Projeto_4_Classificação_de_Texto_e_Análise_de_Sentimentos.ipynb

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      "source": [
        "# Projeto 4: Classificação de Texto e Análise de Sentimentos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNl52nl3qiyL"
      },
      "source": [
        "- Base de dados: www.kaggle.com/sid321axn/amazon-alexa-reviews"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKmFmyaGunc7"
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      "source": [
        "# Etapa 1: Importação das bibliotecas"
      ]
    },
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          "height": 35
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        "outputId": "ca00af4f-e57c-42ef-cddc-63336c83d63c"
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "tf.__version__"
      ],
      "execution_count": 3,
      "outputs": [
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              "'2.7.0'"
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          "execution_count": 3
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    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "# Etapa 2: Importação da base de dados"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/amazon-alexa.tsv', sep='\\t')"
      ],
      "metadata": {
        "id": "xOvOU7xz7kpF"
      },
      "execution_count": 4,
      "outputs": []
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      "source": [
        "df.head()"
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              "   rating  ... feedback\n",
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              "      rating  ... feedback\n",
              "3140       4  ...        1\n",
              "3141       3  ...        1\n",
              "3142       4  ...        1\n",
              "3143       5  ...        1\n",
              "3144       5  ...        1\n",
              "3145       5  ...        1\n",
              "3146       5  ...        1\n",
              "3147       5  ...        1\n",
              "3148       5  ...        1\n",
              "3149       4  ...        1\n",
              "\n",
              "[10 rows x 5 columns]"
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              "Index(['rating', 'date', 'variation', 'verified_reviews', 'feedback'], dtype='object')"
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        "df['verified_reviews']\n",
        "#Apenas os textos"
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        },
        "id": "wnlff0jn8KM0",
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      "execution_count": 8,
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          "data": {
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              "0                                           Love my Echo!\n",
              "1                                               Loved it!\n",
              "2       Sometimes while playing a game, you can answer...\n",
              "3       I have had a lot of fun with this thing. My 4 ...\n",
              "4                                                   Music\n",
              "                              ...                        \n",
              "3145    Perfect for kids, adults and everyone in betwe...\n",
              "3146    Listening to music, searching locations, check...\n",
              "3147    I do love these things, i have them running my...\n",
              "3148    Only complaint I have is that the sound qualit...\n",
              "3149                                                 Good\n",
              "Name: verified_reviews, Length: 3150, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlszUhNNyrl_"
      },
      "source": [
        "# Etapa 3: Visualização dos dados"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "positive = df[df['feedback'] == 1]"
      ],
      "metadata": {
        "id": "RqJJtAqE8ZUN"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "positive"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "cnhvIwlM8hYe",
        "outputId": "0fa4bd48-e4d3-4718-88fa-1e61318502c0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>31-Jul-18</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3145</th>\n",
              "      <td>5</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>Black  Dot</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3146</th>\n",
              "      <td>5</td>\n",
              "      <td>30-Jul-18</td>\n",
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              "      <td>I do love these things, i have them running my...</td>\n",
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              "      <td>5</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>White  Dot</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3149</th>\n",
              "      <td>4</td>\n",
              "      <td>29-Jul-18</td>\n",
              "      <td>Black  Dot</td>\n",
              "      <td>Good</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "          const element = document.querySelector('#df-d76654f0-387a-44f8-8180-4d950ac0134d');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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            "text/plain": [
              "      rating  ... feedback\n",
              "0          5  ...        1\n",
              "1          5  ...        1\n",
              "2          4  ...        1\n",
              "3          5  ...        1\n",
              "4          5  ...        1\n",
              "...      ...  ...      ...\n",
              "3145       5  ...        1\n",
              "3146       5  ...        1\n",
              "3147       5  ...        1\n",
              "3148       5  ...        1\n",
              "3149       4  ...        1\n",
              "\n",
              "[2893 rows x 5 columns]"
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    {
      "cell_type": "code",
