TFIDF.ipynb

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    "colab": {
      "name": "Untitled9.ipynb",
      "version": "0.3.2",
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      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BottleSpink/Product-Review/blob/master/TFIDF.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USrZ1snAstQ-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import re\n",
        "import numpy as np\n",
        "from pprint import pprint\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rEu4OdJ7CZm4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('train.csv')\n",
        "df_test = pd.read_csv('test.csv')\n",
        "\n",
        "#df_input.head()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YMkCZubEIFR",
        "colab_type": "code",
        "outputId": "6ed96634-e147-48e4-a326-a18ad6bc5982",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "df.shape, df_test.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5959, 3), (2553, 2))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvbH3e3xCjJX",
        "colab_type": "code",
        "outputId": "39727f87-ee58-4086-c891-3e3af0a766ac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "\n",
        "\n",
        "#df = df_input.drop_duplicates()\n",
        "#df_test = df_input_test.drop_duplicates()\n",
        "df.shape, df_test.shape\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5959, 3), (2553, 2))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "50Mrw1BECrOL",
        "colab_type": "code",
        "outputId": "259b7d0a-24f8-4d42-b94a-a0eb3b8d95bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5959 entries, 0 to 5958\n",
            "Data columns (total 3 columns):\n",
            "Review Text     5959 non-null object\n",
            "Review Title    5959 non-null object\n",
            "topic           5959 non-null object\n",
            "dtypes: object(3)\n",
            "memory usage: 139.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJ6ds7E-Cvzf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import string\n",
        "from string import digits\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "import re\n",
        "\n",
        "def token(text):\n",
        "    text = text.split(' ')\n",
        "    table = str.maketrans('', '', string.punctuation)\n",
        "    table1 = str.maketrans('','', digits)\n",
        "    reg = re.compile('[^a-zA-Z ]')\n",
        "    stripped = [w.translate(table) for w in text]\n",
        "    stripped1 = [w.translate(table1) for w in stripped]\n",
        "    stripped2 = [reg.sub('', w) for w in stripped1]\n",
        "    stripped3 = [w for w in stripped2 if len(w)>1]\n",
        "    return stripped3"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxCaRTYpCzWb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vect_Title = TfidfVectorizer( max_features= 200, tokenizer=token , stop_words = 'english').fit(df[\"Review Title\"].values.astype('U'))\n",
        "\n",
        "vect_Title_Train = vect_Title.transform(df[\"Review Title\"])\n",
        "vect_Title_Test = vect_Title.transform(df_test[\"Review Title\"])\n",
        "\n",
        "vect_Title_Train=vect_Title_Train.toarray()\n",
        "vect_Title_Test=vect_Title_Test.toarray()\n",
        "\n",
        "words = vect_Title.get_feature_names()\n",
        "words=[\"T_\"+w for w in words]\n",
        "\n",
        "keywords_Train=pd.DataFrame(vect_Title_Train, columns=words)\n",
        "keywords_Test=pd.DataFrame(vect_Title_Test, columns=words)\n",
        "\n",
        "keys_Train=[w for w in words if keywords_Train[w].sum()>60] \n",
        "#keys_Test=[w for w in words if keywords_Test[w].sum()] \n",
        "\n",
        "col_train=pd.concat([df, keywords_Train[keys_Train]],axis=1)\n",
        "col_test=pd.concat([df_test, keywords_Test[keys_Train]],axis=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nlkmiPw71MTq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "vect_Title = TfidfVectorizer( max_features= 200, tokenizer=token , stop_words = 'english').fit(df[\"Review Text\"].values.astype('U'))\n",
        "\n",
        "vect_Title_Train = vect_Title.transform(df[\"Review Text\"])\n",
        "vect_Title_Test = vect_Title.transform(df_test[\"Review Text\"])\n",
        "\n",
        "vect_Title_Train=vect_Title_Train.toarray()\n",
        "vect_Title_Test=vect_Title_Test.toarray()\n",
        "\n",
        "words = vect_Title.get_feature_names()\n",
        "words=[\"R_\"+w for w in words]\n",
        "\n",
        "keywords_Train=pd.DataFrame(vect_Title_Train, columns=words)\n",
        "keywords_Test=pd.DataFrame(vect_Title_Test, columns=words)\n",
        "\n",
        "keys_Train=[w for w in words if keywords_Train[w].sum()>60] \n",
        "#keys_Test=[w for w in words if keywords_Test[w].sum()] \n",
        "\n",
        "col_train=pd.concat([col_train, keywords_Train[keys_Train]],axis=1)\n",
        "col_test=pd.concat([col_test, keywords_Test[keys_Train]],axis=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOCftRYjDlIj",
        "colab_type": "code",
        "outputId": "ff6cb98e-f9f8-46dd-b57a-4270528e356e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "keys_Train"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['R_arrived',\n",
              " 'R_away',\n",
              " 'R_awful',\n",
              " 'R_bad',\n",
              " 'R_better',\n",
              " 'R_big',\n",
              " 'R_bottle',\n",
              " 'R_bottles',\n",
              " 'R_bought',\n",
              " 'R_box',\n",
              " 'R_br',\n",
              " 'R_brand',\n",
              " 'R_broken',\n",
              " 'R_buy',\n",
              " 'R_buying',\n",
              " 'R_came',\n",
              " 'R_chocolate',\n",
              " 'R_day',\n",
              " 'R_delivered',\n",
              " 'R_did',\n",
              " 'R_didnt',\n",
              " 'R_different',\n",
              " 'R_disappointed',\n",
              " 'R_does',\n",
              " 'R_doesnt',\n",
              " 'R_dont',\n",
              " 'R_energy',\n",
              " 'R_feel',\n",
              " 'R_fish',\n",
              " 'R_flavor',\n",
              " 'R_gave',\n",
              " 'R_going',\n",
              " 'R_good',\n",
              " 'R_got',\n",
              " 'R_great',\n",
              " 'R_gross',\n",
              " 'R_gummies',\n",
              " 'R_gummy',\n",
              " 'R_hard',\n",
              " 'R_help',\n",
              " 'R_horrible',\n",
              " 'R_huge',\n",
