Sentimental_Analysis_Twitter_Data_(2_vast).ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
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              "           textID                                               text  \\\n",
              "0      cb774db0d1                I`d have responded, if I were going   \n",
              "1      549e992a42      Sooo SAD I will miss you here in San Diego!!!   \n",
              "2      088c60f138                          my boss is bullying me...   \n",
              "3      9642c003ef                     what interview! leave me alone   \n",
              "4      358bd9e861   Sons of ****, why couldn`t they put them on t...   \n",
              "...           ...                                                ...   \n",
              "27476  4eac33d1c0   wish we could come see u on Denver  husband l...   \n",
              "27477  4f4c4fc327   I`ve wondered about rake to.  The client has ...   \n",
              "27478  f67aae2310   Yay good for both of you. Enjoy the break - y...   \n",
              "27479  ed167662a5                         But it was worth it  ****.   \n",
              "27480  6f7127d9d7     All this flirting going on - The ATG smiles...   \n",
              "\n",
              "                                           selected_text sentiment  \\\n",
              "0                    I`d have responded, if I were going   neutral   \n",
              "1                                               Sooo SAD  negative   \n",
              "2                                            bullying me  negative   \n",
              "3                                         leave me alone  negative   \n",
              "4                                          Sons of ****,  negative   \n",
              "...                                                  ...       ...   \n",
              "27476                                             d lost  negative   \n",
              "27477                                      , don`t force  negative   \n",
              "27478                          Yay good for both of you.  positive   \n",
              "27479                         But it was worth it  ****.  positive   \n",
              "27480  All this flirting going on - The ATG smiles. Y...   neutral   \n",
              "\n",
              "      Time of Tweet Age of User      Country  Population -2020  \\\n",
              "0           morning        0-20  Afghanistan          38928346   \n",
              "1              noon       21-30      Albania           2877797   \n",
              "2             night       31-45      Algeria          43851044   \n",
              "3           morning       46-60      Andorra             77265   \n",
              "4              noon       60-70       Angola          32866272   \n",
              "...             ...         ...          ...               ...   \n",
              "27476         night       31-45        Ghana          31072940   \n",
              "27477       morning       46-60       Greece          10423054   \n",
              "27478          noon       60-70      Grenada            112523   \n",
              "27479         night      70-100    Guatemala          17915568   \n",
              "27480       morning        0-20       Guinea          13132795   \n",
              "\n",
              "       Land Area (Km²)  Density (P/Km²)  \n",
              "0             652860.0               60  \n",
              "1              27400.0              105  \n",
              "2            2381740.0               18  \n",
              "3                470.0              164  \n",
              "4            1246700.0               26  \n",
              "...                ...              ...  \n",
              "27476         227540.0              137  \n",
              "27477         128900.0               81  \n",
              "27478            340.0              331  \n",
              "27479         107160.0              167  \n",
              "27480         246000.0               53  \n",
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              "      <td>negative</td>\n",
              "      <td>night</td>\n",
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              "      <td>negative</td>\n",
              "      <td>morning</td>\n",
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              "      <td>4eac33d1c0</td>\n",
              "      <td>wish we could come see u on Denver  husband l...</td>\n",
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              "      <td>negative</td>\n",
              "      <td>night</td>\n",
              "      <td>31-45</td>\n",
              "      <td>Ghana</td>\n",
