Malaysia EDA/SamVerse/Assignment_EDA_Aiman_Hafizi_&_Luqman_Ariff.ipynb

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        "<a href=\"https://colab.research.google.com/github/drshahizan/Python_EDA/blob/main/Malaysia%20EDA/team%2017/Assignment_EDA_Aiman_Hafizi_%26_Luqman_Ariff.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Restaurant Customer Review Analysis in Malaysia <a href=\"https://www.utm.my\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "\n",
        "The majority of restaurants request client reviews so they may better understand how to satisfy their patrons. Therefore, reviews are essential to the restaurant's successful development."
      ]
    },
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      },
      "source": [
        "\n",
        "> Import all the libraries that will be use for dataset analysia processes."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D8SHgsKIRvjH",
        "outputId": "e54f4706-2d79-47c8-ec34-9e216e7653b2"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Import the dataset from Google Drive for which will be use for this assignment."
      ],
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        "id": "LPbaxgvcqxyl"
      }
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              "                  Author  Rating  \\\n",
              "0            Jia Pin Lee     4.0   \n",
              "1            Chui Yi Lum     2.0   \n",
              "2            liezel wong     1.0   \n",
              "3              Nazri Nor     1.0   \n",
              "4  Fakru Imran's Channel     5.0   \n",
              "\n",
              "                                              Review           Restaurant  \\\n",
              "0  Came here for the High Tea. Great service espe...  Cuisines Restaurant   \n",
              "1  5 stars for the service, even though some of t...  Cuisines Restaurant   \n",
              "2  Hi, thank you for your service. But! i feel so...  Cuisines Restaurant   \n",
              "3  I have the worse buffer dinner ever so far. Th...  Cuisines Restaurant   \n",
              "4  That's are Known 5 Elmark \" 9H72 \" & KDK \" 3 K...  Cuisines Restaurant   \n",
              "\n",
              "  Location  \n",
              "0     Ipoh  \n",
              "1     Ipoh  \n",
              "2     Ipoh  \n",
              "3     Ipoh  \n",
              "4     Ipoh  "
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              "                            Author  \\\n",
              "0                        kmc1e2018   \n",
              "1  MizOthmanKuala Lumpur, Malaysia   \n",
              "2  Relax36268533224Hjarno, Denmark   \n",
              "3                           tstrry   \n",
              "4                   Naim_123456789   \n",
              "\n",
              "                                               Title  \\\n",
              "0                             Chambers Bar and Grill   \n",
              "1             Always a pleasure. Place to celebrate.   \n",
              "2  Enjoying good service and good food after a lo...   \n",
              "3                            Great Steak for Couples   \n",
              "4                                             Dining   \n",
              "\n",
              "                                              Review  Rating  \\\n",
              "0  David, Thanga, Mikail and Chef Steven gave bri...     5.0   \n",
              "1  We visited for family celebration and as usual...     5.0   \n",
              "2  Perfect dinner after a long journey.After trav...     5.0   \n",
              "3  Had a great Tomahawk for me and hubby thanks t...     5.0   \n",
              "4  Excellent ambient. Excellent service by David,...     5.0   \n",
              "\n",
              "                  Dates      Restaurant Location  \n",
              "0   Reviewed yesterday   Chambers Grill       KL  \n",
              "1  Reviewed 5 days ago   Chambers Grill       KL  \n",
              "2  Reviewed 1 week ago   Chambers Grill       KL  \n",
              "3  Reviewed 1 week ago   Chambers Grill       KL  \n",
              "4  Reviewed 1 week ago   Chambers Grill       KL  "
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              "      <td>Always a pleasure. Place to celebrate.</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Relax36268533224Hjarno, Denmark</td>\n",
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              "      <th>3</th>\n",
              "      <td>tstrry</td>\n",
              "      <td>Great Steak for Couples</td>\n",
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              "    </tr>\n",
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              "      <th>4</th>\n",
              "      <td>Naim_123456789</td>\n",
              "      <td>Dining</td>\n",
              "      <td>Excellent ambient. Excellent service by David,...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Reviewed 1 week ago</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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      "source": [
        "# Google\n",
        "file_google = \"/content/drive/MyDrive/Colab Notebooks/GoogleReview_data_cleaned.csv\"\n",
        "df_G =pd.read_csv(file_google)\n",
        "# TripAdvisor\n",
        "file_TA = \"/content/drive/MyDrive/Colab Notebooks/TripAdvisor_data_cleaned.csv\"\n",
        "df_TA =pd.read_csv(file_TA)\n",
        "# Read raw data\n",
        "master_dir = os.getcwd()\n",
        "\n",
        "display(df_G.head(),df_TA.head())"
      ]
    },
    {
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      "source": [
        "## Data Preparation and Cleaning\n",
        "\n",
        "**Step 1** - Concatenate the two dataset into one big dataset for easier analysis process.\n",
        "\n"
      ]
    },
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            "                                 Author  \\\n",
            "0                             kmc1e2018   \n",
            "1       MizOthmanKuala Lumpur, Malaysia   \n",
            "2       Relax36268533224Hjarno, Denmark   \n",
            "3                                tstrry   \n",
            "4                        Naim_123456789   \n",
            "...                                 ...   \n",
            "222015                       Arun Sagar   \n",
            "222016                   Asif Amir Khan   \n",
            "222017                           saads7   \n",
            "222018                            S. R.   \n",
            "222019                     Nurul Jannah   \n",
            "\n",
            "                                                    Title  \\\n",
            "0                                  Chambers Bar and Grill   \n",
            "1                  Always a pleasure. Place to celebrate.   \n",
            "2       Enjoying good service and good food after a lo...   \n",
            "3                                 Great Steak for Couples   \n",
            "4                                                  Dining   \n",
            "...                                                   ...   \n",
            "222015                                                NaN   \n",
            "222016                                                NaN   \n",
            "222017                                                NaN   \n",
            "222018                                                NaN   \n",
            "222019                                                NaN   \n",
            "\n",
            "                                                   Review  Rating  \\\n",
            "0       David, Thanga, Mikail and Chef Steven gave bri...     5.0   \n",
            "1       We visited for family celebration and as usual...     5.0   \n",
            "2       Perfect dinner after a long journey.After trav...     5.0   \n",
            "3       Had a great Tomahawk for me and hubby thanks t...     5.0   \n",
            "4       Excellent ambient. Excellent service by David,...     5.0   \n",
            "...                                                   ...     ...   \n",
            "222015    Good lamb kebabs, nice traditional mughlai food     5.0   \n",
            "222016  Pakistani food lovers must visit this place in...     5.0   \n",
            "222017       Great food, open air dining, freshly cooked.     5.0   \n",
            "222018               Good desi food. Ask for salam waiter     5.0   \n",
            "222019  A lot of tasty Pakistani dishes for ramadhan b...     5.0   \n",
            "\n",
            "                       Dates      Restaurant       Location  \n",
            "0        Reviewed yesterday   Chambers Grill             KL  \n",
            "1       Reviewed 5 days ago   Chambers Grill             KL  \n",
            "2       Reviewed 1 week ago   Chambers Grill             KL  \n",
            "3       Reviewed 1 week ago   Chambers Grill             KL  \n",
            "4       Reviewed 1 week ago   Chambers Grill             KL  \n",
            "...                      ...             ...            ...  \n",
            "222015                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
            "222016                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
            "222017                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
            "222018                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
            "222019                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
            "\n",
            "[361784 rows x 7 columns]\n"
          ]
        }
      ],
      "source": [
        "# combine reviews\n",
        "df = pd.concat([df_TA, df_G], axis=0)\n",
        "df_ori = df.copy()\n",
        "print(df_ori)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Rsgjn7WZVJJ4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4fbac0f-adf8-4ea4-c7da-5f0fae76a9e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample Number: 361784\n"
          ]
        }
      ],
      "source": [
        "# number of records\n",
        "sampleNum = df.index.size\n",
        "print(f\"Sample Number: {sampleNum}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8BlXZJt_VJJ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d08e214d-e0c7-4794-d872-033c7e035bdb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique Restaurants: 3515\n"
          ]
        }
      ],
      "source": [
        "# number of restaurants\n",
        "uniqueRestaurants = df['Restaurant'].unique().size\n",
        "print(f\"Unique Restaurants: {uniqueRestaurants}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 2** - Display the dataset first to have an overview of what kind of data we have."