              " 'R_im',\n",
              " 'R_ingredients',\n",
              " 'R_item',\n",
              " 'R_ive',\n",
              " 'R_just',\n",
              " 'R_know',\n",
              " 'R_like',\n",
              " 'R_little',\n",
              " 'R_lot',\n",
              " 'R_love',\n",
              " 'R_make',\n",
              " 'R_money',\n",
              " 'R_oil',\n",
              " 'R_opened',\n",
              " 'R_order',\n",
              " 'R_ordered',\n",
              " 'R_package',\n",
              " 'R_pills',\n",
              " 'R_powder',\n",
              " 'R_price',\n",
              " 'R_product',\n",
              " 'R_products',\n",
              " 'R_protein',\n",
              " 'R_quality',\n",
              " 'R_really',\n",
              " 'R_received',\n",
              " 'R_return',\n",
              " 'R_sent',\n",
              " 'R_skin',\n",
              " 'R_smell',\n",
              " 'R_stomach',\n",
              " 'R_stuck',\n",
              " 'R_sugar',\n",
              " 'R_sure',\n",
              " 'R_swallow',\n",
              " 'R_sweet',\n",
              " 'R_taking',\n",
              " 'R_taste',\n",
              " 'R_tasted',\n",
              " 'R_tastes',\n",
              " 'R_terrible',\n",
              " 'R_texture',\n",
              " 'R_thought',\n",
              " 'R_time',\n",
              " 'R_took',\n",
              " 'R_tried',\n",
              " 'R_try',\n",
              " 'R_use',\n",
              " 'R_used',\n",
              " 'R_using',\n",
              " 'R_vitamin',\n",
              " 'R_vitamins',\n",
              " 'R_waste',\n",
              " 'R_water',\n",
              " 'R_way',\n",
              " 'R_weird',\n",
              " 'R_wont',\n",
              " 'R_work']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPpvtHpbK9l_",
        "colab_type": "code",
        "outputId": "2395cdb7-b41e-4294-f03a-bb4288e5f6c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        }
      },
      "source": [
        "col_test.head()"
      ],
      "execution_count": 74,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Review Text</th>\n",
              "      <th>Review Title</th>\n",
              "      <th>T_arrived</th>\n",
              "      <th>T_awful</th>\n",
              "      <th>T_bad</th>\n",
              "      <th>T_broken</th>\n",
              "      <th>T_buy</th>\n",
              "      <th>T_did</th>\n",
              "      <th>T_didnt</th>\n",
              "      <th>T_doesnt</th>\n",
              "      <th>T_dont</th>\n",
              "      <th>T_flavor</th>\n",
              "      <th>T_good</th>\n",
              "      <th>T_great</th>\n",
              "      <th>T_gross</th>\n",
              "      <th>T_horrible</th>\n",
              "      <th>T_item</th>\n",
              "      <th>T_like</th>\n",
              "      <th>T_packaging</th>\n",
              "      <th>T_product</th>\n",
              "      <th>T_received</th>\n",
              "      <th>T_smell</th>\n",
              "      <th>T_sweet</th>\n",
              "      <th>T_taste</th>\n",
              "      <th>T_tastes</th>\n",
              "      <th>T_terrible</th>\n",
              "      <th>T_texture</th>\n",
              "      <th>T_work</th>\n",
              "      <th>T_wrong</th>\n",
              "      <th>R_arrived</th>\n",
              "      <th>R_away</th>\n",
              "      <th>R_awful</th>\n",
              "      <th>R_bad</th>\n",
              "      <th>R_better</th>\n",
              "      <th>R_big</th>\n",
              "      <th>R_bottle</th>\n",
              "      <th>R_bottles</th>\n",
              "      <th>R_bought</th>\n",
              "      <th>R_box</th>\n",
              "      <th>R_br</th>\n",
              "      <th>...</th>\n",
              "      <th>R_powder</th>\n",
              "      <th>R_price</th>\n",
              "      <th>R_product</th>\n",
              "      <th>R_products</th>\n",
              "      <th>R_protein</th>\n",
              "      <th>R_quality</th>\n",
              "      <th>R_really</th>\n",
              "      <th>R_received</th>\n",
              "      <th>R_return</th>\n",
              "      <th>R_sent</th>\n",
              "      <th>R_skin</th>\n",
              "      <th>R_smell</th>\n",
              "      <th>R_stomach</th>\n",
              "      <th>R_stuck</th>\n",
              "      <th>R_sugar</th>\n",
              "      <th>R_sure</th>\n",
              "      <th>R_swallow</th>\n",
              "      <th>R_sweet</th>\n",
              "      <th>R_taking</th>\n",
              "      <th>R_taste</th>\n",
              "      <th>R_tasted</th>\n",
              "      <th>R_tastes</th>\n",
              "      <th>R_terrible</th>\n",
              "      <th>R_texture</th>\n",
              "      <th>R_thought</th>\n",
              "      <th>R_time</th>\n",
              "      <th>R_took</th>\n",
              "      <th>R_tried</th>\n",
              "      <th>R_try</th>\n",
              "      <th>R_use</th>\n",
              "      <th>R_used</th>\n",
              "      <th>R_using</th>\n",
              "      <th>R_vitamin</th>\n",
              "      <th>R_vitamins</th>\n",
              "      <th>R_waste</th>\n",
              "      <th>R_water</th>\n",
              "      <th>R_way</th>\n",
              "      <th>R_weird</th>\n",
              "      <th>R_wont</th>\n",
              "      <th>R_work</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>0.528945</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.613034</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.484262</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Don’t waste your money.</td>\n",
              "      <td>No change. No results.</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.66896</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.59759</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.387826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.279578</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.115578</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.088192</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.260714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.183276</td>\n",
