Amazon_Fine_Food_Review.ipynb

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      "source": [
        "The Amazon Fine Food Reviews dataset consisit s of reviews of fine foods from Amazon."
      ],
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        "id": "dUuBEsadPYCM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Objective:**\n",
        "Given a review ,Determine whether the review is positive (Rating of 4 or 5) or Negative (Rating of 1 to 2)."
      ],
      "metadata": {
        "id": "bRxpUIdKQOfg"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "**Loading The DataSet**"
      ],
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      "source": [
        "The dataset is availabe in two form\n",
        "\n",
        "\n",
        "1.   .csv file\n",
        "2.   SQLite database\n",
        "\n",
        "In order to load the data, we have used the SQLite dataset as it easier to query the data and visualize the data efficiently.\n",
        "\n",
        "\n"
      ],
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      "source": [
        "%matplotlib inline\n",
        "\n",
        "import sqlite3\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import nltk\n",
        "import string\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_curve,auc\n",
        "from nltk.stem.porter import PorterStemmer\n"
      ],
      "metadata": {
        "id": "MuVB8lDuPxQA"
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      "execution_count": 3,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "con= sqlite3.connect(\"/content/drive/MyDrive/Amazon Fine Food Review/database.sqlite\")\n",
        "\n",
        "data = pd.read_sql_query(\"\"\"\n",
        "SELECT *\n",
        "FROM Reviews\n",
        "\"\"\",con)\n",
        "print(data.shape)\n",
        "data.head()"
      ],
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        "id": "rmZk1Wd4UN0B",
        "outputId": "44ea8c4b-738e-41a3-be46-78e59e479368"
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(568454, 10)\n"
          ]
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              "   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...  "
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              "      <td>\"Delight\" says it all</td>\n",
              "      <td>This is a confection that has been around a fe...</td>\n",
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              "      <td>Michael D. Bigham \"M. Wassir\"</td>\n",
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              "      <td>Great taffy</td>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "#By looking the dataset we can see that\n",
        "\n",
        "1.Number of datapoint  = 568454\n",
        "\n",
        "2.Numbere of Attributes/columns/Features = 10\n",
        "\n",
        "#Featurs look like :\n",
        "\n",
        "1. Id :identification number.\n",
        "\n",
        "2. ProductId :Unique identification number for the product.\n",
        "\n",
        "3. UserId : Unique identification number of the user.\n",
        "\n",
        "4. ProfileName :Name of the user on Amazon website .\n",
        "\n",
        "5. HelpfulnessNumerator :Number of \"YES\"\n",
        "\n",
        "6. HelpfulnessDenominator :Number of (\"YES + No\").\n",
        "\n",
        "7. Score :Score is nothing but the rating in stars\n",
        "\n",
        "8. Time : time at which message written by the user .\n",
        "\n",
        "9. Summary : Overall message crucks.\n",
        "\n",
        "10. Text : Message given by the user for the product."
      ],
      "metadata": {
        "id": "KwZM3A_9qxUj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Cleaning of the DataSet:\n",
        "filtering only positive and negative reviews i.e\n",
        "not taking into consideration those review with score  = 3"
      ],
      "metadata": {
        "id": "ENj5RWCxt4-N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_data = pd.read_sql_query(\"\"\"\n",
        "SELECT *\n",
        "FROM Reviews\n",
        "WHERE Score != 3\n",
        "\"\"\",con)"
      ],
      "metadata": {
        "id": "pmu9YbzUWGde"
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      "execution_count": 5,
      "outputs": []
    },
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      "source": [
        "filtered_data.head()"
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              "   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...  "
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>B001E4KFG0</td>\n",
              "      <td>A3SGXH7AUHU8GW</td>\n",
              "      <td>delmartian</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1303862400</td>\n",
              "      <td>Good Quality Dog Food</td>\n",
              "      <td>I have bought several of the Vitality canned d...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>B00813GRG4</td>\n",
              "      <td>A1D87F6ZCVE5NK</td>\n",
              "      <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",
              "    </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",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "    </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",
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              "\n",
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              "\n",
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              "    .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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-573fa30f-9cfe-47b6-8698-5d51c16b5f1f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-573fa30f-9cfe-47b6-8698-5d51c16b5f1f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-26eafdfb-80ab-4b46-8af3-4019ac42f076\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26eafdfb-80ab-4b46-8af3-4019ac42f076')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-26eafdfb-80ab-4b46-8af3-4019ac42f076 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "filtered_data"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we are going to define the function which makes/convert Score into Positive and Negative Reviews.like (1 to 2):-give Negative most of the time and (4 to 5 ) rating most of the time it will give Pesitive Reviews amd for the Rating (3) Score Either it will be Positive/Negative or neutral.so, we are not going to consider this .\n"
      ],
      "metadata": {
        "id": "v1AfoUtdZoC7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def partition(x):\n",
        "  if x<3:\n",
        "    return 'negative'\n",
        "  return 'positive'\n",
        "\n",
        "actualScore = filtered_data['Score']\n",
        "positiveNegative = actualScore.map(partition)\n",
        "filtered_data['Score'] = positiveNegative"
      ],
      "metadata": {
        "id": "FYhpy6EiX0QS"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now the data look like this :"
      ],
      "metadata": {
        "id": "LbU9laT3aslv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(filtered_data.shape)\n",
        "filtered_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "id": "YwlDlg9HZiHy",
        "outputId": "3b3bef28-7065-4bfa-ec2e-086f7bed4999"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(525814, 10)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "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  positive  1303862400   \n",
              "1                     0                       0  negative  1346976000   \n",
              "2                     1                       1  positive  1219017600   \n",
              "3                     3                       3  negative  1307923200   \n",
              "4                     0                       0  positive  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...  "
            ],
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-378b8071-9714-47d8-8be8-af1bd5561562 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-378b8071-9714-47d8-8be8-af1bd5561562');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9c4ab149-2b08-4a39-94b1-d91fa5fa45df\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c4ab149-2b08-4a39-94b1-d91fa5fa45df')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9c4ab149-2b08-4a39-94b1-d91fa5fa45df button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "filtered_data"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now , the column \"Score\" value either \"positive\" or \"Negative\""
      ],
      "metadata": {
        "id": "7A4R4cZnuX1J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Data Cleaning:\n",
        "\n",
        "1.De-duplication."