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              "summary": "{\n  \"name\": \"data_train\",\n  \"rows\": 27481,\n  \"fields\": [\n    {\n      \"column\": \"textID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27481,\n        \"samples\": [\n          \"a7f72a928a\",\n          \"ef42dee96c\",\n          \"07d17131b1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 27480,\n        \"samples\": [\n          \" Enjoy! Family trumps everything\",\n          \" --of them kinda turns me off of it all.  And then I buy more of them and dig a deeper hole, etc. ;;\",\n          \"Clive it`s my birthday pat me  http://apps.facebook.com/dogbook/profile/view/6386106\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"selected_text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 22430,\n        \"samples\": [\n          \"that is why I drive a (teeny tiny) honda civic\",\n          \"Sorry...but, I bet they aren`t that bad...\",\n          \"yummy\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"negative\",\n          \"positive\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time of Tweet\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"morning\",\n          \"noon\",\n          \"night\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age of User\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"0-20\",\n          \"21-30\",\n          \"70-100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 195,\n        \"samples\": [\n          \"Philippines\",\n          \"Belgium\",\n          \"Sierra Leone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population -2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 150494590,\n        \"min\": 801,\n        \"max\": 1439323776,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          109581078,\n          11589623,\n          7976983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Area (Km\\u00b2)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1807424.6900064405,\n        \"min\": 0.0,\n        \"max\": 16376870.0,\n        \"num_unique_values\": 193,\n        \"samples\": [\n          2267050.0,\n          1280000.0,\n          100250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Density (P/Km\\u00b2)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2013,\n        \"min\": 2,\n        \"max\": 26337,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          400,\n          71,\n          331\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "# Loading the Training Dataset\n",
        "data_train=  pd.read_csv('/content/Twitter_Analysis_Training.csv',encoding='latin-1')\n",
        "data_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "2Y8vuoReUTm9",
        "outputId": "c7aa496f-8817-4f77-e3dc-fa13ea39594a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          textID                                               text sentiment  \\\n",
              "0     f87dea47db  Last session of the day  http://twitpic.com/67ezh   neutral   \n",
              "1     96d74cb729   Shanghai is also really exciting (precisely -...  positive   \n",
              "2     eee518ae67  Recession hit Veronique Branquinho, she has to...  negative   \n",
              "3     01082688c6                                        happy bday!  positive   \n",
              "4     33987a8ee5             http://twitpic.com/4w75p - I like it!!  positive   \n",
              "...          ...                                                ...       ...   \n",
              "4810         NaN                                                NaN       NaN   \n",
              "4811         NaN                                                NaN       NaN   \n",
              "4812         NaN                                                NaN       NaN   \n",
              "4813         NaN                                                NaN       NaN   \n",
              "4814         NaN                                                NaN       NaN   \n",
              "\n",
              "     Time of Tweet Age of User      Country  Population -2020  \\\n",
              "0          morning        0-20  Afghanistan        38928346.0   \n",
              "1             noon       21-30      Albania         2877797.0   \n",
              "2            night       31-45      Algeria        43851044.0   \n",
              "3          morning       46-60      Andorra           77265.0   \n",
              "4             noon       60-70       Angola        32866272.0   \n",
              "...            ...         ...          ...               ...   \n",
              "4810           NaN         NaN          NaN               NaN   \n",
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              "4812           NaN         NaN          NaN               NaN   \n",
              "4813           NaN         NaN          NaN               NaN   \n",
              "4814           NaN         NaN          NaN               NaN   \n",
              "\n",
              "      Land Area (Km²)  Density (P/Km²)  \n",
              "0            652860.0             60.0  \n",
              "1             27400.0            105.0  \n",
              "2           2381740.0             18.0  \n",
              "3               470.0            164.0  \n",
              "4           1246700.0             26.0  \n",
              "...               ...              ...  \n",
              "4810              NaN              NaN  \n",
              "4811              NaN              NaN  \n",
              "4812              NaN              NaN  \n",
              "4813              NaN              NaN  \n",
              "4814              NaN              NaN  \n",
              "\n",
              "[4815 rows x 9 columns]"
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6ceae095-a665-4a0b-b698-4155c0b39061 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3a2efbf5-8d43-40c7-9a97-67311a4ef14a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('data_test')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        document.querySelector('#id_3a2efbf5-8d43-40c7-9a97-67311a4ef14a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('data_test');\n",
              "      }\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_test",