              "      <td>0.095166</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.321587</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.59759</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.387826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.279578</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.115578</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.088192</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.260714</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.183276</td>\n",
              "      <td>0.095166</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.321587</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 129 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Review Text  ... R_work\n",
              "0  I use chia seed in my protein shakes. These ta...  ...    0.0\n",
              "1  I use chia seed in my protein shakes. These ta...  ...    0.0\n",
              "2                            Don’t waste your money.  ...    0.0\n",
              "3  I use the book 'Fortify Your Life' by Tieraona...  ...    0.0\n",
              "4  I use the book 'Fortify Your Life' by Tieraona...  ...    0.0\n",
              "\n",
              "[5 rows x 129 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qiWaMzIIDvE6",
        "colab_type": "code",
        "outputId": "7b7a902d-e198-42f0-a0ea-16f332fb130c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "col_train.shape, col_test.shape\n",
        "#col_train.to_csv('train_chk.csv')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5959, 131), (2553, 130))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XgOVUUjMGE_C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "32500e3b-5c61-4b5b-a0c3-52319e62d57b"
      },
      "source": [
        "df['topic'].unique()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Shipment and delivery', 'Not Effective', 'Customer Service',\n",
              "       'Allergic', 'Texture', 'Quality/Contaminated', 'Color and texture',\n",
              "       'Bad Taste/Flavor', 'Too big to swallow', 'Smells Bad',\n",
              "       'Too Sweet', 'Ingredients', 'Expiry', 'Packaging',\n",
              "       'Wrong Product received', 'Pricing', 'False Advertisement',\n",
              "       'Inferior to competitors', \"Didn't Like\", 'Customer Issues',\n",
              "       'Hard to Chew'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5SINUFJTD4dt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "43fb1264-db47-4f5e-fb7e-89ac2ca60136"
      },
      "source": [
        "label_encoder = LabelEncoder()\n",
        "integer_encoded = label_encoder.fit_transform(col_train['topic'].astype(str))\n",
        "y = integer_encoded.ravel()\n",
        "np.unique(y)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbhWyiq8GAvw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x = col_train.drop('topic', axis = 1)\n",
        "x = x.drop('Review Text', axis = 1)\n",
        "x = x.drop('Review Title', axis = 1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wi-edxoBExzA",
        "colab_type": "code",
        "outputId": "ff55064a-988c-44c1-818f-b962ce2b7e6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 247
        }
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>T_arrived</th>\n",
              "      <th>T_awful</th>\n",
              "      <th>T_bad</th>\n",
              "      <th>T_broken</th>\n",
              "      <th>T_buy</th>\n",
              "      <th>T_did</th>\n",
              "      <th>T_didnt</th>\n",
              "      <th>T_doesnt</th>\n",
              "      <th>T_dont</th>\n",
              "      <th>T_flavor</th>\n",
              "      <th>T_good</th>\n",
              "      <th>T_great</th>\n",
              "      <th>T_gross</th>\n",
              "      <th>T_horrible</th>\n",
              "      <th>T_item</th>\n",
              "      <th>T_like</th>\n",
              "      <th>T_packaging</th>\n",
              "      <th>T_product</th>\n",
              "      <th>T_received</th>\n",
              "      <th>T_smell</th>\n",
              "      <th>T_sweet</th>\n",
              "      <th>T_taste</th>\n",
              "      <th>T_tastes</th>\n",
              "      <th>T_terrible</th>\n",
              "      <th>T_texture</th>\n",
              "      <th>T_work</th>\n",
              "      <th>T_wrong</th>\n",
              "      <th>R_arrived</th>\n",
              "      <th>R_away</th>\n",
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              "      <th>R_bad</th>\n",
              "      <th>R_better</th>\n",
              "      <th>R_big</th>\n",
              "      <th>R_bottle</th>\n",
              "      <th>R_bottles</th>\n",
              "      <th>R_bought</th>\n",
              "      <th>R_box</th>\n",
              "      <th>R_br</th>\n",
              "      <th>R_brand</th>\n",
              "      <th>R_broken</th>\n",
              "      <th>...</th>\n",
              "      <th>R_price</th>\n",
              "      <th>R_product</th>\n",
              "      <th>R_products</th>\n",
              "      <th>R_protein</th>\n",
              "      <th>R_quality</th>\n",
              "      <th>R_really</th>\n",
              "      <th>R_received</th>\n",
              "      <th>R_recommend</th>\n",
              "      <th>R_return</th>\n",
              "      <th>R_sent</th>\n",
              "      <th>R_skin</th>\n",
              "      <th>R_smell</th>\n",
              "      <th>R_stomach</th>\n",
              "      <th>R_stuck</th>\n",
              "      <th>R_sugar</th>\n",
              "      <th>R_sure</th>\n",
              "      <th>R_swallow</th>\n",
              "      <th>R_sweet</th>\n",
              "      <th>R_taking</th>\n",
              "      <th>R_taste</th>\n",
              "      <th>R_tasted</th>\n",
              "      <th>R_tastes</th>\n",
              "      <th>R_terrible</th>\n",
              "      <th>R_texture</th>\n",
              "      <th>R_thought</th>\n",
              "      <th>R_time</th>\n",
              "      <th>R_took</th>\n",
              "      <th>R_tried</th>\n",
              "      <th>R_try</th>\n",
              "      <th>R_use</th>\n",
              "      <th>R_used</th>\n",
              "      <th>R_using</th>\n",
              "      <th>R_vitamin</th>\n",
              "      <th>R_vitamins</th>\n",
              "      <th>R_waste</th>\n",
              "      <th>R_water</th>\n",
              "      <th>R_way</th>\n",
              "      <th>R_weird</th>\n",
              "      <th>R_wont</th>\n",