      ],
      "metadata": {
        "id": "7xuvm9gDvGf_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "display = pd.read_sql_query(\"\"\"\n",
        "SELECT *\n",
        "FROM Reviews\n",
        "WHERE Score != 3 AND UserId = 'AR5J8UI46CURR'\n",
        "ORDER BY ProductID\n",
        "\"\"\",con)\n",
        "display"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "We50Fkr1vSyC",
        "outputId": "737e16b3-d838-4d6f-cae7-a719b4493e23"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Id   ProductId         UserId      ProfileName  HelpfulnessNumerator  \\\n",
              "0   78445  B000HDL1RQ  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
              "1  138317  B000HDOPYC  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
              "2  138277  B000HDOPYM  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
              "3   73791  B000HDOPZG  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
              "4  155049  B000PAQ75C  AR5J8UI46CURR  Geetha Krishnan                     2   \n",
              "\n",
              "   HelpfulnessDenominator  Score        Time  \\\n",
              "0                       2      5  1199577600   \n",
              "1                       2      5  1199577600   \n",
              "2                       2      5  1199577600   \n",
              "3                       2      5  1199577600   \n",
              "4                       2      5  1199577600   \n",
              "\n",
              "                             Summary  \\\n",
              "0  LOACKER QUADRATINI VANILLA WAFERS   \n",
              "1  LOACKER QUADRATINI VANILLA WAFERS   \n",
              "2  LOACKER QUADRATINI VANILLA WAFERS   \n",
              "3  LOACKER QUADRATINI VANILLA WAFERS   \n",
              "4  LOACKER QUADRATINI VANILLA WAFERS   \n",
              "\n",
              "                                                Text  \n",
              "0  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
              "1  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
              "2  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
              "3  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  \n",
              "4  DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cb299355-5cfb-4ec7-8e75-d6039d271a14\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>78445</td>\n",
              "      <td>B000HDL1RQ</td>\n",
              "      <td>AR5J8UI46CURR</td>\n",
              "      <td>Geetha Krishnan</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1199577600</td>\n",
              "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
              "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>138317</td>\n",
              "      <td>B000HDOPYC</td>\n",
              "      <td>AR5J8UI46CURR</td>\n",
              "      <td>Geetha Krishnan</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1199577600</td>\n",
              "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
              "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>138277</td>\n",
              "      <td>B000HDOPYM</td>\n",
              "      <td>AR5J8UI46CURR</td>\n",
              "      <td>Geetha Krishnan</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1199577600</td>\n",
              "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
              "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>73791</td>\n",
              "      <td>B000HDOPZG</td>\n",
              "      <td>AR5J8UI46CURR</td>\n",
              "      <td>Geetha Krishnan</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1199577600</td>\n",
              "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
              "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>155049</td>\n",
              "      <td>B000PAQ75C</td>\n",
              "      <td>AR5J8UI46CURR</td>\n",
              "      <td>Geetha Krishnan</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>1199577600</td>\n",
              "      <td>LOACKER QUADRATINI VANILLA WAFERS</td>\n",
              "      <td>DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb299355-5cfb-4ec7-8e75-d6039d271a14')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cb299355-5cfb-4ec7-8e75-d6039d271a14 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cb299355-5cfb-4ec7-8e75-d6039d271a14');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-0bc6c621-ca34-48fa-b969-5e96ba0055bd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0bc6c621-ca34-48fa-b969-5e96ba0055bd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
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              "summary": "{\n  \"name\": \"display\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37776,\n        \"min\": 73791,\n        \"max\": 155049,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          138317,\n          155049,\n          138277\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ProductId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"B000HDOPYC\",\n          \"B000PAQ75C\",\n          \"B000HDOPYM\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UserId\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"AR5J8UI46CURR\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ProfileName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Geetha Krishnan\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessNumerator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessDenominator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1199577600,\n        \"max\": 1199577600,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1199577600\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Summary\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"LOACKER QUADRATINI VANILLA WAFERS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"DELICIOUS WAFERS. I FIND THAT EUROPEAN WAFERS ARE LESS SWEET ( LESS SUGAR AND CARBOHYDRATES) BUT VERY DELECTABLE.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "By Seeing the above dataset we can easily say that dataset contains \"dublicate\" datapoint.so we need to remove those datapoint which contains dublicasy and redundancy ."
