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        "import pandas as pd\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from wordcloud import WordCloud\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np"
      ],
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        "colab": {
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        {
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          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
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        "train=pd.read_csv('train.csv',usecols=[0,3,4])\n",
        "test=pd.read_csv('test.csv',usecols=[0,3])"
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        "id": "Yrex7Vw9mA13"
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        "train.head()"
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-689593bb-1492-4525-8d92-7988a70897f1 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",
              "variable_name": "train",
              "summary": "{\n  \"name\": \"train\",\n  \"rows\": 7613,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3137,\n        \"min\": 1,\n        \"max\": 10873,\n        \"num_unique_values\": 7613,\n        \"samples\": [\n          3796,\n          3185,\n          7769\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7503,\n        \"samples\": [\n          \"Three Homes Demolished in Unrecognized Arab Village - International Middle East Media Center http://t.co/ik8m4Yi9T4\",\n          \"Reid Lake fire prompts campground evacuation order http://t.co/jBODKM6rBU\",\n          \"FAAN orders evacuation of abandoned aircraft at MMA http://t.co/dEvYbnVXGQ via @todayng\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sj_DoweEmyyx",
        "outputId": "6c513557-d93a-4f71-9175-50e8313fdbc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id                                               text\n",
              "0   0                 Just happened a terrible car crash\n",
              "1   2  Heard about #earthquake is different cities, s...\n",
              "2   3  there is a forest fire at spot pond, geese are...\n",
              "3   9           Apocalypse lighting. #Spokane #wildfires\n",
              "4  11      Typhoon Soudelor kills 28 in China and Taiwan"
            ],
            "text/html": [
              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "      <td>Heard about #earthquake is different cities, s...</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>there is a forest fire at spot pond, geese are...</td>\n",
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              "      <th>3</th>\n",
              "      <td>9</td>\n",
              "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11</td>\n",
              "      <td>Typhoon Soudelor kills 28 in China and Taiwan</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-ff7cb6d3-58dd-459d-ae2d-255f6314c150')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\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-ff7cb6d3-58dd-459d-ae2d-255f6314c150 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-ff7cb6d3-58dd-459d-ae2d-255f6314c150');\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-849896eb-dfa8-4f5e-af8d-b636fd84f789\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-849896eb-dfa8-4f5e-af8d-b636fd84f789')\"\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",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </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-849896eb-dfa8-4f5e-af8d-b636fd84f789 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",
              "variable_name": "test",
              "summary": "{\n  \"name\": \"test\",\n  \"rows\": 3263,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3146,\n        \"min\": 0,\n        \"max\": 10875,\n        \"num_unique_values\": 3263,\n        \"samples\": [\n          8051,\n          425,\n          1330\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3243,\n        \"samples\": [\n          \"Latest: USA: Huge sinkhole swallows up Brooklyn intersection http://t.co/vspKHg3nZy\",\n          \"I liked a @YouTube video http://t.co/a5YTAw9Vih S.O.S. Rona Guide - The Red Whirlwind\",\n          \"HitchBot travels Europe and greeted with open arms. Gets destroyed after two weeks in america. There's a lesson to be learned here.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-mkHJb7m4ZU",
        "outputId": "5bdd20cc-0369-45d5-8177-b11a3a8c2cf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7613 entries, 0 to 7612\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   id      7613 non-null   int64 \n",
            " 1   text    7613 non-null   object\n",
            " 2   target  7613 non-null   int64 \n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 178.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P8-c4w0xm58Q",
        "outputId": "f5bad1e0-2428-437e-902d-4ec7fc069909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3263 entries, 0 to 3262\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   id      3263 non-null   int64 \n",
            " 1   text    3263 non-null   object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 51.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    text = re.sub(\"\\'\", \"\", text)\n",
        "    text = re.sub(\"[^a-zA-Z]\",\" \",text)\n",
        "    text = re.sub(r'\\s+', ' ', text)\n",
        "    text = text.lower()\n",
        "    return text"
      ],
      "metadata": {
        "id": "YFaTL1EanQMS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=train['text']\n",
        "y=train['target']"
      ],
      "metadata": {
        "id": "iAnF439sni0c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=x.apply(lambda x: clean_text(x))\n",
        "test['text']=test['text'].apply(lambda x: clean_text(x))"
      ],
      "metadata": {
        "id": "ULzarAXknrTY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.corpus import stopwords\n",
        "stop = stopwords.words('english')\n",
        "x=x.apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))\n",
        "test['text']=test['text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))"