              "      <th>R_work</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.604199</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.231318</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.201442</td>\n",
              "      <td>0.23962</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.206459</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25623</td>\n",
              "      <td>0.266251</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 128 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   T_arrived  T_awful  T_bad  T_broken  ...  R_way  R_weird  R_wont  R_work\n",
              "0        0.0      0.0    0.0       0.0  ...    0.0      0.0     0.0     0.0\n",
              "1        0.0      0.0    0.0       0.0  ...    0.0      0.0     0.0     0.0\n",
              "2        0.0      0.0    0.0       0.0  ...    0.0      0.0     0.0     0.0\n",
              "3        0.0      0.0    0.0       0.0  ...    0.0      0.0     0.0     0.0\n",
              "4        0.0      0.0    0.0       0.0  ...    0.0      0.0     0.0     0.0\n",
              "\n",
              "[5 rows x 128 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4fgRvKtqE8qs",
        "colab_type": "code",
        "outputId": "b45063b9-0140-4252-ba0b-1974ed3e05f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train_b, x_test_b, y_train_b, y_test_b = train_test_split(x, y, test_size = 0.33)\n",
        "\n",
        "x_train_b"
      ],
      "execution_count": 16,
      "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>T_arrived</th>\n",
              "      <th>T_awful</th>\n",
              "      <th>T_bad</th>\n",
              "      <th>T_broken</th>\n",
              "      <th>T_buy</th>\n",
              "      <th>T_did</th>\n",
              "      <th>T_didnt</th>\n",
              "      <th>T_doesnt</th>\n",
              "      <th>T_dont</th>\n",
              "      <th>T_flavor</th>\n",
              "      <th>T_good</th>\n",
              "      <th>T_great</th>\n",
              "      <th>T_gross</th>\n",
              "      <th>T_horrible</th>\n",
              "      <th>T_item</th>\n",
              "      <th>T_like</th>\n",
              "      <th>T_packaging</th>\n",
              "      <th>T_product</th>\n",
              "      <th>T_received</th>\n",
              "      <th>T_smell</th>\n",
              "      <th>T_sweet</th>\n",
              "      <th>T_taste</th>\n",
              "      <th>T_tastes</th>\n",
              "      <th>T_terrible</th>\n",
              "      <th>T_texture</th>\n",
              "      <th>T_work</th>\n",
              "      <th>T_wrong</th>\n",
              "      <th>R_arrived</th>\n",
              "      <th>R_away</th>\n",
              "      <th>R_awful</th>\n",
              "      <th>R_bad</th>\n",
              "      <th>R_better</th>\n",
              "      <th>R_big</th>\n",
              "      <th>R_bottle</th>\n",
              "      <th>R_bottles</th>\n",
              "      <th>R_bought</th>\n",
              "      <th>R_box</th>\n",
              "      <th>R_br</th>\n",
              "      <th>R_brand</th>\n",
              "      <th>R_broken</th>\n",
              "      <th>...</th>\n",
              "      <th>R_price</th>\n",
              "      <th>R_product</th>\n",
              "      <th>R_products</th>\n",
              "      <th>R_protein</th>\n",
              "      <th>R_quality</th>\n",
              "      <th>R_really</th>\n",
              "      <th>R_received</th>\n",
              "      <th>R_recommend</th>\n",
              "      <th>R_return</th>\n",
              "      <th>R_sent</th>\n",
              "      <th>R_skin</th>\n",
              "      <th>R_smell</th>\n",
              "      <th>R_stomach</th>\n",
              "      <th>R_stuck</th>\n",
              "      <th>R_sugar</th>\n",
              "      <th>R_sure</th>\n",
              "      <th>R_swallow</th>\n",
              "      <th>R_sweet</th>\n",
              "      <th>R_taking</th>\n",
              "      <th>R_taste</th>\n",
              "      <th>R_tasted</th>\n",
              "      <th>R_tastes</th>\n",
              "      <th>R_terrible</th>\n",
              "      <th>R_texture</th>\n",
              "      <th>R_thought</th>\n",
              "      <th>R_time</th>\n",
              "      <th>R_took</th>\n",
              "      <th>R_tried</th>\n",
              "      <th>R_try</th>\n",
              "      <th>R_use</th>\n",
              "      <th>R_used</th>\n",
              "      <th>R_using</th>\n",
              "      <th>R_vitamin</th>\n",
              "      <th>R_vitamins</th>\n",
              "      <th>R_waste</th>\n",
              "      <th>R_water</th>\n",
              "      <th>R_way</th>\n",
              "      <th>R_weird</th>\n",
              "      <th>R_wont</th>\n",
              "      <th>R_work</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
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              "    <tr>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3992 rows × 128 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      T_arrived   T_awful     T_bad  ...   R_weird    R_wont    R_work\n",
              "2286        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "3393        0.0  0.000000  1.000000  ...  0.000000  0.000000  0.000000\n",
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              "486         0.0  0.723697  0.000000  ...  0.000000  0.000000  0.000000\n",
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              "616         0.0  0.000000  0.000000  ...  0.000000  0.000000  0.287387\n",
              "697         0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "1738        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "3755        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "1250        0.0  1.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "2464        0.0  0.000000  0.000000  ...  0.000000  0.285224  0.000000\n",
              "5645        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "1098        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "5027        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "1800        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.325665\n",
              "3734        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "2705        0.0  0.000000  0.733671  ...  0.000000  0.000000  0.000000\n",
              "...         ...       ...       ...  ...       ...       ...       ...\n",
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              "946         0.0  0.000000  0.000000  ...  0.000000  0.000000  0.175865\n",
