ml_pln_03.ipynb

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        "<a href=\"https://colab.research.google.com/github/GuidoGuarda/machinelearning/blob/main/ml_pln_03.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "\n",
        "\n",
        "# IA - Conceito\n",
        "### Inteligência Artificial (IA) é a tecnologia que permite a máquinas e sistemas realizarem tarefas que normalmente exigiriam a inteligência humana, como reconhecer padrões, tomar decisões, resolver problemas, entender linguagem natural e até aprender com a experiência e com erros.\n",
        "\n",
        "# Machine Learning -  Conceito\n",
        "\n",
        "#### Machine Learning é uma área da IA onde computadores aprendem com dados, analisam padrões e melhoram suas decisões de forma autônoma, sem precisar de programação detalhada.\n",
        "\n",
        "### Machine Learning (Aprendizado de Máquina) é uma parte da Inteligência Artificial onde os computadores aprendem sozinhos a partir de dados. Em vez de serem programados para fazer tudo passo a passo, eles analisam informações, encontram padrões e melhoram suas respostas ou decisões com o tempo, sem a necessidade de intervenção humana direta. É como ensinar um computador a \"aprender\" com a experiência, assim como fazemos. Exemplos incluem filtros de spam de e-mail ou sistemas de recomendação em lojas online.\n"
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        "# Situação Problema de Aprendizagem\n",
        "\n",
        "1. Temos um dataset com 50 mil filmes.\n",
        "2. As resenhas foram traduzidas por um membro da comunidade Kaggle.\n",
        "3. O dataset possui uma coluna, chamada \"sentiment\", essa coluna tem os seguintes valores: neg e pos.\n",
        "4. Variável qualitativa nominal: text_en, text_pt, sentiment (ruim - neg), (bom - pos).\n",
        "\n",
        "Analisar sentimentos em resenhas de filmes, o dataset terá 50.000 filmes para serem analisados. O dataset possui uma coluna alvo que é o sentimento (pos - positivo e neg - negativo).\n",
        "\n",
        "NO final, devemos treinar um modelo de regressão logística, mas antes de entender e transformar o texto em um espaço vetorial (números). Para isso, ultilizaremos técnicas de Bag Of Word (sacola de palavras). Nosso objetivo é treinar um modelo que tenha pelo menos 60% de acurácia.\n",
        "\n",
        "Devemos também relembrar  o Word Cloud (Nuvem de palavras) para categorizar nosso texto (corpus).\n",
        "Utilizaremos outras técnicas e visualização em gráficos, essas técnicas téra como objetivo aumentar a qualidade do modelo."
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)"
      ],
      "metadata": {
        "id": "XHwLjiT-ZZ7b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "https://www.kaggle.com/datasets/luisfredgs/imdb-ptbr"
      ],
      "metadata": {
        "id": "2N5xoUA73EBq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oAfvXfGUXPD",
        "outputId": "d0553cd5-ed04-4ad3-fa52-99802ee0310b"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DATASET no Kaggle"
      ],
      "metadata": {
        "id": "FcPJzMDd2P5L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install pandas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EL9VrJ2j4jV2",
        "outputId": "e7221df7-e64a-48c7-9613-23aba674ecd3"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
      "metadata": {
        "id": "oYwoxZU24qqi"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "source": [
        "\n",
        "# Try to read the CSV, handling potential errors\n",
        "try:\n",
        "    df_resenhas = pd.read_csv('/content/imdb-pt-br.csv')\n",
        "except pd.errors.ParserError as e:\n",
        "    # If a ParserError occurs, try reading with error_bad_lines=False\n",
        "    print(f\"Error reading CSV: {e}\")\n",
        "    print(\"Trying to read with error_bad_lines=False to skip problematic lines...\")\n",
        "    df_resenhas = pd.read_csv('/content/sample_data/imdb-reviews-pt-br.csv', error_bad_lines=False)\n",
        "    print(\"Successfully read the CSV, but some lines may have been skipped.\")\n",
        "\n",
        "# Now you can work with the df_resenhas DataFrame"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "gvEce3EiMZig"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_resenhas.head()"
      ],
      "metadata": {
        "id": "gW7lAQ7Y6JGN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "c103e90b-2df3-43b3-881b-eef65a6dbdf2"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                            text_en  \\\n",
              "0   1  Once again Mr. Costner has dragged out a movie...   \n",
              "1   2  This is an example of why the majority of acti...   \n",
              "2   3  First of all I hate those moronic rappers, who...   \n",
              "3   4  Not even the Beatles could write songs everyon...   \n",
              "4   5  Brass pictures movies is not a fitting word fo...   \n",
              "\n",
              "                                             text_pt sentiment  \n",
              "0  Mais uma vez, o Sr. Costner arrumou um filme p...       neg  \n",
              "1  Este é um exemplo do motivo pelo qual a maiori...       neg  \n",
              "2  Primeiro de tudo eu odeio esses raps imbecis, ...       neg  \n",
              "3  Nem mesmo os Beatles puderam escrever músicas ...       neg  \n",
              "4  Filmes de fotos de latão não é uma palavra apr...       neg  "
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              "    [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",
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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",
              "          + ' 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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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    60% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9753e8b3-538d-4ed2-93cc-62b6aa71220f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_resenhas",
              "summary": "{\n  \"name\": \"df_resenhas\",\n  \"rows\": 49459,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14277,\n        \"min\": 1,\n        \"max\": 49460,\n        \"num_unique_values\": 49459,\n        \"samples\": [\n          12534,\n          35447,\n          20281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_en\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49043,\n        \"samples\": [\n          \"One of the commenters is wrong. This is not the only Pat Patterson film and he didnt die two years after this was made. He shot a film called the \\\"Electric Chair\\\" in Pineville, NC. He shot this a few years after Doctor Gore. Patterson died in the late 70s. I know this because he used my house and he left a cat there!! It sucked also. This was a big deal when the movie came out. No independent horror films were being made in NC. This movie didnt help matters. Patterson used to do gore effects for H. G. Lewis. He was also good at magic. His gore scenes in Body shop were actually well done. The film was shot in a building that also housed a 7/11. You can actually see the tops of the walls in some scenes. The budget was less that $20,000 and the script looked like a child wrote it. Only Patterson could understand it. Still...its entertainment and its a classic.\",\n          \"I just re-watched 08th MS Gundam for the 2nd time. It is so much better than Gundam Wing. I cant wait to get the DVD and see what was edited out of the series. This is great to see the Gundams actually move about clumsily through the land. Somebody really thought over writing this move script.See this today,.\",\n          \"The always delightful Kevin Spacey makes us once again question certain truths in our lives. After driving us into believing he is a mumbling small time crook only to expose the true power of cinematic deception in the classic The Usual Suspect; this time around he leads us into believing he is Prot, a likable alien who assumes a human form whilst strolling around the Earth.Prot is a peculiar stranger who has seemingly appeared out of nowhere, only to be hospitalized in the Manhattan Psychiatric Institution. Dr. Mark Powell Jeff Bridges soon takes extreme interest in him, and even finds himself pondering if Prot is actually a supreme being from planet K-PAX, or perhaps he is only a forgotten human being with severe psychiatric issues. While the ending can be seen and discussed in many ways, it remains a complex, enlightening journey which leaves a gr eat deal of intriguing/philosophical questions behind it.