              "summary": "{\n  \"name\": \"data_test\",\n  \"rows\": 4815,\n  \"fields\": [\n    {\n      \"column\": \"textID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3534,\n        \"samples\": [\n          \"142108215\",\n          \"fb08563a7b\",\n          \"9a2c6ae21c\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3534,\n        \"samples\": [\n          \" Thank you so much phaoloo !!!!\",\n          \"Midnight ice-cream weather! So **** bored\",\n          \"Ohh i forgot to tell you last night that when i was a alton towers i touched a shark  it was amazing !!!! it was nt a massive one tho\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"neutral\",\n          \"positive\",\n          \"negative\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time of Tweet\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"morning\",\n          \"noon\",\n          \"night\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age of User\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"0-20\",\n          \"21-30\",\n          \"70-100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 195,\n        \"samples\": [\n          \"Philippines\",\n          \"Belgium\",\n          \"Sierra Leone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population -2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146875664.36244348,\n        \"min\": 801.0,\n        \"max\": 1439323776.0,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          109581078.0,\n          11589623.0,\n          7976983.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Area (Km\\u00b2)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839133.911427382,\n        \"min\": 0.0,\n        \"max\": 16376870.0,\n        \"num_unique_values\": 193,\n        \"samples\": [\n          2267050.0,\n          1280000.0,\n          100250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Density (P/Km\\u00b2)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1967.012367010644,\n        \"min\": 2.0,\n        \"max\": 26337.0,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          400.0,\n          71.0,\n          331.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# Loading the Testing Dataset\n",
        "data_test=  pd.read_csv('/content/Twitter_Analysis_Testing.csv',encoding='latin-1')\n",
        "data_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jEnYTHPdB6jV",
        "outputId": "3bb97c31-25d2-434a-92da-bd411d43217a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 27481 entries, 0 to 27480\n",
            "Data columns (total 10 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   textID            27481 non-null  object \n",
            " 1   text              27480 non-null  object \n",
            " 2   selected_text     27480 non-null  object \n",
            " 3   sentiment         27481 non-null  object \n",
            " 4   Time of Tweet     27481 non-null  object \n",
            " 5   Age of User       27481 non-null  object \n",
            " 6   Country           27481 non-null  object \n",
            " 7   Population -2020  27481 non-null  int64  \n",
            " 8   Land Area (Km²)   27481 non-null  float64\n",
            " 9   Density (P/Km²)   27481 non-null  int64  \n",
            "dtypes: float64(1), int64(2), object(7)\n",
            "memory usage: 2.1+ MB\n",
            "None \n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4815 entries, 0 to 4814\n",
            "Data columns (total 9 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   textID            3534 non-null   object \n",
            " 1   text              3534 non-null   object \n",
            " 2   sentiment         3534 non-null   object \n",
            " 3   Time of Tweet     3534 non-null   object \n",
            " 4   Age of User       3534 non-null   object \n",
            " 5   Country           3534 non-null   object \n",
            " 6   Population -2020  3534 non-null   float64\n",
            " 7   Land Area (Km²)   3534 non-null   float64\n",
            " 8   Density (P/Km²)   3534 non-null   float64\n",
            "dtypes: float64(3), object(6)\n",
            "memory usage: 338.7+ KB\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "print(data_train.info(), \"\\n\")\n",
        "\n",
        "print(data_test.info())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p39yaAkBCgYP",
        "outputId": "42d0e1fc-9a0a-4877-deb4-12688e759aaf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "textID              0\n",
            "text                1\n",
            "selected_text       1\n",
            "sentiment           0\n",
            "Time of Tweet       0\n",
            "Age of User         0\n",
            "Country             0\n",
            "Population -2020    0\n",
            "Land Area (Km²)     0\n",
            "Density (P/Km²)     0\n",
            "dtype: int64 \n",
            "\n",
            "textID              1281\n",
            "text                1281\n",
            "sentiment           1281\n",
            "Time of Tweet       1281\n",
            "Age of User         1281\n",
            "Country             1281\n",
            "Population -2020    1281\n",
            "Land Area (Km²)     1281\n",
            "Density (P/Km²)     1281\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Chack for Null Values for Training and Testing Dataset\n",
        "print(data_train.isnull().sum(), \"\\n\")\n",
        "\n",
        "print(data_test.isnull().sum())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "t611ebE7CpI_"
      },
      "outputs": [],
      "source": [
        "# Deleting the Nan Values form both DataSet\n",