      ],
      "metadata": {
        "id": "qKz-91NsxN0S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#sorting the data according to ProductId in ascending order\n",
        "sorted_data = filtered_data.sort_values('ProductId',axis = 0,ascending = True,inplace =False,kind='quicksort',na_position='last')"
      ],
      "metadata": {
        "id": "-dDJq45GwREH"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Deduplication of entries\n",
        "final = sorted_data.drop_duplicates(subset ={\"UserId\",\"ProfileName\",\"Time\",\"Text\"},keep = 'first',inplace = False,ignore_index=True)\n",
        "final.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CT45J3LozkpW",
        "outputId": "c0a96645-5ab2-4fd8-85b6-ab3809d652cc"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(364173, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analysis**:\n",
        "\n",
        "By looking the new size of the dataset we can surely say that Amazon Fine Food Review dataset contains sone percentage of Dublication of the data.i.e\n",
        "\n",
        "%of Dublication = ((525814-364173)/525814)*100 = 30.74%\n",
        "\n",
        "This means that almost one fourth of the data is dublicate in the Amazon Fine Food Review dataset."
      ],
      "metadata": {
        "id": "h3pPjxz70-5W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking how much data still remains for the further process\n",
        "(final['Id'].size*1.0)/(filtered_data['Id'].size*1.0)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDFyEBkA0mLY",
        "outputId": "985f7bb9-ee15-4dd8-9262-72d0d5f6d66c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "69.25890143662969"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Almost we have 70% data of the total dataset which will use for the further data preprocessing ."
      ],
      "metadata": {
        "id": "6_A4IJsj2yft"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "id": "x0IpjRvp2ojk",
        "outputId": "0580de2b-155e-467b-e198-cdef48925e9b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Id   ProductId          UserId                  ProfileName  \\\n",
              "0  150524  0006641040   ACITT7DI6IDDL              shari zychinski   \n",
              "1  150506  0006641040  A2IW4PEEKO2R0U                        Tracy   \n",
              "2  150507  0006641040  A1S4A3IQ2MU7V4        sally sue \"sally sue\"   \n",
              "3  150508  0006641040     AZGXZ2UUK6X  Catherine Hallberg \"(Kate)\"   \n",
              "4  150509  0006641040  A3CMRKGE0P909G                       Teresa   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator     Score        Time  \\\n",
              "0                     0                       0  positive   939340800   \n",
              "1                     1                       1  positive  1194739200   \n",
              "2                     1                       1  positive  1191456000   \n",
              "3                     1                       1  positive  1076025600   \n",
              "4                     3                       4  positive  1018396800   \n",
              "\n",
              "                                      Summary  \\\n",
              "0                   EVERY book is educational   \n",
              "1  Love the book, miss the hard cover version   \n",
              "2               chicken soup with rice months   \n",
              "3      a good swingy rhythm for reading aloud   \n",
              "4             A great way to learn the months   \n",
              "\n",
              "                                                Text  \n",
              "0  this witty little book makes my son laugh at l...  \n",
              "1  I grew up reading these Sendak books, and watc...  \n",
              "2  This is a fun way for children to learn their ...  \n",
              "3  This is a great little book to read aloud- it ...  \n",
              "4  This is a book of poetry about the months of t...  "
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bebe80c6-56cd-402f-9df6-7f5a9d093e81 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display = pd.read_sql(\"\"\"\n",
        "SELECT *\n",
        "FROM Reviews\n",
        "WHERE Score != 3 AND Id = 44737 OR Id = 64422\n",
        "ORDER BY ProductId\n",
        "\"\"\",con)\n",
        "\n",
        "print(final.shape)\n",
        "display"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "kbfYSeAt5YLk",
        "outputId": "af111aa7-6886-4823-cd1a-5bc7dbbb5d35"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(364173, 10)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Id   ProductId          UserId              ProfileName  \\\n",
              "0  64422  B000MIDROQ  A161DK06JJMCYF  J. E. Stephens \"Jeanne\"   \n",
              "1  44737  B001EQ55RW  A2V0I904FH7ABY                      Ram   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator  Score        Time  \\\n",
              "0                     3                       1      5  1224892800   \n",
              "1                     3                       2      4  1212883200   \n",
              "\n",
              "                                        Summary  \\\n",
              "0             Bought This for My Son at College   \n",
              "1  Pure cocoa taste with crunchy almonds inside   \n",
              "\n",
              "                                                Text  \n",
              "0  My son loves spaghetti so I didn't hesitate or...  \n",
              "1  It was almost a 'love at first bite' - the per...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ad78c876-4aa1-4294-b3b8-3d0c932662dc\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>ProductId</th>\n",
              "      <th>UserId</th>\n",
              "      <th>ProfileName</th>\n",
              "      <th>HelpfulnessNumerator</th>\n",
              "      <th>HelpfulnessDenominator</th>\n",
              "      <th>Score</th>\n",
              "      <th>Time</th>\n",
              "      <th>Summary</th>\n",
              "      <th>Text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>64422</td>\n",
              "      <td>B000MIDROQ</td>\n",