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_pt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49045,\n        \"samples\": [\n          \"Alguns spoilers A Soap tem momentos maravilhosos para recomend\\u00e1-lo. Quando Charlotte e Veronica se aproximam da intimidade, \\u00e9 uma cena linda, discreta e verdadeiramente er\\u00f3tica. Eu tamb\\u00e9m adorava a partitura e a cinematografia suave e sem som. N\\u00e3o estou claro se a curiosa estrutura de parada e in\\u00edcio do filme vem de ser digerida originalmente em forma serializada, o locutor descrevendo a a\\u00e7\\u00e3o em se\\u00e7\\u00f5es \\u00e9 bem chato. Meu maior problema com esse filme \\u00e9 a representa\\u00e7\\u00e3o bastante absurda de uma mulher trans e de sua vida. Claro, ela tem que ser mostrada como uma trabalhadora sexual o que mais, desmiolada, impratic\\u00e1vel, absurdamente cheia de babados, completamente sem esperan\\u00e7a quando se trata de se vestir e incapaz de fazer quaisquer liga\\u00e7\\u00f5es interpessoais no mundo. Al\\u00e9m disso, ela sempre mostrava dois dias de crescimento de barba por algum motivo bizarro, como se quisesse enfatizar o quanto a vida dela \\u00e9 de mau gosto. E ela est\\u00e1 esperando para fazer uma cirurgia de redesigna\\u00e7\\u00e3o de g\\u00eanero quando aparentemente n\\u00e3o fez mais nada para encaminhar sua transi\\u00e7\\u00e3o. Na verdade, o personagem se assemelha a uma drag queen, n\\u00e3o a algu\\u00e9m que est\\u00e1 em transi\\u00e7\\u00e3o. Essas s\\u00e3o fantasias t\\u00edpicas de pessoas de fora que realmente n\\u00e3o est\\u00e3o ligadas a transpeople. Os filmes de fetichiza\\u00e7\\u00e3o da cirurgia de GRS s\\u00e3o uma forma de objetivar as pessoas que est\\u00e3o passando pela transi\\u00e7\\u00e3o. N\\u00e3o impressionado com este aspecto do filme no m\\u00ednimo. No m\\u00ednimo, por que n\\u00e3o ter o personagem interpretado por algu\\u00e9m que realmente \\u00e9 transg\\u00eanero ... Eu pensei que o ator que interpreta Veronica estava bem, mas n\\u00e3o melhor do que isso. Muito melhor era a mulher retratando Charlotte, um personagem muito complexo cheio de energia, auto-avers\\u00e3o, desejo e contradi\\u00e7\\u00f5es. Se ela estava t\\u00e3o fascinada por algu\\u00e9m com energia feminina, uma Charlotte poderia ir a um bar feminino em dois segundos e encontr\\u00e1-lo. Sim, ela foi atra\\u00edda por Veronica mas mais como uma id\\u00e9ia abstrata de algu\\u00e9m com caracter\\u00edsticas masculinas / femininas um toque gentil, mas com um bom soco, n\\u00e3o como uma pessoa \\u00fanica. Sim, o que A Soap diz sobre o amor \\u00e9 muitas vezes am\\u00e1vel e comovente, mas isso n\\u00e3o significa que uma minoria j\\u00e1 estereotipada tenha de ser estereotipada um pouco mais no processo. Este filme tamb\\u00e9m prova que os dinamarqueses s\\u00e3o maus dan\\u00e7arinos. Para uma premissa t\\u00e3o promissora melhor executada em um filme como \\\"Different For Girls\\\", o filme final \\u00e9 uma decep\\u00e7\\u00e3o.\",\n          \"Este filme reuniu alguns dos antigos tripulantes da Spinal para outro filme de mockumentary, desta vez girando em torno do mundo do Dog Show, como seus donos se preparam e treinam para o show antes de passar para o show em si. Conhecemos v\\u00e1rias equipes como eles esperam para ganhar o pr\\u00eamio principal - The Flecks, Cookie, que parece ter dormido com todos os homens de sempre, e Gerry, que tenta lidar com as antigas escapadas de sua esposa e com o fato de que ele literalmente tem dois p\\u00e9s esquerdos. Harlan, cujo cachorro fala com ele e gosta de ventriloquismo. Os cisnes que tomam muito caf\\u00e9 e gritam um com o outro. Donalan e Vanderhoof, o casal gay, e Cabot e Cummings, que venceram os dois \\u00faltimos anos. Fred Willard comenta sobre o show e \\u00e9 muito engra\\u00e7ado como sempre. Cenas engra\\u00e7adas incluem o Look at me! cena, e qualquer com Levy. Infelizmente algumas das melhores cenas foram apagadas ou filmadas depois - Willard entrevistando Leslie Cabot, e o ep\\u00edlogo alternativo com Gerry \\u00e9 uma das coisas mais engra\\u00e7adas que eu j\\u00e1 vi. Se estas tivessem sido inclu\\u00eddas, daria ao filme uma marca extra. Mas ... 7 de 10\",\n          \"Eu n\\u00e3o pensava assim, mas meu amigo disse a primeira coisa que ele pensou quando ouviu que o t\\u00edtulo \\\"Midnight Cowboy\\\" era um porn\\u00f4 gay. Naquele momento, tudo que eu sabia era a refer\\u00eancia feita naquele epis\\u00f3dio de \\\"Seinfeld\\\" com Jerry tentando levar Kramer para a Fl\\u00f3rida naquele \\u00f4nibus e Kramers todo doente e com uma hemorragia nasal. O filme foi \\u00f3timo, e surpreendentemente otimista. e nem todos pessimistas pessimistas pretensiosos como alguns filmes que eu n\\u00e3o consigo lembrar porque s\\u00e3o todos uma porcaria.O enredo basicamente consistia de um jovem cowboy ing\\u00eanuo Joe Buck indo para Nova York tentando ser um prostituto prostituta, basicamente, pensando que ser\\u00e1 f\\u00e1cil pickings, s\\u00f3 para bater duro na parede de tijolos quando uma mulher acaba apressando-o, acusando-o de seu encontro sexual. Ent\\u00e3o ele conhece Enrico Salvatore Rizzo, chamado \\\"Ratso\\\" por todos e os gays bonitos que fazem piada dele o tempo todo. Voc\\u00ea pensa nele como um canalha, mas um am\\u00e1vel como Han Solo ou Lando Calrissian e surpreendentemente ele e Joe tornam-se amigos, e o filme \\u00e9 t\\u00e3o doce e emocionante ao v\\u00ea-los mais amig\\u00e1veis \\u200b\\u200be tal e tal. Rizzo se revela realmente um homem triste e lastim\\u00e1vel, muito doente, muito deprimido e constrangido, odeia ser chamado de \\\"Ratso\\\" e quer ir para a Fl\\u00f3rida, onde acha que a vida ser\\u00e1 muito melhor e todos os seus problemas resolvidos, e inferno aprender a ser um cozinheiro e ser famoso por l\\u00e1. \\u00c9 reconfortante ver Joe fazer tudo o que ele faz para lev\\u00e1-los para a Fl\\u00f3rida, junto com muitos momentos hilariantes como Ratso tentando roubar comida naquela festa hippie, e ser pego pelo mulher que diz \\\"Nossa, bem, voc\\u00ea sabe, \\u00e9 gr\\u00e1tis. Voc\\u00ea n\\u00e3o tem que roub\\u00e1-lo.\\\" e ele diz \\\"Bem, se estiver livre, ent\\u00e3o eu n\\u00e3o vou roubar\\\", e aquele momento cl\\u00e1ssico completamente improvisado e n\\u00e3o programado, onde Hoffman quase \\u00e9 atingido por aquele T\\u00e1xi, e grita \\\"Ei, eu estou andando aqui! Estou andando aqui!\\\" \\u00e9 t\\u00e3o cr\\u00edvel, que voc\\u00ea nunca acreditaria que Joe Buck cresceria para ser o distinto e respeitado ator Jon Voight, e Ratso Rizzo cresceria para ser o lend\\u00e1rio e amado Dustin Hoffman. N\\u00e3o \\u00e9 a primeira vez que eles trabalham juntos em pap\\u00e9is principais, mas a qu\\u00edmica \\u00e9 t\\u00e3o espessa e intensa. Ent\\u00e3o, a parte triste que eu acredito \\u00e9 um exagero de cham\\u00e1-la de \\\"deprimente\\\". Ratso Rizzo est\\u00e1 desmoronando durante todo o filme, mal consegue andar, mal come, tosse muito, est\\u00e1 doente e atinge um ponto de cabe\\u00e7a no \\u00f4nibus a caminho da Fl\\u00f3rida. Ele est\\u00e1 doendo muito, e a apenas quil\\u00f4metros de Miami, ele finalmente morre no \\u00f4nibus. O motorista do \\u00f4nibus garante a todos que nada est\\u00e1 errado e continua. Triste, mas n\\u00e3o do tipo que faz voc\\u00ea ir para casa e chorar e lamentar miseravelmente como se tivesse acabado de perder seu cachorro de 13 anos. Tudo em todos, \\u00f3timo filme. E a trilha sonora consiste basicamente de \\\"Everybodys Talking\\\", tocada durante todo o filme nos momentos apropriados. Um movimento estranho, mas \\u00f3timo, j\\u00e1 que a m\\u00fasica \\u00e9 boa e se encaixa perfeitamente no tom do filme. V\\u00e1 ver, \\u00e9 \\u00f3timo, vai comprar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"pos\",\n          \"neg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Análise do DataSet:\n",
        "- Os índices do dataframe antecedem a coluna id.\n",
        "- text_en: é uma coluna que contém um texto em inglês\n",
        "- text_pt: é uma coluna em inglês só que traduzida para português\n",
        "- sentimnet: mostra um sentimento de texto, sendo neg(negativo) e pos (positivo)\n",
        "\n",
        "Análise das Variáveis\n",
        "\n",
        "- **text_en**: qualitativa nominal\n",
        "- **text_pt**: qualitativa nominal\n",
        "- **sentiment**: qualitativa"
      ],
      "metadata": {
        "id": "Ntd8l_vQ6300"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "dTMNGCt3_s8N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_resenhas.tail()"
      ],
      "metadata": {
        "id": "D1tvPtPx7fHk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9fa27282-4286-448c-bf14-554f22254444"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id                                            text_en  \\\n",
              "49454  49456  Seeing as the vote average was pretty low, and...   \n",
              "49455  49457  The plot had some wretched, unbelievable twist...   \n",
              "49456  49458  I am amazed at how this movieand most others h...   \n",
              "49457  49459  A Christmas Together actually came before my t...   \n",
              "49458  49460  Working-class romantic drama from director Mar...   \n",
              "\n",
              "                                                 text_pt sentiment  \n",
              "49454  Como a média de votos era muito baixa, e o fat...       pos  \n",
              "49455  O enredo teve algumas reviravoltas infelizes e...       pos  \n",
              "49456  Estou espantado com a forma como este filme e ...       pos  \n",
              "49457  A Christmas Together realmente veio antes do m...       pos  \n",
              "49458  O drama romântico da classe trabalhadora do di...       pos  "
            ],
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <td>pos</td>\n",
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              "      <th>49456</th>\n",
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              "      <th>49457</th>\n",
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              "      <td>A Christmas Together actually came before my t...</td>\n",
              "      <td>A Christmas Together realmente veio antes do m...</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>49458</th>\n",
              "      <td>49460</td>\n",