      ],
      "metadata": {
        "id": "W2n_Dzeorq7Y"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 424
        },
        "outputId": "b9e76f9c-fd5e-4fe3-a63a-cc4c53249998"
      },
      "outputs": [
        {
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              "                                 Author  \\\n",
              "0                             kmc1e2018   \n",
              "1       MizOthmanKuala Lumpur, Malaysia   \n",
              "2       Relax36268533224Hjarno, Denmark   \n",
              "3                                tstrry   \n",
              "4                        Naim_123456789   \n",
              "...                                 ...   \n",
              "222015                       Arun Sagar   \n",
              "222016                   Asif Amir Khan   \n",
              "222017                           saads7   \n",
              "222018                            S. R.   \n",
              "222019                     Nurul Jannah   \n",
              "\n",
              "                                                    Title  \\\n",
              "0                                  Chambers Bar and Grill   \n",
              "1                  Always a pleasure. Place to celebrate.   \n",
              "2       Enjoying good service and good food after a lo...   \n",
              "3                                 Great Steak for Couples   \n",
              "4                                                  Dining   \n",
              "...                                                   ...   \n",
              "222015                                                NaN   \n",
              "222016                                                NaN   \n",
              "222017                                                NaN   \n",
              "222018                                                NaN   \n",
              "222019                                                NaN   \n",
              "\n",
              "                                                   Review  Rating  \\\n",
              "0       David, Thanga, Mikail and Chef Steven gave bri...     5.0   \n",
              "1       We visited for family celebration and as usual...     5.0   \n",
              "2       Perfect dinner after a long journey.After trav...     5.0   \n",
              "3       Had a great Tomahawk for me and hubby thanks t...     5.0   \n",
              "4       Excellent ambient. Excellent service by David,...     5.0   \n",
              "...                                                   ...     ...   \n",
              "222015    Good lamb kebabs, nice traditional mughlai food     5.0   \n",
              "222016  Pakistani food lovers must visit this place in...     5.0   \n",
              "222017       Great food, open air dining, freshly cooked.     5.0   \n",
              "222018               Good desi food. Ask for salam waiter     5.0   \n",
              "222019  A lot of tasty Pakistani dishes for ramadhan b...     5.0   \n",
              "\n",
              "                       Dates      Restaurant       Location  \n",
              "0        Reviewed yesterday   Chambers Grill             KL  \n",
              "1       Reviewed 5 days ago   Chambers Grill             KL  \n",
              "2       Reviewed 1 week ago   Chambers Grill             KL  \n",
              "3       Reviewed 1 week ago   Chambers Grill             KL  \n",
              "4       Reviewed 1 week ago   Chambers Grill             KL  \n",
              "...                      ...             ...            ...  \n",
              "222015                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
              "222016                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
              "222017                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
              "222018                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
              "222019                   NaN      BBQ NIGHTS  Petaling Jaya  \n",
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              "      <th>Author</th>\n",
              "      <th>Title</th>\n",
              "      <th>Review</th>\n",
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              "      <td>5.0</td>\n",
              "      <td>Reviewed 1 week ago</td>\n",
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              "      <th>4</th>\n",
              "      <td>Naim_123456789</td>\n",
              "      <td>Dining</td>\n",
              "      <td>Excellent ambient. Excellent service by David,...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Reviewed 1 week ago</td>\n",
              "      <td>Chambers Grill</td>\n",
              "      <td>KL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>222015</th>\n",
              "      <td>Arun Sagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Good lamb kebabs, nice traditional mughlai food</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222016</th>\n",
              "      <td>Asif Amir Khan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pakistani food lovers must visit this place in...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222017</th>\n",
              "      <td>saads7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Great food, open air dining, freshly cooked.</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222018</th>\n",
              "      <td>S. R.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Good desi food. Ask for salam waiter</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
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              "    <tr>\n",
              "      <th>222019</th>\n",
              "      <td>Nurul Jannah</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A lot of tasty Pakistani dishes for ramadhan b...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {}
        }
      ],
      "source": [
        "display(df_ori)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 3** - Clean the dataset by adding a new column name \"Label\" which contain value \"Positive\", \"Negative\", or \"Neutral\" to reflect the rating of each review using the \"Review\" and \"Rating\" column"
      ],
      "metadata": {
        "id": "RzbbhNiGr1F4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Subset wanted columns\n",
        "df = df[['Review', 'Rating']]\n",
        "df.drop_duplicates(inplace=True)\n",
        "df.reset_index(drop=True, inplace=True)\n",
        "display(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "NTx40-_VXfaH",
        "outputId": "ff5b764a-3637-47ec-f6d5-8a2a680dde13"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                   Review  Rating\n",
              "0       David, Thanga, Mikail and Chef Steven gave bri...     5.0\n",
              "1       We visited for family celebration and as usual...     5.0\n",
              "2       Perfect dinner after a long journey.After trav...     5.0\n",
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              "339690  Delicious food. I passed by this place while s...     5.0\n",
              "339691  To put it mildly, Horrible food, Horrible cust...     1.0\n",
              "339692  Good vegetarian selection and the food was gre...     4.0\n",
              "339693  Simple atmosphere with fresh air (on a rainy n...     5.0\n",
              "339694                             Very good Cheese Naan!     5.0\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "    }\n",
              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-411565b4-9fc5-4c91-af41-5e1ac91289d1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding labels\n",
        "def labelFunc(x):\n",
        "    if int(x) > 3:\n",
        "        return \"Positive\"\n",
        "    elif int(x) < 3:\n",
        "        return \"Negative\"\n",
        "    elif int(x) == 3:\n",
        "        return \"Neutral\"\n",
        "\n",
        "# Add sentiment label\n",
        "df_ori[\"Label\"] = df_ori[\"Rating\"].apply(labelFunc)\n",
        "display(df_ori)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "PTsJ1dQpXqPn",
        "outputId": "b6db494f-03f5-4c16-d147-a321c2560c79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                 Author  \\\n",
              "0                             kmc1e2018   \n",
              "1       MizOthmanKuala Lumpur, Malaysia   \n",
              "2       Relax36268533224Hjarno, Denmark   \n",
              "3                                tstrry   \n",
              "4                        Naim_123456789   \n",
              "...                                 ...   \n",
              "222015                       Arun Sagar   \n",
              "222016                   Asif Amir Khan   \n",
              "222017                           saads7   \n",
              "222018                            S. R.   \n",
              "222019                     Nurul Jannah   \n",
              "\n",
              "                                                    Title  \\\n",
              "0                                  Chambers Bar and Grill   \n",
              "1                  Always a pleasure. Place to celebrate.   \n",
              "2       Enjoying good service and good food after a lo...   \n",
              "3                                 Great Steak for Couples   \n",
              "4                                                  Dining   \n",
              "...                                                   ...   \n",
              "222015                                                NaN   \n",
              "222016                                                NaN   \n",
              "222017                                                NaN   \n",
              "222018                                                NaN   \n",
              "222019                                                NaN   \n",
              "\n",
              "                                                   Review  Rating  \\\n",
              "0       David, Thanga, Mikail and Chef Steven gave bri...     5.0   \n",
              "1       We visited for family celebration and as usual...     5.0   \n",
              "2       Perfect dinner after a long journey.After trav...     5.0   \n",
              "3       Had a great Tomahawk for me and hubby thanks t...     5.0   \n",
              "4       Excellent ambient. Excellent service by David,...     5.0   \n",
              "...                                                   ...     ...   \n",
              "222015    Good lamb kebabs, nice traditional mughlai food     5.0   \n",
              "222016  Pakistani food lovers must visit this place in...     5.0   \n",
              "222017       Great food, open air dining, freshly cooked.     5.0   \n",
              "222018               Good desi food. Ask for salam waiter     5.0   \n",
              "222019  A lot of tasty Pakistani dishes for ramadhan b...     5.0   \n",
              "\n",
              "                       Dates      Restaurant       Location     Label  \n",
              "0        Reviewed yesterday   Chambers Grill             KL  Positive  \n",
              "1       Reviewed 5 days ago   Chambers Grill             KL  Positive  \n",
              "2       Reviewed 1 week ago   Chambers Grill             KL  Positive  \n",
              "3       Reviewed 1 week ago   Chambers Grill             KL  Positive  \n",
              "4       Reviewed 1 week ago   Chambers Grill             KL  Positive  \n",
              "...                      ...             ...            ...       ...  \n",
              "222015                   NaN      BBQ NIGHTS  Petaling Jaya  Positive  \n",
              "222016                   NaN      BBQ NIGHTS  Petaling Jaya  Positive  \n",
              "222017                   NaN      BBQ NIGHTS  Petaling Jaya  Positive  \n",
              "222018                   NaN      BBQ NIGHTS  Petaling Jaya  Positive  \n",
              "222019                   NaN      BBQ NIGHTS  Petaling Jaya  Positive  \n",
              "\n",
              "[361784 rows x 8 columns]"
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              "      <th>Author</th>\n",
              "      <th>Title</th>\n",
              "      <th>Review</th>\n",
              "      <th>Rating</th>\n",
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              "      <td>kmc1e2018</td>\n",
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              "      <th>1</th>\n",
              "      <td>MizOthmanKuala Lumpur, Malaysia</td>\n",
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              "      <td>Reviewed 5 days ago</td>\n",
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              "      <td>KL</td>\n",
              "      <td>Positive</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Relax36268533224Hjarno, Denmark</td>\n",
              "      <td>Enjoying good service and good food after a lo...</td>\n",
              "      <td>Perfect dinner after a long journey.After trav...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Reviewed 1 week ago</td>\n",
              "      <td>Chambers Grill</td>\n",
              "      <td>KL</td>\n",
              "      <td>Positive</td>\n",
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              "      <td>tstrry</td>\n",
              "      <td>Great Steak for Couples</td>\n",
              "      <td>Had a great Tomahawk for me and hubby thanks t...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Reviewed 1 week ago</td>\n",
              "      <td>Chambers Grill</td>\n",
              "      <td>KL</td>\n",
              "      <td>Positive</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Naim_123456789</td>\n",
              "      <td>Dining</td>\n",
              "      <td>Excellent ambient. Excellent service by David,...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>Reviewed 1 week ago</td>\n",
              "      <td>Chambers Grill</td>\n",
              "      <td>KL</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>222015</th>\n",
              "      <td>Arun Sagar</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Good lamb kebabs, nice traditional mughlai food</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222016</th>\n",
              "      <td>Asif Amir Khan</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Pakistani food lovers must visit this place in...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222017</th>\n",
              "      <td>saads7</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Great food, open air dining, freshly cooked.</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222018</th>\n",
              "      <td>S. R.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Good desi food. Ask for salam waiter</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>222019</th>\n",
              "      <td>Nurul Jannah</td>\n",
              "      <td>NaN</td>\n",
              "      <td>A lot of tasty Pakistani dishes for ramadhan b...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>BBQ NIGHTS</td>\n",
              "      <td>Petaling Jaya</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>361784 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abb81f26-b83f-4ebc-8cf0-753c7e688d22')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-abb81f26-b83f-4ebc-8cf0-753c7e688d22');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t26lvNVLVJJ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d40d793f-385a-4887-dbd0-728ce06ff68d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "361784 rows\n"
          ]
        }
      ],
      "source": [
        "#number of rows\n",
        "count_row = df_ori.shape[0]\n",
        "print(count_row , \"rows\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KribcC_XVJJ5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f03a352-d71f-4c52-8a55-1a37efd4d930"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8 columns\n"
          ]
        }
      ],
      "source": [
        "#number of columns\n",
        "count_col = df_ori.shape[1]\n",
        "print(count_col , \"columns\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7mUpY22aVJJ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a10a042-5242-4f1a-c57f-ff8641ade384"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sample Number: 361784\n"
          ]
        }
      ],
      "source": [
        "# range of values\n",
        "sampleNum = df_ori.index.size\n",
        "print(f\"Sample Number: {sampleNum}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_0m5LkbVVJJ6"
      },
      "source": [
        "## Exploratory Analysis and Visualization\n",
        "\n",
        "**Step 4** - Import statistical and visual library function to create graphs and bar charts. Begin by importingmatplotlib.pyplot and seaborn\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6knKGHajVJJ6"
      },
      "source": [
        "> Instructions (delete this cell)\n",
        "> \n",
        "> - Compute the mean, sum, range and other interesting statistics for numeric columns\n",
        "> - Explore distributions of numeric columns using histograms etc.\n",
        "> - Explore relationship between columns using scatter plots, bar charts etc.\n",
        "> - Make a note of interesting insights from the exploratory analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zOTtwqAMVJJ7"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['font.size'] = 14\n",
        "matplotlib.rcParams['figure.figsize'] = (9, 5)\n",
        "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 5** - Compute the mean, sum, range and other interesting statistics for \"Rating\" column."