              "      <td>A161DK06JJMCYF</td>\n",
              "      <td>J. E. Stephens \"Jeanne\"</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1224892800</td>\n",
              "      <td>Bought This for My Son at College</td>\n",
              "      <td>My son loves spaghetti so I didn't hesitate or...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44737</td>\n",
              "      <td>B001EQ55RW</td>\n",
              "      <td>A2V0I904FH7ABY</td>\n",
              "      <td>Ram</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>1212883200</td>\n",
              "      <td>Pure cocoa taste with crunchy almonds inside</td>\n",
              "      <td>It was almost a 'love at first bite' - the per...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ad78c876-4aa1-4294-b3b8-3d0c932662dc')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ad78c876-4aa1-4294-b3b8-3d0c932662dc button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ad78c876-4aa1-4294-b3b8-3d0c932662dc');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a7cc407c-67d9-4c54-a40e-01897cc57e80\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a7cc407c-67d9-4c54-a40e-01897cc57e80')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a7cc407c-67d9-4c54-a40e-01897cc57e80 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_da75091f-599d-4398-ac45-2c1fc3dc4075\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('display')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_da75091f-599d-4398-ac45-2c1fc3dc4075 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('display');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "display",
              "summary": "{\n  \"name\": \"display\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13919,\n        \"min\": 44737,\n        \"max\": 64422,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          44737,\n          64422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ProductId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"B001EQ55RW\",\n          \"B000MIDROQ\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"UserId\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"A2V0I904FH7ABY\",\n          \"A161DK06JJMCYF\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ProfileName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Ram\",\n          \"J. E. Stephens \\\"Jeanne\\\"\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessNumerator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"HelpfulnessDenominator\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8492069,\n        \"min\": 1212883200,\n        \"max\": 1224892800,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1212883200\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Summary\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Pure cocoa taste with crunchy almonds inside\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"It was almost a 'love at first bite' - the perfectly roasted almond with a nice thin layer of pure flavorful cocoa on the top.<br /><br />You can smell the cocoa as soon as you open the canister - making you want to take a bite.<br /><br />You may or may not like the taste of this cocoa roasted almonds depending on your likingness for cocoa.  We are so much used to the taste of chocolate (which is actually cocoa + many other ingredients like milk ...) - that you might have never really tasted really cocoa.<br /><br />Tasting this item it like tasting and enjoying flavorful pure raw cocoa with crunchy almonds in the center.  Get yourself a box and see for yourself what real cocoa + almonds is !<br /><br />Where this product loses a star is in its packaging - the external sleeve is kind of comes in one piece, so if you try to remove the lid, the external sleeve kind of tends to come off fully - so careful when you are removing the external sleeve for the canister.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see that HelpfulnessDenominator of ProductId=B000MIDROQ is less than the HelpfulnessNuminator. this is totally wrong mathematically why Because as we know that :\n",
        "\n",
        "\n",
        "1.  HelpfulnessNumirator = no of YES\n",
        "2.  HelpfulnessDenominator = no of (YES+NO)\n",
        "\n",
        "this means that HelpfulnessNumerator is less than HelpfulnessDenomirator always.\n",
        "\n",
        "This problem not only for the one productId it might be chance it may be various ProductId.So we need to sort this problem from our dataset.\n",
        "\n"
      ],
      "metadata": {
        "id": "u-gtbC3T86zq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Before shape of final dataset:\",final.shape)\n",
        "final  = final[final.HelpfulnessNumerator<=final.HelpfulnessDenominator]\n",
        "final.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mqTJ_7T88ZGf",
        "outputId": "b158ff3d-1c9a-4650-ea81-0f03bd66f629"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before shape of final dataset: (364173, 10)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(364171, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis: we can see here clearly there is two datapoint which contains this type of problem ."
      ],
      "metadata": {
        "id": "uF8ck3ke_l7L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#before going to the next step of Data Preprocessing\n",
        "print(final.shape)\n",
        "\n",
        "final['Score'].value_counts()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8H_XVR-_D1t",
        "outputId": "4cb6d267-b01a-488f-da3b-30b3596d0118"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(364171, 10)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Score\n",
              "positive    307061\n",
              "negative     57110\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize =(5,5))\n",
        "sns.countplot(x = 'Score',data= final,color ='red')\n",
        "plt.title(\"Score Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "cH2sF87HAE8W",
        "outputId": "6681d4e7-cf50-4a0a-d290-e22ffc7f7d6a"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis:By looking the score Distribution we can clearly say that our dataset is imbalance dataset."
