amazon_review_classification.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rayxtn/amazon_review_classification/blob/main/amazon_review_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "24PDoEKXGSEz",
        "outputId": "110bf418-cb1d-4e40-97db-996b8be26195"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n",
            "[nltk_data]   Package vader_lexicon is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "#importing libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "from nltk import corpus, tokenize\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer , PorterStemmer , porter\n",
        "import re\n",
        "import sklearn\n",
        "nltk.download('vader_lexicon')\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qI7TE1wb_h6S"
      },
      "outputs": [],
      "source": [
        "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
        "sen_analayzer = SentimentIntensityAnalyzer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bdGfC97h_8hG",
        "outputId": "b5023e58-1c9e-4977-d0f4-50f87fb5822e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'neg': 0.0, 'neu': 0.393, 'pos': 0.607, 'compound': 0.4753}"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_text =  \"this is a nice !\"\n",
        "sen_analayzer.polarity_scores(test_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "puoegQWFLgr8",
        "outputId": "cc27db0b-3a71-4e0b-810b-3ee15f06ba76"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'neg': 0.558, 'neu': 0.442, 'pos': 0.0, 'compound': -0.5848}"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "test_text2 = \"this is too bad ! \"\n",
        "sen_analayzer.polarity_scores(test_text2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "94CCH-104UEE"
      },
      "source": [
        "Importing 10% of the data set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "8d8fdef5-da54-49a0-9d13-28c28cbb0937"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "(56845, 10)"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "amazon_review = pd.read_csv('/content/drive/MyDrive/csv/Amazon_reviews.csv')\n",
        "\n",
        "# Select the first less than 10% of the rows\n",
        "product = amazon_review.head(int(0.10 * len(amazon_review)))\n",
        "# Display the first few rows of the subset\n",
        "product.head()\n",
        "# Check the shape of the subset\n",
        "product.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        "id": "UglLfPaRPtWm",
        "outputId": "8a11d918-c358-45a9-de60-2333f0e762f0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Original DataFrame:\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(56845, 10)"
            ]
          },
          "execution_count": 37,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "print(\"Original DataFrame:\")\n",
        "product.head()\n",
        "product.shape\n",
        "# cleaning data since there is some errors of missing values and non numerical values in scores"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yycoWCmX4WYO"
      },
      "source": [
        "cleaning the data set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "Y3rhHeuq-ETl",
        "outputId": "21a7b749-a6e5-41b3-8e33-c115e5d08e08"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "DataFrame after cleaning (removed columns with missing data):\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "(56838, 10)"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Drop columns with any missing values\n",
        "#df_cleaned = product.dropna(axis=1, how='any')\n",
        "# Remove duplicate rows\n",
        "df_cleaned = product.drop_duplicates()\n",
        "# Convert non-numeric values in 'Score' to NaN and then fill missing values with the mean\n",
        "df_cleaned['Score'] = pd.to_numeric(df_cleaned['Score'], errors='coerce')  # Convert to numeric, invalid parsing becomes NaN\n",
        "df_cleaned['Score'] = df_cleaned['Score'].fillna(df_cleaned['Score'].mean())  # Fill NaN values with the mean\n",
        "df_cleaned = df_cleaned.dropna(subset=['ProfileName'])\n",
        "df_cleaned = df_cleaned.dropna(subset=['Summary'])\n",
        "\n",
        "print(\"\\nDataFrame after cleaning (removed columns with missing data):\")\n",
        "df_cleaned.head()\n",
        "df_cleaned.shape"
      ]
    },
    {
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              "summary": "{\n  \"name\": \"df_cleaned['Score']\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": 5,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 35521,\n        \"max\": 35521,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          35521\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8165,\n        \"max\": 8165,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8165\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 5468,\n        \"max\": 5468,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5468\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 4510,\n        \"max\": 4510,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4510\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 3174,\n        \"max\": 3174,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "Score      5     4     1     3     2\n",
              "count  35521  8165  5468  4510  3174"
            ]
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          "execution_count": 55,
          "metadata": {},
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      ],
      "source": [
        "# due to this anything less than 3 would be a negative review , just for testing and comparing\n",
        "df_cleaned['Score'].value_counts().to_frame().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "O7kn7NaXBx7M",
        "outputId": "5f401a33-586c-46a2-c7bb-e273275a8f94"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'compound': 0.4404}"
            ]
          },
          "execution_count": 56,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#applying the nltk sentiment analsis to the first review test\n",
        "sen_analayzer.polarity_scores(df_cleaned.loc[0]['Summary'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a9wxPNZ7J1g7"