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        "negative = df[df['feedback'] == 0]"
      ],
      "metadata": {
        "id": "RVk7ed5V8kkv"
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      "outputs": []
    },
    {
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        "negative"
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        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "      <th></th>\n",
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              "      <th>46</th>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>30-Jul-18</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "      <th>162</th>\n",
              "      <td>1</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>Sandstone Fabric</td>\n",
              "      <td>Stopped working after 2 weeks ,didn't follow c...</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>2</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>Heather Gray Fabric</td>\n",
              "      <td>Sad joke. Worthless.</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>3047</th>\n",
              "      <td>1</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>Black  Dot</td>\n",
              "      <td>Echo Dot responds to us when we aren't even ta...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3048</th>\n",
              "      <td>1</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>White  Dot</td>\n",
              "      <td>NOT CONNECTED TO MY PHONE PLAYLIST :(</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3067</th>\n",
              "      <td>2</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>Black  Dot</td>\n",
              "      <td>The only negative we have on this product is t...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3091</th>\n",
              "      <td>1</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>Black  Dot</td>\n",
              "      <td>I didn’t order it</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3096</th>\n",
              "      <td>1</td>\n",
              "      <td>30-Jul-18</td>\n",
              "      <td>White  Dot</td>\n",
              "      <td>The product sounded the same as the emoji spea...</td>\n",
              "      <td>0</td>\n",
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              "<p>257 rows × 5 columns</p>\n",
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              "          const element = document.querySelector('#df-d695397a-6107-47ca-b203-7edcc2b49b55');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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            "text/plain": [
              "      rating  ... feedback\n",
              "46         2  ...        0\n",
              "111        2  ...        0\n",
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              "162        1  ...        0\n",
              "176        2  ...        0\n",
              "...      ...  ...      ...\n",
              "3047       1  ...        0\n",
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              "3067       2  ...        0\n",
              "3091       1  ...        0\n",
              "3096       1  ...        0\n",
              "\n",
              "[257 rows x 5 columns]"
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          "metadata": {},
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      ]
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    {
      "cell_type": "code",
      "source": [
        "sns.countplot(df['feedback'], label='Count')\n",
        "#A base de dados é desbalanceada"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "lZ0vVmdD8xrT",
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      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f204c8eaa10>"
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          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = 'rating', data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "oVWe5VVk9FAu",
        "outputId": "747dc3c6-aa61-4bfb-afd8-ac64b043c753"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f204b3411d0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['rating'].hist(bins=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "eiLV3gpO9PHY",
        "outputId": "af65230e-5473-4e71-d79e-f9577d698507"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f204b2d2e10>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(30, 12))\n",
        "sns.barplot(x = 'variation', y='rating', data=df, palette='deep');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "8BLwKDrH9Uq_",
        "outputId": "857a1a1b-be3d-441f-985c-2912e9a58c8f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 2160x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UpAKD1MNdE09"
      },
      "source": [
        "# Etapa 4: Limpeza dos dados "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['date', 'rating'], axis=1)"
      ],
      "metadata": {
        "id": "jg-b2Tzs9qA3"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EGW4tOoG91RQ",
        "outputId": "5ffd753f-a64b-4ef7-8770-35d5688e56f8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6507bcda-8982-49a0-acb9-687a371d2f1d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>variation</th>\n",
              "      <th>verified_reviews</th>\n",
              "      <th>feedback</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Charcoal Fabric</td>\n",
              "      <td>Love my Echo!</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Charcoal Fabric</td>\n",
              "      <td>Loved it!</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Walnut Finish</td>\n",
              "      <td>Sometimes while playing a game, you can answer...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Charcoal Fabric</td>\n",
              "      <td>I have had a lot of fun with this thing. My 4 ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Charcoal Fabric</td>\n",