              "      <td>Working-class romantic drama from director Mar...</td>\n",
              "      <td>O drama romântico da classe trabalhadora do di...</td>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f09f593-aa5f-4bf5-98bd-498e2a8eaf25')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2f09f593-aa5f-4bf5-98bd-498e2a8eaf25 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-2f09f593-aa5f-4bf5-98bd-498e2a8eaf25');\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",
              "\n",
              "\n",
              "<div id=\"df-c1825bcf-9413-4d7f-bbd0-29907341fda6\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c1825bcf-9413-4d7f-bbd0-29907341fda6')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c1825bcf-9413-4d7f-bbd0-29907341fda6 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_resenhas\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 49456,\n        \"max\": 49460,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          49457,\n          49460,\n          49458\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_en\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"The plot had some wretched, unbelievable twists. However, the chemistry between Mel Brooks and Leslie Ann Warren was excellent. The insight that she comes to, \\\"There are just moments,\\\" provides a philosophical handle by which anyone could pick up, and embrace, life.That was one of several moments that were wonderfully memorable.\",\n          \"Working-class romantic drama from director Martin Ritt is as unbelievable as they come, yet there are moments of pleasure due mostly to the charisma of stars Jane Fonda and Robert De Niro both terrific. Shes a widow who cant move on, hes illiterate and a closet-inventor--you can probably guess the rest. Adaptation of Pat Barkers novel \\\"Union Street\\\" a better title! is so laid-back it verges on bland, and the films editing is a mess, but its still pleasant; a rosy-hued blue-collar fantasy. There are no overtures to serious issues even the illiteracy angle is just a plot-tool for the ensuing love story and no real fireworks, though the characters are intentionally a bit colorless and the leads are toned down to an interesting degree. The finale is pure fluff--and cynics will find it difficult to swallow--though these two characters deserve a happy ending and the picture wouldnt really be satisfying any other way.  from\",\n          \"I am amazed at how this movieand most others has a average 5 stars and lower when there are crappy movies averaging 7 to 10 stars on IMDb. The fanboy mentality strikes again. When this movie came out just about everyone slammed it. Even my ex-girlfriend said this movie questionable. Years later I sat down to watch this movie and I found myself enjoying. Even laughing quite a bit. This and The Replacement Killers are the movies that had people labeling the director Antoine Fuqua as the black Michael Bay. I dont see how since most of Fuquas movies are smarter than anything Michael Bay has came up with. At any rate...Story: Alvin SandersJamie Foxx is former convict that is used by a no-nonsense Treasury agent EdgarDavid Morse as a pawn to catch a killer named BristolDoug Hutchinson. Alvins every moves are tracked by a bug implanted in his jaw after an accident. While these agents are after Bristol, Bristol is after the gold bricks that were taken in a heist gone awry.Jamie Foxx is funny as well as great as Alvin Sanders. Alvin is a fast-talker that is a lot smarter than he lets on. Doug Hutchinson is okay as Bristol. He can be over-the-top sometimes in his John Malkovitchesque demeanor. He was better here than he was as Looney Bin Jim in Punisher: War Zone. David Morse is good as the hard edged treasury agent. Even Mike Epps is funny as Alvins brother Stevie. Both him and Jamie had some funny moments on screen.The only flaw of the movie is the some of the attempts at a thriller fall flat. The scenario at the horse race track is way over-the-top but I couldnt look away. The director went all out there so he gets points for that. Plus the bomb scene with the treasury agent tied to a chair while the detonator rests on the door was pretty nifty.All in all Bait is not a bad movie by a long shot. Its never boring, its always funny and I wasnt checking my watch every minute. That should count for something. Bait is one of the most underrated movies of 2000 period.PS: to the reviewer that claimed this movie is too violent.... How long have you been living under a rock? Im pretty sure youve seen the Die Hard series and EVERY movie by Quentin Tarantino. But those movies arent violent right? Weirdo.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_pt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"O enredo teve algumas reviravoltas infelizes e inacredit\\u00e1veis. No entanto, a qu\\u00edmica entre Mel Brooks e Leslie Ann Warren foi excelente. A percep\\u00e7\\u00e3o de que ela vem, \\\"H\\u00e1 apenas alguns momentos\\\", fornece uma abordagem filos\\u00f3fica pela qual qualquer pessoa poderia captar e abra\\u00e7ar a vida. Esse foi um dos v\\u00e1rios momentos que foram maravilhosamente memor\\u00e1veis.\",\n          \"O drama rom\\u00e2ntico da classe trabalhadora do diretor Martin Ritt \\u00e9 t\\u00e3o inacredit\\u00e1vel quanto eles s\\u00e3o, mas h\\u00e1 momentos de prazer devido principalmente ao carisma das estrelas Jane Fonda e Robert De Niro, ambos \\u00f3timos. Ela \\u00e9 uma vi\\u00fava que n\\u00e3o pode seguir em frente, \\u00e9 analfabeta e \\u00e9 uma inventora de arm\\u00e1rios - provavelmente voc\\u00ea pode adivinhar o resto. Adapta\\u00e7\\u00e3o do romance de Pat Barkers \\\"Union Street\\\" um t\\u00edtulo melhor! \\u00e9 t\\u00e3o descontra\\u00eddo que beirar sem gra\\u00e7a, e a edi\\u00e7\\u00e3o de filmes \\u00e9 uma bagun\\u00e7a, mas ainda \\u00e9 agrad\\u00e1vel; uma fantasia de colarinho azul em tons rosados. N\\u00e3o h\\u00e1 aberturas para quest\\u00f5es s\\u00e9rias, mesmo o \\u00e2ngulo do analfabetismo \\u00e9 apenas uma ferramenta de enredo para a hist\\u00f3ria de amor que se segue e n\\u00e3o h\\u00e1 fogos de artif\\u00edcio reais, embora os personagens sejam intencionalmente um pouco incolores e as deriva\\u00e7\\u00f5es reduzidas a um n\\u00edvel interessante. O final \\u00e9 puro fluff - e os c\\u00ednicos achar\\u00e3o dif\\u00edcil de engolir - embora esses dois personagens mere\\u00e7am um final feliz e a foto n\\u00e3o seria realmente satisfat\\u00f3ria de outra maneira. a partir de\",\n          \"Estou espantado com a forma como este filme e a maioria dos outros tem uma m\\u00e9dia de 5 estrelas e menor quando h\\u00e1 filmes de baixa qualidade com m\\u00e9dia de 7 a 10 estrelas no IMDb. A mentalidade de fanboy ataca novamente. Quando este filme saiu quase todo mundo bateu. At\\u00e9 minha ex-namorada disse esse filme question\\u00e1vel. Anos depois, sentei-me para assistir a esse filme e me vi aproveitando. Mesmo rindo um pouco. Este e os assassinos de substitui\\u00e7\\u00e3o s\\u00e3o os filmes que tinham pessoas rotulando o diretor Antoine Fuqua como o negro Michael Bay. Eu n\\u00e3o vejo como a maioria dos filmes de Fuquas s\\u00e3o mais inteligentes do que qualquer coisa que Michael Bay tenha inventado. De qualquer forma ... Hist\\u00f3ria: Alvin SandersJamie Foxx \\u00e9 o ex-presidi\\u00e1rio que \\u00e9 usado por um no-nonsense agente do Tesouro EdgarDavid Morse como um pe\\u00e3o para pegar um assassino chamado BristolDoug Hutchinson. Alvins todos os movimentos s\\u00e3o rastreados por um bug implantado em sua mand\\u00edbula ap\\u00f3s um acidente. Enquanto esses agentes est\\u00e3o atr\\u00e1s de Bristol, Bristol est\\u00e1 atr\\u00e1s dos tijolos de ouro que foram levados em um assalto que deu errado. Jamie Foxx \\u00e9 t\\u00e3o divertido quanto Alvin Sanders. Alvin \\u00e9 um falador r\\u00e1pido que \\u00e9 muito mais esperto do que ele deixa transparecer. Doug Hutchinson est\\u00e1 bem como Bristol. Ele pode ser over-the-top, por vezes, em seu comportamento John Malkovitchesque. Ele era melhor aqui do que como Looney Bin Jim em Punisher: War Zone. David Morse \\u00e9 bom como o agente do Tesouro. At\\u00e9 mesmo Mike Epps \\u00e9 engra\\u00e7ado como o irm\\u00e3o de Alvin Stevie. Tanto ele quanto Jamie tiveram alguns momentos engra\\u00e7ados na tela. A \\u00fanica falha do filme \\u00e9 que algumas das tentativas de um suspense caem de surpresa. O cen\\u00e1rio na pista de corrida de cavalos \\u00e9 muito exagerado, mas n\\u00e3o consegui desviar o olhar. O diretor foi todo l\\u00e1 fora, ent\\u00e3o ele ganha pontos por isso. Al\\u00e9m disso, a cena da bomba com o agente do tesouro amarrado a uma cadeira enquanto o detonador descansa na porta era bem bacana.Tudo em todas as iscas n\\u00e3o \\u00e9 um filme ruim por um longo tiro. Sua nunca chato, \\u00e9 sempre engra\\u00e7ado e eu n\\u00e3o estava verificando o meu rel\\u00f3gio a cada minuto. Isso deve contar para alguma coisa. Bait \\u00e9 um dos filmes mais subestimados do per\\u00edodo 2000.PS: para o cr\\u00edtico que alegou que este filme \\u00e9 muito violento ... H\\u00e1 quanto tempo voc\\u00ea vive sob uma rocha? Tenho certeza que voc\\u00ea viu a s\\u00e9rie Die Hard e cada filme de Quentin Tarantino. Mas esses filmes n\\u00e3o s\\u00e3o violentos, certo? Esquisito.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"pos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Relembrando Pandas"
      ],
      "metadata": {
        "id": "PE4LrGmfSdtc"
      }
    },
    {
      "source": [