      ],
      "metadata": {
        "id": "u7gcBKevlVE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The median for Rating column is:\",df['Rating'].median())\n",
        "print(\"The mean for Rating column is:\",round(df['Rating'].mean(), 2))\n",
        "print(\"The mode for Rating column is:\",df['Rating'].mode())\n",
        "\n",
        "round(df.describe(),2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "7QdsQoSlnCDW",
        "outputId": "599b0610-f40e-4a50-ea36-9bc5a43401a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The median for Rating column is: 5.0\n",
            "The mean for Rating column is: 4.16\n",
            "The mode for Rating column is: 0    5.0\n",
            "dtype: float64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Rating\n",
              "count  339695.00\n",
              "mean        4.16\n",
              "std         1.10\n",
              "min         1.00\n",
              "25%         4.00\n",
              "50%         5.00\n",
              "75%         5.00\n",
              "max         5.00"
            ],
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              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>339695.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
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              "      <th>75%</th>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.00</td>\n",
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              "  </tbody>\n",
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              "          document.querySelector('#df-e4d0cc4d-9d11-4435-9aa2-17cc412503d8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-e4d0cc4d-9d11-4435-9aa2-17cc412503d8');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 6** - Explore distributions of numeric columns using histograms."
      ],
      "metadata": {
        "id": "A_C7ZFIjkvbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.hist(column='Rating')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "g8RVt4Mek2Nl",
        "outputId": "6a204a29-355b-4fe1-b5f5-655f44de1376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fbd894ae460>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fv4NPHasVJJ7"
      },
      "source": [
        "**Step 7** - Get the distribution of restaurant reviews across different location."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NKN6RIplVJJ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ebe67b71-82e2-408a-fb0d-94f100accdee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KL               98750\n",
              "Petaling Jaya    49932\n",
              "Penang           44502\n",
              "JB               35238\n",
              "Ipoh             30796\n",
              "Kuching          29603\n",
              "Langkawi         29303\n",
              "Melaka           26690\n",
              "Shah Alam         8658\n",
              "Miri              8312\n",
              "Name: Location, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ],
      "source": [
        "#Distribution across locations\n",
        "df_ori[\"Location\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Scf_lKnVJJ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 583
        },
        "outputId": "ca9af378-a36d-4c90-c9c2-7363ae4ffef7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_ori[\"Location\"].value_counts().plot(kind='bar',\n",
        "                                       figsize=(10, 8),\n",
        "                                       ylabel=\"Number of reviews\",\n",
        "                                       title=\"Review Distribution\",\n",
        "                                      );"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dn_AW-5uVJJ7"
      },
      "source": [
        "**Step 7** - Explore the rating distribution of all restaurants in Malaysia."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c7fugjWzVJJ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81b82ed4-7106-43a6-9aa3-6ff2832ce83d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KL               98750\n",
              "Petaling Jaya    49932\n",
              "Penang           44502\n",
              "JB               35238\n",
              "Ipoh             30796\n",
              "Kuching          29603\n",
              "Langkawi         29303\n",
              "Melaka           26690\n",
              "Shah Alam         8658\n",
              "Miri              8312\n",
              "Name: Location, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ],
      "source": [
        "# Rating Distribution\n",
        "df_ori[\"Location\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pPnlFwy2VJJ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 406
        },
        "outputId": "b22f100d-a8e8-4a77-a48a-05663b60a333"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Rating Distribution Graph\n",
        "df_ori[\"Rating\"].value_counts().plot(kind='bar',\n",
        "                                     figsize=(8, 6),\n",
        "                                     ylabel=\"Number of reviews\",\n",
        "                                    title=\"Rating Distribution\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN1bUu8RVJJ7"
      },
      "source": [
        "**Step 8** - Get the average rating of reviews."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kMrm6CiAVJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e1f1248c-eab9-4334-b549-6165963604d0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Rating: 4.18\n"
          ]
        }
      ],
      "source": [
        "# Average Rating\n",
        "avgRating = df_ori[\"Rating\"].mean().round(2)\n",
        "print(f\"Average Rating: {avgRating}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RSt7KlcfVJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "outputId": "0ca3579d-0033-4025-c15b-3ed84aa1242e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Average rating across different locations\n",
        "\n",
        "df_ori.pivot_table(index='Location', values='Rating', aggfunc='mean') \\\n",
        "    .plot(kind='bar', ylabel=\"Average Rating\",\n",
        "          title=\"Rating Distribution\", figsize=(15, 5),\n",
        "         );"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tLNLw0EOVJJ8"
      },
      "source": [
        "**Step 9** - Get the review summary uing the \"Label\" column in a form of barchart."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "61sgf555VJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1f01512-fc95-4c94-d9ba-0a321e8c56e8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Positive    289745\n",
              "Neutral      39835\n",
              "Negative     32204\n",
              "Name: Label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ],
      "source": [
        "df_ori[\"Label\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fOQ9cTWiVJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "d4920df3-0de5-4705-f476-bd191c907a5a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "df_ori[\"Label\"].value_counts().plot(kind='bar',\n",
        "                                     figsize=(8, 5),\n",
        "                                     ylabel=\"Number of reviews\",\n",
        "                                    title=\"Review Summary\");"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nt_CVhi-VJJ8"
      },
      "source": [
        "**Step 10** - Find the percentage review type using the review summary data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Zatc4Si_VJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "9d98f1fe-e615-4ae8-ced1-03fc0588f63a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Label\n",
              "Positive  289745\n",
              "Neutral    39835\n",
              "Negative   32204"
            ],
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              "    <tr>\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b98810ae-3ed6-4f2c-9ed5-cce1ee76fd1c 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-b98810ae-3ed6-4f2c-9ed5-cce1ee76fd1c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ],
      "source": [
        "# Review Type\n",
        "pd.DataFrame(df_ori[\"Label\"].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RBYoxztOVJJ8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "687b52b6-5c91-443a-b485-d5464f0b5f10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Percentage\n",
              "Positive   80.087842\n",
              "Neutral    11.010714\n",
              "Negative    8.901444"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-afe80743-fc75-4239-9dfc-2d80f1dfd135\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Positive</th>\n",
              "      <td>80.087842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Neutral</th>\n",
              "      <td>11.010714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Negative</th>\n",
              "      <td>8.901444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-afe80743-fc75-4239-9dfc-2d80f1dfd135')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-afe80743-fc75-4239-9dfc-2d80f1dfd135 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-afe80743-fc75-4239-9dfc-2d80f1dfd135');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "# Percentage of Review Type\n",
        "pd.DataFrame(df_ori[\"Label\"].value_counts() / df_ori.index.size * 100) \\\n",
        "    .rename(columns={'Label': \"Percentage\"})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E0U3IWm8VJJ9"
      },
      "source": [
        "## Asking and Answering Questions\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SjXvLl4dVJJ9"
      },
      "source": [
        "> Instructions (delete this cell)\n",
        ">\n",
        "> - Ask at least 5 interesting questions about your dataset\n",
        "> - Answer the questions either by computing the results using Numpy/Pandas or by plotting graphs using Matplotlib/Seaborn\n",
        "> - Create new columns, merge multiple dataset and perform grouping/aggregation wherever necessary\n",
        "> - Wherever you're using a library function from Pandas/Numpy/Matplotlib etc. explain briefly what it does\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Lor5rHMVJJ9"
      },
      "source": [
        "#### Q1: TODO - ask a question here and answer it below"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the top 10 restaurants based on average rating"
      ],
      "metadata": {
        "id": "6erGUnURNtnI"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D-PdOKqCVJJ9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "aa691747-545f-4936-ee7a-ff9842915906"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            Rating\n",
              "Restaurant                        \n",
              "Spiced Pumpkin Cafe            5.0\n",
              "Baan Carabao                   5.0\n",
              "Latte Be Light                 5.0\n",
              "Ayam Penyet                    5.0\n",
              "Restaurant Pin Wei Seafood     5.0\n",
              "Restaurant Smk Corner          5.0\n",
              "Restaurant Soon Tong           5.0\n",
              "Thirty8 Fashion                5.0\n",
              "Canard eat & roll              5.0\n",
              "Restoran Bendang               5.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-58f9e450-e2ca-49c7-a1f9-c74c904ccd0a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Restaurant</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Spiced Pumpkin Cafe</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Baan Carabao</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Latte Be Light</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ayam Penyet</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Restaurant Pin Wei Seafood</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Restaurant Smk Corner</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Restaurant Soon Tong</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Thirty8 Fashion</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Canard eat &amp; roll</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Restoran Bendang</th>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-58f9e450-e2ca-49c7-a1f9-c74c904ccd0a')\"\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 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-58f9e450-e2ca-49c7-a1f9-c74c904ccd0a 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-58f9e450-e2ca-49c7-a1f9-c74c904ccd0a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ],
      "source": [
        "averageRating_restaurants = df_ori[[\"Restaurant\", \"Rating\"]] \\\n",
        "                .pivot_table(index=['Restaurant'], values='Rating', aggfunc='mean') \\\n",
        "                .sort_values('Rating', ascending=False)\n",
        "averageRating_restaurants.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_REVnrhVJJ-"
      },
      "source": [
        "#### Q2: TODO - ask a question here and answer it below"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Does a longer review results in better ratings?"