      },
      "source": [
        "Result compoud seems > than 0 it means positive\n",
        "going to check the score and compare :"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        {
          "name": "stdout",
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          "text": [
            "5\n"
          ]
        }
      ],
      "source": [
        "print(df_cleaned.loc[0]['Score'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WCHufbgMKATE"
      },
      "source": [
        "Result of score included says 5 means it is correct"
      ]
    },
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              "summary": "{\n  \"name\": \"df_cleaned\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ProductId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UserId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ProfileName\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessNumerator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessDenominator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Summary\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "</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-1d8792c6-3c59-487d-a245-24c7d0f607a5')\"\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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-1d8792c6-3c59-487d-a245-24c7d0f607a5');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Id  ProductId  UserId  ProfileName  HelpfulnessNumerator  \\\n",
              "0   0          0       0            0                     0   \n",
              "\n",
              "   HelpfulnessDenominator  Score  Time  Summary  Text  \n",
              "0                       0      0     0        0     0  "
            ]
          },
          "execution_count": 58,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned.isnull().sum().to_frame().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "vRX06oh6MTEg",
        "outputId": "96686c7c-680d-4ba8-92fc-169357dafd80"
      },
      "outputs": [
        {
          "data": {
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It's a good one!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scores_analysis\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "      <td>positive</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "    height: 32px;\n",
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              "    border-color: transparent;\n",
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              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "    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-b7f5e522-6cc3-46ad-97d3-a650429daeaf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Id   ProductId          UserId                      ProfileName  \\\n",
              "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
              "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
              "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
              "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
              "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
              "0                     1                       1      5  1303862400   \n",
              "1                     0                       0      1  1346976000   \n",
              "2                     1                       1      4  1219017600   \n",
              "3                     3                       3      2  1307923200   \n",
              "4                     0                       0      5  1350777600   \n",
              "\n",
              "                 Summary                                               Text  \\\n",
              "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
              "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
              "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
              "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
              "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
              "\n",
              "  scores_analysis  \n",
              "0        positive  \n",
              "1        negative  \n",
              "2        positive  \n",
              "3        negative  \n",
              "4        positive  "
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned['scores_analysis'] = df_cleaned['Score'].apply(lambda x: 'positive' if x > 3 else 'negative')\n",
        "df_cleaned.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L6FaH2tR4Ijw",
        "outputId": "7adb83b8-79cb-4ec0-a9e8-d4cf0b9f40ab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(56838, 11)"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cP85dSSc7oLF"
      },
      "source": [
        "# **Creating a new variable in the data frame to check the text analysis and the results**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "crfssCit48po",
        "outputId": "dea6afea-c8fc-422c-b7be-f904627e4215"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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Our precious pets deserve the best, and this is packaged and marketed like it is, but Chinese food products, and pet products in particular are potentially very dangerous.<br /><br />I threw out a half a bag when I read where it came from.\",\n          \"My 7-1/2 month old absolutely loves this one...by far, it is her favorite jarred baby food. I like it because the lentils add a bit more protein than your average baby food, and I'm not ready to introduce her to meats yet.<br /><br />I do subscribe & save, which brings the price down to $0.80/jar. That's a fair price - not an amazing deal, but roughly equivalent to sale prices at the grocery store.  The bigger issue is that this particular blend is hard to find - I have to go to Whole Foods. My regular grocery store and Target carry Earth's Best, but not this blend. My daughter likes it so much, though, that I would still buy even if the price were a bit higher than store prices. It's a good one!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scores_analysis\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mysentiment_scores\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_cleaned"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-08154c70-1e8c-4fb0-92be-4a63a76783f9\" class=\"colab-df-container\">\n",