        "# RESENHA 189\n",
        "# Acessando a linha pela posição numérica\n",
        "print(\"Negativa \\n\")\n",
        "print(df_resenhas.text_pt[188])\n"
      ],
      "cell_type": "code",
      "metadata": {
        "id": "oQbZ3OaOTgvP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6b5ef13b-d29a-4758-ad55-fc1beb79c6d7"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Negativa \n",
            "\n",
            "O filme é uma canção de ninar feliz, foi feito para nos fazer dormir. E é isso que fazemos, enquanto sonhamos com a bela Natasha Henstridge. Nenhum roteiro, nenhum personagem profundo, nada de especial. Então, vamos dormir.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# RESENHA 49002\n",
        "print(\"Positivo \\n\")\n",
        "print(df_resenhas.text_pt[49002])"
      ],
      "metadata": {
        "id": "G1XvbMgUSmAL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fcf1643-b95a-4512-ccbf-b53b887a78a0"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Positivo \n",
            "\n",
            "Crescendo em Nova York no final dos anos 80 e início dos anos 90, posso dizer pessoalmente que este é um dos documentários mais importantes feitos para cobrir esse lugar neste período de tempo. Não Madonna não veio com a idéia de Voguing, mas é de onde ela tirou! Em vez de combater a violência uns dos outros ou em brigas de gato, o voguing permitia que as pessoas \"lutassem\" dentro dos confins de tudo, menos que tocassem umas às outras, o que justificaria uma desqualificação automática. Vendo este tipo de extraordinariamente talentosas / bem orquestradas \"jogadas\" nos clubes foi nada menos do que espetacular e todos os grandes nomes de antigamente estão aqui ... Pepper La Beija, Paris Duprée, Xtragavaganza, etc. ..tudo comemorado nos gostos de peças de época como a música de Malcom McLarens \"Deep in Vogue\" ... não importava quem você era, ou de onde você era porque quando você passava por aquelas portas nesse \"reino mágico\" de De certa forma, você se tornou parte de algo maior que você / você era importante / e, o mais importante, a criação de seus próprios movimentos e imaginação ... e qualquer pessoa de qualquer lugar poderia se tornar Rei ou Rainha como poderia ter sido. As palavras e inteligência eram tão afiadas quanto os movimentos no chão. Toda a tensão, excitação e magia dessa energia urbana de NYC é capturada neste filme. BRILHANTE!!! POR FAVOR, LIBERE EM DVD para o mundo ver !!! Obrigado!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# QUANTIDADE DE SENTIMENTOS\n",
        "quantidade = df_resenhas['sentiment'].count()\n",
        "print(quantidade)\n"
      ],
      "metadata": {
        "id": "Cisa5qdlStjp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "49ffdc81-6835-4326-b7cc-6f2c88ac0933"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49459\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# QUANTIDADE DE SENTIMENTO DISCRIMINANDO POR VALORES\n",
        "print(df_resenhas.sentiment.value_counts())"
      ],
      "metadata": {
        "id": "KZvBOtd3Wm3z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e1636a9-47e7-4ee5-f119-f3b6ffb6a771"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sentiment\n",
            "neg    24765\n",
            "pos    24694\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TODAS AS INFORMAÇÕES DO DATAFRAME\n",
        "print(df_resenhas)\n"
      ],
      "metadata": {
        "id": "q-R4pouaS0yy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4da2012-0369-487c-ef39-a4b8cb609d6f"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          id                                            text_en  \\\n",
            "0          1  Once again Mr. Costner has dragged out a movie...   \n",
            "1          2  This is an example of why the majority of acti...   \n",
            "2          3  First of all I hate those moronic rappers, who...   \n",
            "3          4  Not even the Beatles could write songs everyon...   \n",
            "4          5  Brass pictures movies is not a fitting word fo...   \n",
            "...      ...                                                ...   \n",
            "49454  49456  Seeing as the vote average was pretty low, and...   \n",
            "49455  49457  The plot had some wretched, unbelievable twist...   \n",
            "49456  49458  I am amazed at how this movieand most others h...   \n",
            "49457  49459  A Christmas Together actually came before my t...   \n",
            "49458  49460  Working-class romantic drama from director Mar...   \n",
            "\n",
            "                                                 text_pt sentiment  \n",
            "0      Mais uma vez, o Sr. Costner arrumou um filme p...       neg  \n",
            "1      Este é um exemplo do motivo pelo qual a maiori...       neg  \n",
            "2      Primeiro de tudo eu odeio esses raps imbecis, ...       neg  \n",
            "3      Nem mesmo os Beatles puderam escrever músicas ...       neg  \n",
            "4      Filmes de fotos de latão não é uma palavra apr...       neg  \n",
            "...                                                  ...       ...  \n",
            "49454  Como a média de votos era muito baixa, e o fat...       pos  \n",
            "49455  O enredo teve algumas reviravoltas infelizes e...       pos  \n",
            "49456  Estou espantado com a forma como este filme e ...       pos  \n",
            "49457  A Christmas Together realmente veio antes do m...       pos  \n",
            "49458  O drama romântico da classe trabalhadora do di...       pos  \n",
            "\n",
            "[49459 rows x 4 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# INFORMAÇÕES\n",
        "df_resenhas.info()\n"
      ],
      "metadata": {
        "id": "iEeqpfRUUjnt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "142e3e06-361d-4c26-8239-b5cfef775b44"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 49459 entries, 0 to 49458\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   id         49459 non-null  int64 \n",
            " 1   text_en    49459 non-null  object\n",
            " 2   text_pt    49459 non-null  object\n",
            " 3   sentiment  49459 non-null  object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 1.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# DESCRIÇÃO\n",
        "df_resenhas.describe()\n"
      ],
      "metadata": {
        "id": "KCIXNCDiUovL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "7e8da941-402e-4118-8c9a-9f455d99d7f5"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id\n",
              "count  49459.000000\n",
              "mean   24730.960917\n",
              "std    14277.792868\n",
              "min        1.000000\n",
              "25%    12366.500000\n",
              "50%    24731.000000\n",
              "75%    37095.500000\n",
              "max    49460.000000"
            ],
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              "      <td>24730.960917</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
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              "        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",
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              "\n",
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              "\n",
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              "\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ce58952-7c79-41ff-913b-4180df4773f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_resenhas\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17825.078957729485,\n        \"min\": 1.0,\n        \"max\": 49460.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          24730.960917123273,\n          24731.0,\n          49459.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# COLUNAS DO DATAFRAME\n",
        "df_resenhas.columns\n"
      ],
      "metadata": {
        "id": "ZTLjafqdUwyz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac041a09-dd3b-4c0a-9c42-a15152c04e3a"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['id', 'text_en', 'text_pt', 'sentiment'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Machine Learning\n",
        "\n",
        "scikit-learn\n",
        "Machine Learning in Python\n",
        "\n",
        "Documentação: https://scikit-learn.org/stable/\n",
        "\n",
        "- Ferramentas simples e eficientes para análise preditiva de dados\n",
        "- Acessível a todos e reutilizável em vários contextos\n",
        "- Construído em NumPy, SciPy e matplotlib\n",
        "- Código aberto, comercialmente utilizável - licença BSD\n"
      ],
      "metadata": {
        "id": "nelDjzVV_uFS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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      ],
      "metadata": {
        "id": "SpuadJg6YZTD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "HNMGHPXfYToD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Machine Learning\n",
        "\n",
        "Inteligència artifical é um conceito geral, resumidamente são máquinas que imitam os seres humanos.\n",
        "A inteligência artificial é dividida em duas grandes categorias:\n",
        "\n",
        "* Machine Learning: Modelos Preditivos (Modelos que vão prever alto)\n",
        "* Deep Learning: Modelo de redes neurais (ex: visão computacional - CNN)\n",
        "\n",
        "- Machine Learning Supervisionado: rotulos(labels)\n",
        "-  Algoritimo escolher?"