      ],
      "metadata": {
        "id": "yGBPHaoYn8qG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_val,y_train,y_val=train_test_split(x,y,test_size=0.2,random_state=42,shuffle=True,stratify=y)"
      ],
      "metadata": {
        "id": "YIixlRj7pgAs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Try multiple C parameters, select one that minimizes validation error\n",
        "# Often, you need to try a few values and see those results to determine what other values to try\n",
        "\n",
        "def calc_error(C):\n",
        "  # Train linear SVM\n",
        "  c=Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,1))),('model',LogisticRegression(C=C,solver='sag',max_iter=100))])\n",
        "  c.fit(x_train,y_train)\n",
        "\n",
        "  # Evaluate on validation set\n",
        "  val_pred = c.predict(x_val)\n",
        "  val_error = np.mean(val_pred != y_val)\n",
        "  print('C: ' + str(C))\n",
        "  print('val_error: ' + str(val_error))\n",
        "  return val_error\n",
        "\n",
        "# start with 1\n",
        "# Values of C to try0.015625, 0.03125, 0.0625, 0.125,\n",
        "C_values = [1.0]\n",
        "\n",
        "# Track validation errors for each C value\n",
        "val_errors = [calc_error(1.0)]\n",
        "least_error = calc_error(1.0)\n",
        "second_least_error = float('inf')\n",
        "best_C = 1.0\n",
        "second_best_C = 0\n",
        "\n",
        "while True:\n",
        "\n",
        "  if best_C/2 in C_values and best_C*2 in C_values:\n",
        "    new_C = (best_C + second_best_C)/2\n",
        "    if new_C in C_values:\n",
        "      break\n",
        "    new_error = calc_error(new_C)\n",
        "    C_values.append(new_C)\n",
        "    val_errors.append(new_error)\n",
        "    val_errors = [x for _,x in sorted(zip(C_values, val_errors))]\n",
        "    C_values.sort()\n",
        "\n",
        "    if new_error < least_error:\n",
        "      second_least_error = least_error\n",
        "      second_best_C = best_C\n",
        "      least_error = new_error\n",
        "      best_C = new_C\n",
        "    elif new_error < second_least_error:\n",
        "      second_least_error = new_error\n",
        "      second_best_C = new_C\n",
        "\n",
        "  if best_C/2 not in C_values:\n",
        "    new_C = best_C/2\n",
        "    new_error = calc_error(new_C)\n",
        "    C_values.append(new_C)\n",
        "    val_errors.append(new_error)\n",
        "    val_errors = [x for _,x in sorted(zip(C_values, val_errors))]\n",
        "    C_values.sort()\n",
        "\n",
        "    if new_error < least_error:\n",
        "      second_least_error = least_error\n",
        "      second_best_C = best_C\n",
        "      least_error = new_error\n",
        "      best_C = new_C\n",
        "    elif new_error < second_least_error:\n",
        "      second_least_error = new_error\n",
        "      second_best_C = new_C\n",
        "\n",
        "  if best_C*2 not in C_values:\n",
        "    new_C = best_C*2\n",
        "    new_error = calc_error(new_C)\n",
        "    C_values.append(new_C)\n",
        "    val_errors.append(new_error)\n",
        "    val_errors = [x for _,x in sorted(zip(C_values, val_errors))]\n",
        "    C_values.sort()\n",
        "\n",
        "    if new_error < least_error:\n",
        "      second_least_error = least_error\n",
        "      second_best_C = best_C\n",
        "      least_error = new_error\n",
        "      best_C = new_C\n",
        "    elif new_error < second_least_error:\n",
        "      second_least_error = new_error\n",
        "      second_best_C = new_C\n",
        "\n",
        "  if abs(least_error - second_least_error)*100 < 0.1:\n",
        "    break\n",
        "\n",
        "# Plot validation error for each C value\n",
        "plt.semilogx(C_values, val_errors, marker='o', linestyle='-', color='b')\n",
        "plt.xlabel('C Values')\n",
        "plt.ylabel('Validation Error')\n",
        "plt.title('Validation Error vs C Values')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Report the best C value and its corresponding validation error\n",
        "print(\"Best C value:\", best_C)\n",
        "print(\"Validation Error with Best C value:\", least_error)\n",
        "\n",
        "print(\"Second best C value:\", second_best_C)\n",
        "print(\"Validation Error with second best C value:\", second_least_error)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "HpNKy48xz8Nf",
        "outputId": "ede41b43-0728-4894-8f9f-9cc0094466da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "C: 1.0\n",
            "val_error: 0.1969796454366382\n",
            "C: 1.0\n",
            "val_error: 0.1969796454366382\n",
            "C: 0.5\n",
            "val_error: 0.20748522652659226\n",
            "C: 2.0\n",
            "val_error: 0.18844386080105055\n",
            "C: 4.0\n",
            "val_error: 0.18319107025607353\n",
            "C: 8.0\n",
            "val_error: 0.18844386080105055\n",
            "C: 3.0\n",
            "val_error: 0.18647406434668418\n",
            "C: 3.5\n",
            "val_error: 0.1871306631648063\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best C value: 4.0\n",
            "Validation Error with Best C value: 0.18319107025607353\n",
            "Second best C value: 3.0\n",
            "Validation Error with second best C value: 0.18647406434668418\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "c=Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,1))),('model',LogisticRegression(C=best_C,solver='sag',max_iter=1000))])\n",
        "c.fit(x_train,y_train)\n",
        "\n",
        "training_score = c.score(x_train,y_train)\n",
        "testing_score = c.score(x_val,y_val)\n",
        "print(training_score)\n",
        "print(testing_score)\n",
        "\n",
        "y_pred_tfidf=c.predict(x_val)\n",
        "print(confusion_matrix(y_val,y_pred_tfidf))\n",
        "print(classification_report(y_val,y_pred_tfidf))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZSQF_xEZplIh",
        "outputId": "d73e18ba-0a69-4e26-c9d5-b61c6d42d049"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9625615763546798\n",
            "0.8168089297439265\n",
            "[[752 117]\n",
            " [162 492]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.87      0.84       869\n",
            "           1       0.81      0.75      0.78       654\n",
            "\n",
            "    accuracy                           0.82      1523\n",
            "   macro avg       0.82      0.81      0.81      1523\n",
            "weighted avg       0.82      0.82      0.82      1523\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Additional Analysis"
      ],
      "metadata": {
        "id": "zZ_BxC3f25It"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sizes = [0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\n",
        "val_scores = []\n",
        "\n",
        "for size in sizes:\n",
        "  x_train,x_val,y_train,y_val=train_test_split(x,y,test_size=0.2, train_size=size*0.8,random_state=42,shuffle=True,stratify=y)\n",
        "  c=Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,1))),('model',LogisticRegression(C=best_C,solver='sag',max_iter=1000))])\n",
        "  c.fit(x_train,y_train)\n",
        "\n",
        "  val_score = c.score(x_val,y_val)\n",
        "  val_scores.append(val_score)\n",
        "\n",
        "plt.plot(sizes, 1-np.array(val_scores))\n",
        "plt.xlabel('Training size')\n",
        "plt.ylabel('Validation error')\n",
        "plt.title('Training size vs Validation error (Logistic Regression)')"
      ],
      "metadata": {
        "id": "fdVhj1QS283N",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "06800e59-6ec8-4c95-b30b-19d6c91832d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training size vs Validation error (Logistic Regression)')"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model 2 - SVM Linear SVC"
      ],
      "metadata": {
        "id": "vw9CcQEp2Pc4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Try multiple C parameters, select one that minimizes validation error\n",
        "# Often, you need to try a few values and see those results to determine what other values to try\n",
        "# from sklearn import LinearSVC\n",
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "def calc_error(C):\n",
        "  # Train linear SVM\n",
        "  c = Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,2))), ('model', LinearSVC(C = C, max_iter = 1000, random_state = 42))])\n",
        "  c.fit(x_train,y_train)\n",
        "\n",
        "  # Evaluate on validation set\n",
        "  val_pred = c.predict(x_val)\n",
        "  val_error = np.mean(val_pred != y_val)\n",
        "  return val_error\n",
        "\n",
        "# start with 1\n",
        "# Values of C to try0.015625, 0.03125, 0.0625, 0.125,\n",
        "C_values = [0.125]\n",
        "\n",
        "# Track validation errors for each C value\n",
        "val_errors = [calc_error(1.0)]\n",
        "least_error = calc_error(1.0)\n",
        "second_least_error = float('inf')\n",
        "best_C = 1.0\n",
        "second_best_C = 0\n",
        "\n",
        "while True:\n",
        "\n",
        "  if best_C/2 in C_values and best_C*2 in C_values:\n",
        "    new_C = (best_C + second_best_C)/2\n",
        "    new_error = calc_error(new_C)\n",
        "    C_values.append(new_C)\n",
        "    val_errors.append(new_error)\n",
        "    val_errors = [x for _,x in sorted(zip(C_values, val_errors))]\n",
        "    C_values.sort()\n",
        "\n",
        "    if new_error < least_error:\n",
        "      second_least_error = least_error\n",
        "      second_best_C = best_C\n",
        "      least_error = new_error\n",
        "      best_C = new_C\n",
        "    elif new_error < second_least_error:\n",
        "      second_least_error = new_error\n",
        "      second_best_C = new_C\n",
        "\n",
        "  if best_C/2 not in C_values:\n",
        "    new_C = best_C/2\n",
        "    new_error = calc_error(new_C)\n",
        "    C_values.append(new_C)\n",
        "    val_errors.append(new_error)\n",
        "    val_errors = [x for _,x in sorted(zip(C_values, val_errors))]\n",
        "    C_values.sort()\n",
        "\n",
        "    if new_error < least_error:\n",
        "      second_least_error = least_error\n",
        "      second_best_C = best_C\n",
        "      least_error = new_error\n",
        "      best_C = new_C\n",
        "    elif new_error < second_least_error:\n",
        "      second_least_error = new_error\n",
        "      second_best_C = new_C\n",
        "\n",
        "  if best_C*2 not in C_values:\n",
        "    new_C = best_C*2\n",
        "    new_error = calc_error(new_C)\n",
        "    C_values.append(new_C)\n",
        "    val_errors.append(new_error)\n",
        "    val_errors = [x for _,x in sorted(zip(C_values, val_errors))]\n",
        "    C_values.sort()\n",
        "\n",
        "    if new_error < least_error:\n",
        "      second_least_error = least_error\n",
        "      second_best_C = best_C\n",
        "      least_error = new_error\n",
        "      best_C = new_C\n",
        "    elif new_error < second_least_error:\n",
        "      second_least_error = new_error\n",
        "      second_best_C = new_C\n",
        "\n",
        "  if abs(least_error - second_least_error)*100 < 0.1:\n",
        "    break\n",
        "\n",
        "# Plot validation error for each C value\n",
        "plt.semilogx(C_values, val_errors, marker='o', linestyle='-', color='b')\n",
        "plt.xlabel('C Values')\n",
        "plt.ylabel('Validation Error')\n",
        "plt.title('Validation Error vs C Values')\n",
        "plt.grid(True)\n",
        "plt.show()\n",
        "\n",
        "# Report the best C value and its corresponding validation error\n",
        "print(\"Best C value:\", best_C)\n",
        "print(\"Validation Error with Best C value:\", least_error)\n",
        "\n",
        "print(\"Second best C value:\", second_best_C)\n",
        "print(\"Validation Error with second best C value:\", second_least_error)"
      ],