        "data_train.dropna(inplace=True)\n",
        "\n",
        "data_test.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvc7wGD7C0Oo",
        "outputId": "34b75efe-1ae7-490f-8729-6f085241e48f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sentiment\n",
            "neutral     11117\n",
            "positive     8582\n",
            "negative     7781\n",
            "Name: count, dtype: int64 \n",
            "\n",
            "sentiment\n",
            "neutral     1430\n",
            "positive    1103\n",
            "negative    1001\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Checking the number of type of Sentiments present in Datasets\n",
        "print(data_train[\"sentiment\"].value_counts(), \"\\n\")\n",
        "\n",
        "print(data_test[\"sentiment\"].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FAmWUH2nVh-f",
        "outputId": "7b5a11ac-d298-4a79-b829-a69ea454cbb5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "#Check for duplicates in the training dataset\n",
        "data_train.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "2NVPr_gUV4Gr",
        "outputId": "46707482-7ff0-4da3-92a6-dba7d1cba7b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizing tweet time distribution with a histogram to determine its influence on sentiment\n",
        "sns.countplot(x=data_train[\"Time of Tweet\"], hue=data_train[\"sentiment\"], palette=\"pastel\")\n",
        "plt.xlabel(\"Time of Tweet\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Tweet Time Distribution by Sentiment\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UbOiO-OSXDMv",
        "outputId": "489925fc-70d4-4016-ad55-6fa8150e2731"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizing age-of-user distribution with a histogram to determine its influence on sentiment\n",
        "sns.countplot(x=data_train[\"Age of User\"], hue=data_train[\"sentiment\"], palette=\"pastel\")\n",
        "plt.xlabel(\"Age of User\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.title(\"Age of User Distribution by Sentiment\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "0bie0mCXa51G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e878768-c1a6-4039-8e57-e2bbef26779e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-13-bbbf82af92a7>:2: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  data_train[\"sentiment\"]= data_train[\"sentiment\"].replace({\"neutral\": 0, \"positive\": 1, \"negative\":2})\n",
            "<ipython-input-13-bbbf82af92a7>:4: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
            "  data_test[\"sentiment\"]= data_test[\"sentiment\"].replace({\"neutral\": 0, \"positive\": 1, \"negative\":2})\n"
          ]
        }
      ],
      "source": [
        "# Applying the Label Encoding to sentiment Column for both Training and Testing Dataset\n",
        "data_train[\"sentiment\"]= data_train[\"sentiment\"].replace({\"neutral\": 0, \"positive\": 1, \"negative\":2})\n",
        "\n",
        "data_test[\"sentiment\"]= data_test[\"sentiment\"].replace({\"neutral\": 0, \"positive\": 1, \"negative\":2})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "nHtDFN3WXWKy"
      },
      "outputs": [],
      "source": [
        "# Keep only the 'text' and 'sentiment' columns for further analysis.\n",
        "df_train=data_train[['text','sentiment']].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GDG5LxOXV2-",
        "outputId": "3a440a50-9985-4d8e-8f43-72419325da2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ],
      "source": [
        "# Perform cleaning and preprocessing on the 'text' column\n",
        "import re\n",
        "import string\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import LancasterStemmer\n",
        "\n",
        "nltk.download('stopwords')\n",
        "def clean_text(text):\n",
        "    # Remove URLs\n",
        "    text = re.sub(r'https?://\\S+|www\\.\\S+', '', text)\n",
        "    # Remove HTML tags\n",
        "    text = re.sub(r'<.*?>', '', text)\n",
        "    # Remove punctuation\n",
        "    text = re.sub(f'[{string.punctuation}]', '', text)\n",
        "    # Remove newlines\n",
        "    text = re.sub(r'\\n', '', text)\n",
        "    # Remove alphanumeric words (words containing digits)\n",
        "    text = re.sub(r'\\b\\w*\\d\\w*\\b', '', text)\n",
        "    # Convert to lowercase\n",
        "    text = text.lower()\n",
        "    # Remove remaining non-alphabetic characters (except spaces)\n",
        "    text = re.sub(r'[^a-z\\s]', '', text)\n",
        "    # Normalize repeated characters (e.g., \"soooo\" -> \"so\")\n",
        "    text = re.sub(r'(.)\\1+', r'\\1\\1', text)\n",
        "\n",
        "    # Remove stopwords\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    words = text.split()\n",
        "    words = [word for word in words if word not in stop_words]\n",
        "\n",
        "    # Stem words using LancasterStemmer\n",
        "    stemmer = LancasterStemmer()\n",
        "    words = [stemmer.stem(word) for word in words]\n",
        "\n",
        "    # Join words back into a single string\n",
        "    text = ' '.join(words)\n",
        "\n",
        "    return text\n",
        "\n",
        "# Ensure the 'text' column is of string type\n",
        "df_train['text'] = df_train['text'].astype(str)\n",
        "data_test['text'] = data_test['text'].astype(str)\n",
        "\n",
        "# Apply the clean_text function and assign it back to the DataFrame\n",
        "df_train['text'] = df_train['text'].apply(clean_text)\n",