              "414         0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "2852        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
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              "1778        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
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              "3140        0.0  0.000000  0.000000  ...  0.000000  0.387564  0.000000\n",
              "999         0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
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              "2739        0.0  0.000000  0.000000  ...  0.189145  0.000000  0.000000\n",
              "2760        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.253767\n",
              "2175        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "3266        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.133991\n",
              "1198        0.0  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "\n",
              "[3992 rows x 128 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0a5IHthFMKr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "# fit model no training data\n",
        "model = XGBClassifier(objective ='multi:softprob', learning_rate = 0.8, max_depth = 5)\n",
        "xgb_mod = model.fit(x_train_b, y_train_b)\n",
        "pred_xgb = xgb_mod.predict(x_test_b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jb-A3sMCFSsq",
        "colab_type": "code",
        "outputId": "4751dec6-3601-4a51-8e60-f01db398dbe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        }
      },
      "source": [
        "pred_xgb = xgb_mod.predict(x_test_b)\n",
        "#pred_rf = rf.predict(x_test_b)\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import accuracy_score\n",
        "xg = f1_score ( y_test_b, pred_xgb, average = 'weighted')\n",
        "xg_a = accuracy_score ( y_test_b, pred_xgb)\n",
        "xg_a, xg"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
            "  'precision', 'predicted', average, warn_for)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.3335027961362481, 0.3283528387265982)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XqeSAqOvHOQ-",
        "colab_type": "code",
        "outputId": "073c0dd4-6488-4db4-8e61-20b06d8eb9ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "x_test_b"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>T_arrived</th>\n",
              "      <th>T_awful</th>\n",
              "      <th>T_bad</th>\n",
              "      <th>T_broken</th>\n",
              "      <th>T_buy</th>\n",
              "      <th>T_did</th>\n",
              "      <th>T_didnt</th>\n",
              "      <th>T_doesnt</th>\n",
              "      <th>T_dont</th>\n",
              "      <th>T_flavor</th>\n",
              "      <th>T_good</th>\n",
              "      <th>T_great</th>\n",
              "      <th>T_gross</th>\n",
              "      <th>T_horrible</th>\n",
              "      <th>T_item</th>\n",
              "      <th>T_like</th>\n",
              "      <th>T_packaging</th>\n",
              "      <th>T_product</th>\n",
              "      <th>T_received</th>\n",
              "      <th>T_smell</th>\n",
              "      <th>T_sweet</th>\n",
              "      <th>T_taste</th>\n",
              "      <th>T_tastes</th>\n",
              "      <th>T_terrible</th>\n",
              "      <th>T_texture</th>\n",
              "      <th>T_work</th>\n",
              "      <th>T_wrong</th>\n",
              "      <th>R_arrived</th>\n",
              "      <th>R_away</th>\n",
              "      <th>R_awful</th>\n",
              "      <th>R_bad</th>\n",
              "      <th>R_better</th>\n",
              "      <th>R_big</th>\n",
              "      <th>R_bottle</th>\n",
              "      <th>R_bottles</th>\n",
              "      <th>R_bought</th>\n",
              "      <th>R_box</th>\n",
              "      <th>R_br</th>\n",
              "      <th>R_brand</th>\n",
              "      <th>R_broken</th>\n",
              "      <th>...</th>\n",
              "      <th>R_price</th>\n",
              "      <th>R_product</th>\n",
              "      <th>R_products</th>\n",
              "      <th>R_protein</th>\n",
              "      <th>R_quality</th>\n",
              "      <th>R_really</th>\n",
              "      <th>R_received</th>\n",
              "      <th>R_recommend</th>\n",
              "      <th>R_return</th>\n",
              "      <th>R_sent</th>\n",
              "      <th>R_skin</th>\n",
              "      <th>R_smell</th>\n",
              "      <th>R_stomach</th>\n",
              "      <th>R_stuck</th>\n",
              "      <th>R_sugar</th>\n",
              "      <th>R_sure</th>\n",
              "      <th>R_swallow</th>\n",
              "      <th>R_sweet</th>\n",
              "      <th>R_taking</th>\n",
              "      <th>R_taste</th>\n",
              "      <th>R_tasted</th>\n",
              "      <th>R_tastes</th>\n",
              "      <th>R_terrible</th>\n",
              "      <th>R_texture</th>\n",
              "      <th>R_thought</th>\n",
              "      <th>R_time</th>\n",
              "      <th>R_took</th>\n",
              "      <th>R_tried</th>\n",
              "      <th>R_try</th>\n",
              "      <th>R_use</th>\n",
              "      <th>R_used</th>\n",
              "      <th>R_using</th>\n",
              "      <th>R_vitamin</th>\n",
              "      <th>R_vitamins</th>\n",
              "      <th>R_waste</th>\n",
              "      <th>R_water</th>\n",
              "      <th>R_way</th>\n",
              "      <th>R_weird</th>\n",
              "      <th>R_wont</th>\n",
              "      <th>R_work</th>\n",
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              "<p>1967 rows × 128 columns</p>\n",
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              "5813   0.000000  0.000000  0.000000  ...  0.000000  0.000000  0.000000\n",
              "...         ...       ...       ...  ...       ...       ...       ...\n",
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              "\n",
              "[1967 rows x 128 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j9HODaNTAUOO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "7944899e-f1cb-4c2b-c611-38340a378417"
      },
      "source": [
        "np.unique(y_train_b)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