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              "      <td>1303862400</td>\n",
              "      <td>Good Quality Dog Food</td>\n",
              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "      <td>positive</td>\n",
              "      <td>{'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'comp...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>dll pa</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
              "      <td>Product arrived labeled as Jumbo Salted Peanut...</td>\n",
              "      <td>negative</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>3</td>\n",
              "      <td>B000LQOCH0</td>\n",
              "      <td>ABXLMWJIXXAIN</td>\n",
              "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1219017600</td>\n",
              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
              "      <td>positive</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>B000UA0QIQ</td>\n",
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              "      <td>Karl</td>\n",
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              "      <td>2</td>\n",
              "      <td>1307923200</td>\n",
              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
              "      <td>negative</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B006K2ZZ7K</td>\n",
              "      <td>A1UQRSCLF8GW1T</td>\n",
              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1350777600</td>\n",
              "      <td>Great taffy</td>\n",
              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "      <td>positive</td>\n",
              "      <td>{'neg': 0.0, 'neu': 0.196, 'pos': 0.804, 'comp...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-08154c70-1e8c-4fb0-92be-4a63a76783f9 button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-08154c70-1e8c-4fb0-92be-4a63a76783f9');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-21fc7821-8f34-4fe7-b3d7-b2fd438b4193')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
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              "      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-21fc7821-8f34-4fe7-b3d7-b2fd438b4193 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Id   ProductId          UserId                      ProfileName  \\\n",
              "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
              "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
              "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
              "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
              "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
              "0                     1                       1      5  1303862400   \n",
              "1                     0                       0      1  1346976000   \n",
              "2                     1                       1      4  1219017600   \n",
              "3                     3                       3      2  1307923200   \n",
              "4                     0                       0      5  1350777600   \n",
              "\n",
              "                 Summary                                               Text  \\\n",
              "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
              "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
              "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
              "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
              "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
              "\n",
              "  scores_analysis                                 mysentiment_scores  \n",
              "0        positive  {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'comp...  \n",
              "1        negative  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
              "2        positive  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
              "3        negative  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...  \n",
              "4        positive  {'neg': 0.0, 'neu': 0.196, 'pos': 0.804, 'comp...  "
            ]
          },
          "execution_count": 60,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned['mysentiment_scores'] = df_cleaned['Summary'].apply(lambda x: sen_analayzer.polarity_scores(x))\n",
        "df_cleaned.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oUXsU7u_k6U",
        "outputId": "9f18933f-3749-4f6a-a6ea-0f9f77de1232"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "56838"
            ]
          },
          "execution_count": 61,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(df_cleaned['mysentiment_scores'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "uV3nATer_x2N",
        "outputId": "7b0046a7-c463-4876-8b86-94fe52f701ba"
      },
      "outputs": [
        {
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Our precious pets deserve the best, and this is packaged and marketed like it is, but Chinese food products, and pet products in particular are potentially very dangerous.<br /><br />I threw out a half a bag when I read where it came from.\",\n          \"My 7-1/2 month old absolutely loves this one...by far, it is her favorite jarred baby food. I like it because the lentils add a bit more protein than your average baby food, and I'm not ready to introduce her to meats yet.<br /><br />I do subscribe & save, which brings the price down to $0.80/jar. That's a fair price - not an amazing deal, but roughly equivalent to sale prices at the grocery store.  The bigger issue is that this particular blend is hard to find - I have to go to Whole Foods. My regular grocery store and Target carry Earth's Best, but not this blend. My daughter likes it so much, though, that I would still buy even if the price were a bit higher than store prices. It's a good one!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scores_analysis\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mysentiment_scores\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compound\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37151904476048986,\n        \"min\": -0.9157,\n        \"max\": 0.9701,\n        \"num_unique_values\": 2183,\n        \"samples\": [\n          0.8374,\n          0.4777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "\n",