      ],
      "metadata": {
        "id": "hl0QgOKrEXhq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize =(3,2))\n",
        "plt.hist(final['Score'].values,color='green')\n",
        "plt.title(\"Score Distribution\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "HnlLRS969sEe",
        "outputId": "3cd4d39c-a4a1-4842-9897-4de9b6523ca6"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 300x200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis:Here we can clearly see that the difference between the counts of Positive sentiment and Negative sentiment is huge.That means the Amazon fine food review dataset is Imbalance dataset."
      ],
      "metadata": {
        "id": "AZSqRV41-igP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sns.pairplot(data = final,hue = 'Score')"
      ],
      "metadata": {
        "id": "PE_Q2-n_A61X"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis:By looking the pair plot we can surely say that our dataset is imbalance dataset."
      ],
      "metadata": {
        "id": "a661L3VEE1b8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Conversion of Text To a Numeric Vector:\n",
        "\n",
        "1. BoW(Bag of Words).\n",
        "\n",
        "2. TF-IDF(Term Frequency-Inverse Document Frequency).\n",
        "\n",
        "3. W2V(Word2Vec).\n",
        "\n",
        "4. Average W2V(Word2Vec).\n",
        "\n",
        "5. (TF-IDF)-W2V."
      ],
      "metadata": {
        "id": "ztprMv9ABN94"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#1. Bag of Words(BoW)"
      ],
      "metadata": {
        "id": "K6niYrLVCdM5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install scikit-learn\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "count_vect = CountVectorizer()\n",
        "final_counts = count_vect.fit_transform(final['Text'].values)"
      ],
      "metadata": {
        "id": "22KbexozCR1h"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(final_counts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "id": "izSELZJ-C5iH",
        "outputId": "6f46a6b9-478f-48f7-a2dc-c59323d0f326"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "scipy.sparse._csr.csr_matrix"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>scipy.sparse._csr.csr_matrix</b><br/>def __init__(arg1, shape=None, dtype=None, copy=False)</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/scipy/sparse/_csr.py</a>Compressed Sparse Row matrix\n",
              "\n",
              "This can be instantiated in several ways:\n",
              "    csr_array(D)\n",
              "        with a dense matrix or rank-2 ndarray D\n",
              "\n",
              "    csr_array(S)\n",
              "        with another sparse matrix S (equivalent to S.tocsr())\n",
              "\n",
              "    csr_array((M, N), [dtype])\n",
              "        to construct an empty matrix with shape (M, N)\n",
              "        dtype is optional, defaulting to dtype=&#x27;d&#x27;.\n",
              "\n",
              "    csr_array((data, (row_ind, col_ind)), [shape=(M, N)])\n",
              "        where ``data``, ``row_ind`` and ``col_ind`` satisfy the\n",
              "        relationship ``a[row_ind[k], col_ind[k]] = data[k]``.\n",
              "\n",
              "    csr_array((data, indices, indptr), [shape=(M, N)])\n",
              "        is the standard CSR representation where the column indices for\n",
              "        row i are stored in ``indices[indptr[i]:indptr[i+1]]`` and their\n",
              "        corresponding values are stored in ``data[indptr[i]:indptr[i+1]]``.\n",
              "        If the shape parameter is not supplied, the matrix dimensions\n",
              "        are inferred from the index arrays.\n",
              "\n",
              "Attributes\n",
              "----------\n",
              "dtype : dtype\n",
              "    Data type of the matrix\n",
              "shape : 2-tuple\n",
              "    Shape of the matrix\n",
              "ndim : int\n",
              "    Number of dimensions (this is always 2)\n",
              "nnz\n",
              "    Number of stored values, including explicit zeros\n",
              "data\n",
              "    CSR format data array of the matrix\n",
              "indices\n",
              "    CSR format index array of the matrix\n",
              "indptr\n",
              "    CSR format index pointer array of the matrix\n",
              "has_sorted_indices\n",
              "    Whether indices are sorted\n",
              "\n",
              "Notes\n",
              "-----\n",
              "\n",
              "Sparse matrices can be used in arithmetic operations: they support\n",
              "addition, subtraction, multiplication, division, and matrix power.\n",
              "\n",
              "Advantages of the CSR format\n",
              "  - efficient arithmetic operations CSR + CSR, CSR * CSR, etc.\n",
              "  - efficient row slicing\n",
              "  - fast matrix vector products\n",
              "\n",
              "Disadvantages of the CSR format\n",
              "  - slow column slicing operations (consider CSC)\n",
              "  - changes to the sparsity structure are expensive (consider LIL or DOK)\n",
              "\n",
              "Canonical Format\n",
              "    - Within each row, indices are sorted by column.\n",