      "metadata": {
        "id": "g_TiaCy82O0P",
        "outputId": "ea8a03d5-c84a-4dbf-ee6f-a3c937fe670c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best C value: 2.0\n",
            "Validation Error with Best C value: 0.1943532501641497\n",
            "Second best C value: 1.0\n",
            "Validation Error with second best C value: 0.19500984898227183\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "\n",
        "linearSVC = Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,2))), ('model', LinearSVC(C = best_C, max_iter = 1000, random_state = 42))])\n",
        "linearSVC.fit(x_train, y_train)\n",
        "\n",
        "training_svc_score = linearSVC.score(x_train, y_train)\n",
        "testing_svc_score = linearSVC.score(x_val, y_val)\n",
        "\n",
        "print(\"Training Linear SVC Score\", training_svc_score)\n",
        "print(\"Testing SVC Score\", testing_svc_score)\n",
        "\n",
        "y_pred_svc = linearSVC.predict(x_val)\n",
        "print(confusion_matrix(y_val,y_pred_svc))\n",
        "print(classification_report(y_val,y_pred_svc))\n"
      ],
      "metadata": {
        "id": "OHvDW-wl2O3p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8211ff30-4993-4e75-882e-3b76016ea563"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Linear SVC Score 0.9957307060755337\n",
            "Testing SVC Score 0.8056467498358503\n",
            "[[718 151]\n",
            " [145 509]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.83      0.83       869\n",
            "           1       0.77      0.78      0.77       654\n",
            "\n",
            "    accuracy                           0.81      1523\n",
            "   macro avg       0.80      0.80      0.80      1523\n",
            "weighted avg       0.81      0.81      0.81      1523\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sizes = [0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\n",
        "val_scores = []\n",
        "\n",
        "for size in sizes:\n",
        "  x_train,x_val,y_train,y_test = train_test_split(x,y,test_size=0.2, train_size=size*0.8,random_state=42,shuffle=True,stratify=y)\n",
        "  linearSVC = Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,2))), ('model', LinearSVC(C = best_C, max_iter = 1000, random_state = 42))])\n",
        "  linearSVC.fit(x_train,y_train)\n",
        "\n",
        "  val_score = c.score(x_val,y_val)\n",
        "  val_scores.append(val_score)\n",
        "\n",
        "plt.plot(sizes, 1-np.array(val_scores))\n",
        "plt.xlabel('Training size')\n",
        "plt.ylabel('Validation error')\n",
        "plt.title('Training size vs Validation error (LinearSVC)')"
      ],
      "metadata": {
        "id": "1gwznjnZ2Os7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "outputId": "706283e3-c8d8-4a03-d5db-7e44887d04c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training size vs Validation error (LinearSVC)')"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model 3: Random Forest Classifier"
      ],
      "metadata": {
        "id": "UaJICxkOxuPT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "x_train,x_val,y_train,y_val=train_test_split(x,y,test_size=0.2,random_state=42,shuffle=True,stratify=y)\n",
        "\n",
        "accuracies = []\n",
        "for i in [1, 50, 100, 150, 200, 250, 300, 350, 400]:\n",
        "  nb = Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,2))), ('model', RandomForestClassifier(random_state=42, n_estimators=i))])\n",
        "\n",
        "  nb.fit(x_train, y_train)\n",
        "  print(x_val.shape)\n",
        "  print(y_val.shape)\n",
        "\n",
        "  y_pred = nb.predict(x_val)\n",
        "  correct = np.sum(np.array(y_pred) == np.array(y_val))\n",
        "  print(f'Model with No of Estimators: {i}')\n",
        "  print(correct / len(y_val))\n",
        "  print(confusion_matrix(y_val, y_pred))\n",
        "  print(classification_report(y_val,y_pred))\n",
        "  accuracies.append(correct / len(y_val))\n",
        "\n",
        "# Plot validation error for each C value\n",
        "plt.semilogx([1, 50, 100, 150, 200, 250, 300, 350, 400], 1-np.array(accuracies), marker='o', linestyle='-', color='b')\n",
        "plt.xlabel('Number of Estimators')\n",
        "plt.ylabel('Validation error')\n",
        "plt.title('Validation error vs Num_Estimators')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "NvwuEfFO2Ope",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9fc77584-848b-46bd-a519-4a8e881891ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 1\n",
            "0.6894287590282338\n",
            "[[653 216]\n",
            " [257 397]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.72      0.75      0.73       869\n",
            "           1       0.65      0.61      0.63       654\n",
            "\n",
            "    accuracy                           0.69      1523\n",
            "   macro avg       0.68      0.68      0.68      1523\n",
            "weighted avg       0.69      0.69      0.69      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 50\n",
            "0.7839789888378201\n",
            "[[808  61]\n",
            " [268 386]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.93      0.83       869\n",
            "           1       0.86      0.59      0.70       654\n",
            "\n",
            "    accuracy                           0.78      1523\n",
            "   macro avg       0.81      0.76      0.77      1523\n",
            "weighted avg       0.80      0.78      0.78      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 100\n",
            "0.7866053841103086\n",
            "[[813  56]\n",
            " [269 385]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.94      0.83       869\n",
            "           1       0.87      0.59      0.70       654\n",
            "\n",
            "    accuracy                           0.79      1523\n",
            "   macro avg       0.81      0.76      0.77      1523\n",
            "weighted avg       0.80      0.79      0.78      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 150\n",
            "0.7852921864740644\n",