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              "\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",
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              "\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-255a3dd7-2f8f-4901-abf6-b94820fd4e9a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Id   ProductId          UserId                      ProfileName  \\\n",
              "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
              "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
              "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
              "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
              "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
              "0                     1                       1      5  1303862400   \n",
              "1                     0                       0      1  1346976000   \n",
              "2                     1                       1      4  1219017600   \n",
              "3                     3                       3      2  1307923200   \n",
              "4                     0                       0      5  1350777600   \n",
              "\n",
              "                 Summary                                               Text  \\\n",
              "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
              "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
              "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
              "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
              "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
              "\n",
              "  scores_analysis                                 mysentiment_scores  compound  \n",
              "0        positive  {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'comp...    0.4404  \n",
              "1        negative  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000  \n",
              "2        positive  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000  \n",
              "3        negative  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...    0.0000  \n",
              "4        positive  {'neg': 0.0, 'neu': 0.196, 'pos': 0.804, 'comp...    0.6249  "
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned['compound'] = df_cleaned['mysentiment_scores'].apply(lambda x: x['compound'])\n",
        "df_cleaned.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "CPM55ur8ELhO",
        "outputId": "ec990092-afb6-4abb-e07c-edd13803f432"
      },
      "outputs": [
        {
          "data": {
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Our precious pets deserve the best, and this is packaged and marketed like it is, but Chinese food products, and pet products in particular are potentially very dangerous.<br /><br />I threw out a half a bag when I read where it came from.\",\n          \"My 7-1/2 month old absolutely loves this one...by far, it is her favorite jarred baby food. I like it because the lentils add a bit more protein than your average baby food, and I'm not ready to introduce her to meats yet.<br /><br />I do subscribe & save, which brings the price down to $0.80/jar. That's a fair price - not an amazing deal, but roughly equivalent to sale prices at the grocery store.  The bigger issue is that this particular blend is hard to find - I have to go to Whole Foods. My regular grocery store and Target carry Earth's Best, but not this blend. My daughter likes it so much, though, that I would still buy even if the price were a bit higher than store prices. It's a good one!\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"scores_analysis\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mysentiment_scores\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compound\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.37151904476048986,\n        \"min\": -0.9157,\n        \"max\": 0.9701,\n        \"num_unique_values\": 2183,\n        \"samples\": [\n          0.8374,\n          0.4777\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"compound_score\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df_cleaned"
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              "\n",
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              "      <th>Text</th>\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>B001E4KFG0</td>\n",
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              "      <td>delmartian</td>\n",
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              "      <td>5</td>\n",
              "      <td>1303862400</td>\n",
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              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "      <td>positive</td>\n",
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              "      <td>2</td>\n",
              "      <td>B00813GRG4</td>\n",
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              "      <td>dll pa</td>\n",
              "      <td>0</td>\n",
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              "      <td>1346976000</td>\n",
              "      <td>Not as Advertised</td>\n",
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              "      <td>negative</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>B000LQOCH0</td>\n",
              "      <td>ABXLMWJIXXAIN</td>\n",
              "      <td>Natalia Corres \"Natalia Corres\"</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>1219017600</td>\n",
              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