              "      <td>Music</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6507bcda-8982-49a0-acb9-687a371d2f1d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6507bcda-8982-49a0-acb9-687a371d2f1d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6507bcda-8982-49a0-acb9-687a371d2f1d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "          variation  ... feedback\n",
              "0  Charcoal Fabric   ...        1\n",
              "1  Charcoal Fabric   ...        1\n",
              "2    Walnut Finish   ...        1\n",
              "3  Charcoal Fabric   ...        1\n",
              "4  Charcoal Fabric   ...        1\n",
              "\n",
              "[5 rows x 3 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "variation_dummies = pd.get_dummies(df['variation'])\n",
        "variation_dummies"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "aC53p7BM99Ev",
        "outputId": "faef4887-f6ad-4df1-9478-efee5fdfac1c"
      },
      "execution_count": 19,
      "outputs": [
        {
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          "data": {
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              "      <td>0</td>\n",
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              "<p>3150 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-383c48e7-94b7-4863-b49f-b867d3b0d0fb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-383c48e7-94b7-4863-b49f-b867d3b0d0fb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-383c48e7-94b7-4863-b49f-b867d3b0d0fb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      Black  Black  Dot  Black  Plus  ...  White  Plus  White  Show  White  Spot\n",
              "0         0           0            0  ...            0            0            0\n",
              "1         0           0            0  ...            0            0            0\n",
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              "\n",
              "[3150 rows x 16 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['variation'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "IDWZIodR-Nig"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "54qecMvK-SRR",
        "outputId": "9fb7b47f-068a-4adf-dc24-66d8386b3d5d"
      },
      "execution_count": 21,
      "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",
              "\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>verified_reviews</th>\n",
              "      <th>feedback</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Love my Echo!</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Loved it!</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Sometimes while playing a game, you can answer...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>I have had a lot of fun with this thing. My 4 ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Music</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2b1f2f72-40df-4cf7-abb9-edeb4cda8d80')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2b1f2f72-40df-4cf7-abb9-edeb4cda8d80 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2b1f2f72-40df-4cf7-abb9-edeb4cda8d80');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                    verified_reviews  feedback\n",
              "0                                      Love my Echo!         1\n",
              "1                                          Loved it!         1\n",
              "2  Sometimes while playing a game, you can answer...         1\n",
              "3  I have had a lot of fun with this thing. My 4 ...         1\n",
              "4                                              Music         1"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df, variation_dummies], axis=1)"
      ],
      "metadata": {
        "id": "vOVRGnJn-Zjf"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "id": "wtCnIHJ8-goM",
        "outputId": "e6820f98-f2ca-4a8a-f7e7-3afb809c6620"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c71c7594-9148-48a1-bf3c-0b54709efa97 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c71c7594-9148-48a1-bf3c-0b54709efa97');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                    verified_reviews  ...  White  Spot\n",
              "0                                      Love my Echo!  ...            0\n",
              "1                                          Loved it!  ...            0\n",
              "2  Sometimes while playing a game, you can answer...  ...            0\n",
              "3  I have had a lot of fun with this thing. My 4 ...  ...            0\n",
              "4                                              Music  ...            0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWrhtE7J76nK"
      },
      "source": [
        "# Tokenização da base de dados"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "#usado para a tokenização\n",
        "vectorizer = CountVectorizer()\n",
        "df_vec = vectorizer.fit_transform(df['verified_reviews'])\n"
      ],
      "metadata": {
        "id": "xHdiz0ce-6Iu"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_vec.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iYCgFX8JaQAa",
        "outputId": "44e903c7-26e6-4f7a-bf50-8d29488354d8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3150, 4044)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(df_vec)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yXpRKx3VaTMU",
        "outputId": "2692807d-d9da-477e-ca7e-f5890b764a18"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "scipy.sparse.csr.csr_matrix"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(vectorizer.get_feature_names())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7kyOF26aZMK",
