IMBD,amazon_and_Yelp_review_Classification.ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import spacy\n",
        "import nltk"
      ]
    },
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        " data1= '/content/drive/MyDrive/Colab Notebooks/Udemy_Natural_Language_Processing_NLP_in_Python_with_8_Projects_2021-11/project_3/data3.TXT'\n",
        " data2 = '/content/drive/MyDrive/Colab Notebooks/Udemy_Natural_Language_Processing_NLP_in_Python_with_8_Projects_2021-11/project_3/data3.TXT'\n",
        " data3 = '/content/drive/MyDrive/Colab Notebooks/Udemy_Natural_Language_Processing_NLP_in_Python_with_8_Projects_2021-11/project_3/data3.TXT'\n",
        " data_yelp = pd.read_csv(data1 , sep=\"\\t\",header=None)\n",
        " data_yelp"
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .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-2f2aba42-a8e7-4484-afcc-3934d22e2130 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-2f2aba42-a8e7-4484-afcc-3934d22e2130');\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-480c7c5f-a208-4197-95d2-472b6962bae0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-480c7c5f-a208-4197-95d2-472b6962bae0')\"\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-480c7c5f-a208-4197-95d2-472b6962bae0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_yelp.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmxPhsUL8aeg",
        "outputId": "f069a5dd-62f6-476c-c415-529c29d76164"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_yelp[1].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FlpOWujB8fY4",
        "outputId": "f86613b7-1aa7-44b5-a9b3-795e9ee38196"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    500\n",
              "1    500\n",
              "Name: 1, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_name = [\"Review\",\"Sentiment\"]\n",
        "data_yelp.columns = column_name"
      ],
      "metadata": {
        "id": "hCGg2My18nlb"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_yelp.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaYJLNUQ84pw",
        "outputId": "ede40f43-f081-4be6-952b-001298a7f73a"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Review', 'Sentiment'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_IMBD = pd.read_csv(data2 , sep=\"\\t\",header=None)\n",
        "data_amazon = pd.read_csv(data3 , sep=\"\\t\",header=None)"
      ],
      "metadata": {
        "id": "E0gyjm-J9I_g"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_IMBD[1].value_counts() , data_amazon[1].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jKi4-U6V9pgM",
        "outputId": "20ee3250-5429-41a7-9d5c-8a4a37ff0bea"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0    500\n",
              " 1    500\n",
              " Name: 1, dtype: int64,\n",
              " 0    500\n",
              " 1    500\n",
              " Name: 1, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = data_yelp[\"Review\"]\n",
        "y = data_yelp[\"Sentiment\"]\n",
        "x,y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXtXwO2--Dox",
        "outputId": "967ecea2-6c29-45fb-9e66-fb9709af02a0"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0      So there is no way for me to plug it in here i...\n",
              " 1                            Good case, Excellent value.\n",
              " 2                                 Great for the jawbone.\n",
              " 3      Tied to charger for conversations lasting more...\n",
              " 4                                      The mic is great.\n",
              "                              ...                        \n",
              " 995    The screen does get smudged easily because it ...\n",
              " 996    What a piece of junk.. I lose more calls on th...\n",
              " 997                         Item Does Not Match Picture.\n",
              " 998    The only thing that disappoint me is the infra...\n",
              " 999    You can not answer calls with the unit, never ...\n",
              " Name: Review, Length: 1000, dtype: object,\n",
              " 0      0\n",
              " 1      1\n",
              " 2      1\n",
              " 3      0\n",
              " 4      1\n",
              "       ..\n",
              " 995    0\n",
              " 996    0\n",
              " 997    0\n",
              " 998    0\n",
              " 999    0\n",
              " Name: Sentiment, Length: 1000, dtype: int64)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Cleaning**"
      ],
      "metadata": {
        "id": "1o0JvtK499-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import string"
      ],
      "metadata": {
        "id": "hExRX3k292yF"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "punct = string.punctuation\n",
        "punct"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "H3RW8soY-jem",
        "outputId": "fc1c4ef3-9489-4d32-f4d6-1684b4050c68"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from spacy.lang.en.stop_words import STOP_WORDS"
      ],
      "metadata": {
        "id": "IfM9ipm0-nW_"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stopword = list(STOP_WORDS)"
      ],
      "metadata": {
        "id": "M91i9Nqn-tsQ"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy.cli\n",
        "spacy.cli.download(\"en_core_web_lg\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2SO6rSWTAMuI",
        "outputId": "f4eaa5a7-7bb1-4c3b-a7f2-c345a5e7ab34"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_lg')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_lg\")"
      ],