            "[[812  57]\n",
            " [270 384]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.93      0.83       869\n",
            "           1       0.87      0.59      0.70       654\n",
            "\n",
            "    accuracy                           0.79      1523\n",
            "   macro avg       0.81      0.76      0.77      1523\n",
            "weighted avg       0.80      0.79      0.78      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 200\n",
            "0.7905449770190414\n",
            "[[814  55]\n",
            " [264 390]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.94      0.84       869\n",
            "           1       0.88      0.60      0.71       654\n",
            "\n",
            "    accuracy                           0.79      1523\n",
            "   macro avg       0.82      0.77      0.77      1523\n",
            "weighted avg       0.81      0.79      0.78      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 250\n",
            "0.7872619829284307\n",
            "[[810  59]\n",
            " [265 389]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.93      0.83       869\n",
            "           1       0.87      0.59      0.71       654\n",
            "\n",
            "    accuracy                           0.79      1523\n",
            "   macro avg       0.81      0.76      0.77      1523\n",
            "weighted avg       0.80      0.79      0.78      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 300\n",
            "0.7892317793827971\n",
            "[[813  56]\n",
            " [265 389]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.94      0.84       869\n",
            "           1       0.87      0.59      0.71       654\n",
            "\n",
            "    accuracy                           0.79      1523\n",
            "   macro avg       0.81      0.77      0.77      1523\n",
            "weighted avg       0.81      0.79      0.78      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 350\n",
            "0.7872619829284307\n",
            "[[812  57]\n",
            " [267 387]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.93      0.83       869\n",
            "           1       0.87      0.59      0.70       654\n",
            "\n",
            "    accuracy                           0.79      1523\n",
            "   macro avg       0.81      0.76      0.77      1523\n",
            "weighted avg       0.80      0.79      0.78      1523\n",
            "\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with No of Estimators: 400\n",
            "0.783322390019698\n",
            "[[810  59]\n",
            " [271 383]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.93      0.83       869\n",
            "           1       0.87      0.59      0.70       654\n",
            "\n",
            "    accuracy                           0.78      1523\n",
            "   macro avg       0.81      0.76      0.76      1523\n",
            "weighted avg       0.80      0.78      0.77      1523\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Additional Analysis"
      ],
      "metadata": {
        "id": "uKC0tHc6xnIm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sizes = [0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\n",
        "\n",
        "print(x.shape)\n",
        "print(y.shape)\n",
        "\n",
        "accuracies = []\n",
        "errors = []\n",
        "\n",
        "for size in sizes:\n",
        "  x_train, x_val, y_train, y_val = train_test_split(x,y,test_size=0.2, train_size=size*0.8,random_state=42,shuffle=True,stratify=y)\n",
        "  nb = Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,2))), ('model', RandomForestClassifier(random_state=42, n_estimators=200))])\n",
        "\n",
        "  nb.fit(x_train, y_train)\n",
        "\n",
        "  y_pred = nb.predict(x_val)\n",
        "  print(x_val.shape)\n",
        "  print(y_val.shape)\n",
        "  correct = np.sum(np.array(y_pred) == np.array(y_val))\n",
        "  print(f'Model with Training Size: {size}')\n",
        "  accuracies.append(correct / len(y_val))\n",
        "  errors.append(1.0 - (correct / len(y_val)))\n",
        "\n",
        "# Plot validation error for each C value\n",
        "plt.semilogx(sizes, errors, marker='o', linestyle='-', color='b')\n",
        "plt.xlabel('Training Size')\n",
        "plt.ylabel('Validation Error')\n",
        "plt.title('Validation Error vs Training Size')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "u_wexHJC2OnL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "outputId": "77ba93bf-97ed-40fc-8cfe-f660f40d8dc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7613,)\n",
            "(7613,)\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with Training Size: 0.01\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with Training Size: 0.05\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with Training Size: 0.1\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with Training Size: 0.25\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with Training Size: 0.5\n",
            "(1523,)\n",
            "(1523,)\n",
            "Model with Training Size: 1.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Model 4: KNeighborsClassifier\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "CV40UQQsIMUH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x_train,x_val,y_train,y_val=train_test_split(x,y,test_size=0.2,random_state=42,shuffle=True,stratify=y)\n",
        "\n",
        "k_values = [10, 15, 20, 25, 30, 35, 40]\n",
        "accuracy_scores = []\n",
        "for k in k_values:\n",
        "    # Create KNeighborsClassifier pipeline\n",
        "    knn_pipeline = Pipeline([\n",
        "        ('tfidf', TfidfVectorizer(ngram_range=(1, 2))),\n",
        "        ('model', KNeighborsClassifier(n_neighbors=k))\n",
        "    ])\n",
        "    knn_pipeline.fit(x_train, y_train)\n",
        "    y_pred_val = knn_pipeline.predict(x_val)\n",
        "\n",
        "\n",
        "    accuracy = accuracy_score(y_val, y_pred_val)\n",