        "outputId": "a61580cb-3f24-488d-d5f5-81788318d547"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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'simply', 'simultaneously', 'sin', 'since', 'sincerely', 'sing', 'singing', 'single', 'singley', 'sink', 'sinqued', 'siri', 'sirius', 'sirrius', 'sister', 'sit', 'site', 'sits', 'sitting', 'situations', 'six', 'size', 'sized', 'skeptical', 'skill', 'skills', 'skips', 'skype', 'sleek', 'sleep', 'sleeper', 'sleeping', 'sleeps', 'sleepy', 'sliced', 'slide', 'slideshow', 'slight', 'slightly', 'sling', 'slow', 'slowly', 'sm', 'small', 'smaller', 'smart', 'smartbon', 'smarter', 'smarthome', 'smartphone', 'smartthing', 'smartthings', 'smells', 'smiths', 'smooth', 'smoothly', 'snap', 'snarls', 'sneaky', 'snell', 'snooze', 'snoozed', 'snoozes', 'so', 'soaked', 'soaking', 'soccer', 'social', 'socket', 'sofa', 'soft', 'softly', 'software', 'sold', 'solely', 'solid', 'solo', 'solución', 'solution', 'solved', 'solves', 'some', 'somebody', 'somehow', 'someone', 'something', 'sometime', 'sometimes', 'somewhat', 'son', 'song', 'songs', 'sonos', 'sons', 'sony', 'soon', 'sooner', 'sooo', 'sooooo', 'sooooooo', 'sopt', 'sore', 'sorely', 'sorprendió', 'sorry', 'sort', 'sound', 'soundbar', 'sounded', 'sounding', 'soundlink', 'sounds', 'soundtouch', 'source', 'sources', 'southern', 'spa', 'space', 'spaces', 'spacing', 'spam', 'span', 'spanish', 'spanking', 'spark', 'sparks', 'speak', 'speaker', 'speakers', 'speaking', 'speaks', 'special', 'specially', 'specific', 'specifically', 'specifily', 'specify', 'specifying', 'specs', 'spectacular', 'speech', 'speed', 'speeds', 'speedy', 'spell', 'spelling', 'spend', 'spending', 'spent', 'spiel', 'spilled', 'spin', 'spins', 'split', 'spoiled', 'spoke', 'spoken', 'sport', 'sports', 'spot', 'spotify', 'spotlight', 'spots', 'spouse', 'sprinkler', 'sprint', 'spur', 'spying', 'square', 'squirms', 'sry', 'ssdi', 'st', 'staff', 'stage', 'staging', 'stairs', 'stand', 'standalone', 'standard', 'standards', 'standing', 'stands', 'star', 'stark', 'stars', 'start', 'started', 'starting', 'starts', 'stat', 'state', 'statement', 'states', 'station', 'stationary', 'stationed', 'stations', 'stay', 'stayed', 'staying', 'steaming', 'steep', 'stellar', 'step', 'steps', 'stereo', 'stick', 'sticks', 'still', 'stimulus', 'stinks', 'stoled', 'stop', 'stopped', 'stops', 'storage', 'store', 'stories', 'storm', 'story', 'stove', 'straight', 'straightforward', 'strange', 'stream', 'streaming', 'streamline', 'strictly', 'string', 'strips', 'strong', 'strongly', 'structure', 'struggle', 'stubborn', 'stuck', 'students', 'stuff', 'stump', 'stupid', 'sturdy', 'style', 'stylish', 'su', 'sub', 'subject', 'subpar', 'subscriber', 'subscribing', 'subscription', 'subscriptiondoes', 'subscriptions', 'subsequently', 'substitute', 'success', 'successful', 'successfully', 'successor', 'such', 'suck', 'sucks', 'suffer', 'sufficient', 'suffolk', 'suggest', 'suggested', 'suggesting', 'suggestions', 'suggests', 'suitable', 'summoning', 'sunroom', 'supberb', 'super', 'superb', 'superior', 'supplied', 'supplying', 'support', 'supported', 'supporting', 'supports', 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'them', 'themes', 'themselves', 'then', 'theories', 'there', 'therefore', 'thermostat', 'these', 'thestand', 'thete', 'they', 'thick', 'thing', 'things', 'think', 'thinking', 'third', 'this', 'thongs', 'thorough', 'thoroughly', 'those', 'thou', 'though', 'thought', 'thoughts', 'thousands', 'three', 'thrilled', 'through', 'throughout', 'throw', 'thrown', 'thru', 'thu', 'thumb', 'thumbs', 'thunderstorm', 'thursday', 'ti', 'tickled', 'tiempo', 'tiene', 'ties', 'til', 'till', 'time', 'timer', 'timers', 'times', 'timing', 'tin', 'ting', 'tinker', 'tinkering', 'tinny', 'tiny', 'tipping', 'tips', 'tired', 'title', 'tivo', 'to', 'toda', 'today', 'toddler', 'together', 'toilet', 'told', 'tomorrow', 'tomy', 'ton', 'tones', 'tons', 'tony', 'too', 'took', 'tool', 'tools', 'tooth', 'top', 'topic', 'tosca', 'total', 'totallly', 'totally', 'tou', 'touch', 'touching', 'touted', 'toward', 'towards', 'tower', 'town', 'toy', 'tp', 'track', 'traditional', 'traffic', 'trailer', 'trailers', 'trained', 'trainees', 'training', 'transferring', 'travel', 'traveling', 'travelling', 'través', 'treadmill', 'treat', 'treble', 'trek', 'tremendous', 'trending', 'trial', 'tricks', 'tricky', 'tried', 'tries', 'trigger', 'trip', 'trivia', 'trouble', 'troubleshooting', 'troublesome', 'troubling', 'true', 'truly', 'trust', 'try', 'trying', 'tube', 'tubi', 'tune', 'tunein', 'tunes', 'turn', 'turned', 'turning', 'turns', 'tv', 'tvs', 'tweeter', 'tweeters', 'twice', 'twist', 'twitter', 'two', 'ty', 'type', 'typed', 'types', 'typical', 'typically', 'typing', 'títulos', 'udefulness', 'ugly', 'uhyour', 'ummm', 'un', 'unable', 'unacceptable', 'unavailable', 'unbelievable', 'uncle', 'under', 'underestimated', 'understand', 'understanding', 'understands', 'understood', 'unexpected', 'unfortunately', 'unhappy', 'unhelpful', 'unico', 'unimportant', 'uninstall', 'unique', 'unit', 'units', 'universal', 'unless', 'unlike', 'unlimited', 'unlocking', 'unnannounced', 'unnecessary', 'unobtrusive', 'unplug', 'unplugged', 'unresponsive', 'unsettling', 'untapped', 'until', 'unusable', 'unused', 'unwitty', 'unwrapped', 'up', 'upcoming', 'update', 'updated', 'updates', 'updating', 'upgrade', 'upgraded', 'upgrades', 'upgrading', 'upload', 'upon', 'upset', 'upsetting', 'upstairs', 'urge', 'us', 'usa', 'usable', 'usage', 'usb', 'usde', 'use', 'used', 'useful', 'useless', 'user', 'users', 'uses', 'using', 'usual', 'usually', 'utility', 'utilización', 'utilize', 'utilizing', 'vacation', 'vacations', 'vacuum', 'value', 'variant', 'variety', 'various', 'vast', 've', 'vehicle', 'verbal', 'verbalize', 'verbally', 'versa', 'versatile', 'versatility', 'verse', 'verses', 'version', 'versions', 'versus', 'very', 'vetted', 'vez', 'via', 'vibrating', 'vice', 'viceo', 'video', 'videos', 'view', 'viewed', 'viewing', 'views', 'vintage', 'viola', 'virtual', 'virtually', 'visa', 'visible', 'vision', 'visiting', 'visits', 'visual', 'visuals', 'vlan', 'voice', 'voices', 'voiceview', 'voila', 'voltage', 'voltson', 'volume', 'vs', 'vudu', 'wait', 'waited', 'waiting', 'waits', 'wake', 'wakes', 'waking', 'walk', 'walked', 'walking', 'walks', 'wall', 'walls', 'want', 