              "       17, 18, 19, 20])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5FQak_GwG1SM",
        "colab_type": "code",
        "outputId": "41e7b901-8750-4294-d6e0-200f5e45c97d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        }
      },
      "source": [
        "probs = xgb_mod.predict_proba(x_test_b)\n",
        "ind = x_test_b.index\n",
        "#labels = np.unique(df['topic'])\n",
        "df_cm = pd.DataFrame(probs, ind)\n",
        "df_cm.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.007629</td>\n",
              "      <td>0.475200</td>\n",
              "      <td>0.011927</td>\n",
              "      <td>3.042222e-06</td>\n",
              "      <td>0.464043</td>\n",
              "      <td>0.000072</td>\n",
              "      <td>0.001230</td>\n",
              "      <td>0.000032</td>\n",
              "      <td>2.437654e-06</td>\n",
              "      <td>0.000051</td>\n",
              "      <td>0.001199</td>\n",
              "      <td>0.000211</td>\n",
              "      <td>0.000446</td>\n",
              "      <td>0.000146</td>\n",
              "      <td>0.033266</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.003063</td>\n",
              "      <td>0.000999</td>\n",
              "      <td>0.000065</td>\n",
              "      <td>0.000380</td>\n",
              "      <td>0.000012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4816</th>\n",
              "      <td>0.001565</td>\n",
              "      <td>0.556443</td>\n",
              "      <td>0.000198</td>\n",
              "      <td>4.144402e-06</td>\n",
              "      <td>0.000091</td>\n",
              "      <td>0.000026</td>\n",
              "      <td>0.000187</td>\n",
              "      <td>0.000878</td>\n",
              "      <td>9.266069e-04</td>\n",
              "      <td>0.000137</td>\n",
              "      <td>0.430594</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.001502</td>\n",
              "      <td>0.004421</td>\n",
              "      <td>0.000435</td>\n",
              "      <td>0.000634</td>\n",
              "      <td>0.000711</td>\n",
              "      <td>0.000906</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>0.000190</td>\n",
              "      <td>0.000039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5033</th>\n",
              "      <td>0.011472</td>\n",
              "      <td>0.010668</td>\n",
              "      <td>0.001747</td>\n",
              "      <td>1.840521e-05</td>\n",
              "      <td>0.000605</td>\n",
              "      <td>0.000068</td>\n",
              "      <td>0.002513</td>\n",
              "      <td>0.000059</td>\n",
              "      <td>1.954339e-05</td>\n",
              "      <td>0.000154</td>\n",
              "      <td>0.011596</td>\n",
              "      <td>0.020323</td>\n",
              "      <td>0.802249</td>\n",
              "      <td>0.002380</td>\n",
              "      <td>0.124922</td>\n",
              "      <td>0.004088</td>\n",
              "      <td>0.001223</td>\n",
              "      <td>0.004701</td>\n",
              "      <td>0.000063</td>\n",
              "      <td>0.000723</td>\n",
              "      <td>0.000405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3746</th>\n",
              "      <td>0.003428</td>\n",
              "      <td>0.000247</td>\n",
              "      <td>0.000103</td>\n",
              "      <td>7.989808e-07</td>\n",
              "      <td>0.000092</td>\n",
              "      <td>0.000105</td>\n",
              "      <td>0.000175</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>6.037975e-07</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000071</td>\n",
              "      <td>0.000247</td>\n",
              "      <td>0.011942</td>\n",
              "      <td>0.000069</td>\n",
              "      <td>0.000044</td>\n",
              "      <td>0.982789</td>\n",
              "      <td>0.000131</td>\n",
              "      <td>0.000386</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>0.000079</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5188</th>\n",
              "      <td>0.046052</td>\n",
              "      <td>0.016482</td>\n",
              "      <td>0.009749</td>\n",
              "      <td>1.149571e-05</td>\n",
              "      <td>0.001521</td>\n",
              "      <td>0.000068</td>\n",
              "      <td>0.788446</td>\n",
              "      <td>0.000175</td>\n",
              "      <td>2.184707e-05</td>\n",
              "      <td>0.000151</td>\n",
              "      <td>0.001657</td>\n",
              "      <td>0.002213</td>\n",
              "      <td>0.000861</td>\n",
              "      <td>0.002519</td>\n",
              "      <td>0.127106</td>\n",
              "      <td>0.000114</td>\n",
              "      <td>0.001254</td>\n",
              "      <td>0.000910</td>\n",
              "      <td>0.000038</td>\n",
              "      <td>0.000578</td>\n",
              "      <td>0.000071</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            0         1         2   ...        18        19        20\n",
              "2564  0.007629  0.475200  0.011927  ...  0.000065  0.000380  0.000012\n",
              "4816  0.001565  0.556443  0.000198  ...  0.000042  0.000190  0.000039\n",
              "5033  0.011472  0.010668  0.001747  ...  0.000063  0.000723  0.000405\n",
              "3746  0.003428  0.000247  0.000103  ...  0.000014  0.000047  0.000079\n",
              "5188  0.046052  0.016482  0.009749  ...  0.000038  0.000578  0.000071\n",
              "\n",
              "[5 rows x 21 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZ-FVpmuHJqH",
        "colab_type": "code",
        "outputId": "ae10e46a-16d8-4c93-f0da-a1dcdc5d32ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "df.iloc[1297]"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Review Text     Awful! Do not buy this! Just got this package ...\n",
              "Review Title                                       Waste of money\n",
              "topic                                            Customer Service\n",
              "Name: 1297, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vGa0D8IkIbGU",
        "colab_type": "code",
        "outputId": "0d5eb5c6-f0f9-4538-9aba-749d04fbf4d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "pred_xgb"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  1, 12, ...,  1, 12,  2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q_s-duejIIAJ",
