A7EB2no7UoL9me42jviPcsR/NPk4j9kVD/KFFUyxVVBmKZ3wJDfjTaVSYbPosVn0zCi3c8s1lURjQzScCXOkMcBHjQGOt4Zpau9m34Fm1CoVU0sLWFBVTKXbwvQym6yF/wlIUBdCiM+IXqOnoqCcioLylO3dAz3xmffhpuQEvQNt73Kg7V0AdGodFQVlTLFVMLWwkqm2CuwGWyaaMK60GjUzyu3MKLdzx+KpDAzGONEa4khTgCONARpOd3HidDjxWBXTy2zMTvTkp5YWyqI6F0GCuhBCjDOr3sLnXFV8zlUFxBfO6ej10hBqoiER7E+GGjkROpXcx26wpQzZVxSUodfoM9SC8aHXaaia4qRqihOIT8rr6Brg7fdbOdIY4OOmIB81BfnfNxvQ69TMLLdTVelgdqWDyuKCjFxHn+0kqAshxARTq9TJTHbXTroagP5ohKauFk4lAn1DuJH/ej/gv94PkvuUWUvjQ/aFFczTz0I9ZESnzp+PcZNBy1XlDipd8TkH3X2D8cDeGOBIU+BswhviM+zLXBbK3RbK3FbK3VbK3RYKzPn1xedS5c+rQQghcphRa2CmYxozHdOA+EQzf3+QU+HGeG8+1ERzVyvNXa38X+t+XjgSXzinyOjAY3FTbHLjMbvwmOO3doMtp9a9T8dq0vHFWW6+OCueAyTUHeFIU/x8/InTYRrbujiZGK4fZrPok4G+zG2h3G1lksuC4TI5Py9BXQghspBKpaLI5KDI5OCLxfOAs8vgNoSa8Ed9NPpP09Hr43Dnxxzm45T9dWpdPMibzgZ6j9lNsdmds7PvbVYDi+aUsGhOCRC/pK6ts5cWbzetvh5aOrpp8fZw6FSAQ6cCyf1UgMdhonxEoC/3WPHYTXk3hC9BXQghcoROrU1koatIWXq0d7APb5+P9l4vHb0+Onq9dPR6ae/z0do9OvGLRWfGc07Pvtjsxm1yoR8jfW020mrUlHuso1bM64tEafX20OLtTvz00Ort5uBRLwePepOP02nVTCoaMYTviQd8m0WfsyviSVAXQogcZ9aZqNSNvsROURRCA+ERgT4R+Pu8NHY10xBuHPVcDoOdYvPIgO+m2OzCaXTkzHC+yaBlermN6eVnryBQFIVg9wCtiSA/HPBbfT00tqeuy24xailznR3CH/49FxbLkaAuhBB5SqVSYTfYsBtsyXP1w2JDMTr7/cmA397no6PHS0efj48Cx/gocCzl8VqVBpfZRbHpbLAf7uFbdZas79mqVKpkNrrPXVGU3B4bGqIj0Jfs2bd6e2jx9XCsNZ6/fiSbVU/5cLBP3E5ymTHqsyeUZk9NhBBCTBiNWpMMzlCVUtYfjeDtG9m7TwT+Xi9tPe2jnsukNSXO37vP6eW7MGT5ZXgatZrSIgulRRaumu1Jbh8YjHGms5dWXzzQt/riQ/jnnq8HcNmMo3r2pUUWdNqJH9mQoC6EECKFUWtgckEZkwvKUrYrikL3YE/i3P3Z8/ftvV5auk7TGG4e9Vx2gw2PyYXb7MJjduE2uXCbinCbitBl8fl7vU5DZUkBlSWpa973RaK0+no47Tvbs2/19VB/opP6E53Jx6lVKoqdJspcFm5fOp3JTtOE1FuCuhBCiIuiUqko0Fsp0FuZbk9d7D02FCMQCaZM1hv+/WjwBEeDJ1Kfi/ipAY85EfATwX6WoTKrr783GbRML7MxvSx1xb9w7wCnR/ToW3w9tHp7ONPZi06v5X9uqxrjGT9b2flfE0IIkVM0ag0uUxEuUxFzi1LLBmKD+Po66ejz4e310dHrSwzv+0ant/0gHvCdRjtu03DvvigZ+ItMTrRZGPALzXoKK/XMrnQktw1Pzpta4SAY6J2QemTff0YIIURe0Wt0TLKWMMlaMqosEhuIB/zeeMAPKyGa/GfwjjFhb3jBHZepCIfRjt1gw2G0YTfYcSR+N2knZqj7QoYn5+m0E7fwjQR1IYQQGWPQ6FNy2Y+8/j4+Ya8z2av39vqSvf1zg/1IRo0hEewTQd9gw2604TDYk9tNWuOEtG+iSVAXQgiRleIT9iYxuWDSqLJIbIBgf5BAJEQgEiLYHyIQCRKMhAj0x2/bejvGfm6NMRHoh4N+vKfvNDooMjpxGu05mQ5XgroQQoicY9DoKbZ4KLZ4xnzM6MA//Hsw8SUglPYSPTg7ka/IFA/yRUYHTlP8tsjoxG4ozMqgL0FdCCFEXrqYwN8fjRCMhAhGQvj7g/j7A/j7A/j6/Pj7A5wInuI4DaP2U6vUOAz2eJAfDvYmZ6Kn78BmKMzICnwS1IUQQly2jFoDJVoPJWME/uhQlEB/iM5+P539fvx9AXz98YDf2eePX6p3zuV6EF+Bz2G0U2R0cmvVDUwzzhjvpsT/7oT8FSGEECIHadVa3OYi3OaitOWDsUH8kSCdfX46E4He3x9I/v5R4BiuVgfTpklQF0IIIbKaTqOjOJHSNp2B2CCTih34fN0TUp/cSLkjhBBC5CC9RjehyW4kqAshhBB5QoK6EEIIkSckqAshhBB5Ylwnyj3++OPU19ejUql49NFH+fznP58si0QibNq0iWPHjrFnzx4A6urq+OEPf8iMGfFZgjNnzmTjxo3jWUUhhBAib4xbUD9w4ACNjY3s2rWLEydO8Oijj7Jr165k+bZt26iqquLYsdT1excuXMgzzzwzXtUSQggh8ta4Db/v37+fr3zlKwBMmzaNUChEd/fZKf1r1qxJlgshhBDi0xu3nrrP52Pu3LnJ+06nE6/Xi9VqBcBqtRIMBkftd/z4cX7wgx8QCoVYuXIlixcvPu/fcTjMaCcwrd1EcLsLMl2FcZGP7ZI25Y58bFc+tgnys10T1aYJW3xGUZQLPmbKlCmsXLmSW265hebmZu677z5ef/119Hr9mPsEJijx/EQZmXYwn+Rju6RNuSMf25WPbYL8bNdn3abzfUEYt+F3j8eDz+dL3u/o6MDtTr/izrDi4mJuvfVWVCoVFRUVuFwu2tvTZ9ARQgghRKpxC+qLFy9m3759ABw6dAiPx5Mceh/L3r17ef755wHwer10dnZSXFw8XlUUQggh8sq4Db8vWLCAuXPncs8996BSqdi8eTN79uyhoKCAm266idWrV9PW1kZDQwP33nsvd911F8uWLePHP/4x//znPxkcHKS2tva8Q+9CCCGEOEulXMzJbiGEEEJkPVlRTgghhMgTEtSFEEKIPCFBXQghhMgTEtSFEEKIPCFBXQghhMgTEtSFEEKIPDFhy8SK0bZt28bBgweJRqN8//vf56tf/WqybNmyZZSUlKDRxNe13759e9YvxHOh1LlvvfUWTz31FBqNhiVLlvDggw9mqqoX7c9//jN79+5N3v/www957733kvfnzp3LggULkvf/8Ic/JI9ZNjp69CgrVqzgO9/5DjU1NZw5c4Z169YRi8Vwu908+eSTo9aGOF8K5WyRrl0bNmwgGo2i1Wp58sknU1a0zIU0z+e2af369Rw6dAi73Q7A8uXLueGGG1L2ycVjtXr1agKBAADBYJB58+bx05/+NPn4PXv28PTTT1NRUQHAddddxwMPPJCRuo/l3M/yK6+8MnPvK0VkxP79+5X7779fURRF8fv9ytKlS1PKb7zxRqW7uzsDNfvk3n77bWXVqlVjlt9yyy3K6dOnlVgsplRXVyvHjh2bwNp9enV1dUptbW3KtoULF2aoNpeup6dHqampUR577DFl586diqIoyvr165XXXntNURRF+fnPf6689NJLKfvU1dUp3/ve9xRFUZTjx48rd91118RW+iKka9e6deuUv/3tb4qiKMqLL76obN26NWWfC71WMy1dmx555BHljTfeGHOfXD1WI61fv16pr69P2faXv/xF2bJly0RV8ZKl+yzP5PtKht8z5Oqrr+bpp58GoLCwkL6+PmKxWIZrNX6am5ux2WyUlpaiVqtZunQp+/fvz3S1Lsmvf/1rVqxYkelqfGJ6vZ4dO3bg8XiS2+rq6vjyl78MwI033jjqmFwohXI2SNeuzZs387WvfQ0Ah8ORNiNkNkvXpgvJ1WM17OTJk3R1dWXl6ML5pPssz+T7SoJ6hmg0GsxmMwC7d+9myZIlo4ZtN2/eTHV1Ndu3b7+oLHfZYDh1bnV1Nf/5z3+S271eL06nM3l/OBVvrnj//fcpLS0dlZRoYGCAtWvXcs899/D73/8+Q7W7OFqtFqPRmLKtr68vOSxYVFQ06pj4fD4cDkfyfjYet3TtMpvNaDQaYrEYL7/8Mrfffvuo/cZ6rWaDdG0CePHFF7nvvvtYs2YNfr8/pSxXj9WwF154gZqamrRlBw4cYPny5Xz729/m8OHD41nFS5buszyT7ys5p55h//jHP9i9eze/+93vUravXr2a66+/HpvNxoMPPsi+ffu4+eabM1TLi/NJUufmit27d/ONb3xj1PZ169Zxxx13oFKpqKmp4aqrruLKK6/MQA0/vYv54pgrXy4BYrEY69atY9GiRVx77bUpZbn4Wv3617+O3W6nqqqK5557jl/96lds2rRpzMfn0rEaGBjg4MGD1NbWjir7whe+gNPp5IYbbuC9997jkUce4a9//evEV/ICRn6Wj5wfNdHvK+mpZ9Cbb77Jb3/7W3bs2EFBQWp+3DvvvJOioiK0Wi1Llizh6NGjGarlxTtf6txzU/G2t7df0tBiptXV1TF//vxR26urq7FYLJjNZhYtWpQTx2kks9lMf38/kP6YfJIUytliw4YNVFZWsnLlylFluZjm+dprr6WqqgqIT6Q997WWy8fqnXfeGXPYfdq0ackJgfPnz8fv92fdqcpzP8sz+b6SoJ4hXV1dbNu2jWeffTY5m3Vk2fLlyxkYGADiL/jhWbrZ7Hypc8vLy+nu7qalpYVoNMq//vUvFi9enMnqXrT29nYsFsuoXtzJkydZu3YtiqIQjUZ59913c+I4jXTdddclUyS//vrrXH/99SnlnySFcjbYu3cvOp2O1atXj1mea2meV61aRXNzMxD/knnuay1XjxXABx98wOzZs9OW7dixg1dffRWIz5x3Op1ZdYVJus/yTL6vZPg9Q1577TUCgQAPPfRQcts111zDrFmzuOmmm1iyZAl33303BoOBOXPmZP3QO5A2de6rr76aTLdbW1vL2rVrAbj11luZOnVqhmt8cc6dD/Dcc89x9dVXM3/+fEpKSvjWt76FWq1m2bJlWT3J58MPP2Tr1q20trai1WrZt28f27dvZ/369ezatYtJkyZx5513ArBmzRqeeOKJtCmUs026dnV2dmIwGLj33nuBeG+vtrY22a5sT/Ocrk01NTU89NBDmEwmzGYzTzzxBJD7x+qXv/wlXq83ecnasAceeIDf/OY33H777Tz88MP88Y9/JBqN8rOf/SxDtU8v3Wf5li1beOyxxzLyvpLUq0IIIUSekOF3IYQQIk9IUBdCCCHyhAR1IYQQIk9IUBdCCCHyhAR1IYQQIk/IJW1CXMZaWlq4+eabRy2ss3TpUu6///5P/fx1dXX84he/4JVXXvnUzyWEuDAJ6kJc5pxOJzt37sx0NYQQnwEJ6kKItObMmcOKFSuoq6ujp6eHLVu2MHPmTOrr69myZQtarRaVSsWmTZuYPn06p06dYuPGjQwNDWEwGJKLowwNDbF582aOHDmCXq/n2WefxWKxZLh1QuQnOacuhEgrFosxY8YMdu7cSXV1Nc888wwQT2KzYcMGdu7cyXe/+11+8pOfAPGsgsuXL+ell17im9/8Jn//+98BOHHiBKtWreJPf/oTWq2Wf//73xlrkxD5TnrqQlzm/H5/cjnVYQ8//DAAX/rSlwBYsGABzz//POFwmM7OzuRyuAsXLuRHP/oREE9Pu3DhQgBuu+02IH5O/YorrsDlcgFQUlJCOBwe/0YJcZmSoC7EZe5859RHriKtUqlQqVRjlkN8qP1c2ZR8Q4h8J8PvQogxvf322wAcPHiQWbNmUVBQgNvtpr6+HoD9+/czb948IN6bf/PNN4F4kounnnoqM5UW4jImPXUhLnPpht/Ly8sBOHz4MK+88gqhUIitW7cCsHXrVrZs2YJGo0GtVlNbWwvAxo0b2bhxIy+//DJarZbHH3+cpqamCW2LEJc7ydImhEhr1qxZHDp0CK1WvvsLkStk+F0IIYTIE9JTF0IIIfKE9NSFEEKIPCFBXQghhMgTEtSFEEKIPCFBXQghhMgTEtSFEEKIPCFBXQghhMgT/w/owF9OAcnkCgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NkBZMjrvqHQD"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}