      ],
      "metadata": {
        "id": "Qku5rj96N9zf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ShA3QyQpVJJ-"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "\n",
        "tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "\n",
        "df_ori['Word_count'] = df_ori['Review'].apply(lambda x: len(tokenizer.tokenize(x)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dvyLd9MdVJJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "833429c8-9166-41e1-95c1-ed446750eca2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(x=df_ori['Word_count'], y=df_ori['Rating'], alpha=0.02)\n",
        "plt.xlabel('Word count')\n",
        "plt.ylabel('Rating')\n",
        "plt.title('Relationship between rating and word count');"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tdjrhzySVJJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "b589f2bf-f485-49d0-c3e9-3367c9178455"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Word_count    Rating\n",
              "Word_count    1.000000 -0.111255\n",
              "Rating       -0.111255  1.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4501d121-28a7-46af-8775-8994c0ebdf35\">\n",
              "    <div 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>Word_count</th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Word_count</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.111255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rating</th>\n",
              "      <td>-0.111255</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4501d121-28a7-46af-8775-8994c0ebdf35')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4501d121-28a7-46af-8775-8994c0ebdf35');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ],
      "source": [
        "df_ori[['Word_count', 'Rating']].corr()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From our findings, there seems to be no correlation bbetween the two variables."
      ],
      "metadata": {
        "id": "xvU8sab7OvtD"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ya3o0SurVJJ-"
      },
      "source": [
        "#### Q3: TODO - ask a question here and answer it below"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What is the most common phrase/word used by users in the reviews"
      ],
      "metadata": {
        "id": "nHQZv_TsO5Wy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CaF9SiPeVJJ-"
      },
      "outputs": [],
      "source": [
        "from wordcloud import WordCloud\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_positive = df[df[\"Rating\"] > 3]\n",
        "df_neutral = df[df[\"Rating\"] == 3]\n",
        "df_negative = df[df[\"Rating\"] < 3]"
      ],
      "metadata": {
        "id": "fn_aOPc9TBVP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bGV1nEzqVJJ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff318e60-4371-41c5-a787-4cde84ca631d"
      },
      "outputs": [
        {
          "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"
          ]
        }
      ],
      "source": [
        "nltk.download('stopwords')\n",
        "stopwordz = set(stopwords.words(\"english\"))\n",
        "stopwordz.remove('not') # remove negative"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1y8gWqGcVJJ-"
      },
      "outputs": [],
      "source": [
        "texts = \" \".join(review.lower() for review in df[\"Review\"])\n",
        "# remove punctuation\n",
        "tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "tokens = tokenizer.tokenize(texts)\n",
        "# remove stopwords\n",
        "tokens = [token for token in tokens if token not in stopwordz]\n",
        "\n",
        "overall_unigram = nltk.FreqDist(tokens).most_common(20)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "overall_unigram\n",
        "overallUnigram_df = pd.DataFrame(overall_unigram, columns=['Unigram', 'Count'])\n",
        "overallUnigram_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "lRSWDktVSYwS",
        "outputId": "12bb7406-966f-40c1-e7cb-476c2b90140f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unigram   Count\n",
              "0         food  208145\n",
              "1         good  145511\n",
              "2          not   87382\n",
              "3      service   83760\n",
              "4        place   82728\n",
              "5        great   71729\n",
              "6         nice   67433\n",
              "7   restaurant   64852\n",
              "8        staff   46725\n",
              "9    delicious   41794\n",
              "10    friendly   38565\n",
              "11         one   37525\n",
              "12        best   37105\n",
              "13      really   36711\n",
              "14       price   35100\n",
              "15     chicken   34627\n",
              "16        time   33902\n",
              "17       taste   32343\n",
              "18        like   31600\n",
              "19         try   31507"
            ],
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              "      <td>83760</td>\n",
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              "      <td>64852</td>\n",
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              "      <td>46725</td>\n",
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              "      <td>delicious</td>\n",
              "      <td>41794</td>\n",
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              "      <td>really</td>\n",
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              "      <td>chicken</td>\n",
              "      <td>34627</td>\n",
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              "    <tr>\n",
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              "      <td>33902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>taste</td>\n",