      ],
      "metadata": {
        "id": "zsnn8zm9BzVM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        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)\n",
        "\n"
      ],
      "metadata": {
        "id": "JdYvmPZmGH6L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Escolhemos Machine Learning Supervisionado - Classificação.\n",
        "## Qual algoritimo escolher\n",
        "\n",
        "\n",
        "| **Método**                   | **Descrição**                                                                 | **Quando Usar**                                                | **Exemplo**                                                        |\n",
        "|------------------------------|-------------------------------------------------------------------------------|--------------------------------------------------------------|--------------------------------------------------------------------|\n",
        "| **Regressão Linear Simples** | Modela a relação entre uma variável dependente e uma independente usando uma linha reta. | Quando há uma relação linear clara entre duas variáveis.    | Prever o preço de uma casa com base em sua área.                  |\n",
        "| **Regressão Linear Múltipla** | Extensão da regressão linear simples que usa várias variáveis independentes. | Quando várias variáveis influenciam a variável dependente.  | Prever o preço de uma casa com área, localização e número de quartos. |\n",
        "| **Regressão Polinomial**     | Modela a relação usando um polinômio de grau n.                             | Quando a relação entre as variáveis é não linear.           | Prever vendas com base no tempo, onde a relação é curva.          |\n",
        "| **Árvore de Decisão**        | Estrutura hierárquica que faz decisões baseadas em perguntas sobre os dados. | Para classificação e regressão, especialmente com dados categóricos. | Classificar se um e-mail é spam ou não baseado em palavras-chave.  |\n",
        "| **Regressão Logística**      | Modela a probabilidade de um evento binário.                                | Quando a variável dependente é categórica (sim/não).        | Prever se um cliente comprará ou não um produto.                  |\n",
        "| **Naive Bayes**              | Classificação baseada no teorema de Bayes, assumindo independência entre variáveis. | Para tarefas de classificação, especialmente com texto.     | Classificar sentimentos em análises de produtos.                   |\n",
        "\n",
        "\n",
        "variavel independente (depende de outras variaveis)\n",
        "variavel dependente (variavel alvo)"
      ],
      "metadata": {
        "id": "VLw3eGKSGmka"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Treino e Teste"
      ],
      "metadata": {
        "id": "GCK4NWq2JjGO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![traintest.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "C7F4-ZO8bFw3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Separar ou segregar.\n",
        "70 - treinar\n",
        "30 - avaliar (esconder)\n"
      ],
      "metadata": {
        "id": "iyMus7vHSrQZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Documentação:\n",
        "https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html"
      ],
      "metadata": {
        "id": "CErah2BJVdVl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importação\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# definir variáveis de treino e de teste\n",
        "# random_state: passe um int para embaralhamento\n",
        "# criando variáveis\n",
        "treino, teste, classe_treino, classe_teste = train_test_split(df_resenhas.text_pt,\n",
        "                                                              df_resenhas.sentiment,\n",
        "                                                              random_state=42)\n"
      ],
      "metadata": {
        "id": "LeTfPLZvUtXT"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        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)\n"
      ],
      "metadata": {
        "id": "up09N5joY11T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando Treino\n",
        "treino"
      ],
      "metadata": {
        "id": "4KmIYSwpZUfj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "2a0f1623-e421-488c-d8da-fe227b829a7c"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1348     Embora o filme tenha sido apenas assim, o clos...\n",
              "27466    Este é provavelmente um dos piores filmes que ...\n",
              "29998    De vez em quando, um filme irá varrer ao seu r...\n",
              "48186    Este é um conto completamente diabólico de quã...\n",
              "26473    Lenta, chata, extremamente repetitiva. Não adm...\n",
              "                               ...                        \n",
              "11284    Naach teria ganhado um Razzie para o Pior Film...\n",
              "44732    Apenas assisti a esse filme em DVD e achei a a...\n",
              "38158    Melhor show desde Seinfeld. Ela é realmente mu...\n",
              "860      Eu pareço estar discordando com muitas pessoas...\n",
              "15795    Minhas duas filhas de 11 e 13 anos e eu tive s...\n",
              "Name: text_pt, Length: 37094, dtype: object"
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "      <th>text_pt</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>1348</th>\n",
              "      <td>Embora o filme tenha sido apenas assim, o clos...</td>\n",
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              "    <tr>\n",
              "      <th>27466</th>\n",
              "      <td>Este é provavelmente um dos piores filmes que ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29998</th>\n",
              "      <td>De vez em quando, um filme irá varrer ao seu r...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48186</th>\n",
              "      <td>Este é um conto completamente diabólico de quã...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26473</th>\n",
              "      <td>Lenta, chata, extremamente repetitiva. Não adm...</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11284</th>\n",
              "      <td>Naach teria ganhado um Razzie para o Pior Film...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44732</th>\n",
              "      <td>Apenas assisti a esse filme em DVD e achei a a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38158</th>\n",
              "      <td>Melhor show desde Seinfeld. Ela é realmente mu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>Eu pareço estar discordando com muitas pessoas...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15795</th>\n",
              "      <td>Minhas duas filhas de 11 e 13 anos e eu tive s...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37094 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Verificando Teste\n",
        "teste"
      ],
      "metadata": {
        "id": "HSKVrA5VZlXT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "f04b64d8-17e3-4c07-fd9a-f84d0a7b255f"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12532    Isso era incomum: um filme moderno que era ult...\n",
              "35445    Alguns dos meus velhos amigos sugeriram que eu...\n",
              "20279    Que prazer. Isto é realmente uma paródia. Some...\n",
              "2969     Há cerca de dez minutos a meio da Strangeland,...\n",
              "45161    Otelo, a clássica história de Shakespearen sob...\n",
              "                               ...                        \n",
              "16421    Crescendo como filho do cinema, uma das trilog...\n",
              "39861    Este filme é o melhor filme de todos os tempos...\n",
              "309      \"Electra Glide in Blue\" é um movimento lento e...\n",
              "20638    Eu amo esse filme ! Eu acho que já vi 5 vezes ...\n",
              "38935    Eu vi alguns filmes sobre transtornos alimenta...\n",
              "Name: text_pt, Length: 12365, dtype: object"
            ],
            "text/html": [
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              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>text_pt</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12532</th>\n",
              "      <td>Isso era incomum: um filme moderno que era ult...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35445</th>\n",
              "      <td>Alguns dos meus velhos amigos sugeriram que eu...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20279</th>\n",
              "      <td>Que prazer. Isto é realmente uma paródia. Some...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2969</th>\n",
              "      <td>Há cerca de dez minutos a meio da Strangeland,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45161</th>\n",
              "      <td>Otelo, a clássica história de Shakespearen sob...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16421</th>\n",
              "      <td>Crescendo como filho do cinema, uma das trilog...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39861</th>\n",
              "      <td>Este filme é o melhor filme de todos os tempos...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>309</th>\n",
              "      <td>\"Electra Glide in Blue\" é um movimento lento e...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>Eu amo esse filme ! Eu acho que já vi 5 vezes ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38935</th>\n",
              "      <td>Eu vi alguns filmes sobre transtornos alimenta...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12365 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_resenhas.info()"