        "    accuracy_scores.append(accuracy)\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(k_values, 1-np.array(accuracy_scores), marker='o', linestyle='-')\n",
        "plt.title('Error vs. K Value')\n",
        "plt.xlabel('K Value')\n",
        "plt.ylabel('Validation Error')\n",
        "plt.grid(True)\n",
        "plt.xticks(k_values)\n",
        "plt.show()\n",
        "\n",
        "# hyperparameter\n",
        "best_k = k_values[np.argmax(accuracy_scores)]\n",
        "\n",
        "X_train_union = pd.concat([x_train, x_val], axis=0)\n",
        "y_train_union = pd.concat([y_train, y_val], axis=0)\n",
        "best_knn_model = Pipeline([\n",
        "    ('tfidf', TfidfVectorizer(ngram_range=(1, 2))),\n",
        "    ('model', KNeighborsClassifier(n_neighbors=best_k))\n",
        "])\n",
        "best_knn_model.fit(X_train_union, y_train_union)\n",
        "\n",
        "y_pred_test = best_knn_model.predict(x_val)\n",
        "test_accuracy = accuracy_score(y_val, y_pred_test)\n",
        "\n",
        "print(test_accuracy)"
      ],
      "metadata": {
        "id": "SBGvf5CN2OlI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "af3adb13-83dd-42f9-cc3b-9fcde4b42e66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8089297439264609\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "train_sizes = [0.01, 0.05, 0.1, 0.25, 0.5, 1.0]\n",
        "\n",
        "accuracy_scores = []\n",
        "\n",
        "for size in train_sizes:\n",
        "    X_train_partial, X_val_partial, y_train_partial, y_val_partial = train_test_split(x_train, y_train, test_size=0.2, train_size=size*0.8, random_state=42)\n",
        "\n",
        "    knn_pipeline = Pipeline([\n",
        "        ('tfidf', TfidfVectorizer(ngram_range=(1, 2))),\n",
        "        ('model', KNeighborsClassifier(n_neighbors=35))\n",
        "    ])\n",
        "\n",
        "    knn_pipeline.fit(X_train_partial, y_train_partial)\n",
        "    y_pred_val = knn_pipeline.predict(X_val_partial)\n",
        "    accuracy_scores.append(accuracy_score(y_val_partial, y_pred_val))\n",
        "\n",
        "\n",
        "results_df = pd.DataFrame({\n",
        "    'Training Size': [f'{size * 100}%' for size in train_sizes],\n",
        "    'Validation Accuracy': accuracy_scores\n",
        "})\n",
        "\n",
        "print(results_df)\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(train_sizes, 1-np.array(accuracy_scores), linestyle='-')\n",
        "plt.title('Training Size vs. Validation Error (KNearestNeighbors)')\n",
        "plt.xlabel('Training Size')\n",
        "plt.ylabel('Validation Error')\n",
        "plt.xticks(train_sizes, [f'{size * 100}%' for size in train_sizes])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "WBAbEXbIIUxB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 685
        },
        "outputId": "41229d8f-ecfe-4734-ab41-6ef310a3ca4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Training Size  Validation Accuracy\n",
            "0          1.0%             0.559113\n",
            "1          5.0%             0.652709\n",
            "2         10.0%             0.698686\n",
            "3         25.0%             0.727422\n",
            "4         50.0%             0.756158\n",
            "5        100.0%             0.776683\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Best Model and Hyperparameter"
      ],
      "metadata": {
        "id": "1SQJd-1-QOI1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c=Pipeline([('tfidf',TfidfVectorizer(ngram_range=(1,1))),('model',LogisticRegression(C=4.0,solver='sag',max_iter=1000))])\n",
        "c.fit(x,y)\n",
        "\n",
        "submission=pd.DataFrame()\n",
        "submission['id']=test['id']\n",
        "submission['target']=c.predict(test['text'])\n",
        "submission.to_csv('submission.csv',index=False)"
      ],
      "metadata": {
        "id": "2jYjcvFyMgRn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stretch Goal - Innovation"
      ],
      "metadata": {
        "id": "ezy5fRAz-eTX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09xbXTIz-d49",
        "outputId": "7f5bbe44-14e3-4d43-8ac3-d89c75dc94ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.40.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.2)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.2.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "x_train,x_val,y_train,y_val=train_test_split(x,y,test_size=0.2,random_state=42,shuffle=True,stratify=y)\n",
        "tweets_train = [tweet for tweet in x_train]\n",
        "\n",
        "sentiment_pipeline = pipeline(\"sentiment-analysis\")\n",
        "sentiment_analysis_train = sentiment_pipeline(tweets_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GoqhcKe_SNo1",
        "outputId": "d0ec0299-8a27-4ec3-aac2-18c48f589f6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_val = [tweet for tweet in x_val]\n",
        "\n",
        "sentiment_pipeline = pipeline(\"sentiment-analysis\")\n",
        "sentiment_analysis_val = sentiment_pipeline(tweets_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0O-r1FD7-xvA",
        "outputId": "02b5fe6c-043c-4c43-b48b-3fa6b67a1b49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
            "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_train = []\n",
        "i = 0\n",
        "for tweet, result in zip(tweets_train, sentiment_analysis_train):\n",
        "  if sentiment_analysis_train[i]['label'] == 'NEGATIVE':\n",
        "    scores_train.append(sentiment_analysis_train[i]['score'] * -1)\n",
        "  else:\n",
        "    scores_train.append(sentiment_analysis_train[i]['score'])\n",
        "  i+=1"
      ],
      "metadata": {
        "id": "P1mgXMygAk--"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores_val = []\n",
        "i = 0\n",
        "for tweet, result in zip(tweets_val, sentiment_analysis_val):\n",
        "  if sentiment_analysis_val[i]['label'] == 'NEGATIVE':\n",
        "    scores_val.append(sentiment_analysis_val[i]['score'] * -1)\n",
        "  else:\n",
        "    scores_val.append(sentiment_analysis_val[i]['score'])\n",
        "  i+=1"
      ],
      "metadata": {