'wanted', 'wanting', 'warehouse', 'warning', 'warns', 'warranty', 'was', 'wasconcerned', 'wasn', 'wasnt', 'waste', 'wasted', 'watch', 'watched', 'watching', 'water', 'wattage', 'wave', 'way', 'ways', 'we', 'weak', 'wealth', 'wear', 'weary', 'weather', 'web', 'website', 'websites', 'wedding', 'week', 'weekday', 'weekdays', 'weekend', 'weekly', 'weeks', 'weight', 'weird', 'welcome', 'well', 'wellfour', 'went', 'were', 'weren', 'what', 'whatever', 'whats', 'whatsoever', 'whe', 'when', 'whenever', 'where', 'wherever', 'whether', 'which', 'while', 'whisper', 'whistles', 'white', 'who', 'whole', 'whom', 'whos', 'whose', 'why', 'wi', 'wide', 'widespread', 'wife', 'wifi', 'wikipedia', 'will', 'willing', 'wind', 'window', 'winds', 'wink', 'wireless', 'wish', 'wished', 'wishing', 'with', 'within', 'without', 'woke', 'woken', 'won', 'wonder', 'wonderful', 'wonderfully', 'wonders', 'wont', 'woofer', 'woofers', 'woohoo', 'word', 'words', 'work', 'workarounds', 'worked', 'worker', 'working', 'workout', 'workreat', 'works', 'world', 'worried', 'worry', 'worse', 'worst', 'worth', 'worthless', 'worthy', 'would', 'wouldn', 'wow', 'writes', 'writing', 'wrong', 'www', 'xbox', 'xfinity', 'xm', 'yale', 'yard', 'yards', 'yeah', 'year', 'years', 'yell', 'yelling', 'yellow', 'yep', 'yes', 'yesterday', 'yet', 'yhe', 'york', 'you', 'young', 'younger', 'youngest', 'your', 'yourself', 'youtube', 'yr', 'yrs', 'yup', 'zero', 'zigbee', 'zonked', 'zzzz', 'zzzzzzz', 'útil']\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_vec.toarray())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kLU-8TZ2apWl",
        "outputId": "a8245708-434e-476f-fc50-fe9862c7e6dc"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['verified_reviews'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "qQ01fqmoayw4"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reviews = pd.DataFrame(df_vec.toarray())"
      ],
      "metadata": {
        "id": "Bd9-3pXqa6bO"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "reviews.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-Pi4PMGea_XI",
        "outputId": "3290bbeb-b62c-44fb-e809-584708e9bbac"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<p>5 rows × 4044 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41913552-4421-4bff-a52d-284d5ff85a13')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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          "metadata": {},
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        "df = pd.concat([df, reviews], axis=1)"
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      "outputs": []
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        "outputId": "6ad81bf3-8619-4b64-8194-f5da93fc500d"
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              "\n",
              "[5 rows x 4061 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['feedback'], axis=1)\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "R1mS-vOObUkd",
        "outputId": "fe9e403c-1643-401d-84f0-82b0d2000eac"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 4060 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14b14f09-2265-421d-8a4d-71305bcd905a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-14b14f09-2265-421d-8a4d-71305bcd905a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-14b14f09-2265-421d-8a4d-71305bcd905a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Black  Black  Dot  Black  Plus  Black  Show  ...  4040  4041  4042  4043\n",
              "0      0           0            0            0  ...     0     0     0     0\n",
              "1      0           0            0            0  ...     0     0     0     0\n",
              "2      0           0            0            0  ...     0     0     0     0\n",
              "3      0           0            0            0  ...     0     0     0     0\n",
              "4      0           0            0            0  ...     0     0     0     0\n",
              "\n",
              "[5 rows x 4060 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df['feedback']\n",
        "y.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruKJlOA6bgFN",
        "outputId": "43852111-7b2c-4a29-e85b-757ca6faabe5"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: feedback, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "0O_Di4zJbi44"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lI7ZqY2bvl2",
        "outputId": "64a883a6-60dd-4320-f888-76ad444eeefc"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2520, 4060)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BebzwGxbxqF",
        "outputId": "19e81058-c2f4-4abc-d9d3-2a6545b37b71"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(630, 4060)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0GmpAjG3GiH"
      },
      "source": [
        "# Etapa 5: Construção e treinamento do modelo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#4060 -> 400 -> 400 -> 1\n",
        "\n",
        "classifier = tf.keras.models.Sequential()\n",
        "classifier.add(tf.keras.layers.Dense(units=400, activation='relu', input_shape=(4060, )))\n",
        "classifier.add(tf.keras.layers.Dense(units=400, activation='relu'))\n",
        "classifier.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "lvmW1B9Cb9ip"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dL8gXmRaceUy",
        "outputId": "0056faae-ff1f-4dec-a090-b2e22b4053ba"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_2 (Dense)             (None, 400)               1624400   \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 400)               160400    \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 401       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,785,201\n",
            "Trainable params: 1,785,201\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classifier.compile(optimizer='Adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "#binary porque retorna 1 ou 0\n",
        "#crossentropy é uma fórmula que executa um pouco mais rápido que outras métricas usando log"