        "colab_type": "code",
        "outputId": "222303c4-2eee-4aba-e4a9-ec489987547b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "source": [
        "best_n = np.argsort(probs, axis=1)[:,-6:]\n",
        "#best_n = np.argsort(probs, axis=1)[::-1][:6]\n",
        "best_n[0], probs[0]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([16,  0,  2, 14,  4,  1]),\n",
              " array([7.6292856e-03, 4.7519976e-01, 1.1926617e-02, 3.0422216e-06,\n",
              "        4.6404302e-01, 7.2495932e-05, 1.2296824e-03, 3.1648029e-05,\n",
              "        2.4376543e-06, 5.1342540e-05, 1.1988062e-03, 2.1139966e-04,\n",
              "        4.4552120e-04, 1.4563135e-04, 3.3266138e-02, 2.4190409e-05,\n",
              "        3.0634857e-03, 9.9854078e-04, 6.4877313e-05, 3.7995659e-04,\n",
              "        1.2079475e-05], dtype=float32))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q64E7Bp0IOrl",
        "colab_type": "code",
        "outputId": "87614f91-5148-448f-a42b-7627360ba418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.mean(np.array([1 if y_test_b[k] in best_n[k] else 0 for k in range(len(best_n))]))"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93ry1ZCMISGV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "col_test = col_test.drop('Review Text', axis = 1)\n",
        "col_test = col_test.drop('Review Title', axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gzFNHkm21PD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from xgboost import XGBClassifier\n",
        "# fit model no training data\n",
        "#model = XGBClassifier(objective ='multi:softprob', learning_rate = 0.8, max_depth = 5)\n",
        "xgb_mod_t = model.fit(x, y)\n",
        "pred_xgb_t = xgb_mod_t.predict(col_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MI7SGqPpMQ3m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "outputId": "2f364d03-3c2f-44ae-f3f1-3b37135f3a30"
      },
      "source": [
        "#pred_xgb_t = xgb_mod.predict(col_test)\n",
        "probs_t = xgb_mod.predict_proba(col_test)\n",
        "probs_t"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3.7970517e-03, 4.8785347e-01, 1.2706043e-03, ..., 6.9610763e-04,\n",
              "        6.2204979e-04, 4.3552162e-04],\n",
              "       [3.7970517e-03, 4.8785347e-01, 1.2706043e-03, ..., 6.9610763e-04,\n",
              "        6.2204979e-04, 4.3552162e-04],\n",
              "       [2.0640822e-02, 2.2993227e-02, 7.8465808e-03, ..., 1.4876986e-04,\n",
              "        1.4292122e-01, 6.3076528e-04],\n",
              "       ...,\n",
              "       [8.5511550e-02, 8.7438697e-01, 4.9546434e-05, ..., 1.6822923e-05,\n",
              "        3.9136771e-02, 3.4643388e-05],\n",
              "       [5.8522047e-03, 2.7237287e-02, 1.2413155e-03, ..., 7.1495604e-05,\n",
              "        5.8726109e-05, 4.2142605e-05],\n",
              "       [8.6148858e-02, 1.7568758e-01, 1.3159249e-03, ..., 3.3276979e-04,\n",
              "        5.6945566e-02, 1.8477059e-04]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59QNWm0aNniN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e525d185-ddb2-4d11-cd66-ef799b4d71c0"
      },
      "source": [
        "pred_xgb_t"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  1, 11, ...,  1, 14,  4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ZvWnW_9P-ba",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "84ffd3c6-2e38-4d99-dd48-abc29dea8154"
      },
      "source": [
        "best_t = np.argsort(probs_t, axis=1)[:,-6:]\n",
        "best_t"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[10,  6, 17, 16, 14,  1],\n",
              "       [10,  6, 17, 16, 14,  1],\n",
              "       [ 1, 10, 13, 14, 19, 11],\n",
              "       ...,\n",
              "       [17, 11, 14, 19,  0,  1],\n",
              "       [ 6, 17,  0,  9,  1, 14],\n",
              "       [11, 10, 19,  0,  1,  4]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFSR8zPuQolI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "453ae6ad-c944-46dd-ea20-3bd07b789d62"
      },
      "source": [
        "df_test.head()"
      ],
      "execution_count": 32,
      "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>Review Text</th>\n",
              "      <th>Review Title</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Don’t waste your money.</td>\n",
              "      <td>No change. No results.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Review Text                              Review Title\n",
              "0  I use chia seed in my protein shakes. These ta...                                  Bad tast\n",
              "1  I use chia seed in my protein shakes. These ta...                                  Bad tast\n",
              "2                            Don’t waste your money.                    No change. No results.\n",
              "3  I use the book 'Fortify Your Life' by Tieraona...  Good Vegan Choice, Poor Non Vegan Choice\n",
              "4  I use the book 'Fortify Your Life' by Tieraona...  Good Vegan Choice, Poor Non Vegan Choice"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5bAb1sjQ7wy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#df_test['cnt'] = df_test.groupby('Review Text')['Review Text'].transform('count')\n",
        "df_test['new'] = 1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NwabRF0SWxz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_test['wins'] = df_test.groupby('Review Text').cumcount()+1\n",
        "#df_test['wins'] = df_test['wins']-1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m1CEJ6vEUFiq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "outputId": "53d5a4c1-c81d-42bc-ba7d-860bde215941"
      },
      "source": [
        "df_test.head()"
      ],
      "execution_count": 52,
      "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>Review Text</th>\n",
              "      <th>Review Title</th>\n",
              "      <th>new</th>\n",
              "      <th>wins</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Don’t waste your money.</td>\n",
              "      <td>No change. No results.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Review Text  ... wins\n",
              "0  I use chia seed in my protein shakes. These ta...  ...    1\n",
              "1  I use chia seed in my protein shakes. These ta...  ...    2\n",
              "2                            Don’t waste your money.  ...    1\n",
              "3  I use the book 'Fortify Your Life' by Tieraona...  ...    1\n",