        "id": "9K-qfz1X-xrB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_df = x_train.to_frame()\n",
        "x_train_df['Scores'] = scores_train"
      ],
      "metadata": {
        "id": "p8oTC6re-xmF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_val_df = x_val.to_frame()\n",
        "x_val_df['Scores'] = scores_val"
      ],
      "metadata": {
        "id": "dxDQIWa4UV46"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "column_transformer = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('text', TfidfVectorizer(ngram_range=(1, 1)), 'text'),\n",
        "        ('numeric', StandardScaler(), ['Scores'])\n",
        "    ])"
      ],
      "metadata": {
        "id": "NyvWx2rwYsMw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_model = Pipeline([('preprocessor', column_transformer),('model',LogisticRegression(C=4.0, solver='sag',max_iter=1000))])\n",
        "sentiment_model.fit(x_train_df, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "id": "a07u8ZfYUDvy",
        "outputId": "511ed5a1-db9e-4968-83f5-92e3f0507342"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('text', TfidfVectorizer(),\n",
              "                                                  'text'),\n",
              "                                                 ('numeric', StandardScaler(),\n",
              "                                                  ['Scores'])])),\n",
              "                ('model',\n",
              "                 LogisticRegression(C=4.0, max_iter=1000, solver='sag'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;text&#x27;, TfidfVectorizer(),\n",
              "                                                  &#x27;text&#x27;),\n",
              "                                                 (&#x27;numeric&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;Scores&#x27;])])),\n",
              "                (&#x27;model&#x27;,\n",
              "                 LogisticRegression(C=4.0, max_iter=1000, solver=&#x27;sag&#x27;))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;text&#x27;, TfidfVectorizer(),\n",
              "                                                  &#x27;text&#x27;),\n",
              "                                                 (&#x27;numeric&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;Scores&#x27;])])),\n",
              "                (&#x27;model&#x27;,\n",
              "                 LogisticRegression(C=4.0, max_iter=1000, solver=&#x27;sag&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;text&#x27;, TfidfVectorizer(), &#x27;text&#x27;),\n",
              "                                (&#x27;numeric&#x27;, StandardScaler(), [&#x27;Scores&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">text</label><div class=\"sk-toggleable__content\"><pre>text</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numeric</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Scores&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=4.0, max_iter=1000, solver=&#x27;sag&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = sentiment_model.predict(x_val_df)"
      ],
      "metadata": {
        "id": "HCf5V7lQUds1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = np.mean(np.array(y_pred)==np.array(y_val))\n",
        "print(accuracy)"
      ],
      "metadata": {
        "id": "FQQKgBYZUt2v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68958e96-c2fd-45c7-baaa-fa78f529c62c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8168089297439265\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Change to model -"
      ],
      "metadata": {
        "id": "R9Kk7wfrh13-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "x_train,x_val,y_train,y_val=train_test_split(x,y,test_size=0.2,random_state=42,shuffle=True,stratify=y)\n",
        "tweets_train = [tweet for tweet in x_train]\n",
        "\n",
        "sentiment_pipeline = pipeline(\"sentiment-analysis\", model = \"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n",
        "sentiment_analysis_train = sentiment_pipeline(tweets_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 249,
          "referenced_widgets": [
            "d3d9be4e8c754a14b2866c9c196b5624",
            "78f4516270d24c15852089ee78b5f373",
            "db4c04baaa124ab282f36926c5145850",
            "10293042189c4abf9714877b44f54619",
            "24cb126bdf414a62b5899e9cfe0015b8",
            "18b7ec8bbbd54ab687f978bc2382e53c",
            "dc6916e890894b0293b8971e5ac01a38",
            "f58b034cbe65494a956f755b95b50c63",
            "8c69563238344471bb41fb6e1a19df96",
            "ba05cdb13f364f75bc8125a165785cf7",
            "07bd6cb204e7409890ab47252562d928",
            "cd32466b17c9478080cef08a9d2544bc",
            "89fa2bb27aad40c2a5b417aa1471ef08",
            "8cd6cbabb5d3427fb81cd4f805572dcc",
            "c1c68ee370b7455c8955a843d0a1258a",
            "0aac3ace07d949488b8db36c9c163ec7",
            "99232dfa1c7641ae8cf4ac0496ab2a3a",
            "2ad3b8984a554b5c90ecb9e4dae073ba",
            "1fcf713dabca4e13923d7ad3ff363240",
            "54872c73b67f48f09f0c9bc12c9d91a5",
            "53480e1821ac4a28a2c3826ac1f90b50",
            "042258cfe3694075869489a5172a2e24",
            "228606bb37164921bf1e547cbe0ecfff",
            "659e34efb4664520b40f99c28c49cb69",
            "d1ae8d09bff14055a60228a25dadfbee",
            "7cf251bd57af4209bf3d8caad467fdf4",
            "4e3f2ec8dcc54787a0c1b377b7738fb1",
            "c4ff62b33aa448d3bf2454f33f6ba171",
            "0552c3f9868149219c61ab631233188f",
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            "5f18a2ca5dce4670a4a5afb848383522",
            "453ec9754ddc4c828bea626600a0500d",
            "7250f6475b934eba80b375e7cb33355a",
            "655bf7f3b41b4aa1bd8812e59bb30d43",
            "9dde93f4a9234cbbb593d6f5bbf22c48",
            "79274bfc4cc84f7a90bb85ee84cae06a",
            "691a1920e11347cb895c68bac8d369db",
            "33d1351c99364e09b5eaa5667df87528",
            "e89394a48740412ea33177252e4911a6",
            "b9d2f923595c416bb2b6a81a19bce445",
            "c7eaa6e137764c2c8e3914b7ee8241f3",
            "116e5353063343a1bb5eef47bbaf6ca4",