              "      <td>positive</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>negative</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>B000UA0QIQ</td>\n",
              "      <td>A395BORC6FGVXV</td>\n",
              "      <td>Karl</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1307923200</td>\n",
              "      <td>Cough Medicine</td>\n",
              "      <td>If you are looking for the secret ingredient i...</td>\n",
              "      <td>negative</td>\n",
              "      <td>{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>negative</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>B006K2ZZ7K</td>\n",
              "      <td>A1UQRSCLF8GW1T</td>\n",
              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1350777600</td>\n",
              "      <td>Great taffy</td>\n",
              "      <td>Great taffy at a great price.  There was a wid...</td>\n",
              "      <td>positive</td>\n",
              "      <td>{'neg': 0.0, 'neu': 0.196, 'pos': 0.804, 'comp...</td>\n",
              "      <td>0.6249</td>\n",
              "      <td>positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ce0b627-bc56-4c42-92de-aca99ef51bc4')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "        document.querySelector('#df-2ce0b627-bc56-4c42-92de-aca99ef51bc4 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-2ce0b627-bc56-4c42-92de-aca99ef51bc4');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "  }\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-dea4a152-efec-4af3-a798-ad8b0924cce8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   Id   ProductId          UserId                      ProfileName  \\\n",
              "0   1  B001E4KFG0  A3SGXH7AUHU8GW                       delmartian   \n",
              "1   2  B00813GRG4  A1D87F6ZCVE5NK                           dll pa   \n",
              "2   3  B000LQOCH0   ABXLMWJIXXAIN  Natalia Corres \"Natalia Corres\"   \n",
              "3   4  B000UA0QIQ  A395BORC6FGVXV                             Karl   \n",
              "4   5  B006K2ZZ7K  A1UQRSCLF8GW1T    Michael D. Bigham \"M. Wassir\"   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
              "0                     1                       1      5  1303862400   \n",
              "1                     0                       0      1  1346976000   \n",
              "2                     1                       1      4  1219017600   \n",
              "3                     3                       3      2  1307923200   \n",
              "4                     0                       0      5  1350777600   \n",
              "\n",
              "                 Summary                                               Text  \\\n",
              "0  Good Quality Dog Food  I have bought several of the Vitality canned d...   \n",
              "1      Not as Advertised  Product arrived labeled as Jumbo Salted Peanut...   \n",
              "2  \"Delight\" says it all  This is a confection that has been around a fe...   \n",
              "3         Cough Medicine  If you are looking for the secret ingredient i...   \n",
              "4            Great taffy  Great taffy at a great price.  There was a wid...   \n",
              "\n",
              "  scores_analysis                                 mysentiment_scores  \\\n",
              "0        positive  {'neg': 0.0, 'neu': 0.508, 'pos': 0.492, 'comp...   \n",
              "1        negative  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
              "2        positive  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
              "3        negative  {'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...   \n",
              "4        positive  {'neg': 0.0, 'neu': 0.196, 'pos': 0.804, 'comp...   \n",
              "\n",
              "   compound compound_score  \n",
              "0    0.4404       positive  \n",
              "1    0.0000       negative  \n",
              "2    0.0000       negative  \n",
              "3    0.0000       negative  \n",
              "4    0.6249       positive  "
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned['compound_score'] = df_cleaned['compound'].apply(lambda x: 'positive' if x > 0 else 'negative')\n",
        "df_cleaned.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "RSF82n-9gTVU",
        "outputId": "55028241-3ccd-4f7f-afa5-f19f420fb0d3"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>scores_analysis</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>43686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>13152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "scores_analysis\n",
              "positive    43686\n",
              "negative    13152\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned['scores_analysis'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FDOEEIIOF3nZ"
      },
      "source": [
        "# **Evaluation or confusion Matrix**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        },
        "id": "pwHxzvXrE5PR",
        "outputId": "01d7b372-14d6-439e-c3c8-de6976cc4bd5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "    negative       0.43      0.76      0.55     13152\n",
            "    positive       0.91      0.70      0.79     43686\n",
            "\n",
            "    accuracy                           0.71     56838\n",
            "   macro avg       0.67      0.73      0.67     56838\n",
            "weighted avg       0.80      0.71      0.73     56838\n",
            "\n",
            "Accuracy: 0.7115134241176677\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
        "y_true = df_cleaned['scores_analysis']\n",
        "y_pred = df_cleaned['compound_score']\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "# Visualize the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "print(classification_report(y_true, y_pred))\n",
        "print(\"Accuracy:\", accuracy_score(y_true, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ryltntmATcjT",
        "outputId": "f9e7d84e-8bb6-4f6e-ce1c-31bc104a2897"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "scores_analysis\n",