      ],
      "metadata": {
        "id": "4sd4ZWfiaKob",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eebe92a9-b59b-47e7-b9b5-fb8c7ed272e2"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 49459 entries, 0 to 49458\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   id         49459 non-null  int64 \n",
            " 1   text_en    49459 non-null  object\n",
            " 2   text_pt    49459 non-null  object\n",
            " 3   sentiment  49459 non-null  object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 1.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classe_treino"
      ],
      "metadata": {
        "id": "nynUekNOeXVE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "adf8e446-5473-4960-e77b-6b097f8b6768"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1348     neg\n",
              "27466    neg\n",
              "29998    neg\n",
              "48186    pos\n",
              "26473    neg\n",
              "        ... \n",
              "11284    neg\n",
              "44732    pos\n",
              "38158    pos\n",
              "860      neg\n",
              "15795    pos\n",
              "Name: sentiment, Length: 37094, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1348</th>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27466</th>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29998</th>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48186</th>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26473</th>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11284</th>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44732</th>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38158</th>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>neg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15795</th>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>37094 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mostrando os Rótulos (labels) de treino\n",
        "classe_teste"
      ],
      "metadata": {
        "id": "_f_dstiFaVAC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "d06c2a11-8f88-46ed-efcc-b9cef9151077"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12532    pos\n",
              "35445    neg\n",
              "20279    pos\n",
              "2969     neg\n",
              "45161    pos\n",
              "        ... \n",
              "16421    pos\n",
              "39861    pos\n",
              "309      neg\n",
              "20638    pos\n",
              "38935    pos\n",
              "Name: sentiment, Length: 12365, dtype: object"
            ],
            "text/html": [
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              "    .dataframe tbody tr th {\n",
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              "    .dataframe thead th {\n",
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              "      <td>neg</td>\n",
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              "    <tr>\n",
              "      <th>20279</th>\n",
              "      <td>pos</td>\n",
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              "      <td>neg</td>\n",
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              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16421</th>\n",
              "      <td>pos</td>\n",
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              "      <th>39861</th>\n",
              "      <td>pos</td>\n",
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              "      <th>309</th>\n",
              "      <td>neg</td>\n",
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              "    <tr>\n",
              "      <th>20638</th>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38935</th>\n",
              "      <td>pos</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>12365 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Relebrando\n",
        "- Variável dependente: nosso alvo (Observada no experimento)\n",
        "- Variável independente: influênciam o resultado.\n",
        "\n",
        "Exemplo:\n",
        "Estudo: Impacto da temperatura na taxa de crescimento de plantas.\n",
        "\n",
        "independente: temperatura (a temperatura a que as plantas são expostas)\n",
        "dependente: taxa de crescimento das plantas.\n",
        "\n",
        "Qual a diferença entre a regressão linear e logística?\n",
        "\n",
        "- Na regressão linear, a linha de regressão é reta. Qualquer alteração em uma variável independente afeta diretamente a variável dependente.\n",
        "-  Na regressão logística, a linha de regressão é uma curva em forma de S, também conhecida como curva sigmoide.\n",
        "\n",
        "O que são Bag Of Words?\n",
        "\n",
        "Bag of Words (BOW), ou \"saco de palavras\" em português, é um método para extrair recursos de textos e representá-los como uma lista de palavras únicas, sem repetições.\n",
        "\n",
        "o que são matriz esparsa?\n",
        "\n",
        "Uma matriz é considerada esparsa quando a maioria de seus elementos são zeros ou não estão presentes.\n",
        "\n",
        "Princípio de Pareto(teoria e gráficos)\n",
        "\n",
        "O Princípio de Pareto, também conhecido como regra 80/20, é uma técnica de organização que afirma que 80% dos resultados de um trabalho são provenientes de 20% das causas"
      ],
      "metadata": {
        "id": "tU08gy6Aa0Sh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Escolhendo o algorítimo de aprendizado supervisionado - Classificação"
      ],
      "metadata": {
        "id": "ZuYWvYz0sRc6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![tela7.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "ePtvN0-iwzdK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tem pelo?\n",
        "# tem pernas longas?\n",
        "# faz cocoricó?\n",
        "\n",
        "# cahorro\n",
        "# galinha\n",
        "\n",
        "cachorro1 = [1,1,0]\n",
        "cachorro2 = [0,1,0]\n",
        "cachorro3 = [1,0,0]\n",
        "\n",
        "galinha1 = [1,0,1]\n",
        "galinha2 = [0,1,1]\n",
        "galinha3 = [1,1,1]\n",
        "\n",
        "treino_x = [cachorro1, cachorro2, cachorro3, galinha1, galinha2, galinha3]\n",
        "treino_y = [1,1,1,0,0,0]\n"
      ],
      "metadata": {
        "id": "-_VYsTuPbipw"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "# criei um objeto e chamei o construtor da classe LinearSVC\n",
        "model = LinearSVC()\n",
        "\n",
        "# Treinando o modelo\n",
        "model.fit(treino_x, treino_y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "FvQyQkhQdadz",
        "outputId": "3dfb9638-dbfd-44a2-8709-a9e68c25a41c"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearSVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC()</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "animal_misterioso = [0,0,1]\n",
        "model.predict([animal_misterioso])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fX6mdJ7Dgsmm",
        "outputId": "506cfc4c-76d8-43d7-d43b-ab482666a2f3"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "animal_misterioso = [0,1,0]\n",
        "model.predict([animal_misterioso])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jI6od_tdhNpE",
        "outputId": "fb0154a7-bc15-41bd-b008-27b637c4d3d0"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        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)"
      ],
      "metadata": {
        "id": "sipubzWsmD1i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# CountVectorizer - Vetorizar o corpus em números\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "# corpus\n",
        "texto_bag = [\"O filme é muito bom\", \"O filme é muito ruim\"]\n",
        "# Vetorização - pegar o corpus e criar um vetor\n",
        "vetorizar = CountVectorizer(lowercase=False)\n",
        "#bag of words (BoW)\n",
        "bag_of_words = vetorizar.fit_transform(texto_bag)\n",
        "\n"
      ],
      "metadata": {
        "id": "b7xMvBCPvarR"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bag_of_words\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nolaD9IAyWXx",
        "outputId": "450bdc7b-b7b8-4d4b-c26f-fe6222e1896e"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<2x4 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 6 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vetorizar.get_feature_names_out()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "0cd1ff74-728c-4cb2-d5d3-2eb8366c9b98"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['bom', 'filme', 'muito', 'ruim'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_esparsa = pd.DataFrame.sparse.from_spmatrix(bag_of_words, columns=vetorizar.get_feature_names_out())\n",
        "matriz_esparsa"
      ],
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        "id": "-LNjfUad09Bd",
        "outputId": "7acdbc9d-1bc4-4214-b2d2-bb72b79da4d6"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   bom  filme  muito  ruim\n",
              "0    1      1      1     0\n",
              "1    0      1      1     1"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "matriz_esparsa",
              "summary": "{\n  \"name\": \"matriz_esparsa\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"bom\",\n      \"properties\": {\n        \"dtype\": \"Sparse[int64, 0]\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filme\",\n      \"properties\": {\n        \"dtype\": \"Sparse[int64, 0]\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"muito\",\n      \"properties\": {\n        \"dtype\": \"Sparse[int64, 0]\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ruim\",\n      \"properties\": {\n        \"dtype\": \"Sparse[int64, 0]\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "source": [