      ],
      "metadata": {
        "id": "dM5GLVWAc07E"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "epochs_hist = classifier.fit(X_train, y_train, epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WwrWflpndKrE",
        "outputId": "62acb765-278a-4542-f33d-573f8388503e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "79/79 [==============================] - 2s 15ms/step - loss: 0.2643 - accuracy: 0.9194\n",
            "Epoch 2/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0836 - accuracy: 0.9679\n",
            "Epoch 3/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0316 - accuracy: 0.9893\n",
            "Epoch 4/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0168 - accuracy: 0.9948\n",
            "Epoch 5/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0123 - accuracy: 0.9948\n",
            "Epoch 6/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0125 - accuracy: 0.9952\n",
            "Epoch 7/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0124 - accuracy: 0.9956\n",
            "Epoch 8/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0108 - accuracy: 0.9956\n",
            "Epoch 9/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0114 - accuracy: 0.9956\n",
            "Epoch 10/10\n",
            "79/79 [==============================] - 1s 15ms/step - loss: 0.0116 - accuracy: 0.9960\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-wRQqOeB5Zh5"
      },
      "source": [
        "# Etapa 6: Avaliação do modelo "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epochs_hist.history['loss'])\n",
        "plt.title('Model loss progress during training')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Training loss')\n",
        "plt.legend(['Training loss']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "zMuSRuDqfvpR",
        "outputId": "6bd97603-1bae-4976-f59a-375e4125cd9f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(epochs_hist.history['accuracy'])\n",
        "plt.title('Model loss progress during training')\n",
        "plt.xlabel('accuracy')\n",
        "plt.ylabel('Training accuracy')\n",
        "plt.legend(['Training accuracy']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "FPAMAWadgPc3",
        "outputId": "bd6d8e54-8a0b-4277-decc-825f0551e454"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1bBt1OM65FsYTRJratL2Su19azDEDuvHvx/RvfAXnXNrxBJGm7nn5UzZ/UcWd3zyCDG+F1TkXhyeINPThio08OXsF3znRO/Vxzu2ZJ4g0U11Ty63Pzqdnp2xuOPPgqMNxzrVgniDSzJT3lrNwzRZu+7dh3mS3c65BniDSyLotFfzm1SWccnAPzjqiV9ThOOdaOE8QaeTOfy6isqaWiecc7v06OOca5QkiTcwsLOWFeav54amDGZjXMepwnHOtgCeINFBRVcNt/1jAwNwOXD1mcNThOOdaCa+lTAMPvlXMZ2XbmPzdUd7Pg3MuYf4EkeKWr9/GfdOLOHt4b045uEfU4TjnWhFPECnMzLjtHwtokyF+dvawqMNxzrUySU0QksZK+lRSkaSb48wfIOkNSR9LmiGpX8y8X0taIGmRpD/IX7tpspfnr+VfS0r58VcP8V7inHNNlrQEISkTuB84CxgGjJdU/zZ2EjDZzIYDE4G7w3VPBEYT9EV9BHAsMCZZsaai8h3V/OKFhRzWuzOXnzAg6nCcc61QMp8gRgFFZlZsZpXAk8C59ZYZBrwZDk+PmW9ANtAWaEfQR/W6JMaacu59fQlrt1Rw5zeP8F7inHN7JZlXjr7AypjxknBarHnAeeHwOKCTpFwze5cgYawJf14xs0VJjDWlLF67hYffXsb4Uf05ZkC3qMNxzrVSUd9a3giMkTSXoAhpFVAjaQhwGNCPIKmcLunk+itLulJSgaSC0tLS/Rl3i1Vba9z67Hy6tG/DTV/zXuKcc3svmQliFRDbE02/cNpOZrbazM4zs5HAT8NpmwieJt4zs3IzKwdeAk6ovwMze9DM8s0sv0cPf4UTYOqHJRQs917inHP7LpkJYjYwVNIgSW2Bi4DnYxeQlCepLoZbgIfD4RUETxZZktoQPF14EVMjNm6r5O5pi8gf0I1vHd2v8RWcc64BSUsQZlYNTABeIbi4P21mCyRNlHROuNipwKeSlgA9gbvC6VOBpcAnBPUU88zshWTFmip+/cpitlRUc+c47yXOObfvktrUhplNA6bVm3ZbzPBUgmRQf70a4KpkxpZq5izfyF8/WMkPTh7Eob28lzjn3L6LupLaNYPqmlpufW4+vTpnc8NXvJc451zz8ASRAh57dzmL1mzh5/82jI7eS5xzrpl4gmjl1m6u4Levfsqph/RgrPcS55xrRp4gWrk7/rmQ6lrjF95LnHOumXmCaMXeWlLKPz9ewzWnDWFArvcS55xrXp4gWqmgl7j5DMrryFVjDoo6HOdcCvIazVbqT/9ayrL125nyvVG0y/Je4pxzzc+fIFqhZWXb+OOMpXxjeG9OHupNjDjnksMTRCtjZtz2/ALaZmbws294L3HOueTxBNHKvDR/LW8tKeUnXz2Ynp29lzjnXPI0miAkXSvJOxVoAcp3VDPxhYUM692ZS4/3XuKcc8mVyBNET2C2pKfDPqb9ZfuI/P61JazbWsFd47yXOOdc8jV6lTGzW4GhwEPAFUChpF9KGpzk2FyMRWu28Mg7y7jo2AMZeaA/0Dnnki+h21AzM2Bt+FMNdAOmSvp1EmNzodpa49bngl7i/nPsIVGH45xLE41+ByHpeuAyoAz4C/D/zKwq7OinELgpuSG6Z+asZM7yjfz3t4bTtYP3Euec2z8S+VCuO3CemS2PnWhmtZK+kZywXJ0N2yq5+6XFjBrYnW8d473EOef2n0SKmF4CNtSNSOos6TgAM/NuQJPsnpcWU15RzR3fPMIb43PO7VeJJIgHgPKY8fJwWqPCt54+lVQk6eY48wdIekPSx5JmSOoXTj9N0kcxPxWSvpnIPlPJnOUbeKpgJd87aRCH9OoUdTjOuTSTSIJQWEkNBEVLJFZ3kQncD5wFDAPGS6r/6e8kYLKZDQcmAneH+5huZiPMbARwOrAdeDWBWFNGdU0tP312Pn26ZHPdGUOjDsc5l4YSSRDFkq6T1Cb8uR4oTmC9UUCRmRWbWSXwJHBuvWWGAW+Gw9PjzAf4FvCSmW1PYJ8p49F3lrF47VZu+7fDvZc451wkEkkQVwMnAquAEuA44MoE1usLrIwZLwmnxZoHnBcOjwM6Scqtt8xFwF/j7UDSlZIKJBWUlpYmEFLrsGbzF/zutSWcdkgPvnZ4z6jDcc6lqUQ+lPvczC4yswPMrKeZXWxmnzfT/m8ExkiaC4whSEI1dTMl9QaOBF7ZQ2wPmlm+meX36JE6rZre8WJdL3FeMe2ci04idQnZwPeAw4GdrcOZ2XcbWXUV0D9mvF84bSczW034BCEpBzjfzDbFLHIB8KyZVTUWZ6qY8ennTPtkLT8582AOzO0QdTjOuTSWSBHTFKAX8DXgXwQX+q0JrDcbGCppkKS2BEVFz8cuICkv/OAO4Bbg4XrbGM8eipdSUUVVDT9/fgEH5XXkSu8lzjkXsUQSxBAz+xmwzcweA84mqIdokJlVAxMIiocWAU+b2QJJEyWdEy52KvCppCUEjQLeVbe+pIEETyD/SvhoWrkHZixl+frt3PHNI7yXOOdc5BJ5PaaueGeTpCMI2mM6IJGNm9k0YFq9abfFDE8Fpu5h3WXsXqmdsko2bueBfy3lnKP6MHpIXtThOOdcQgniwbA/iFsJiohygJ8lNao09PL8tVRW13LjV70xPudcy9BgggjrB7aY2UbgLcALxpNkVlEZB+V19Ipp51yL0WAdRPjVtLfWmmQ7qmt4v3gDJw31oiXnXMuRSCX165JulNRfUve6n6RHlkbmLN/IF1U1nDw0db7lcM61fonUQVwY/r4mZprhxU3NZlZhGZkZ4viDPO8651qORhOEmQ3aH4Gks1lFZYzs35VO2W2iDsU553ZK5Evqy+JNN7PJzR9O+tm4rZJPVm3mem+x1TnXwiRSxHRszHA2cAbwIeAJohm8vbQMM7z+wTnX4iRSxHRt7LikrgRNd7tmMKuwjE7ZWRzVr0vUoTjn3C4SeYupvm2A10s0AzNjZmEZJxyUS1bm3vxTOOdc8iRSB/ECwVtLECSUYcDTyQwqXXxWto1Vm77g6lMHRx2Kc87tJpE6iEkxw9XAcjMrSVI8aWVWURkAJ3vbS865FiiRBLECWGNmFQCS2ksaGDam5/bBzMIy+nVrzwBvXsM51wIlUvD9DFAbM14TTnP7oKqmlveWrufkoXnea5xzrkVKJEFkmVll3Ug43DZ5IaWHeSs3sXVHtb/e6pxrsRJJEKUxHfwg6VygLHkhpYeZhWVIcOLg3KhDcc65uBKpg7gaeELSfeF4CRD362qXuFlFZQzv24WuHfxhzDnXMjX6BGFmS83seILXW4eZ2YlmVpTIxiWNlfSppCJJN8eZP0DSG5I+ljRDUr+YeQdKelXSIkkLwy5IU8KWiio+WrnJi5eccy1aowlC0i8ldTWzcjMrl9RN0p0JrJcJ3A+cRZBcxksaVm+xScBkMxsOTATujpk3GfhvMzsMGAV8ntghtXzvLl1PTa15/w/OuRYtkTqIs8xsU91I2Lvc1xNYbxRQZGbFYcX2k8C59ZYZBrwZDk+vmx8mkiwzey3cZ7mZbU9gn63CrMIyOrTN5OgDu0UdinPO7VEiCSJTUru6EUntgXYNLF+nL7AyZrwknBZrHnBeODwO6CQpFzgY2CTp75LmSvrv8