            "positive    43686\n",
            "negative    13152\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "print(df_cleaned['scores_analysis'].value_counts())  # Check the counts of positive and negative\n",
        "\n",
        "# Count the occurrences of 'positive' and 'negative' sentiments\n",
        "sentiment_counts = df_cleaned['scores_analysis'].value_counts()\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(sentiment_counts.index, sentiment_counts.values, color='skyblue')  # Set a specific color\n",
        "\n",
        "plt.title('Distribution of Positive vs Negative Sentiment', fontsize=14)\n",
        "plt.xlabel('Sentiment', fontsize=12)\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "\n",
        "plt.xticks(rotation=0)  # Rotate x-axis labels for better readability\n",
        "plt.tick_params(bottom=False)  # Remove bottom ticks\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djHUlmuspLkl"
      },
      "source": [
        "# Just Creating some balance for better **results**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tr1EoCSIowGi",
        "outputId": "de60d081-2cf5-4c92-ddab-19cfc36f3210"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "scores_analysis\n",
            "positive    13152\n",
            "negative    13152\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "from sklearn.utils import resample\n",
        "\n",
        "positive_reviews = df_cleaned[df_cleaned['scores_analysis'] == 'positive']\n",
        "negative_reviews = df_cleaned[df_cleaned['scores_analysis'] == 'negative']\n",
        "\n",
        "# Undersample the positive reviews\n",
        "positive_downsampled = resample(\n",
        "    positive_reviews,\n",
        "    replace=False,  # nooo duplicatiooon\n",
        "    n_samples=len(negative_reviews),  # match the exact number of negative revviews\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "# combining the downsampled positive and all negative reviews\n",
        "balanced_reviews_undersample = pd.concat([positive_downsampled, negative_reviews])\n",
        "\n",
        "# shuffling the dataset\n",
        "balanced_reviews_undersample = balanced_reviews_undersample.sample(frac=1, random_state=42).reset_index(drop=True)\n",
        "\n",
        "print(balanced_reviews_undersample['scores_analysis'].value_counts())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEtL29KxpR3A"
      },
      "source": [
        "Balance created for positive and negative reviews\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hqmkmlLoRM7g"
      },
      "source": [
        "# Building a machine learning approche"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AMHld1OlBUcP"
      },
      "source": [
        "# **Preprocessing **"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iyZ8qhi6wn6K",
        "outputId": "059de23c-3ef9-45b2-949c-449f5369339b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(56838, 14)"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9bz0Vashj-xl"
      },
      "source": [
        "Cleaning the Summary texts and removing the stop words"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c9PV8NI4RTF1",
        "outputId": "2338a03e-048c-4477-b4e1-f2ecae8afd3b"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 56838/56838 [00:36<00:00, 1537.16it/s]\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "preprocessed_reviews = []\n",
        "for review in tqdm(df_cleaned['Summary']):\n",
        "    review = re.sub('[^a-zA-Z]', ' ', review)\n",
        "    review = review.lower()\n",
        "    review = review.split()\n",
        "    review = [word for word in review if not word in set(stopwords.words('english'))]\n",
        "    review = ' '.join(review)\n",
        "    preprocessed_reviews.append(review)\n",
        "#\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZpLFDUgw4INA"
      },
      "source": [
        "# Testing the difference after processing the **column**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vm0C9HFqu9Jo",
        "outputId": "e7efdd65-a5df-4478-8c03-60dd8d52cf15"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\"Delight\" says it all\n"
          ]
        }
      ],
      "source": [
        "print(df_cleaned['Summary'][2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zHeAI87aRY_H",
        "outputId": "60e75564-a9a6-4de6-f2c5-4343b1c97fda"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "delight says\n"
          ]
        }
      ],
      "source": [
        "print(preprocessed_reviews[2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3YylP7CBcxT"
      },
      "source": [
        "# ***Feature Extraction***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_3tqwdRzB3RT"
      },
      "source": [
        "*Using TF-IDF*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D51MzQr7RY1b",
        "outputId": "d6fc859b-07fc-43b5-da3e-a965355ef8c1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5684, 3216)"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "vectorizer = TfidfVectorizer()\n",
        "X = vectorizer.fit_transform(preprocessed_reviews).toarray()\n",
        "pd.DataFrame(X).shape\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9QWtnyTzRYtu"
      },
      "outputs": [],
      "source": [
        "df_cleaned['scores_analysis'] = df_cleaned['scores_analysis'].astype('category')\n",
        "df_cleaned['scores_analysis'] = df_cleaned['scores_analysis'].cat.codes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "nfriQ7k9RYlC",
        "outputId": "4712f8fd-50e4-4a3f-eb31-cd1a1dd5401c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>scores_analysis</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>852</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "scores_analysis\n",
              "1    4832\n",
              "0     852\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned['scores_analysis'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZSwLf96aL-o"
      },
      "source": [