              "      <td>32343</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>like</td>\n",
              "      <td>31600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>try</td>\n",
              "      <td>31507</td>\n",
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              "      \n",
              "  <style>\n",
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              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      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-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7ad92b50-bfd4-4c3a-9eff-fc0861b8d3b7 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-7ad92b50-bfd4-4c3a-9eff-fc0861b8d3b7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4CcVO1TVJJ-"
      },
      "source": [
        "#### Q4: TODO - ask a question here and answer it below"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What word is often used in positive reviews"
      ],
      "metadata": {
        "id": "gHesCKZmS3Av"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wa96BGX1VJJ-"
      },
      "outputs": [],
      "source": [
        "texts_positive = \" \".join(review.lower() for review in df_positive[\"Review\"])\n",
        "# remove punctuation\n",
        "tokenizer_positive = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "tokens_positive = tokenizer.tokenize(texts_positive)\n",
        "# remove stopwords\n",
        "tokens_positive = [token for token in tokens_positive if token not in stopwordz]\n",
        "\n",
        "positive_unigram = nltk.FreqDist(tokens_positive).most_common(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R_C78KePVJJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "2f207e33-af23-434c-a262-caf303f2e25a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Unigram')"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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OaH6samLeODOIjgG1OA94GtgbWAD4c6MGJSIiIu1HiVn/TQDWMLNlgA/oZt2eu7uZ3QWMBv7s7u8PyAhFRESkLWjxf/+dRMyaPQ1MBJbq4frziT6aWvQvIiIiH6MZs35y92eBtasOj+nmLcOB59z9zt58jpqY952amouISLtQYjZAzGx+YGmi9tnxfbmHmpiLiIjkmxKzgXM6sB1wHXBOX26gXpn9p76ZIiLSypSYDRB33xnYua/vV6/M+lDfTBERaWVa/C8iIiLSIjRjViMzm5vof7kdsBDwGHCwu99lZhsCdwCbAL8DViZ2ae7h7o9k7vF1omn5COC/xGPNQ1Q2Q0REREAzZr1xIrAt0UppVeBJ4CYzG5655gTgUGA14G3gMjMbBGBmKwO3EMnYV4nWTKsAFwxUACIiItLaBpXL5WaPoeWZ2XzEDNdP3f3idGwI8CzwJ+A2YsZsM3e/OZ1fB7gL+IK7v5LaMc10990y910FeBRY3N3f6mEYZa0x678JIzdn5szZPV84AIYMGczs2R3NHsaAUKz5pFjzpyhxQvNjnWuuIQ8DX6s+rkeZtVkOmAu4u3LA3Web2b3AikRiBvBE5j2vpa+LAa8AqwNfMrNtM9cMyty/p8RM6qRVynEUqTSIYs0nxZo/RYkTmh+remU2TnbKcWYnxwdnvp4H/L6Te7zagHGJiIhIm1FiVpvnibZL66TvK48y1wYur/EejwBfdvf/NGSEIiIi0vaUmNXA3aeY2VnAKDObBIwHDgAWB84ErIbbjALuM7OziQKzk4HlgS3cfc/GjFxERETaiRKz2h2Svl4ILEws2t/M3V83sx4TM3d/wszWB44D/gkMAV4Arqnlw9Ursz7UN1NERFqZdmW2iY6Ocvntt/Nfs77ZizEHkmLNJ8WaT0WJtShxQvNjHTZsgU53ZaqOWR2Z2QQzO7ir1yIiIiLd0aPMNqIm5o2hxuYiItIqlJgBZjbU3Wc0exzdURPzxlFjcxERaRWFTMzMbCwwDpgC7ARMMLOdgNHA+sA04HbgAHd/I71nBHA80W5pKFFM9pfufm+Nn3kBsJi7fydzbDAwATjV3U+pS3AiIiLStoq8xmwHovL+esAvgDuBp4A1iGbk8wPXpuQJYAHgknT9GkQT87+Z2SI1ft65wGZVvTU3BZZI9xUREZGCK+SMWTLe3Q8CMLNjgMfdvVISAzPbEXiH2DHxgLv/I/tmM/s5sBXwLeDSnj7M3e81s2eIGbqR6fCuwHXuPrEO8YiIiEibK3Ji9nDm+9WB9c2ss6VGywEPmNliwLHARkRh2SHAp4GlevGZ5wJ7AyPN7LPA94Af9GHsUmcLLzzvgH/mkCGDm/K5zaBY80mx5k9R4oTWjbXIidmUzPeDgRuBzkpbvJm+XkQkZAcQ68KmE+vQhvbiMy8hugesC6wKTARu7tWopSGaUcum2TV0BpJizSfFmj9FiROaH6uamHfvEeCHwIvuPrOLa9YFfuHuNwKY2eLA8C6u7ZS7v2NmVxOPMFcFLnL3jr4PW0RERPJEiVk4A9gduMLMRhEzWcsSydpB7j4ZeBbYwczuB+YDTiQam/fWucBNwFzEGjURERERoNi7Mj/i7q8B6wAdRNL0byJZm57+QMxyzU+sTfszcAHxSLO3xgKvAGPd/YX+jFtERETypVe9Ms3sB8Ti98WoSurc/Yf1HVo+mdmngVeBn7v7ZbW+r6OjXB48eFDjBlZgzar83+z1DQNJseaTYs2fosQJzY+1q16ZNT/KNLOTgZ8DdxML4mfXbXQFkOqhLQrsRxSw/Utv7zFx4uR6D6vlNPs/FBERkWbqzRqznYBt3P3aRg2mVaTOAE+5+751vO1SwHjiMeYu3Wwy6JJ6ZbYO9dcUEZFG6E1iNhV4plEDyTt3n0B0GsDMJpjZ6e5+Uq3vV6/M1qL+miIi0gi9Wfw/EviVmWknp4iIiEgD9CbJOhfYAnjVzJ4FPvYozt2/Uc+BtYBPmdlpwI7p9XnAIe7eYWZDiS4A2wOfJXZxHuHuNwOY2VzAycDWwCLAW8Bl7n5oeky6NDDazEYDuLtW9YuIiEivErOziSKrNxGL/2vfztmetgfGAGsDXyES09eBU4ALiVZNPybWjH0buN7MRrj740RT9B8APyJKanwesHTfLYHHiXIbZw1MKCIiItIOepOYbQv8wN1vbdRgWszrRKX/MvCMmZWAA83sWmA7YBl3fylde7qZbQLsSfTCXJooSPuv9P6XgHvgo+r/s4HJ7v7GwIYk9dTfHmut2qetERRrPinW/ClKnNC6sfYmMZtI1N8qivtSUlVxL/H4cl1iEf/TZpa9fm7gH+n7McCtwLNmdgvwN+Dvar+UL/0t61Gk0iCKNZ8Ua/4UJU5ofqxdVSDozeL/I4FjzGz+uoyovZWBEcAqmT8rEN0BcPdHgGWAw4if8UXAramWmYiIiEinejNj9ksi2XjTzF7ik4v/v1LHcbWCNc1sUGbWbC3gNWLmbBCwhLvf0dWbU3/Nq4CrzGwMcB/wJeIR5wxgSAPHLiIiIm2oN4nZVQ0bRWtaEjjVzM4EViYS0+Pc/VkzuwwYY2YHAY8QOzM3BF5w96vN7EBijdpjRAL7Y+B9YqMAxIaA9czsUmC6u08auLBERESkVdWcmLn70Y0cSAu6jJjVup94dHk+8Pt0bhfg18CJxI7Ld4AHgMoM2mQikfuf9N5HgW+5e+Vh9m+Bc4DnibVpPZbL6OgoM2Hk5v0OSupj2vRZzR6CiIjkUK+amEvzdHSUy2+/nf9a881ejDmQFGs+KdZ8KkqsRYkTmh9rPZqYDyVmibYj+j7OlT3v7gO+Ziotpj+LKOT6WWAjdx/byXUTgF61QOrjeA4G9nX3ZdLro4Ct3X2lRn6uiIiI5ENv1pgdS9QyO4F4pFfZDPAj4Dd1H1ltvk08VtwQeIF4pNiZEcCUARpTw7RDc+96aMc41dRcRETqoTeJ2Q+Bvdz9JjM7CbjW3Z83s3HApsSaqYH2JeB1d7+ns5NmNtTdZ7j7xAEeV92piXlrU1NzERGph94kZosDT6fvPwAWTt/fBIyq56BqkUpQ7JS+LwMvErsdxxGzYzul1yOqH2Wa2ULAaOD7wKeJnZUHuftD6fzOwOnA94DTgC8Si/t3dffxmTH8CjgQmB+4mpi162q86wO3A1/IVvw3s+OBLXJYbkRERER6qTcFT18iSkgA/Af4Zvp+baAZz3D2A44hSlAMJx5XAuxA7HJcjzkNyD9iZoOAG4HPAd8BVgXuBP5hZsMzl85NFIjdlYhxYaJfaOU+PwSOIwrvrgY4kaR1yt3vJHZhfjSmtEZuR2LHp4iIiBRcb2bMrgE2Jgqlngb8ycx2JxKc0Q0YW7fc/T0zmwzMrsxApRZJ4939oG7euhFRqX+Yu1cSyt+Y2RbAT4gSGBA/m33c3dO9TwIuyBSd3R+4yN0rj3CPN7ONiMerXTkP2C3zGd8EFgMurTVuERERya/e1DE7LPP9VWb2MrAO8Ky739CIwfXRwz2cXx2YF5hY1etyHmC5zOvplaQseQ0YCnyG2GSwApFoZd1L94nZRUQC9/W0Lm5X4P+5+9s9jFnaQG+b4bZqA91GUKz5pFjzpyhxQuvGWlNiZmZzEbM6h7v78wDufj9RfLXV9LT7cjDwJvGos9r7me+rK4hWCr71ud+lu080s+uAXc3Mge8CW/T1ftJaelsPp9k1dAaSYs0nxZo/RYkTmh9rVxUIakrM3H2mmf0vseaq3T1CbGTocPcuF+vXYBzRP/OCzLG1anjfuUR7qxeAN4Db+jEGERERyZHerDG7GtgSaGiR1gFwG3A3cG3aVfkMsASwGXCbu/+rxvucBlxsZg