            "ee5390e323b14cb0acf374128d09a562",
            "d2c3d9bdc2dc41c7ac562e03c19aee51",
            "b48b88a3e6db4137b8c2423c5acad6bb",
            "f2a6a20d91214374897d0fa04f6cb100",
            "51f71f48f4c04d128bf1f8d391f2db7c",
            "ef8541466f9f404197c1b73a9209b8a8",
            "04876e0a761c4a67a53590bd492ffed3",
            "2ecac888d5364db4b3a93bcc822b85fc",
            "62721f25fe534e57a1b100c18bd83fdb",
            "97c1154704f343be865e1669f1271a66",
            "fae54fa5026248abbb2a94df1d870419",
            "d646b6f2859b469aa9a697db78b47dd4"
          ]
        },
        "id": "51hToHxfhgsl",
        "outputId": "ccad8e90-169e-4a58-a229-a64e16159bf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/929 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d3d9be4e8c754a14b2866c9c196b5624"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "pytorch_model.bin:   0%|          | 0.00/501M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cd32466b17c9478080cef08a9d2544bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7250f6475b934eba80b375e7cb33355a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/239 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d2c3d9bdc2dc41c7ac562e03c19aee51"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tweets_val = [tweet for tweet in x_val]\n",
        "\n",
        "sentiment_pipeline = pipeline(\"sentiment-analysis\", model = \"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n",
        "sentiment_analysis_val = sentiment_pipeline(tweets_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WfjxT31phk9a",
        "outputId": "6a511315-00e5-42cd-db81-7d9604d78539"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment-latest were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scores_train = []\n",
        "i = 0\n",
        "for tweet, result in zip(tweets_train, sentiment_analysis_train):\n",
        "  if sentiment_analysis_train[i]['label'] == 'NEGATIVE':\n",
        "    scores_train.append(sentiment_analysis_train[i]['score'] * -1)\n",
        "  else:\n",
        "    scores_train.append(sentiment_analysis_train[i]['score'])\n",
        "  i+=1"
      ],
      "metadata": {
        "id": "vhtJa-S6hp_R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scores_val = []\n",
        "i = 0\n",
        "for tweet, result in zip(tweets_val, sentiment_analysis_val):\n",
        "  if sentiment_analysis_val[i]['label'] == 'NEGATIVE':\n",
        "    scores_val.append(sentiment_analysis_val[i]['score'] * -1)\n",
        "  else:\n",
        "    scores_val.append(sentiment_analysis_val[i]['score'])\n",
        "  i+=1"
      ],
      "metadata": {
        "id": "T40k6MJNhp7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_df = x_train.to_frame()\n",
        "x_train_df['Scores'] = scores_train\n",
        "\n",
        "x_val_df = x_val.to_frame()\n",
        "x_val_df['Scores'] = scores_val\n"
      ],
      "metadata": {
        "id": "8GD1FSSYhp58"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "column_transformer = ColumnTransformer(\n",
        "    transformers=[\n",
        "        ('text', TfidfVectorizer(ngram_range=(1, 1)), 'text'),\n",
        "        ('numeric', StandardScaler(), ['Scores'])\n",
        "    ])"
      ],
      "metadata": {
        "id": "m5BodaC4hx01"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_model = Pipeline([('preprocessor', column_transformer),('model',LogisticRegression(C=4.0, solver='sag',max_iter=1000))])\n",
        "sentiment_model.fit(x_train_df, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "id": "jhM_ycbrh0HS",
        "outputId": "8e42d2cd-56a5-42a8-9f1f-c92384cfe8ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('text', TfidfVectorizer(),\n",
              "                                                  'text'),\n",
              "                                                 ('numeric', StandardScaler(),\n",
              "                                                  ['Scores'])])),\n",
              "                ('model',\n",
              "                 LogisticRegression(C=4.0, max_iter=1000, solver='sag'))])"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;text&#x27;, TfidfVectorizer(),\n",
              "                                                  &#x27;text&#x27;),\n",
              "                                                 (&#x27;numeric&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;Scores&#x27;])])),\n",
              "                (&#x27;model&#x27;,\n",
              "                 LogisticRegression(C=4.0, max_iter=1000, solver=&#x27;sag&#x27;))])</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;text&#x27;, TfidfVectorizer(),\n",
              "                                                  &#x27;text&#x27;),\n",
              "                                                 (&#x27;numeric&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;Scores&#x27;])])),\n",
              "                (&#x27;model&#x27;,\n",
              "                 LogisticRegression(C=4.0, max_iter=1000, solver=&#x27;sag&#x27;))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;text&#x27;, TfidfVectorizer(), &#x27;text&#x27;),\n",
              "                                (&#x27;numeric&#x27;, StandardScaler(), [&#x27;Scores&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">text</label><div class=\"sk-toggleable__content\"><pre>text</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">numeric</label><div class=\"sk-toggleable__content\"><pre>[&#x27;Scores&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(C=4.0, max_iter=1000, solver=&#x27;sag&#x27;)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = sentiment_model.predict(x_val_df)"
      ],
      "metadata": {
        "id": "kGeJDL3nnBQb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = np.mean(np.array(y_pred)==np.array(y_val))\n",
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WpxJK_qSnDry",
        "outputId": "85520acc-0b43-4cd0-b182-7d749e637b80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8082731451083388\n"
          ]
        }
      ]
    }
  ]
}