IlkF5KulFQgqaC0tDSBkFqGWUVlHDeoO22zvHkN51zLlcgV6gngDUnfk/Q94DXgsWba/43AGElzgTHAKoLvLLKAk8P5xxJ0TnRF/ZXN7EEzyzez/B49Wkd5/soN2/msbJvXPzjnWrxEWnO9R9LHBM18A9xhZq8ksO1VQP+Y8X7htNhtryZ8gpCUA5xvZpsklQAfmVlxOO854HjgoQT226LtbF7D6x+ccy1cIq+5YmYvAS81cduzgaGSBhEkhouAi2MXkJQHbDCzWuAW4OGYdbtK6mFmpcDpQEET998izSoso2fndgw5ICfqUJxzrkGJvMV0vKTZksolVUqqkbSlsfXMrBqYALwCLAKeNrMFkibGfHh3KvCppCVAT+CucN0aguKlNyR9Agj4814cX4tSU2u8vbSMk4f28OY1nHMtXiJPEPcR3P0/A+QTfCR3cCIbN7NpwLR6026LGZ4KTN3Duq8BwxPZT2sxf9VmNm2v8uIl51yrkNBrNOGHcZlmVmNmjwBjkxtWaqqrfxjtzXs751qBRJ4gtktqC3wk6dfAGvauJ7q0N7OwlMN6dyYvJ5G3hJ1zLlqJXOgvDZebQNDdaH/g/GQGlYq27ahmzvKNnOLFS865ViKR11yXh4MVwC+SG07q+uCzDVTVePMazrnWw4uK9pOZhWW0zcrg2IHdow7FOecS4gliP5lVVMpxg7qT3Wa3FkOcc65F8gSxH6zdXMGSdeWc5G8vOedakUbrICS9AFi9yZsJvmz+XzOrSEZgqaTu9Vavf3DOtSaJPEEUA+UEXzL/GdgCbCX4WK7Vf928P8wqLCUvpy2H9eocdSjOOZewRL6DONHMjo0Zf0HSbDM7VtKCZAWWKmprjVlFZYwekkdGhjev4ZxrPRJ5gsiRdGDdSDhc19JcZVKiSiGL126lrLzS6x+cc61OIk8QPwFmSVpK0GjeIOCHkjrSfP1CpKxZRUFHRt7/g3OutUnkQ7lpkoYCh4aTPo2pmP590iJLETMLyxh6QA69umRHHYpzzjVJQv1BAMcAA8Plj5KEmU1OWlQpoqKqhg8+28DFxx3Y+MLOOdfCJPKa6xRgMPARQXegELz26gmiEQXLNrKjutab93bOtUqJPEHkA8PMrP63EK4RM4tKaZMpjhuUG3UozjnXZIm8xTQf6JXsQFLRrMIyjj6wGx3bJVqS55xzLUciCSIPWCjpFUnP1/0ksnFJYyV9KqlI0s1x5g+Q9IakjyXNkNQvZl6NpI/Cn4T215KUle9gweotXrzknGu1Erm1vX1vNiwpE7gfOBMoAWZLet7MFsYsNgmYbGaPSToduJug/wmAL8xsxN7suyV4e2fzGv56q3OudUrkNdd/7eW2RwFFZlYMIOlJ4FwgNkEMA34cDk8HntvLfbU4swrL6NK+DUf27RJ1KM45t1f2WMQkaVb4e6ukLTE/WyVtSWDbfYGVMeMl4bRY84DzwuFxQCdJdTW62ZIKJL0n6Zt7iPHKcJmC0tLSBELaP8yMmYVljB6SS6Y3r+Gca6X2mCDM7KTwdycz6xzz08nMmqvVuRuBMZLmAmOAVXz5Ku0AM8sHLgZ+L2lwnBgfNLN8M8vv0aPlFOUsLS1n7ZYKThrScmJyzrmmSuj1mrA+oWfs8ma2opHVVhH0X12nXzhtJzNbTfgEISkHON/MNoXzVoW/iyXNAEYCSxOJN2ozC4P6B6+gds61Zo2+xSTpWmAd8Brwz/DnxQS2PRsYKmmQpLbARcAubyNJypNUF8MtwMPh9G6S2tUtA4xm17qLFm1WYRkDczvQv3uHqENxzrm9lsgTxPXAIWa2vikbNrNqSROAV4BM4GEzWyBpIlBgZs8DpwJ3SzLgLeCacPXDgP+VVEuQxH5V7+2nFquyupZ3i9dz3tH1q1ucc651SSRBrCToQa7JzGwaMK3etNtihqcCU+Os9w5w5N7sM2pzV2xke2WN1z8451q9RBJEMTBD0j+BHXUTzey3SYuqFZtVVEZmhjhhsDev4Zxr3RJJECvCn7bhj2vAW4VlHNWvC13at4k6FOec2yeJfCj3i/0RSCrYvL2KT0o2MeH0oVGH4pxz+2yPCULS783sBkkvEDTvvQszOyepkbVC7ywto9bgFH+91TmXAhp6gpgS/p60PwJJBTOLyshpl8VR/btGHYpzzu2zPSYIM5sT/t7btpjSzszCUo4/KJc2mYk0kuuccy1bIh/KDZU0VdJCScV1P/sjuNZk+fptrNzwhX897ZxLGYnc6j4CPABUA6cRdDX6eDKDao28eQ3nXKpJJEG0N7M3AJnZcjO7HTg7uWG1PrMKy+jbtT2D8jpGHYpzzjWLRL6D2BG2l1QYNp2xCshJblitS3VNLW8vLePrR/RG8ua9nXOpIZEniOuBDsB1wDHAJcDlyQyqtfl41Wa2VlRz8sFevOScSx0NPkGEzXxfaGY3AuXAd/ZLVK3MrMIyJBg92BOEcy51NNSjXJaZ1d/d2RYAABPiSURBVAAn7cd4WqWZhaUc0acL3Tp6SyTOudTR0BPEB8DRwFxJzwPPANvqZprZ35McW6tQvqOauSs28YNTDoo6FOeca1aJVFJnA+uB0wma3FD42xME8N7S9VTXmr/e6pxLOQ0liAMk/RiYz5eJoc5ubTOlq1lFZbRvk8kxA7pFHYpzzjWrhhJEJsHrrPHe2/QEEXqrsJRRg7rTLisz6lCcc65ZNZQg1pjZxH3ZuKSxwL0EyeYvZvarevMHEPRD3QPYAFxiZiUx8zsT9EX9nJlN2JdYkmH1pi8oLt3GxaMOjDoU55xrdg19B7FPX3yFr8jeD5wFDAPGSxpWb7FJwGQzGw5MBO6uN/8Ogr6qW6RZO5vX8O5FnXOpp6EEccY+bnsUUGRmxWZWCTwJnFtvmWHAm+Hw9Nj5ko4BegKv7mMcSfNWYSkHdGrHwT39w3LnXOrZY4Iwsw37uO2+wMqY8ZJwWqx5wHnh8Digk6TcsGmP3wA3NrQDSVdKKpBUUFpauo/hNk1trfHO0vWcNDTPm9dwzqWkqDsuuBEYI2kuMIagnaca4IfAtNj6iHjM7EEzyzez/B499m8xz8I1W9iwrdJfb3XOpaxEvoPYW6uA/jHj/cJpO5nZasInCEk5wPlmtknSCcDJkn5I8CZVW0nlZnZzEuNtkrrmvUcP8QThnEtNyUwQs4GhkgYRJIaLgItjF5CUB2wws1rgFoI3mjCzb8cscwWQ35KSAwTNaxzaqxMHdMqOOhTnnEuKpBUxmVk1MAF4BVgEPG1mCyRNlHROuNipwKeSlhBUSN+VrHia0xeVNRQs2+jFS865lJbMJwjMbBowrd6022KGpwJTG9nGo8CjSQhvr32wbAOVNbWc5K+3OudSWNSV1K3SzCWltM3MYNTA7lGH4pxzSeMJYi/MKirj2EHdaN/Wm9dwzqUuTxBN9PnWChav3cpJQ7x4yTmX2jxBNNHbRXXNa3gFtXMutXmCaKKZS8ro3rEtw3p3jjoU55xLKk8QTWBmzCoqY/SQPDIyvHkN51xq8wTRBEvWlfP51h2c7F9PO+fSgCeIJphZGDQIeJLXPzjn0oAniCaYWVjG4B4d6dO1fdShOOdc0nmCSNCO6hre/2y9dw7knEsbniASNGf5RiqqajnJ6x+cc2nCE0SCZhaWkZUhjh+cG3Uozjm3X3iCSNCswjKOPrAbOe2S2r6hc861GJ4gErBxWyXzV2/2t5ecc2nFE0QC3l5ahpm/3uqcSy+eIBIwc0kZnbOzGN63S9ShOOfcfuMJohF1zWucODiPrEw/Xc659JHUK56ksZI+lVQkabc+pSUNkPSGpI8lzZDUL2b6h5I+krRA0tXJjLMhn5VtY9WmL7x4yTmXdpKWICRlAvcDZwHDgPGShtVbbBIw2cyGAxOBu8Ppa4ATzGwEcBxws6Q+yYq1ITMLvXlv51x6SuYTxCigyMyKzawSeBI4t94yw4A3w+HpdfPNrNLMdoTT2yU5zgbNLCzjwO4dGJDbMaoQnHMuEsm88PYFVsaMl4TTYs0DzguHxwGdJOUCSOov6eNwG/eY2er6O5B0paQCSQWlpaXNfgBVNbW8V7zei5ecc2kp6lrXG4ExkuYCY4BVQA2Ama0Mi56GAJdL6ll/ZTN70MzyzSy/R4/mbyNp3spNlO+o9ua9nXNpKZkJYhXQP2a8XzhtJzNbbWbnmdlI4KfhtE31lwHmAycnMda43iosI0Nw4mBPEM659JPMBDEbGCppkKS2wEXA87ELSMqTVBfDLcDD4fR+ktqHw92Ak4BPkxhrXLMKSxnerytdOrTZ37t2zrnIJS1BmFk1MAF4BVgEPG1mCyRNlHROuNipwKeSlgA9gbvC6YcB70uaB/wLmGRmnyQr1ng2f1HFvJLN/vaScy5tJbXlOTObBkyrN+22mOGpwNQ4670GDE9mbI15d+l6amrNm/d2zqWtqCupW6xZRaV0bJvJyAO7RR2Kc85FwhPEHswqLOP4g3Jpm+WnyDmXnvzqF8fKDdtZtn67f//gnEtrniDi+LJ5De9/2jmXvjxBxDGrqJTeXbIZ3MOb13DOpS9PEPXU1BpvF63npCF5SIo6HOeci4wniHrmr9rM5i+qOPlgL15yzqU3TxD1zCwMGv0bPTg34kiccy5aSf1QrjWaWVjG4X06k5vTLupQnEu6qqoqSkpKqKioiDoUl2TZ2dn069ePNm0SbzrIE0SMbTuq+XDFRr570qCoQ3FuvygpKaFTp04MHDjQ69xSmJmxfv16SkpKGDQo8eubFzHFeP+z9VTVGKf4660uTVRUVJCbm+vJIcVJIjc3t8lPip4gYswsLKNdVgbHDPDmNVz68OSQHvbm39kTRIxZhWWMGtSd7DaZUYfinHOR8wQRWru5gsLPy714ybn9aP369YwYMYIRI0bQq1cv+vbtu3O8srKywXULCgq47rrrGt3HiSee2Fzhph2vpA7Vvd7q7S85t//k5uby0UcfAXD77beTk5PDjTfeuHN+dXU1WVnxL1P5+fnk5+c3uo933nmneYLdj2pqasjMjL4kwxNEaFZRGXk57Ti0V6eoQ3EuEr94YQELV29p1m0O69OZn//b4U1a54orriA7O5u5c+cyevRoLrroIq6//noqKipo3749jzzyCIcccggzZsxg0qRJvPjii9x+++2sWLGC4uJiVqxYwQ033LDz6SInJ4fy8nJmzJjB7bffTl5eHvPnz+eYY47h8ccfRxLTpk3jxz/+MR07dmT06NEUFxfz4osv7hLXsmXLuPTSS9m2bRsA9913386nk3vuuYfHH3+cjIwMzjrrLH71q19RVFTE1VdfTWlpKZmZmTzzzDOsXLlyZ8wAEyZMID8/nyuuuIKBAwdy4YUX8tprr3HTTTexdetWHnzwQSorKxkyZAhTpkyhQ4cOrFu3jquvvpri4mIAHnjgAV5++WW6d+/ODTfcAMBPf/pTDjjgAK6//vq9/8cjyQlC0ljgXiAT+IuZ/are/AEE3Yz2ADYAl5hZiaQRwANAZ6AGuMvMnkpWnLW1xttFZd68hnMtRElJCe+88w6ZmZls2bKFmTNnkpWVxeuvv85//dd/8be//W23dRYvXsz06dPZunUrhxxyCP/xH/+x2zv/c+fOZcGCBfTp04fRo0fz9ttvk5+fz1VXXcVbb73FoEGDGD9+fNyYDjjgAF577TWys7MpLCxk/PjxFBQU8NJLL/GPf/yD999/nw4dOrBhwwYAvv3tb3PzzTczbtw4KioqqK2tZeXKlQ0ed25uLh9++CEQFL/94Ac/AODWW2/loYce4tprr+W6665jzJgxPPvss9TU1FBeXk6fPn0477zzuOGGG6itreXJJ5/kgw8+aPJ5ry9pCUJSJnA/cCZQAsyW9LyZLYxZbBIw2cwek3Q6cDdwKbAduMzMCiX1AeZIesXMNiUj1kVrt1BWXumtt7q01tQ7/WT693//951FLJs3b+byyy+nsLAQSVRVVcVd5+yzz6Zdu3a0a9eOAw44gHXr1tGvX79dlhk1atTOaSNGjGDZsmXk