      "metadata": {
        "id": "7oAVT8G7AECJ"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def text_data_cleaning(sentence):\n",
        "  doc = nlp(sentence)\n",
        "  tokens = []\n",
        "  for token in doc:\n",
        "    if token.lemma_ != \"-PRON-\":\n",
        "      temp = token.lemma_.lower().strip()\n",
        "    else:\n",
        "      temp = token.lower_\n",
        "    tokens.append(temp)\n",
        "\n",
        "  cleaned_tokens = []\n",
        "  for token in tokens:\n",
        "    if token not in stopword and token not in punct:\n",
        "      cleaned_tokens.append(token)\n",
        "  return cleaned_tokens\n"
      ],
      "metadata": {
        "id": "clptNvpn-4LP"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "text_data_cleaning(\"HEllo all , it's a good day to die!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uw7WC3Fi_tNb",
        "outputId": "74473c66-c1d2-4c34-8359-cca7c71837a4"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['hello', 'good', 'day', 'die']"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Vectorization Feature Engineering (TF-IDF)"
      ],
      "metadata": {
        "id": "np9L8f1OBIgp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "metadata": {
        "id": "7kbctMyJ_3NI"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf_idf = TfidfVectorizer(tokenizer=text_data_cleaning)\n",
        "model = LinearSVC()"
      ],
      "metadata": {
        "id": "UaJ7MzvrBeG6"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "qcHouIIFBxja"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train , x_test , y_train , y_test = train_test_split(x,y,test_size = 0.22)"
      ],
      "metadata": {
        "id": "UDoM08CfB4bo"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = Pipeline([('tfidf',tf_idf),('clf',model)])"
      ],
      "metadata": {
        "id": "tYfBP5vzCCe3"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.fit(x_train , y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "w7TduA5OCGmn",
        "outputId": "3249ce0f-a9a0-4563-fc3d-547cd2cb97e5"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('tfidf',\n",
              "                 TfidfVectorizer(tokenizer=<function text_data_cleaning at 0x791ea6027760>)),\n",
              "                ('clf', LinearSVC())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;,\n",
              "                 TfidfVectorizer(tokenizer=&lt;function text_data_cleaning at 0x791ea6027760&gt;)),\n",
              "                (&#x27;clf&#x27;, LinearSVC())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item 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-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;tfidf&#x27;,\n",
              "                 TfidfVectorizer(tokenizer=&lt;function text_data_cleaning at 0x791ea6027760&gt;)),\n",
              "                (&#x27;clf&#x27;, LinearSVC())])</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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(tokenizer=&lt;function text_data_cleaning at 0x791ea6027760&gt;)</pre></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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearSVC</label><div class=\"sk-toggleable__content\"><pre>LinearSVC()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score , precision_score , recall_score , f1_score , ConfusionMatrixDisplay , confusion_matrix , classification_report"
      ],
      "metadata": {
        "id": "igMGjetfJn-Q"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(x_test)"
      ],
      "metadata": {
        "id": "aAcSb9x_KCOv"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test , y_pred) *100 , precision_score(y_test , y_pred)*100 , recall_score(y_test , y_pred)*100 , f1_score(y_test , y_pred)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlRNd39XKFvE",
        "outputId": "b7e2f484-e824-435a-f48d-c7b0b336bbba"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(73.18181818181819, 69.0909090909091, 75.24752475247524, 72.03791469194313)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test , y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WX8TXtnUKRrN",
        "outputId": "3767091c-9fb4-4ce8-ed24-20016314bb00"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.77      0.71      0.74       119\n",
            "           1       0.69      0.75      0.72       101\n",
            "\n",
            "    accuracy                           0.73       220\n",
            "   macro avg       0.73      0.73      0.73       220\n",
            "weighted avg       0.74      0.73      0.73       220\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "ConfusionMatrixDisplay(confusion_matrix(y_test , y_pred)).plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "oHxzMGaJKg2k",
        "outputId": "d6de3e57-b97b-46df-8178-fb61c6ef0589"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x791e9f9d0040>"
            ]
          },
          "metadata": {},
          "execution_count": 94
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf.predict([\"The BMW M3 GT3 is a true testament to precision engineering and unparalleled performance. From its sleek exterior design to its finely tuned mechanics, every aspect of this machine exudes excellence.\"])\n",
        "# 0 -> bad review , 1-> good review"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fp85EKzRLD--",
        "outputId": "8fcbac1c-47e2-4087-b213-3787aec6c8bb"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EfkBmEERL260"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}