              "    - There are no duplicate entries.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "\n",
              "&gt;&gt;&gt; import numpy as np\n",
              "&gt;&gt;&gt; from scipy.sparse import csr_array\n",
              "&gt;&gt;&gt; csr_array((3, 4), dtype=np.int8).toarray()\n",
              "array([[0, 0, 0, 0],\n",
              "       [0, 0, 0, 0],\n",
              "       [0, 0, 0, 0]], dtype=int8)\n",
              "\n",
              "&gt;&gt;&gt; row = np.array([0, 0, 1, 2, 2, 2])\n",
              "&gt;&gt;&gt; col = np.array([0, 2, 2, 0, 1, 2])\n",
              "&gt;&gt;&gt; data = np.array([1, 2, 3, 4, 5, 6])\n",
              "&gt;&gt;&gt; csr_array((data, (row, col)), shape=(3, 3)).toarray()\n",
              "array([[1, 0, 2],\n",
              "       [0, 0, 3],\n",
              "       [4, 5, 6]])\n",
              "\n",
              "&gt;&gt;&gt; indptr = np.array([0, 2, 3, 6])\n",
              "&gt;&gt;&gt; indices = np.array([0, 2, 2, 0, 1, 2])\n",
              "&gt;&gt;&gt; data = np.array([1, 2, 3, 4, 5, 6])\n",
              "&gt;&gt;&gt; csr_array((data, indices, indptr), shape=(3, 3)).toarray()\n",
              "array([[1, 0, 2],\n",
              "       [0, 0, 3],\n",
              "       [4, 5, 6]])\n",
              "\n",
              "Duplicate entries are summed together:\n",
              "\n",
              "&gt;&gt;&gt; row = np.array([0, 1, 2, 0])\n",
              "&gt;&gt;&gt; col = np.array([0, 1, 1, 0])\n",
              "&gt;&gt;&gt; data = np.array([1, 2, 4, 8])\n",
              "&gt;&gt;&gt; csr_array((data, (row, col)), shape=(3, 3)).toarray()\n",
              "array([[9, 0, 0],\n",
              "       [0, 2, 0],\n",
              "       [0, 4, 0]])\n",
              "\n",
              "As an example of how to construct a CSR matrix incrementally,\n",
              "the following snippet builds a term-document matrix from texts:\n",
              "\n",
              "&gt;&gt;&gt; docs = [[&quot;hello&quot;, &quot;world&quot;, &quot;hello&quot;], [&quot;goodbye&quot;, &quot;cruel&quot;, &quot;world&quot;]]\n",
              "&gt;&gt;&gt; indptr = [0]\n",
              "&gt;&gt;&gt; indices = []\n",
              "&gt;&gt;&gt; data = []\n",
              "&gt;&gt;&gt; vocabulary = {}\n",
              "&gt;&gt;&gt; for d in docs:\n",
              "...     for term in d:\n",
              "...         index = vocabulary.setdefault(term, len(vocabulary))\n",
              "...         indices.append(index)\n",
              "...         data.append(1)\n",
              "...     indptr.append(len(indices))\n",
              "...\n",
              "&gt;&gt;&gt; csr_array((data, indices, indptr), dtype=int).toarray()\n",
              "array([[2, 1, 0, 0],\n",
              "       [0, 1, 1, 1]])</pre>\n",
              "      <script>\n",
              "      if (google.colab.kernel.accessAllowed && google.colab.files && google.colab.files.view) {\n",
              "        for (const element of document.querySelectorAll('.filepath')) {\n",
              "          element.style.display = 'block'\n",
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              "            google.colab.files.view(element.textContent, 370);\n",
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              "        };\n",
              "      }\n",
              "      </script>\n",
              "      </div>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "final_counts.get_shape()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEWvjM5QDxoF",
        "outputId": "ad2a8c4f-a733-44c2-f569-c7d04fc5b861"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(364171, 115281)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analysis: The final_counts is a sparse matrix of dimension (364171,115281).which means\n",
        "\n",
        "\n",
        "1.   364171->it represents,how many number of reviews/datapoints in the Amazon fine food reviews dataset.\n",
        "\n",
        "2.   115281->it represent, how many number of Unique words in the each datapoints or the how many unique words present in the each reviews.\n",
        "\n"
      ],
      "metadata": {
        "id": "eTmU-1XGExFx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "final_counts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TebC8fvZEZZG",
        "outputId": "1c23904f-e73e-4e1a-d41e-9580938d14d6"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<364171x115281 sparse matrix of type '<class 'numpy.int64'>'\n",
              "\twith 19341760 stored elements in Compressed Sparse Row format>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# #finding Sparcity of a sparse matrix\n",
        "# count_K =0\n",
        "# for i in range(final_counts.shape[0]):\n",
        "#   for j in range(final_counts.shape[1]):\n",
        "#     if final_counts[i,j]!=0:\n",
        "#       count_K = count_K+1\n",
        "\n",
        "# Sparsity_of_final_count = count_K/(final_counts.shape[0]*final_counts.shape[1])*100\n",
        "# print(\"Sparsity of the fianl_counts of sparse matric:\",Sparsity_of_final_count)\n",
        "\n",
        "\n",
        ""
      ],
      "metadata": {
        "id": "S_EJlQA1GQ8w"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Text Preprocessing :Stemming,Stop-Word removal and Lemmatization.\n",
        "\n",
        "=>During Preprocessing of the text we are following some simple steps.\n",