8CY4kit2vSdS21iluBt4lNAyPdvaPXEYiIiEgu9SYxewk4wszWAx6i6pGhu59Sz4E1iruXzezbxI7Kc4nF928SydrFvbjPFWa2LHA8sWbtOuAUYOcaPv9C4Cjgwj6EICIiIjlVc69MMxvfzemyuy9bnyHln5mdBXzJ3Tet9T0dHeXy4ME99jqXJulL5f9mr28YSIo1nxRr/hQlTmh+rP3ulenuX6zriAooFbZdkahd9sPevn/ixMl1H1OrafZ/KCIiIs3Um0eZ0g9mtg5wMzAf8Kq735iOnQUsD9zj7ht2d4927CHZF3mLU300RUSkVjUnZmZ2QRenysCHRDeAK9z9tXoMrBVVWjW5+/x9ePtpRJHew5mzPu804HFgc3oo86Feme1LfTRFRKRWvZkxG0bU/uoAnkrHViLaHz1M7Ng8xszWc/fH6jrKfPgScIa7v9zDMRERESmo3iRmdxPNy3dz96kAZjYvsbPxceDbxK7Gk4nWTW0rNRw/kUg8ZxN9MC8G/pjOV3ZMHO3uR5nZDkTvzuWJvqH/BPZ391fNbBmgsnHigjTzuAtzdmR+dMzdxzQ6NhEREWldvalivx9wTCUpA0jfHw8c4O4zgFFEH8q2ZWafAq4F7gK+StQmOxX4F9EfcyrRNH04c2q6DSXqkn2VaIy+KPCndO7ldO3U9P7hwJWdHLuisZGJiIhIq+vNjNn8RAIxrur4EukcREujdt9QsCCwMHB9pf0UUYQWM1uVKA3yRvYN7p5df/eCmf0MGGdmn3f3V4A30izbe5n3TunkmORUZ/3YWrVPWyMo1nxSrPlTlDihdWPtTRJ1DXB+qpb/YDo2gnjkd3V6vQbwbP2GN/Dc/R0zGwPcbGa3A7cDV7n7S129x8xWI2bMVgE+S6y7A1gKeKWxI5Z20FkJkCKVBlGs+aRY86cocULzY+2qAkFvHmXuRZR7uBR4Pv25FLgJ2DtdMw7Yvc+jbBHuvgvxCPNOotG4m9k3O7vWzOYjfi5TgZ8Qyepm6fTQxo9WRERE8qI3BWanAnuZ2UHAcunw8+4+JXNNbnZjuvvjxKaGUWb2d2An4AZgSNWlyxNryg539/EAZrblQI5VRERE8qHX68FSIvZEA8bSEszsi8CeRO/LV4Flga8QhWAnAPOY2abAo8Qs2UvAdGBfMzsDWAE4duBHLiIiIu2u28TMzK4DdnD399P3XXL379Z1ZM0zFSgROycXJRqcXwaMcveZZnY2seNyEeaUy9gJ+B2wD5G0Hkg84q2bjo4yE0ZuXs9bygCZNn1Ws4cgIiJtoqcZs7eJyv6V73PP3d8kiuV2df5nwM+qjl3BJ8tdDKq65hPdAnrbQUC9MkVERPKt28QsLYL/xPfNYmY3AJPcfecarl2GKOw6wt0fqn5dw/uPArZ295X6M2YRERGRWrV7zbHeqBR6nVTj9SeRKv23irw19+5K3uNUU3MREelKb5qYz0NU/98YWIyqUhvu/pX6Dq2+3H02UHMhV3f/AFqn97SamOeHmpqLiEhXejNjdibwA2JR/D3MWXvWEKkP55nA1sAU4LSq80OJ3Y/bE0Vd/w0c4e43d3G/Zah6lGlmyxMFcjcgymA8Cezh7k9WP8o0s8HAr4E9iMT02fR513Z1/3S8DGzj7lel178FdiM6JvwXuMXdd+zrz0lERETyozeJ2feJBOO2Rg2myknApsBWRNmKI4H1mdNl4EKintqPier63wauN7MRqQZZt8xsSaIf5t3pc94lOhdU1ymr2A/4JVFo9yFgB+BqM1u91vptZrYVcDCwHZEELgasVct7RUREJP96k5hNJdZpNZyZzU/MKu1amQEzs11I7Y3MbDkiuVkm0yrpdDPbhKhBtvcn7/oJ+xAzcdukBuzQfTupg4GT3P3y9Pq3ZrZ+Or5DjaEtDbxOzJLNJGqg9bgRQURERIqhN4nZicCBZraXuzf0MSYxEzYUuLdywN0/MLMn08vViHIUT5tZ9n1zA/+o8TNWBe7KJGVdMrMFgSWJ2bWsu4iZulpdScy8jTezm4laZ9e5+/Re3ENyYOGF523ZBrqNoFjzSbHmT1HihNaNtTeJ2abAesBmZvY0MDN7coALzA4m1riNqB4HMNDb3SpJakf6+lH9MjObK3uhu79skUluDGwCnAwcaWZrZltbSf69++7UQtVsU6z5pFjzpyhxQvNj7aoCQW8Ss0nANXUZTc+eJxKutYAX4KNm4Sulc48SCdAS7n5HHz/jUWAHMxva06xZ6nzwGrAOcHvm1L07dlAAACAASURBVLrA0+n7ienr8Mz5VTq514fAjcCNZjaS2Cm6DnBLn6IQERGR3OhNE/MBKzCbHlueTzQQnwi8BvyWtDDf3Z81s8uAMamp+iPEzswNgRfc/erO7/wxZxIL+f9iZscTOyRHAOO6WMw/GjjGzJ4DHibWla1HPFbF3aeZ2X3AIWb2PLAQcEL2Bma2M/Ezv58oxbEtkYA+V8vPRURERPKtx8TMzCbTeWmM9wAHTnT3Rsz2HAzMR8zSTSWKvc6XOb8LUb7iRODzwDvAA0BNM2ju/mpavD86vadMKpfRxVv+ACyQPm9xIvatqnaA7gqcBzxIzOztDdyZOf8ucAix43QuYrZtS3cfX8uYRUREJN8Glcvdr+NPDbo7szCwOjHrs7W7X1/nsUlGR0e5PHjwoJ4vlJZXqfzf7PUNA0mx5pNizZ+ixAnNj3XYsAUeBr5WfbzHGTN3v6i782b2KHA4oMSswdTEXEREJN/q0SvzRmL9lzRY3ntIVhQlTuh/rOq7KSKSL/VIzOYBPqzDfQojbQI43d3nr/U96pUpnVHfTRGRfBnc8yU9+ilQU0siEREREelaLbsy/9DFqYWIUhHLEj0sC8PMxhI7Kt8ldnF2ABcDv3L3DjP7DHAq8F1iRvFuYD93/7eZbUj0+aw0OAc42t2PGsgYREREpPXU8ihz5S6Ovw/8HTiroOUetgdOA75OFJK9nKhv9idgDGDA94j6aMcDN5lZCbgH2B/4HdF6CtDTKBEREaltV+ZGAzGQNvS0u1c2PTxrZrsDG5vZQ8RM2QbufieAmf2EaFi+vbufZ2bvAWV3f6MpI5dcacVeb9VatSddIyjWfCpKrEWJE1o31nos/i+qJ6pevwYsBqxAPNrMNmB/LzVgX3HghidF0Q7lRYpUBkWx5lNRYi1KnND8WLvalV+Pxf9FVd08vUzPP8/uq/mKiIhIoSkxq79xxM917coBM1uQWKtXaXg+g9T3U0RERKRCjzLrzN2fM7NrgXPMbA9i5+bxxGaJy9NlE4B5zGxT4FFgqrsXY+5YREREuqTErDF2IcplXMecchmbufs0AHe/x8zOJnZwLgIcDRzV3Q07OspMGLl5I8csbWja9FnNHoKIiNRRj03MpTV0dJTLb7+d/6oazV6MOZAUaz4p1nwqSqxFiROaH2tXTcy1xkxERESkRbTto8xUQf8OYJi7T2rycHrFzCYQvTJP6s37itLcuyhxwsDHqqbnIiKtrWmJmZkNdfcZzfr83jKzudy9ukTGgFETc6kHNT0XEWltA5aYpf6S44ApwE7ABDPbCRhN9NqcBtwOHFCpiG9mKxOL6EcQj12fJ9oZjSdmywAmmhnARe6+s5ltBvwaWImoG/YgsL+7j0v3XCa9f4S7P5QZXxnYxt2vylzzY2B3ovTFL83sT8DpwHrEov0XgJPc/cKqOLvrozkWWBoYbWajAdx9UN9/siIiIpIXA73GbAdgEJHY/AK4E3gKWAPYBJgfuNbMKuO6HHg9nV+F2Ln4IfAysFW65svAcGC/9Ho+IplbA9gQeA+43syG9mG8JwBnEhX7/x+xw/IR4Dvpc08jymJsXPW+7YFZRB/NfYlkctt0bkvgFeCYNO7hfRiXiIiI5NBAP8oc7+4HAZjZMcDj7n5I5aSZ7Qi8Q+xSeICYWTrJ3Z9Jl/wnc+076du3smvM3P2v2Q80s12IGmJrAHf1crx/dPerqo6Nznz/f2b2DWA7YravotM+msCf3P0dM5sNTFavTBEREcka6MTs4cz3qwPrm1lnS16WIxKzU4Dz0iPP24G/ZpK0TpnZcsCxwJrAMGJWcDCwVB/G+1D2hZkNAQ4lZr8+B8wNDAXGVr2vqz6aIk3XjKa9rdosuBEUaz4VJdaixAmtG+tAJ2ZTMt8PBm4EDu7kujcB3P0oM7sM+BbwTeBIM9vL3S/o5jNuIB4V7gm8SjxSfJpIoCDWfEE8UgViYX8N4yWN9SDisemTwAfA7/hk0tWXPpoiA6IZdXuaXS9oICnWfCpKrEWJE5ofa1e78ptZLuMR4IfAi93tdnT354DngD+Y2VnAT4ELiH6TkOk5aWaLAMsDe7v7HenYanw8zonpa3Zt1yo1jnld4Hp3vyTdexBQIhb694Z6ZYqIiMgnNDMxO4PY8XiFmY0iEqZliWTtIGKm6yTgSqK35OJEYnR/ev+LxEzU5mZ2PbGr87/AJGB3M3uZeNw4Ot0LAHefZmb3AYeY2fPAQsQi/1o8C2xrZuumz/k58EWi32VvTADWM7NLgentVodNREREGqNpj9fc/TVgHeLR4k3Av4lkbXr6Mxv4DDAGcOAa4F7gwPT+V4EjiQbhbxIFWzuI9V9fIXZ7ngH8Jt0va9f09UHgHOCIGod9HLH27e/EjtIpwGU1Bz3Hb4EvEOU/JvZwrYiIiBSEemW2iY6OcnnwYJU7k/5pVuX/Zq/lGEiKNZ+KEmtR4oTmx9pVr8y2bclURBMnTm72EBqu2f+hDKQixSoiIrVRYtYPqYr/U+6+70B8XlF6SBYlTshfrOrFKSLSP0rM+mdLPlkaoyHUK1PagXpxioj0jxKzfnD3d3q+SkRERKQ2Ssy6UWND8o8eZaZ+nEcRvTKXIArcnuruf0jnV6Sbpu0iIiJSbKpG37PuGpJXuwjYkSjpsQKwG6n4rJkNp+em7SIiIlJgmjHrWZcNybMXmdn/AD8CvuXuN6XDL2Qu+Rk9N20XaXud9Z5r1Z50jaBY86kosRYlTmjdWJWY9azWhuSrEo867+jiPrU0bRdpe52VAClSaRDFmk9FibUocULzY23FXpntol4NyXts2i4iIiLFpsSsfh4jkq+NiBZT1Wpq2i4iIiLFpcSsTtz9WTP7C3Ceme1HJGKfB5Zx90vooWm7u+e/rL+IiIh0S4lZfe0IHAv8AVgUeAX4PUTTdjNbBziBmFGbB3gJuIVPNln/hI6OMhNGbt6gYYvUx7Tps5o9BBGRtqYm5m2io6Ncfvvt/NdUb/ZizIGkWPNJseZTUWItSpzQ/Fi7amKu+ln9YGZjzOyGZo9DRERE8kGPMvtnP2DQQH1Y3hped6UocYJirZWao4tIUSgx64SZDXX3GT1d5+7vDcR4QE3MpdjUHF1EiiI3iZmZrQ+cCKwEzAYc2NXdnzKzrxOL7kcA/wWuAw5x9/fTe8cC44ApwE7ABDN7Dpjb3bfKfMZg4EXg9+5+ipmNARZ19++k84OIdkx7AUsROy8vcffD0vnPAScD30y3vAfY392fa8gPRURERNpKLtaYmdmngGuBu4CvAmsCpwKzzWxlYufjdenclsAqwAVVt9mBeCy5HrG78lJgczNbKHPNBsBwqtoxZfwO+A2RBH4Z2AZ4OY1xXqIrwIfpPmsDrwO3pXMiIiJScHmZMVsQWBi43t2fT8eeATCzi4Er3P3kysVm9jPgUTNbzN3fSofHu/tBmWueA94DtgbOT4e3B/7h7q9XD8DM5gcOIGbAKknff4B70/c/IhK/Xdy9nN6zJ/AW8B3gL/2IX0RERHIgF4mZu7+THivebGa3A7cDV7n7S0SPyi+Z2baZt1QW7C9HJEYAD1fdc5aZXUEkY+eb2dzAVsSC/86sCMydPrszqwNfBCabWfb4vGkcItKNVmw23JlWbYzcCIo1f4oSJ7RurLlIzADcfRczOxXYDPgucLyZfZ94XHseqdBrlVcz30/p5PylwL1pbdiawFDg6j4OcTDRtulHnZx7p4/3FCmMdqmt1OzaSANJseZPUeKE5sdaiCbm7v448Dgwysz+TizkfwT4srv/pw/3e8DM/gNsR6wJu9bdu9ocNo6o4L8x0Nli/kfSfSa5+7u9HYuIiIjkXy4SMzP7IrAnscD/VaIH5VeAs9Kx+8zsbOAcYDKwPLCFu+9Zw+0vA34KLENsHOiUu082s9OAE8xsOnAnsAiwurufle5zMHCtmf2WaMf0BeB7wNnamSkiIiK52JUJTAVKwJXAs8BFRCI0yt2fANYnEqt/EjNqJwBv1njvSwEjNgLc0sO1hwGjiJ2Z44C/Eo3McfepaRwvpHE+k8b5GaKEh4iIiBScemW2iY6Ocnnw4AFrMiDSUtqp8n+z160MJMWaP0WJE5ofa1e9MnPxKLMoJk6c3OwhNFyz/0MZSIpVRESqKTFrI0Xpq1iUOEGx9lY7zZyJiPSFErMmMLNlgPHACHd/qJb3qFemiHpmikj+5WXx/4Aws6HNHoOIiIjkV6FnzMxsPqKkxpZEgdlTgXWIWmM7m9kEYAzRkHxL4FZgmxqaom8G/JpoqF4GHiRaNY1LHz0+fX0wdQH4p7tv2MBQRUREpA0UfcbsZKKh+A+AbxBNzteruuZAorTF14DDa2yKPh+R5K0BbEiU2rg+M+O2Rvq6GdEUvcv6aCIiIlIchZ0xS03HdwV2dPdb07HdgFeqLv2nu5+YeV+PTdHd/a9Vn7UL8D6RkN0FTEyn3nb3N+ocmkiutWJvu6xW7b/XCIo1f4oSJ7RurIVNzIjG4XMBD1QOuPsUM3uq6rrqxfk9NkU3s+WAY4n+msOImcnBxCNREemHVi+7UaTSIIo1f4oSJzQ/1kL0ymyQ6ubmtTRFv4GYedszHZsFPE00QRcRERHpVJETs+eBmcQC/hcAzGxeYsH+8928r9um6Ga2CNGLc293vyMdW42P/6xnpK9D+hOAiIiI5EthEzN3/8DMLgBGmdkk4HXgCGJGrLs+VaPovin6f4FJwO5m9jLwOWA0MWtW8RYwDfhm2vn5obu/V8fwREREpA0VNjFLDiZ2UF4HfEA8nlwc+LCrN7j7E2a2PnAc0RR9CDHjdk0635HWn/0BeAr4D3AQ0dC8co9ZZvYL4LfAkcC/iN2bXeroKDNh5OZ9ClIkL6ZNn9XzRSIibUxNzDPMbG7gRWB0dtdlK+joKJfffjv/Nc+bvRhzICnWfFKs+VSUWIsSJzQ/VjUx74SZrQqsQOzMXAA4JH29wszGAk+5+77NG6GIiIgUSaETs+RAwIg1YI8B67v7K6kif0spSsProsQJirVe1NxcRPKi0ImZuz9KJ9OIrUhNzEW6pubmIpIXhU3M0qPKZ4DpwI7p8HlEz8uOTq7fAdiP2IE5jVj4v7+7v5q5ZnngRKLN0xDgSWAPd38ynd8F+CWwLPAS0afztM4+T0RERIqn6L0ytyd+BmsTxWD3APbv4tqhxA7KrwLfARYF/lQ5aWZLEu2WysCmwGrAGaRaZWa2O/A7YifmCsROzUOAvesck4iIiLSpws6YJa8Dv3D3MvCMmZWINWenVF/o7tkm5S+k/pjjzOzz7v4KsA/RJWAbd68UkH02857fAL9y96vS6/FmNpJIzE6va1QiBdQqPe9atf9eIyjW/ClKnNC6sRY9MbsvJWUV9wLHmtmC1Rem6v1HAqsAn2VOf8yliPZLqwJ3ZZKy7HuHAV8AzjGzszKnPpW5j4j0Q6ts8W/2FvyBpFjzpyhxQvNjVa/MfjCz+YCbgduAnxCV+xclCsPW0v+y8sh4L+CeRoxRRERE2l/RE7M1zWxQZtZsLeA1d3+/qlzG8kQidri7jwcwsy2r7vUosIOZDa2eNXP3N83sNWA5d7+4IZGIiIhI2yt6YrYkcKqZnQmsTOyYPK6T614idm/ua2ZnEIv3j6265kxiRuwvZnY80TNzBDDO3R8jHoP+0czeBf4GzEVsEPicu59Q98hERESk7RQ9MbuM2DV5P7Gb8nyiX+bHuPtEM9uJ2FW5D/AEsUngpsw1r6YemqOBO9L9niR2euLu55nZFCL5O4EoufFvalz4r16ZIl1TD00RyYvC9spst5ZL6pWZP4o1nxRrPhUl1qLECc2PtatemUWvYyYiIiLSMor+KLPpzOwp4Cp3P6qna4vSV7EocYJizSvFmk/ZWNWfVRqlsImZu2/Y7DH0hnplioi0DvVnlUbRo0wRERGRFlHYGbNqqYjsWcCWRGulU4F1gEnuvrOZfSYd+y4wD3A3sJ+7/ztzjy2Bo4ESUYT2bOB3lTppZrYYcC7wv+n80QMTnYiIiLQDzZjNcTKwAfAD4BtEs/L1MufHAGsC3wPWAKYCN5nZpwHMbHXgSuBqoibaocBhwL5V9/gSsAnwfWBHYJnGhCMiIiLtRjNmgJnND+wK7Ojut6ZjuxE9MDGz/yFmyjZw9zvTsZ8QhWe3B84j6pr9092PTLd9Nr3vEKKwbAn4FrCuu9+d7rET8MLARCkiIvXUig2w+6tVG3s3QqvGqsQsLEdU4n+gcsDdp6QdkxCV/juIJueV8++Z2ZPAiplrqlfn3wUcmZqiV+6R/YwXU6smERFpM3ms99Xs2l4DqdmxdrWjWY8y+6+WCr3lLr4XERER+YgSs/A8MJPobQmAmc0LrJRejiN+Vmtnzi9IrCV7OnPNOlX3XRd4xd0nA8+ke6yRucdSRL9OERERET3KBHD3D8zsAmCUmU0CXgeOIBKpsrs/Z2bXAueY2R7Au8DxwPvA5ek2JwMPmtlR6dgI4CDg8PQZbmY3Ze4xDTglfRURERFRYpZxMDAfcB3wAdHMfHHgw3R+F6JcxnXMKZexmbtPA3D3R8xsG6IExuHAm8BIPt6kfGeiXMY/gEnp2sVqGZyamIuItI5p02c1ewiSU4VtYt4TM5sbeBEY7e4nN3s8amKeP4o1nxRrPhUl1qLECc2Ptasm5krMEjNbldg5+QCwIPAXYrcmwEbuPrYBn3kDqYBtT9d2dJTLgwcPqvcQREREpBON7ofaVWKmR5kfdyBgwCBgXqK22d+Bd5o5KFCvTBERkYHUrH6oSswSd3+UlLma2f7AAe5+YXNHJSIiIkWixKyKmY0Bdkrfl4l1ZgaMArYDFgIeAw5297sy71sfGE20cnqP2Jl5iLvPSOfnBc4EtiZ6cZ42MBGJiIhIu1Ads0/aDziGaMc0nCh7cSKwLfFoc1XgSaJP5nAAM/sc8cjz0XR+NyKJOyFz35OATYGtgI3Tdes3PhwRERFpF5oxq5JaLU0GZrv7G2Y2H/Az4KfufiOAme1FNDrfh6h3tjfwGrC3u3cA48zsUKJm2W+IBHg3YFd3vzndYxdSL04RERFpPc3opanErGeVPpp3Vw64+2wzu5eP98m8LyVlFXcBQ4EvEZsJhvLxXpsfpF6bIiIi0oIaWU5DvTIbo7d9MkVERES6pMSsZ88DM8j0wTSzIUTfzGyfzLXMLPvzXDe973nm9OJcK3OP+ZjTi1NEREREjzJ74u5TzOws5vTRHA8cQLRrOjNddiawP3CmmZ0GLEtqx+TuUwHM7Px0j4nEerTfAkMGNBgRERFpaUrManNI+nohsDCx+3Izd38dwN1fNbNvEeUyHiOanF9OamCeVHpxXgNMBf6YXtdEvTJFREQGTrP6oaolU5tQr8z8Uaz5pFjzqSixFiVOaH6sXbVk0hozERERkRahxExERESkRSgxExEREWkRSsxEREREWoQSMxEREZEWocRMREREpEUoMRMRERFpEUrMRERERFqEEjMRERGRFqHETERERKRFKDETERERaRHqldk+JgIvNnsQIiIiUhdLA8OqDyoxExEREWkRepQpIiIi0iKUmImIiIi0CCVmIiIiIi1CiZmIiIhIi1BiJiIiItIilJiJiIiItIhPNXsA0j0z2xv4JTAc+Dewv7v/q7mjCmZ2GLAlYMB04D7gMHd/KnPNIOBIYA/gM8D9wD7u/u/MNZ8B/gB8Nx26Dvi5u7+buWZl4HRgDeAd4BzgWHcvZ67ZCjgWWA54Hvi1u19T57Arn3UY8DvgDHffN2+xmtlwYCTwbWAB4AXgZ+7+zzzFamZDgKOAHYj/xl4HLgOOcvdZ7Ryrma0PHAysDiwJ7OLuYzLnWyauWsbS11jNbC7gOOBb6fPfB+4ADnX3lzL3mBs4CdgO+DRwO7C3u7+SuWYp4AzgG8A04HLgYHefkblmA+AU4MvAa8CJ7n521Xj7/Hu9p3+uVdeeQ/xMf+nuJ7VTrLXEaWYl4vfUN4ChwDPA9u4+rl3i7IxmzFqYmW0LnEYkAKsC9wB/T/8itYINgTOBrxP/Us8CbjOzz2au+RVwEPBzYATwFnCrmS2QueZyYDVgs/RnNeCSykkzWxC4FXgz3WM/4j+AAzPXrA1cQfxPdZX09UozW7Nu0c75rLWIX3ZPVJ3KRaxmtjBwNzAI2BxYIcX0Vt5iBQ4B9gF+ASyfxrAPcFgOYp0feCp91rROzrdSXLWMpa+xzpvGfXz6+j3gC8BNZpadnDgV2Ir4n/h6wILADSl5ryTxNxJ/UVkvXbc1cHIm1i8CfyN+V68KnAD8MSWmlWv6+3u9p3+ulc/ZmkiWX+vkdDvE2m2c6fPvBsYT//9ZCTgC+KDN4vykcrmsPy36p1Qq3V8qlc6tOvZcqVQ6odlj62K885dKpdmlUmmL9HpQqVR6vVQq/TpzzadLpdLkUqm0Z3q9QqlUKpdKpXUy16ybjll6/bNSqfR+qVT6dOaaI0ql0qulUmlQen1FqVS6tWo8t5VKpT/VOcaFSqXS86VSaaNSqTS2VCqdnrdYS6XS70ql0t3dnM9TrDeUSqWLqo5dVCqVbshTrKVS6YNSqbRzK/4zrGUs/Ym1i2tWTHGsnF4vVCqVZpRKpe0z13yhVCp1lEqlb6bX30qvv5C5ZodSqfRhqVRaML0eVSqVnqv6rPNKpdK9mdd1+73eVaylUmnp9M9ghVKpNKFUKh2cOdd2sXYWZ6lUurxUKl3WzXvaLs7KH82YtSgzG0pM4d5SdeoWYoaqFS1AzML+N73+IrAEmRjcfRpwJ3NiWJv4G849mfvcDUypuuZf6b0VNxPT28tkrqn+Wd1M/X9W/wdc5e53VB3PU6zfB+43syvM7C0ze8zM9k2PmyBfsd4FbGRmywOY2YrE377/ls7nKdasVoqrlrHU24Lpa+V31erAXFVjeBkYx8djHZeOV9wMzJ3eX7mms1i/ZmZzDcTv9TQL+CfguMojvSptH6uZDQa2AJ42s5vMbKKZPZhmriraNk4lZq1rUWAI8Ygg603il1grOg14DLg3va6Ms7sYlgAmZtejpO/fqrqms3tQwzV1+1mZ2e7Al4jp8mp5inVZYG9iXdk3iX+uI4lHfNlx5CHWUcSjuafNbCaxNuQidz+zahx5iDWrleKqZSx1k/5HejJwfWat0RLAbGBSD+OsHuOk9L6eYv0U8Tt9IH6vHw1Mcvezujifh1gXIx51Hk4kQJsSyehlZrZ5ZnxtGacW/0tdmNkpwLrAuu4+u9njqTczM2L9wLruPrPZ42mwwcBD7l5ZZ/Womf0PkZid3rxhNcS2wI7Aj4mkbBXgNDMb7+7nN3VkUndpNulSYGHmbGjIDTPbENiZ+Pc4zyqTSte6+ynp+8fM7GvAvsS6sbalGbPWVcnaF686vjjwxsAPp2tm9nti0eQ33P2FzKnKOLuL4Q1gWOYxWWWH1mJV13R2D2q4pl4/q7WJvxn928xmmdksYANg7/T921Xj6mwM7RLr68DTVcfGAZWFrHn65zoaOMnd/+zuT7r7JcTuq0pSmqdYs1oprlrG0m+ZR3xfATZ297czp98gZj0W7WGc1WOszJb0FOss4nd6o3+vb0jaXZz5PbU0MMrMKrODeYh1Uvqcnn5PtWWcSsxaVNqq+zAxRZu1KR9f89FUZnYac5KyZ6pOjyf+xdw0c/08xO6XSgz3ElPSa2fetzYwX9U166X3VmxK7DaakLmmkT+r/wesTPxNtPLnIeDP6ftnyU+sdxMlULJKwIvp+zz9c52X+KWaNZs5vxvzFGtWK8VVy1j6JZXMuIJIyjZy9+r/YT4MzKwaw+eJHcnZWFdIx7NxTE/vr1zTWawPufvMAfi9fiYRY/b31GvA74GN0zVtH2u694N0/3uqbeMcVC6Xe75KmiItZLyEWO9zN7AXsBvwZXd/sbv3DgQzOwP4CbFYPPs3lw/c/YN0zSHEOoBdiOTlCGB9wNx9crrm78DniRIUEAvsJ7j7Fun8QoADY4l6RCVgDHC0u5+crvk6sVj4CCKJ+gFwDPHo8f76Rw9mNhZ4KlPHLBexmtkI4hfKUcT/zFYFzgMOd/czchbrGGATYE/iUeaqaZwXu/tB7Ryrmc1PrImE+Oc5kqhD9o67v9RKcdUylr7GSiQmVxFlOLbg4+Uj3qtsXDCzs9L5nYkZ8FOImmqru/vsVFrhMWAiUdpjEeAi4Gp3/3m6xxeJEg/nEvXc1iGSpe3c/a/pmn79Xu/pn2sn108ATq+qY9bysdbw7+/3gb8Qjy7/AWyUPv/77n5ju8TZGc2YtTB3vwLYn/gl9RixhuvbrZCUJXsTOzFvJx5/Vf4cnLnmROJva2cQM0zDgf+t+mX7Y+BxYqfLzen7n1ROuvt7xN8+lkz3OINYvHtK5pp7gB8R/wE+Qawb2rZRSVkXchGruz9IJNs/JH4hHQ/8hvhllKtYibpZV6XYxqXPPxf4dQ5i/RrwaPrzaWJR+KNEUtRqcdUylr7G+nmidtmSxMxG9ndVdhff/sA1xF9G7iZ2pG5RWTObvm4OTE3nrwD+yv9v7+5D9ZzjOI6/Z20U5R+tqEWoL0tmZA15+EeUh388/ENHrfjDQmwSEsliHvKQhzzMQ1FIMVKrMZqnRp4367vGUIwW/hjW0Dn++F43V7fZzr0zzrXO+1V397mu+3df9+86p3Odz/ld1+/6to53mbmeuinzCdQx+1rg0t4f8KbNWI/r2/u5jsausK/b3M/MfIH6h2EB8Cn1uzzUC2W70H7+gyNmkiRJHeGImSRJUkcYzCRJkjrCYCZJktQRBjNJkqSOMJhJkiR1hMFMkiSpIwxmkiRJHWERc0kTXlMB4IKtvDQrMz/6n7sjaQJzxEySyivUHefbj1XtBhExdRz6JWkCccRMksqW/uLWEfF6RKwBfqFG1L4Ejo6IGcBtVJmWzVRZsst7729q8C2i6uVB1d/bHTg0yWbgZwAAA1RJREFUM0/qbZtWrdVm3ePAPpl5erM8CbiSquW5H7AOWJSZTzavH0AVAT+bqs93XNPHyzJzWWu7h1Blj04EJlMlbC6i6ga+Ckxv73tELKRK1xw+6DdR0tg4YiZJ23Y+MAk4HhiKiH2potyrgNlUEfS9gCUR0TumzgcupALVMVQYOm8HPvsmKtzNA2YANwMPRsRpfe0WAvcAM4H3gKebItBExH7Am8AIVdfySKom5eTMXAF8TtWvpGm/W7O8eAf6K2mMHDGTpHJqRPzcWn6jeV6fmfN7KyPiRuDjzLyqtW4I+JEqvPwuVdD41sx8tnn9MuCUQToTEXsCV1DFvP/qS0TMpoJau1jznZn5UvO+a6hgdQQVyOZRI37nZOZvTfu1rfc+QoW/W5vlU4BpwJOD9FfSzmEwk6Sygjq917MZeAp4v6/dUcAJfSGu56CISOr6tHd6KzNzOCJWAtMH6M8MYA9gaUSMtNZPoU5Xtn3S+vrb5nla8zwLeLMVyvo9ASyMiGMz821gLvBCZv4wQF8l7SQGM0kqv2bmuvaKiIAabWrbjRqtWrCVbXzP6C8RGaZOkbZN6fscgDOAr/va/f5vy5k50vR7VP3IzI0R8SIwtwmVZzafKWkcGMwkaTAfAOcCX2Vmf0ACICI2AHOA5c3yJOp6tA2tZhupkbW2mfw9GvYZsAXYPzOXj6G/HwLnR8TUbYyaPQw8B3wBfEfNUJU0DgxmkjSY+6gL+5+JiEVUwDqQCmvzM3MTcDdwdUSspWZAXkyFsHYwWw7cFRFnAklNFJhOE8wyc1NE3A7c3gS7FdQkgznAcGY+NMr+3k/N2Hy2mW35E3A0sKZ1j7ZlwA/A9cAtmTk82LdE0s7irExJGkBmfkvdlmIYWAqspsLaluYBcAfwGHVh/UrqWPtU36YebT3eAjYBz/e1uQ64gTptupoKUGdRt8gYbX+/oW7rMRV4jRpBuwT4o9VmpOnvlOZZ0jiZNDIysv1WkqQxiYh7gcN69zHrmoh4ADg4M08e775IE5mnMiVpAouIvakZoEPU6VhJ48hgJkkT2xJqYsLizHx5e40l/bc8lSlJktQRXvwvSZLUEQYzSZKkjjCYSZIkdYTBTJIkqSMMZpIkSR1hMJMkSeqIPwHz2i0IVw/JlgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "positiveUnigram_df = pd.DataFrame(positive_unigram, columns=['Unigram', 'Count'])\n",
        "plt.barh(positiveUnigram_df['Unigram'], width=positiveUnigram_df[\"Count\"])\n",
        "plt.xlabel('Frequency')\n",
        "plt.ylabel('Unigram')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uFuZBqX1VJJ_"
      },
      "source": [
        "#### Q5: TODO - ask a question here and answer it below"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "What word is often used in negative reviews?"
      ],
      "metadata": {
        "id": "grGkgl2eTWAu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0XCOkgIHVJJ_"
      },
      "outputs": [],
      "source": [
        "texts_negative = \" \".join(review.lower() for review in df_negative[\"Review\"])\n",
        "# remove punctuation\n",
        "tokenizer_negative = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "tokens_negative = tokenizer.tokenize(texts_negative)\n",
        "\n",
        "# remove stopwords\n",
        "tokens_negative = [token for token in tokens_negative if token not in stopwordz]\n",
        "\n",
        "negative_unigram = nltk.FreqDist(tokens_negative).most_common(20)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xZWIg8jJVJJ_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "78b8e038-9205-49f8-f262-4c687a3f4fcd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Unigram')"
            ]
          },
          "metadata": {},
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "negativeUnigram_df = pd.DataFrame(negative_unigram, columns=['Unigram', 'Count'])\n",
        "plt.barh(negativeUnigram_df['Unigram'], width=negativeUnigram_df[\"Count\"])\n",
        "plt.xlabel('Frequency')\n",
        "plt.ylabel('Unigram')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VxoO755tVJJ_"
      },
      "source": [
        "## Inferences and Conclusion\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After further analysis, we have found multiple observations from our findings. Although, not all of our initial assumptions resulted in any useful outcomes such as correlation between review rating and word length. However, we did found some interesting results from observing what words were often used in both positive and negative reviews. "
      ],
      "metadata": {
        "id": "3KqtSWhIUe_u"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xfuc6sJgVJKA"
      },
      "source": [
        "## References and Future Work\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In my opinion, our findings barely scratch the surface of the topic. Indeed, we could perhaps find out the reason of the problems for all the negative reviews by doing a thorough analysis unlike what we did just by using datas from review and not actually finding the cause. Hopefully, this would help the business owners to improve so they could grow their restaurants."
      ],
      "metadata": {
        "id": "15sdbzK4VZ13"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}