        "!pip install pandas\n",
        "!pip install scikit-learn\n",
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Corpus\n",
        "texto = [\"O filme é muito bom\", \"O filme é muito ruim\", \"O filme é bom\", \"O filme é péssimo\", \"O filme é excelente\"]\n",
        "classe = [1, 0, 1, 0, 1] # 1 - positivo; 0 - negativo\n",
        "\n",
        "\n",
        "# 1. Create Bag of Words representation:\n",
        "vetorizar = CountVectorizer(lowercase=False)\n",
        "bag_of_words = vetorizar.fit_transform(texto)\n",
        "\n",
        "# 2. Split the data into training"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNC-Hghc2Jpb",
        "outputId": "3dc95f2d-f96a-45ef-cf82-2dee04926b23"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.1.4)\n",
            "Requirement already satisfied: numpy<2,>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n"
          ]
        }
      ]
    },
    {
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer # or TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# 1. Assuming 'treino' is a Pandas Series or a list of strings:\n",
        "vetorizar = CountVectorizer(lowercase=False) # or TfidfVectorizer(lowercase=False)\n",
        "treino_numerico = vetorizar.fit_transform(treino)\n",
        "\n",
        "# 2. Now use the numerical data for training:\n",
        "regressao_logistica = LogisticRegression()\n",
        "regressao_logistica.fit(treino_numerico, classe_treino)\n",
        "\n",
        "# 3. For predictions, you need to transform the new text using the same vectorizer:\n",
        "animal_misterioso = [\"some text\"] # Example new text, ensure it's a string or a list of strings\n",
        "# Ensure animal_misterioso is a list of strings or a single string before transforming\n",
        "if isinstance(animal_misterioso, str):\n",
        "    animal_misterioso = [animal_misterioso]  # Convert to a list if it's a single string\n",
        "animal_misterioso_numerico = vetorizar.transform(animal_misterioso)\n",
        "prediction = regressao_logistica.predict(animal_misterioso_numerico)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ygGNe2-y2aar",
        "outputId": "daffbc74-5862-40d3-fabc-22b56a200bbb"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer # or TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# 1. Assuming 'treino' is a Pandas Series or a list of strings:\n",
        "vetorizar = CountVectorizer(lowercase=False) # or TfidfVectorizer(lowercase=False)\n",
        "treino_numerico = vetorizar.fit_transform(treino)\n",
        "\n",
        "# 2. Now use the numerical data for training:\n",
        "regressao_logistica = LogisticRegression()\n",
        "regressao_logistica.fit(treino_numerico, classe_treino)\n",
        "\n",
        "# 3. For predictions, you need to transform the new text using the same vectorizer:\n",
        "# animal_misterioso = [\"some text\"] # Example new text\n",
        "animal_misterioso_numerico = vetorizar.transform(animal_misterioso)\n",
        "prediction = regressao_logistica.predict(animal_misterioso_numerico)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hS0J6RpbyNRb",
        "outputId": "2f5e9cd4-95b0-4915-fd5b-dd4188aac9a9"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:469: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Transformando neg r pod para 0 e 1 (Classificação binária)"
      ],
      "metadata": {
        "id": "0MrTmNwZw8Dp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# criando uma variável classificação\n",
        "# Acessar a coluna sentimento\n",
        "# Usar a função replace para trocar \"neg e pos\" para \"0 e 1 \"\n",
        "classificacao = df_resenhas[\"sentiment\"].replace([\"neg\", \"pos\"], [0, 1])\n",
        "\n",
        "classificacao"
      ],
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        "id": "fGScd9hixlmI",
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          "base_uri": "https://localhost:8080/",
          "height": 458
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        "outputId": "3c685f2a-409c-4902-c4da-0337e0ec4e86"
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      "execution_count": 107,
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        "outputId": "f69c6200-4c39-4cb8-c1da-769ac85c5c70"
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      "execution_count": 108,
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      "cell_type": "code",
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        "df_resenhas['clasificação'] = classificacao"
      ],
      "metadata": {
        "id": "lHLl_i8r0iW_"
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      "execution_count": 109,
      "outputs": []
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        "df_resenhas.head()"
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              "   id                                            text_en  \\\n",
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              "2   3  First of all I hate those moronic rappers, who...   \n",
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              "summary": "{\n  \"name\": \"df_resenhas\",\n  \"rows\": 49459,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14277,\n        \"min\": 1,\n        \"max\": 49460,\n        \"num_unique_values\": 49459,\n        \"samples\": [\n          12534,\n          35447,\n          20281\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_en\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49043,\n        \"samples\": [\n          \"One of the commenters is wrong. This is not the only Pat Patterson film and he didnt die two years after this was made. He shot a film called the \\\"Electric Chair\\\" in Pineville, NC. He shot this a few years after Doctor Gore. Patterson died in the late 70s. I know this because he used my house and he left a cat there!! It sucked also. This was a big deal when the movie came out. No independent horror films were being made in NC. This movie didnt help matters. Patterson used to do gore effects for H. G. Lewis. He was also good at magic. His gore scenes in Body shop were actually well done. The film was shot in a building that also housed a 7/11. You can actually see the tops of the walls in some scenes. The budget was less that $20,000 and the script looked like a child wrote it. Only Patterson could understand it. Still...its entertainment and its a classic.\",\n          \"I just re-watched 08th MS Gundam for the 2nd time. It is so much better than Gundam Wing. I cant wait to get the DVD and see what was edited out of the series. This is great to see the Gundams actually move about clumsily through the land. Somebody really thought over writing this move script.See this today,.\",\n          \"The always delightful Kevin Spacey makes us once again question certain truths in our lives. After driving us into believing he is a mumbling small time crook only to expose the true power of cinematic deception in the classic The Usual Suspect; this time around he leads us into believing he is Prot, a likable alien who assumes a human form whilst strolling around the Earth.Prot is a peculiar stranger who has seemingly appeared out of nowhere, only to be hospitalized in the Manhattan Psychiatric Institution. Dr. Mark Powell Jeff Bridges soon takes extreme interest in him, and even finds himself pondering if Prot is actually a supreme being from planet K-PAX, or perhaps he is only a forgotten human being with severe psychiatric issues. While the ending can be seen and discussed in many ways, it remains a complex, enlightening journey which leaves a gr eat deal of intriguing/philosophical questions behind it.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text_pt\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 49045,\n        \"samples\": [\n          \"Alguns spoilers A Soap tem momentos maravilhosos para recomend\\u00e1-lo. Quando Charlotte e Veronica se aproximam da intimidade, \\u00e9 uma cena linda, discreta e verdadeiramente er\\u00f3tica. Eu tamb\\u00e9m adorava a partitura e a cinematografia suave e sem som. N\\u00e3o estou claro se a curiosa estrutura de parada e in\\u00edcio do filme vem de ser digerida originalmente em forma serializada, o locutor descrevendo a a\\u00e7\\u00e3o em se\\u00e7\\u00f5es \\u00e9 bem chato. Meu maior problema com esse filme \\u00e9 a representa\\u00e7\\u00e3o bastante absurda de uma mulher trans e de sua vida. Claro, ela tem que ser mostrada como uma trabalhadora sexual o que mais, desmiolada, impratic\\u00e1vel, absurdamente cheia de babados, completamente sem esperan\\u00e7a quando se trata de se vestir e incapaz de fazer quaisquer liga\\u00e7\\u00f5es interpessoais no mundo. Al\\u00e9m disso, ela sempre mostrava dois dias de crescimento de barba por algum motivo bizarro, como se quisesse enfatizar o quanto a vida dela \\u00e9 de mau gosto. E ela est\\u00e1 esperando para fazer uma cirurgia de redesigna\\u00e7\\u00e3o de g\\u00eanero quando aparentemente n\\u00e3o fez mais nada para encaminhar sua transi\\u00e7\\u00e3o. Na verdade, o personagem se assemelha a uma drag queen, n\\u00e3o a algu\\u00e9m que est\\u00e1 em transi\\u00e7\\u00e3o. Essas s\\u00e3o fantasias t\\u00edpicas de pessoas de fora que realmente n\\u00e3o est\\u00e3o ligadas a transpeople. Os filmes de fetichiza\\u00e7\\u00e3o da cirurgia de GRS s\\u00e3o uma forma de objetivar as pessoas que est\\u00e3o passando pela transi\\u00e7\\u00e3o. N\\u00e3o impressionado com este aspecto do filme no m\\u00ednimo. No m\\u00ednimo, por que n\\u00e3o ter o personagem interpretado por algu\\u00e9m que realmente \\u00e9 transg\\u00eanero ... Eu pensei que o ator que interpreta Veronica estava bem, mas n\\u00e3o melhor do que isso. Muito melhor era a mulher retratando Charlotte, um personagem muito complexo cheio de energia, auto-avers\\u00e3o, desejo e contradi\\u00e7\\u00f5es. Se ela estava t\\u00e3o fascinada por algu\\u00e9m com energia feminina, uma Charlotte poderia ir a um bar feminino em dois segundos e encontr\\u00e1-lo. Sim, ela foi atra\\u00edda por Veronica mas mais como uma id\\u00e9ia abstrata de algu\\u00e9m com caracter\\u00edsticas masculinas / femininas um toque gentil, mas com um bom soco, n\\u00e3o como uma pessoa \\u00fanica. Sim, o que A Soap diz sobre o amor \\u00e9 muitas vezes am\\u00e1vel e comovente, mas isso n\\u00e3o significa que uma minoria j\\u00e1 estereotipada tenha de ser estereotipada um pouco mais no processo. Este filme tamb\\u00e9m prova que os dinamarqueses s\\u00e3o maus dan\\u00e7arinos. Para uma premissa t\\u00e3o promissora melhor executada em um filme como \\\"Different For Girls\\\", o filme final \\u00e9 uma decep\\u00e7\\u00e3o.