              "4  I use the book 'Fortify Your Life' by Tieraona...  ...    2\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6xA2jOs_UGXP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "bbbdfa29-622f-4542-e565-58c495fd96ab"
      },
      "source": [
        "df_test.shape"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2553, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xllr1iBgWagB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "cf00e6dc-103c-499c-a1f3-80c7bbfcbd0a"
      },
      "source": [
        "best_t[9]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0, 11, 17,  6, 12, 15])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zNcZz8QpQYJ7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "9f2ef85a-f5ce-42c5-acad-3fc57fe37641"
      },
      "source": [
        "best = pd.DataFrame(best_t)\n",
        "df_m = pd.concat([df_test, best], axis =1)\n",
        "df_m.columns = ['Review Text','Review Title', 'new', 'wins', 'F', 'E', 'D', 'C', 'B', 'A']\n",
        "df_m.head()"
      ],
      "execution_count": 55,
      "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>Review Text</th>\n",
              "      <th>Review Title</th>\n",
              "      <th>new</th>\n",
              "      <th>wins</th>\n",
              "      <th>F</th>\n",
              "      <th>E</th>\n",
              "      <th>D</th>\n",
              "      <th>C</th>\n",
              "      <th>B</th>\n",
              "      <th>A</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
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              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
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              "      <td>10</td>\n",
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              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Don’t waste your money.</td>\n",
              "      <td>No change. No results.</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Review Text  ...   A\n",
              "0  I use chia seed in my protein shakes. These ta...  ...   1\n",
              "1  I use chia seed in my protein shakes. These ta...  ...   1\n",
              "2                            Don’t waste your money.  ...  11\n",
              "3  I use the book 'Fortify Your Life' by Tieraona...  ...  14\n",
              "4  I use the book 'Fortify Your Life' by Tieraona...  ...  14\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P90y_6yvtQ-i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def fun(win, A, B, C, D, E, F): \n",
        "  \n",
        "    if win == 1:\n",
        "        return A\n",
        "  \n",
        "    elif win == 2: \n",
        "        return B\n",
        "    \n",
        "    elif win == 3: \n",
        "        return C\n",
        "      \n",
        "    elif win == 4: \n",
        "        return D\n",
        "      \n",
        "    elif win == 5: \n",
        "        return E\n",
        "  \n",
        "    else: \n",
        "        return F\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2CyW3Be5l6Fm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_m['try'] = df_m.apply(lambda x: fun(x['wins'], x['A'], x['B'], x['C'], x['D'], x['E'], x['F']), axis = 1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvM0hDjlr3Bu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_s = df_m.drop([ 'new', 'wins', 'A', 'B', 'C', 'D', 'E', 'F'], axis  =1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l0yRq13-yhFm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_s['topic'] = label_encoder.inverse_transform(df_s['try'])\n",
        "df_s = df_s.drop('try', axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V0J8pT-qy_tV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "outputId": "ae092b60-b55f-4c2c-f207-84a7d7f390f1"
      },
      "source": [
        "df_s.head()"
      ],
      "execution_count": 60,
      "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>Review Text</th>\n",
              "      <th>Review Title</th>\n",
              "      <th>topic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "      <td>Bad Taste/Flavor</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I use chia seed in my protein shakes. These ta...</td>\n",
              "      <td>Bad tast</td>\n",
              "      <td>Quality/Contaminated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Don’t waste your money.</td>\n",
              "      <td>No change. No results.</td>\n",
              "      <td>Not Effective</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>Quality/Contaminated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I use the book 'Fortify Your Life' by Tieraona...</td>\n",
              "      <td>Good Vegan Choice, Poor Non Vegan Choice</td>\n",
              "      <td>Allergic</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                         Review Text  ...                 topic\n",
              "0  I use chia seed in my protein shakes. These ta...  ...      Bad Taste/Flavor\n",
              "1  I use chia seed in my protein shakes. These ta...  ...  Quality/Contaminated\n",
              "2                            Don’t waste your money.  ...         Not Effective\n",
              "3  I use the book 'Fortify Your Life' by Tieraona...  ...  Quality/Contaminated\n",
              "4  I use the book 'Fortify Your Life' by Tieraona...  ...              Allergic\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "isW-xx1AzHC8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_s.to_csv('sample_8.csv', index = False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVaL7d_DzrSf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "5ec0c96f-8fbd-40cb-9daf-ba22614303eb"
      },
      "source": [
        "df_s.topic.unique()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Bad Taste/Flavor', 'Quality/Contaminated', 'Not Effective',\n",
              "       'Allergic', 'Smells Bad', 'Texture', 'Shipment and delivery',\n",
              "       'Wrong Product received', 'Expiry', 'Packaging',\n",
              "       'Inferior to competitors', 'Color and texture',\n",
              "       'Too big to swallow', 'Customer Service', 'Customer Issues',\n",
              "       'Pricing', 'Too Sweet', 'Ingredients', \"Didn't Like\",\n",
              "       'False Advertisement', 'Hard to Chew'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irPWRa9gAy54",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "51MMV-yjEQYO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}