5ORw0EEH7fw+YPz48Tz44IO7bb+qqooJEybw0UcfkZmZyZIlSwB4/fXX+c53vkOHDh0A6N69O1u3bmXVqlWMGzcOCD5SS8SFF164c3j+/PnceuutbNq0ifLycr72ta8B8OabbzJ58mQAMjMz6dKlC126dCE3N5e5c+eybt06Ro4cSW7uvrcGkcwniFFAkZkVA0h6EjgXiE0Qw4Afh8PTgecAzGxJ3QJmtlrS5wRPGUlJELPC5r29/sG5lqFjxy9bUv7Zz37GaaedxrPPPsuyZcs49dRT467Trt2XrR9kZmZSXV29V8vsye9+9zt69uzJvHnzqK2tTfiiHysrK4va2tqd4/W/S4g97iuuuILnnnuOo446ikcffZQZM2Y0uO3vf//7PProo6xdu5bvfve7TY4tnmS+xdQXiH2eKgmnxZoHnBcOjwM6Sdol7UkaBbQFltbfgaQrJRVIKigtLd3rQGcVlXFwzxx6dm76P7hzLrk2b95M377BpePRRx9t9u0fcsghFBcXs2zZMgCeeip+afbmzZvp3bs3GRkZTJkyhZqaGgDOPPNMHnnkEbZv3w7Ahg0b6NSpE/369eO5554DYMeOHWzfvp0BAwawcOFCduzYwaZNm3jjjTf2GNfWrVvp3bs3VVVVPPHEEzunn3HGGTzwwANAUJm9efNmAMaNG8fLL7/M7Nmzdz5t7KuoX3O9ERgjaS4wBlhFUOcAgKTewBTgO2ZWW39lM3vQzPLNLL9Hj70rHqqoquGDzzZ48ZJzLdRNN93ELbfcwsiRI5t0x5+o9u3b88c//pGxY8dyzDHH0KlTJ7p06bLbcj/84Q957LHHOOqoo1i8ePHOu/2xY8dyzjnnkJ+fz4gRI5g0aRIAU6ZM4Q9/+APDhw/nxBNPZO3atfTv358LLriAI444ggsuuICRI0fuMa477riD4447jtGjR3PooYfunH7vvfcyffp0jjzySI455hgWLgwKZdq2bctpp53GBRdc0GxvQMnMmmVDu21YOgG43cy+Fo7fAmBmd+9h+RxgsZn1C8c7AzOAX5rZ1Mb2l5+fbwUFBU2O8/OtFdz54iIuGtWfEwd7EZNLL4sWLeKwww6LOozIlZeXk5OTg5lxzTXXMHToUH70ox9FHVaT1NbWcvTRR/PMM88wdOjQuMvE+/eWNMfM4r4vnMwniNnAUEmDJLUFLgKerxdYnqS6GG4heKOJcPlnCSqwG00O++KATtn8YfxITw7OpbE///nPjBgxgsMPP5zNmzdz1VVXRR1SkyxcuJAhQ4Zwxhln7DE57I2kVVKbWbWkCcArBK+5PmxmCyRNBArM7HngVOBuSQa8BVwTrn4BcAqQK+mKcNoVZvZRsuJ1zqWvH/3oR63uiSHWsGHDdn4X0ZyS+h2EmU0DptWbdlvM8FRgtycEM3sceDyZsTnnAmbm3/+kgb2pToi6kto5F6Hs7GzWr1+/VxcP13rU9QfR1FdzvakN59JYv379KCkpYV9eE3etQ12Pck3hCcK5NNamTZsm9TDm0osXMTnnnIvLE4Rzzrm4PEE455yLK2lfUu9vkkqB5fuwiTygrJnCae38XOzKz8eu/Hx8KRXOxQAzi9vWUMokiH0lqWBPn5unGz8Xu/LzsSs/H19K9XPhRUzOOefi8gThnHMuLk8QX9q9C6n05ediV34+duXn40spfS68DsI551xc/gThnHMuLk8Qzjnn4kr7BCFprKRPJRVJujnqeKIkqb+k6ZIWSlog6fqoY4qapExJcyW9GHUsUZPUVdJUSYslLQp7jUxbkn4U/j+ZL+mvklKuU/u0ThCSMoH7gbOAYcB4ScOijSpS1cBPzGwYcDxwTZqfD4DrgUVRB9FC3Au8bGaHAkeRxudFUl/gOiDfzI4g6BTtomijan5pnSCAUUCRmRWbWSXwJHBuxDFFxszWmNmH4fBWggtA32ijio6kfsDZwF+ijiVqkroQ9PL4EICZVZrZpmijilwW0F5SFtABWB1xPM0u3RNEX2BlzHgJaXxBjCVpIDASeD/aSCL1e+AmoDbqQFqAQUAp8EhY5PYXSR2jDioqZrYKmASsANYAm83s1Wijan7pniBcHJJygL8BN5jZlqjjiYKkbwCfm9mcqGNpIbKAo4EHzGwksA1I2zo7Sd0IShsGAX2AjpIuiTaq5pfuCWIV0D9mvF84LW1JakOQHJ4ws79HHU+ERgPnSFpGUPR4uqR07ie9BCgxs7onyqkECSNdfQX4zMxKzawK+DtwYsQxNbt0TxCzgaGSBklqS1DJ9HzEMUVGQc/1DwGLzOy3UccTJTO7xcz6mdlAgr+LN80s5e4QE2Vma4GVkg4JJ50BLIwwpKitAI6X1CH8f3MGKVhpn9ZdjppZtaQJwCsEbyE8bGYLIg4rSqOBS4FPJH0UTvsvM5sWYUyu5bgWeCK8mSoGvhNxPJExs/clTQU+JHj7by4p2OyGN7XhnHMurnQvYnLOObcHniCcc87F5QnCOedcXJ4gnHPOxeUJwjnnXFyeIJxzzsXlCcK5CIQNvDnXonmCcK4eSc9JmhO29X9lOG2spA8lzZP0RjgtR9Ijkj6R9LGk88Pp5THb+pakR8PhRyX9SdL7wK8ljZL0btj43Tt1XymHfVBMCvsZ+FjStZJOl/RczHbPlPTs/jsrLh35XYxzu/uumW2Q1B6YLekfwJ+BU8zsM0ndw+V+RtCK55GwswG3xvQDTjSzGkmdgZPDL/q/AvwSOB+4EhgIjAjndQc2An+U1MPMSgm+Yn64+Q7Zud15gnBud9dJGhcO9ye4YL9lZp8BmNmGcN5XiOkkxsw2JrDtZ8ysJhzuAjwmaShgQJuY7f7JzKpj9ydpCnCJpEeAE4DL9vL4nEuIJwjnYkg6leACfYKZbZc0A/gIOLQJm4ltv6Z+N5TbYobvAKab2biw/40ZjWz3EeAFoIIg0VQ3ISbnmszrIJzbVRdgY5gcDiXoejUbOEXSIICYIqbXgGvqVowpYlon6TBJGcA49qwLXzYvf0XM9NeAq+oqsuv2Z2arCXotu5UgWTiXVJ4gnNvVy0CWpEXAr4D3CHpSuxL4u6R5wFPhsncC3cLK5HnAaeH0m4EXgXcIehvbk18Dd0uay65P838haE7643C7F8fMewJYaWYp17S0a3m8NVfnWhFJ9wFzzeyhqGNxqc8ThHOthKQ5BHUYZ5rZjqjjcanPE4Rzzrm4vA7COedcXJ4gnHPOxeUJwjnnXFyeIJxzzsXlCcI551xc/x94O2D4jwi39wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "metadata": {
        "id": "By3k5rCNdWzb"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = classifier.predict(X_train)"
      ],
      "metadata": {
        "id": "w9VVDnqidcFE"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train\n",
        "#retorna as probabilidades, precisa definir um threshold\n",
        "#ou seja um valor que divide True e False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eRSmqD9UeBBq",
        "outputId": "e16179ae-51a5-42ba-dd30-086b0a95ea87"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.9998927e-01],\n",
              "       [1.0000000e+00],\n",
              "       [4.6753883e-04],\n",
              "       ...,\n",
              "       [9.9789345e-01],\n",
              "       [1.0000000e+00],\n",
              "       [9.9999022e-01]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_train = (y_pred_train > 0.5)\n",
        "y_pred_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dVxbaUitd-pX",
        "outputId": "60c13888-8e49-4288-a017-9411e236927d"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True],\n",
              "       [ True],\n",
              "       [False],\n",
              "       ...,\n",
              "       [ True],\n",
              "       [ True],\n",
              "       [ True]])"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_train, y_pred_train)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qJbAASGQeywI",
        "outputId": "b65327f3-358d-4707-e651-4cc6305607c4"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 192,    7],\n",
              "       [   2, 2319]])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title('confusion matrix - in the train data')\n",
        "sns.heatmap(cm, annot=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "UshAkO4kef-f",
        "outputId": "cbce4cdf-d576-42af-ab98-99df105ec52d"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = classifier.predict(X_test)\n",
        "y_pred = (y_pred > 0.5)"
      ],
      "metadata": {
        "id": "KbDVs3Z5fDLA"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm_test = confusion_matrix(y_test, y_pred)\n",
        "cm_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-B0pqsf1fOCd",
        "outputId": "fc2cb94b-73c2-41b9-a0fc-86b11db966b5"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 32,  26],\n",
              "       [ 14, 558]])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.title('confusion matrix - in the test data')\n",
        "sns.heatmap(cm_test, annot=True);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "4_wln9T0fkeG",
        "outputId": "64f7d8d7-b94b-44bb-8f07-baa4d7e66bda"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}