\",\n          \"Este filme reuniu alguns dos antigos tripulantes da Spinal para outro filme de mockumentary, desta vez girando em torno do mundo do Dog Show, como seus donos se preparam e treinam para o show antes de passar para o show em si. Conhecemos v\\u00e1rias equipes como eles esperam para ganhar o pr\\u00eamio principal - The Flecks, Cookie, que parece ter dormido com todos os homens de sempre, e Gerry, que tenta lidar com as antigas escapadas de sua esposa e com o fato de que ele literalmente tem dois p\\u00e9s esquerdos. Harlan, cujo cachorro fala com ele e gosta de ventriloquismo. Os cisnes que tomam muito caf\\u00e9 e gritam um com o outro. Donalan e Vanderhoof, o casal gay, e Cabot e Cummings, que venceram os dois \\u00faltimos anos. Fred Willard comenta sobre o show e \\u00e9 muito engra\\u00e7ado como sempre. Cenas engra\\u00e7adas incluem o Look at me! cena, e qualquer com Levy. Infelizmente algumas das melhores cenas foram apagadas ou filmadas depois - Willard entrevistando Leslie Cabot, e o ep\\u00edlogo alternativo com Gerry \\u00e9 uma das coisas mais engra\\u00e7adas que eu j\\u00e1 vi. Se estas tivessem sido inclu\\u00eddas, daria ao filme uma marca extra. Mas ... 7 de 10\",\n          \"Eu n\\u00e3o pensava assim, mas meu amigo disse a primeira coisa que ele pensou quando ouviu que o t\\u00edtulo \\\"Midnight Cowboy\\\" era um porn\\u00f4 gay. Naquele momento, tudo que eu sabia era a refer\\u00eancia feita naquele epis\\u00f3dio de \\\"Seinfeld\\\" com Jerry tentando levar Kramer para a Fl\\u00f3rida naquele \\u00f4nibus e Kramers todo doente e com uma hemorragia nasal. O filme foi \\u00f3timo, e surpreendentemente otimista. e nem todos pessimistas pessimistas pretensiosos como alguns filmes que eu n\\u00e3o consigo lembrar porque s\\u00e3o todos uma porcaria.O enredo basicamente consistia de um jovem cowboy ing\\u00eanuo Joe Buck indo para Nova York tentando ser um prostituto prostituta, basicamente, pensando que ser\\u00e1 f\\u00e1cil pickings, s\\u00f3 para bater duro na parede de tijolos quando uma mulher acaba apressando-o, acusando-o de seu encontro sexual. Ent\\u00e3o ele conhece Enrico Salvatore Rizzo, chamado \\\"Ratso\\\" por todos e os gays bonitos que fazem piada dele o tempo todo. Voc\\u00ea pensa nele como um canalha, mas um am\\u00e1vel como Han Solo ou Lando Calrissian e surpreendentemente ele e Joe tornam-se amigos, e o filme \\u00e9 t\\u00e3o doce e emocionante ao v\\u00ea-los mais amig\\u00e1veis \\u200b\\u200be tal e tal. Rizzo se revela realmente um homem triste e lastim\\u00e1vel, muito doente, muito deprimido e constrangido, odeia ser chamado de \\\"Ratso\\\" e quer ir para a Fl\\u00f3rida, onde acha que a vida ser\\u00e1 muito melhor e todos os seus problemas resolvidos, e inferno aprender a ser um cozinheiro e ser famoso por l\\u00e1. \\u00c9 reconfortante ver Joe fazer tudo o que ele faz para lev\\u00e1-los para a Fl\\u00f3rida, junto com muitos momentos hilariantes como Ratso tentando roubar comida naquela festa hippie, e ser pego pelo mulher que diz \\\"Nossa, bem, voc\\u00ea sabe, \\u00e9 gr\\u00e1tis. Voc\\u00ea n\\u00e3o tem que roub\\u00e1-lo.\\\" e ele diz \\\"Bem, se estiver livre, ent\\u00e3o eu n\\u00e3o vou roubar\\\", e aquele momento cl\\u00e1ssico completamente improvisado e n\\u00e3o programado, onde Hoffman quase \\u00e9 atingido por aquele T\\u00e1xi, e grita \\\"Ei, eu estou andando aqui! Estou andando aqui!\\\" \\u00e9 t\\u00e3o cr\\u00edvel, que voc\\u00ea nunca acreditaria que Joe Buck cresceria para ser o distinto e respeitado ator Jon Voight, e Ratso Rizzo cresceria para ser o lend\\u00e1rio e amado Dustin Hoffman. N\\u00e3o \\u00e9 a primeira vez que eles trabalham juntos em pap\\u00e9is principais, mas a qu\\u00edmica \\u00e9 t\\u00e3o espessa e intensa. Ent\\u00e3o, a parte triste que eu acredito \\u00e9 um exagero de cham\\u00e1-la de \\\"deprimente\\\". Ratso Rizzo est\\u00e1 desmoronando durante todo o filme, mal consegue andar, mal come, tosse muito, est\\u00e1 doente e atinge um ponto de cabe\\u00e7a no \\u00f4nibus a caminho da Fl\\u00f3rida. Ele est\\u00e1 doendo muito, e a apenas quil\\u00f4metros de Miami, ele finalmente morre no \\u00f4nibus. O motorista do \\u00f4nibus garante a todos que nada est\\u00e1 errado e continua. Triste, mas n\\u00e3o do tipo que faz voc\\u00ea ir para casa e chorar e lamentar miseravelmente como se tivesse acabado de perder seu cachorro de 13 anos. Tudo em todos, \\u00f3timo filme. E a trilha sonora consiste basicamente de \\\"Everybodys Talking\\\", tocada durante todo o filme nos momentos apropriados. Um movimento estranho, mas \\u00f3timo, j\\u00e1 que a m\\u00fasica \\u00e9 boa e se encaixa perfeitamente no tom do filme. V\\u00e1 ver, \\u00e9 \\u00f3timo, vai comprar\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"pos\",\n          \"neg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"clasifica\\u00e7\\u00e3o\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# torray()- conversão considerando uma matriz densa (inclui tudo)\n",
        "# sparse.from_matrix()- cria DataFrame sparso diretamente da matriz sparsa\n",
        "\n",
        "# Vantagens:\n",
        "\n",
        "\n",
        "\n",
        "1.   Eficiência\n",
        "2.   Memória\n",
        "3.   Especializa em muitos zeros\n",
        "4.   Performance\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "g4-H0lST2xbi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exercício\n",
        "1 - Considere o corpus sendo o dataset de filmes(49459)\n",
        "\n",
        "2 - Aplique o mesmo código \"filme é muito bom\"\n",
        "\n",
        "3 - E visualize o Bag of Words"
      ],
      "metadata": {
        "id": "eGm9vWCr4DSR"
      }
    },
    {
      "source": [
        "import pandas as pd\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "# Assuming 'text_pt' is the correct column name for your movie reviews\n",
        "# If the reviews are stored in a file, you need to load it into a pandas DataFrame first.\n",
        "df_resenhas = pd.read_csv(\"/content/imdb-pt-br.csv\")\n",
        "\n",
        "# Check existing columns in df_resenhas\n",
        "print(df_resenhas.columns)\n",
        "\n",
        "# Replace 'text' with the actual column name 'text_pt' if it's different\n",
        "corpus = df_resenhas['text_pt'].tolist()\n",
        "\n",
        "# Continue with the rest of your code\n",
        "vetorizar = CountVectorizer(lowercase=False)\n",
        "bag_of_words = vetorizar.fit_transform(corpus)\n",
        "\n",
        "matriz_esparsa = pd.DataFrame.sparse.from_spmatrix(bag_of_words, columns=vetorizar.get_feature_names_out())\n",
        "# print(matriz_esparsa)\n"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cMEfrqBi6Jfl",
        "outputId": "5398fac8-d78c-451f-80ef-fdb83bdcc5be"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['id', 'text_en', 'text_pt', 'sentiment'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matriz_esparsa"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "1bu2jzgr6e1T",
        "outputId": "5ef82539-6bed-49d8-f312-08233befb997"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  </tbody>\n",
              "</table>\n",
              "<p>49459 rows × 156923 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4b72cc96-1ea6-4ab4-ad3f-e330fa6fea3e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-4b72cc96-1ea6-4ab4-ad3f-e330fa6fea3e 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-4b72cc96-1ea6-4ab4-ad3f-e330fa6fea3e');\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",
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              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4e30d72c-5173-4f39-9fc2-a5a900310a8a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8a5a5001-b6b1-40bb-b7fc-89f21cf75dec\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('matriz_esparsa')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8a5a5001-b6b1-40bb-b7fc-89f21cf75dec button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('matriz_esparsa');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "matriz_esparsa"
            }
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    }
  ]
}