        "# **Split the data into train and test now**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LoGzuATEaH4L"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, df_cleaned['scores_analysis'], test_size=0.25, random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "j3cZDTSqkGWc",
        "outputId": "8846064c-41d6-4d85-9414-eb08e3e07064"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vb0z62jHkCMy"
      },
      "outputs": [],
      "source": [
        "y_pred_train = rf.predict(X_train)\n",
        "y_pred_test = rf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PAr9pQxRflYu",
        "outputId": "243b76ee-cd2a-407c-ffed-5dc3e09d7bd4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9870982875908985\n",
            "****************************************\n",
            "0.9809992962702322\n"
          ]
        }
      ],
      "source": [
        "print(accuracy_score(y_train, y_pred_train))\n",
        "print('****************************************')\n",
        "print(accuracy_score(y_test, y_pred_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PuXuBKXIkkli"
      },
      "source": [
        "**Testing Random forest with the balanced DATA**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CqubNYJkgXnu"
      },
      "outputs": [],
      "source": [
        "df_cleaned = balanced_reviews_undersample"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "id": "FR8cifMuhqGx",
        "outputId": "a933c42b-e4e5-4b7e-b18b-584c82eeb0a7"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>scores_analysis</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>positive</th>\n",
              "      <td>13152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>negative</th>\n",
              "      <td>13152</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "scores_analysis\n",
              "positive    13152\n",
              "negative    13152\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_cleaned['scores_analysis'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xje8906YsMtq",
        "outputId": "91727605-e999-4fc1-c698-a243f46345f8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 26304/26304 [00:23<00:00, 1102.14it/s]\n"
          ]
        }
      ],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "preprocessed_reviews = []\n",
        "for review in tqdm(df_cleaned['Summary']):\n",
        "    review = re.sub('[^a-zA-Z]', ' ', review)\n",
        "    review = review.lower()\n",
        "    review = review.split()\n",
        "    review = [word for word in review if not word in set(stopwords.words('english'))]\n",
        "    review = ' '.join(review)\n",
        "    preprocessed_reviews.append(review)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1rzypAshrvhQ",
        "outputId": "0336bad7-bc24-4408-cb44-ddea3f744a36"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(26304, 7807)"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "vectorizer = TfidfVectorizer()\n",
        "X = vectorizer.fit_transform(preprocessed_reviews).toarray()\n",
        "pd.DataFrame(X).shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sWTdDcBdrwil"
      },
      "outputs": [],
      "source": [
        "df_cleaned['scores_analysis'] = df_cleaned['scores_analysis'].astype('category')\n",
        "df_cleaned['scores_analysis'] = df_cleaned['scores_analysis'].cat.codes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "moAsVyGzmRLQ"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, df_cleaned['scores_analysis'], test_size=0.25, random_state=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 57
        },
        "id": "f8ssyW5DmMtG",
        "outputId": "8a7f13c8-3584-4de8-f030-80ac0001e8ce"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: black;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: block;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 1ex;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "execution_count": 113,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "id": "QmHKI09um1hh",
        "outputId": "ea126171-614f-4d39-dac8-d3140d61a47b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9684712084347121\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.97      0.97      9839\n",
            "           1       0.97      0.97      0.97      9889\n",
            "\n",
            "    accuracy                           0.97     19728\n",
            "   macro avg       0.97      0.97      0.97     19728\n",
            "weighted avg       0.97      0.97      0.97     19728\n",
            "\n",
            "Accuracy: 0.9684712084347121\n",
            "****************************************\n",
            "Accuracy test: 0.798205596107056\n"
          ]
        }
      ],
      "source": [
        "y_pred_train = rf.predict(X_train)\n",
        "y_pred_test = rf.predict(X_test)\n",
        "print(accuracy_score(y_train, y_pred_train))\n",
        "\n",
        "cm = confusion_matrix(y_train, y_pred_train)\n",
        "# Visualize the confusion matrix\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "print(classification_report(y_train, y_pred_train))\n",
        "print(\"Accuracy:\", accuracy_score(y_train, y_pred_train))\n",
        "print('****************************************')\n",
        "print(\"Accuracy test:\",accuracy_score(y_test, y_pred_test))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aNrgc0Zbm-EE"
      },
      "source": [
        "![Capture d'écran 2024-12-13 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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tKc0Awannwmc"
      },
      "source": [
        "**using cross validation methode on the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yGzFo0jpjGij",
        "outputId": "d9f1cd81-b8a8-46a9-e752-1dcefd9843a8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Cross-Validation Accuracy: 0.7811741270137991\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "test_accuracy = cross_val_score(rf, X_train, y_train, cv=5)\n",
        "mean_accuracy = np.mean(test_accuracy)\n",
        "print(\"Mean Cross-Validation Accuracy:\", mean_accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N6z1rWmpuIsJ"
      },
      "source": [