        "\n",
        "1. Begin by removal of the html tags.\n",
        "\n",
        "2. Remove any punctuation or limited set of interval characters like or,# etc.\n",
        "\n",
        "3. checking for the Alpha-numeric\n",
        "4. Checking the length of the letter is more than two or not .\n",
        "5. Convert the words into the lowercase.\n",
        "6. Removal of stopWords.\n",
        "7. Snowball/Porter Stemming of word Algorithm.\n"
      ],
      "metadata": {
        "id": "k5uo6oWtQIz2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#1. finding sentences containing HTML tags.\n",
        "import re #regular expression\n",
        "i = 0;\n",
        "for sent in final['Text'].values:\n",
        "  if(len(re.findall('<.*?',sent))):\n",
        "    print(i)\n",
        "    print(sent)\n",
        "    break;\n",
        "  i = i+1;\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wyVXZdJFKdIJ",
        "outputId": "e777f9f4-cc75-44e4-87cc-9b4427c69541"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "6\n",
            "I set aside at least an hour each day to read to my son (3 y/o). At this point, I consider myself a connoisseur of children's books and this is one of the best. Santa Clause put this under the tree. Since then, we've read it perpetually and he loves it.<br /><br />First, this book taught him the months of the year.<br /><br />Second, it's a pleasure to read. Well suited to 1.5 y/o old to 4+.<br /><br />Very few children's books are worth owning. Most should be borrowed from the library. This book, however, deserves a permanent spot on your shelf. Sendak's best.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "\n",
        "stop = set(stopwords.words('english'))\n",
        "sno = PorterStemmer()\n",
        "wordnet = WordNetLemmatizer()\n",
        "\n",
        "def cleanhtml(sentence):\n",
        "  cleanr =re.compile('<.*?>')\n",
        "  cleantext = re.sub(cleanr,'',sentence)\n",
        "  return cleantext\n",
        "\n",
        "def cleanpunc(sentence):\n",
        "  cleaned = re.sub(r'[?|!|\\|\"|#]',r'',sentence)\n",
        "  cleaned = re.sub(r'[.|,|)|(|\\|/]',r'',cleaned)\n",
        "  return cleaned\n",
        "\n",
        "print(stop)\n",
        "print('********************************')\n",
        "print(sno.stem('tasty'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JVF3tI2MTLas",
        "outputId": "4c917f15-622d-4eba-b0c9-bf4615da04b3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'each', 're', 'haven', 'up', \"wasn't\", \"you're\", 'any', 'them', 'than', 'if', \"you'll\", 'your', 'him', 'then', 'why', 'or', 'do', 'himself', 'so', 'what', 'be', 'off', 've', \"she's\", 'myself', 's', 'other', \"should've\", 'own', 'our', 'they', 'theirs', 'having', \"it's\", \"won't\", \"that'll\", 'does', 'his', 'more', 'weren', 'because', 'a', 'where', 'is', 'itself', 'into', \"you've\", 'until', 'did', 'over', 'hers', 'doing', 'shouldn', 'my', 'wasn', 'll', 'should', 'hasn', 'yourself', 'nor', \"mustn't\", 'not', 'after', 'am', 'can', 'once', 'down', 'don', 'o', \"needn't\", 'during', 'was', \"shan't\", 'that', 'whom', 'being', 'have', 'here', 'couldn', 'under', 'has', 'ma', 'these', 'it', 'i', 'needn', \"didn't\", 'those', 'most', 'were', 'some', 'how', 'yours', 'above', 'to', \"haven't\", 'themselves', \"couldn't\", 'their', 'who', 'while', 'hadn', 'won', 'mightn', 'but', 'of', 'both', 'you', 'only', \"doesn't\", 'against', 'y', 'had', 'will', 'we', 'such', 'as', 'didn', 'which', 'before', 'very', 'are', 'm', 'and', 'herself', 'ours', \"aren't\", 'doesn', 'all', 'too', 'ain', 'an', 'about', \"wouldn't\", 'out', 'wouldn', 'by', \"you'd\", 'just', 'when', 'me', 'below', \"hadn't\", \"hasn't\", 'been', 'her', 'for', 'no', 'again', \"weren't\", 'the', 'd', 'from', 'isn', 'on', \"don't\", 'she', 'its', 'mustn', 'same', 'there', 't', 'yourselves', 'in', \"shouldn't\", 'shan', 'through', 'aren', 'with', 'ourselves', 'at', 'few', \"mightn't\", 'further', \"isn't\", 'he', 'this', 'now', 'between'}\n",
            "********************************\n",
            "tasti\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "i =0;\n",
        "strl = ''\n",
        "final_string = []\n",
        "all_positive_words = []\n",
        "all_negative_words =[]\n",
        "s = ''\n",
        "for sent in final['Text'].values:\n",
        "  filtered_sentence =[]\n",
        "  #print(sent)\n",
        "  sent =cleanhtml(sent)\n",
        "  for w in sent.split():\n",
        "    for cleaned_words in cleanpunc(w).split():\n",
        "      if((cleaned_words.isalpha())& (len(cleaned_words)>2)):\n",
        "        if(cleaned_words.lower() not in stop):\n",
        "          s = (sno.stem(cleaned_words.lower())).encode('utf8')\n",
        "          filtered_sentence.append(s)\n",
        "          # Moved the conditional checks outside the loop\n",
        "          if(final['Score'].values[i]=='positive'):\n",
        "            all_positive_words.append(s)\n",
        "          if(final['Score'].values[i]=='negative'):\n",
        "            all_negative_words.append(s)\n",
        "\n",
        "        else:\n",
        "          continue\n",
        "    else:\n",
        "      continue\n",
        "    #print(filtered_sentences)\n",
        "    # Convert the list of bytes to a single byte string\n",
        "    if filtered_sentence:  # Check if filtered_sentence is not empty\n",