        "![Capture d'écran 2024-12-13 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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-seXDmtuzvJy"
      },
      "source": [
        "Why Is This Improvement Happening?\n",
        "\n",
        "\n",
        "1.  More Data = Better Generalization\n",
        "2.  More Balanced Data Exposure\n",
        "3.  Reduced Overfitting\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "buZMUGjd4we9",
        "outputId": "ebd5222b-f068-42a3-ce2e-67ee8d1cd119"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.interpolate import make_interp_spline\n",
        "\n",
        "y_pred_test = rf.predict(X_test)\n",
        "# Get predicted probabilities for the positive class (class 1) for the test set\n",
        "y_pred_prob_test = rf.predict_proba(X_test)[:, 1]  # Probabilities for class 1\n",
        "x = np.arange(len(y_test))\n",
        "#creting a smooth curve\n",
        "spl_actual = make_interp_spline(x, y_test.values, k=3)\n",
        "smooth_actual = spl_actual(x)\n",
        "\n",
        "# Interpolation for predicted labels (to create a smooth curve)\n",
        "spl_pred = make_interp_spline(x, y_pred_prob_test, k=3)\n",
        "smooth_pred = spl_pred(x)\n",
        "\n",
        "plt.figure(figsize=(14, 6))\n",
        "plt.plot(x, smooth_actual, label='Actual Labels', color='blue', lw=2)\n",
        "\n",
        "plt.plot(x, smooth_pred, label='Predicted Probabilities', color='red', lw=2)\n",
        "\n",
        "plt.title('Actual vs Predicted Labels (Continuous Curves)')\n",
        "plt.xlabel('Index of Test Data')\n",
        "plt.ylabel('Labels / Predicted Probability')\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksbem2XFjfFI"
      },
      "source": [
        "**Using Naive Bayes method**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlgaKR0W5dqm",
        "outputId": "b70ca522-a899-46b5-d8b3-386a49cae9fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.39      0.52      3355\n",
            "           1       0.84      0.97      0.90     10855\n",
            "\n",
            "    accuracy                           0.83     14210\n",
            "   macro avg       0.83      0.68      0.71     14210\n",
            "weighted avg       0.83      0.83      0.81     14210\n",
            "\n",
            "Accuracy: 0.8344827586206897\n"
          ]
        }
      ],
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "# Create a Naive Bayes classifier\n",
        "model = MultinomialNB()\n",
        "# Train the model\n",
        "model.fit(X_train, y_train)\n",
        "# Make predictions on the test set\n",
        "y_pred = model.predict(X_test)\n",
        "# Evaluate the model\n",
        "print(classification_report(y_test, y_pred))\n",
        "print(\"Accuracy:\", accuracy_score(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NbfrVuUtyH6c"
      },
      "source": [
        "**===>> As Expected naise bayes would perform faster but less precise than random forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "YMJIWJAe3zow",
        "outputId": "be01fbda-d1da-4a0e-c3a5-a6bd1bd13ad8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n",
            "landing.html\n",
            "Starting Flask server...\n",
            "Open the app in your browser: https://bu3ig3gnim-496ff2e9c6d22116-5000-colab.googleusercontent.com/\n",
            " * Serving Flask app '__main__'\n",
            " * Debug mode: on\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on all addresses (0.0.0.0)\n",
            " * Running on http://127.0.0.1:5000\n",
            " * Running on http://172.28.0.12:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n",
            "INFO:werkzeug: * Restarting with stat\n"
          ]
        }
      ],
      "source": [
        "from flask import Flask, render_template, request\n",
        "from google.colab.output import eval_js\n",
        "from google.colab import drive\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "import re\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "STOPWORDS = set(stopwords.words(\"english\"))\n",
        "stemmer = PorterStemmer()\n",
        "\n",
        "app = Flask(__name__, template_folder='/content/drive/MyDrive/landingpage')\n",
        "\n",
        "@app.route(\"/\", methods=[\"GET\", \"POST\"])\n",
        "def home():\n",
        "    print(\"Rendering landing page...\")\n",
        "    if request.method == \"POST\":\n",
        "        try:\n",
        "            text_input = request.form.get(\"review_text\")\n",
        "            if not text_input:\n",
        "                return render_template(\"landing.html\", result=\"Error: No input provided\", text=None)\n",
        "\n",
        "            prediction = single_prediction(text_input)\n",
        "            return render_template(\"landing.html\", result=prediction, text=text_input)\n",
        "        except Exception as e:\n",
        "            print(f\"Error occurred: {e}\")\n",
        "            return f\"An error occurred: {str(e)}\"\n",
        "    return render_template(\"landing.html\", result=None, text=None)\n",
        "\n",
        "def single_prediction(text_input):\n",
        "    review = re.sub(\"[^a-zA-Z]\", \" \", text_input).lower().split()\n",
        "    review = [stemmer.stem(word) for word in review if word not in STOPWORDS]\n",
        "    processed_text = \" \".join(review)\n",
        "\n",
        "    if \"good\" in processed_text:\n",
        "        return \"Positive\"\n",
        "    elif \"bad\" in processed_text:\n",
        "        return \"Negative\"\n",
        "    else:\n",
        "        return \"Neutral\"\n",
        "\n",
        "print(\"Starting Flask server...\")\n",
        "proxy_url = eval_js(\"google.colab.kernel.proxyPort(5000)\")\n",
        "print(f\"Open the app in your browser: {proxy_url}\")\n",
        "\n",
        "app.run(host=\"0.0.0.0\", port=5000, debug=True)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1v_UQ3GGbKhOYHWScZ4JCW-sv6xW1WITy",
      "authorship_tag": "ABX9TyOl1zb9BwwKIMfBTJhMo7VJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}