        "        str1 = b\" \".join(filtered_sentence)\n",
        "        final_string.append(str1)\n",
        "    else:\n",
        "        final_string.append(b'') # Append an empty byte string if no words are found\n",
        "    print(\"***********************************************************\")\n",
        "\n",
        "  # Increment the index 'i' after processing each sentence\n",
        "  i = i+1\n",
        "\n",
        "# Handle potential length mismatch after the loop\n",
        "if len(final_string) < len(final):\n",
        "    final_string += [b''] * (len(final) - len(final_string))\n",
        "\n",
        "final['CleanedText'] = final_string\n",
        "final.head(3)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "quLMkiXPXLJQ",
        "outputId": "c08ec75b-2f5a-40e6-ad37-63431906194d"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Id   ProductId          UserId            ProfileName  \\\n",
              "0  150524  0006641040   ACITT7DI6IDDL        shari zychinski   \n",
              "1  150506  0006641040  A2IW4PEEKO2R0U                  Tracy   \n",
              "2  150507  0006641040  A1S4A3IQ2MU7V4  sally sue \"sally sue\"   \n",
              "\n",
              "   HelpfulnessNumerator  HelpfulnessDenominator     Score        Time  \\\n",
              "0                     0                       0  positive   939340800   \n",
              "1                     1                       1  positive  1194739200   \n",
              "2                     1                       1  positive  1191456000   \n",
              "\n",
              "                                      Summary  \\\n",
              "0                   EVERY book is educational   \n",
              "1  Love the book, miss the hard cover version   \n",
              "2               chicken soup with rice months   \n",
              "\n",
              "                                                Text CleanedText  \n",
              "0  this witty little book makes my son laugh at l...         b''  \n",
              "1  I grew up reading these Sendak books, and watc...         b''  \n",
              "2  This is a fun way for children to learn their ...         b''  "
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              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-89a4d477-792f-4f0a-aa70-61984d60cfc2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final"
            }
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#store final table into an SQLite table for the future implementation\n",
        "conn = sqlite3.connect('final.sqlite')\n",
        "c =conn.cursor()\n",
        "conn.text_factory = str\n",
        "final.to_sql('Reviews',conn,schema = None,if_exists='replace',\n",
        "             index = True,index_label =None,\n",
        "             chunksize = None,dtype =None)\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7R7qJDSHcVwP",
        "outputId": "839b85a8-e651-4f37-eb8f-f6baa49aef8b"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "364171"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Now We are going to appy Bi-Gram and N-Gram\n",
        "==>To understand the concept of word frequency distribution with the help of bi_gram and the N_gram"
      ],
      "metadata": {
        "id": "ce8udKzCt8_a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "freq_dist_positive = nltk.FreqDist(all_positive_words)\n",
        "freq_dist_negative =nltk.FreqDist(all_negative_words)\n",
        "print(\"Most common Positive Words:\",freq_dist_positive.most_common(20))\n",
        "print(\"Most Common Positive Words: \",freq_dist_negative.most_common(20))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJt6kOt4dcrb",
        "outputId": "b3889033-a931-4fe5-ffa2-416ba7dfb7ca"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most common Positive Words: [(b'like', 138317), (b'tast', 125929), (b'good', 109735), (b'love', 106433), (b'flavor', 106267), (b'use', 102847), (b'great', 100414), (b'one', 93825), (b'product', 88276), (b'tri', 85096), (b'tea', 81348), (b'coffe', 76246), (b'make', 74439), (b'get', 71647), (b'food', 62863), (b'would', 55116), (b'buy', 53550), (b'time', 53533), (b'realli', 52384), (b'eat', 51295)]\n",
            "Most Common Positive Words:  [(b'tast', 33813), (b'like', 32051), (b'product', 27313), (b'one', 20064), (b'flavor', 18864), (b'would', 17856), (b'tri', 17512), (b'use', 15136), (b'good', 14594), (b'coffe', 14222), (b'get', 13703), (b'buy', 13603), (b'order', 12695), (b'food', 12331), (b'tea', 11288), (b'even', 10884), (b'box', 10542), (b'make', 9784), (b'time', 9555), (b'bag', 9467)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation:**:=From the above it can be seen that the most common positive and negative word:Eg ='like' or 'not like' etc\n",
        "\n",
        "so, it is good idea to consider the pair of words (bi-grams) or k-Sequence of the n-consecutive words(n-grams)"
      ],
      "metadata": {
        "id": "c92Cwawsx4mp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#code\n",
        "count_vect =CountVectorizer(ngram_range =(1,2))\n",
        "final_Bi_gram_counts =count_vect.fit_transform(final['Text'].values)\n"
      ],
      "metadata": {
        "id": "T52cx-B0w6d4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DY25bVqHzFAT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}