        "data_test['text'] = data_test['text'].apply(clean_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QMVuE3sYZGGw",
        "outputId": "afa564fa-476c-4f4e-80bd-36c0a7f255e0",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    text  sentiment\n",
              "0                       id respond going          0\n",
              "1                 soo sad miss san diego          2\n",
              "2                               boss bul          2\n",
              "3                    interview leav alon          2\n",
              "4  son couldnt put releas already bought          2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8d98e2a0-6f6e-4f94-b61b-3eb56d3c6eef\" 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>text</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>id respond going</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>soo sad miss san diego</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>boss bul</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>interview leav alon</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>son couldnt put releas already bought</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d98e2a0-6f6e-4f94-b61b-3eb56d3c6eef')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d98e2a0-6f6e-4f94-b61b-3eb56d3c6eef 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-8d98e2a0-6f6e-4f94-b61b-3eb56d3c6eef');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-d02b7ebf-b56a-402c-b85a-7f1255299893\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d02b7ebf-b56a-402c-b85a-7f1255299893')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d02b7ebf-b56a-402c-b85a-7f1255299893 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train",
              "summary": "{\n  \"name\": \"df_train\",\n  \"rows\": 27480,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 26731,\n        \"samples\": [\n          \"la said wip slat cle jel\",\n          \"cool mark song going stuck head day lov anyway star war day today cool\",\n          \"got paint hair\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "# Displaying the first 5 rows of columns of Training Dataset\n",
        "df_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3ytEcUEOZYAg",
        "outputId": "3eb03e58-d4d4-4e2a-f40d-ec70e00c34bd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       textID                                               text  sentiment  \\\n",
              "0  f87dea47db                                      last sess day          0   \n",
              "1  96d74cb729  shangha also real excit prec skyscrap gal good...          1   \n",
              "2  eee518ae67      recess hit veron branquinho quit company sham          2   \n",
              "3  01082688c6                                         happy bday          1   \n",
              "4  33987a8ee5                                                lik          1   \n",
              "\n",
              "  Time of Tweet Age of User      Country  Population -2020  Land Area (Km²)  \\\n",
              "0       morning        0-20  Afghanistan        38928346.0         652860.0   \n",
              "1          noon       21-30      Albania         2877797.0          27400.0   \n",
              "2         night       31-45      Algeria        43851044.0        2381740.0   \n",
              "3       morning       46-60      Andorra           77265.0            470.0   \n",
              "4          noon       60-70       Angola        32866272.0        1246700.0   \n",
              "\n",
              "   Density (P/Km²)  \n",
              "0             60.0  \n",
              "1            105.0  \n",
              "2             18.0  \n",
              "3            164.0  \n",
              "4             26.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1fac013e-7ab4-4807-8b97-614fc9c00313\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>textID</th>\n",
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              "      <th>Country</th>\n",
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              "      <th>0</th>\n",
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              "      <td>morning</td>\n",
              "      <td>0-20</td>\n",
              "      <td>Afghanistan</td>\n",
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              "      <td>60.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>96d74cb729</td>\n",
              "      <td>shangha also real excit prec skyscrap gal good...</td>\n",
              "      <td>1</td>\n",
              "      <td>noon</td>\n",
              "      <td>21-30</td>\n",
              "      <td>Albania</td>\n",
              "      <td>2877797.0</td>\n",
              "      <td>27400.0</td>\n",
              "      <td>105.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>eee518ae67</td>\n",
              "      <td>recess hit veron branquinho quit company sham</td>\n",
              "      <td>2</td>\n",
              "      <td>night</td>\n",
              "      <td>31-45</td>\n",
              "      <td>Algeria</td>\n",
              "      <td>43851044.0</td>\n",
              "      <td>2381740.0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01082688c6</td>\n",
              "      <td>happy bday</td>\n",
              "      <td>1</td>\n",
              "      <td>morning</td>\n",
              "      <td>46-60</td>\n",
              "      <td>Andorra</td>\n",
              "      <td>77265.0</td>\n",
              "      <td>470.0</td>\n",
              "      <td>164.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33987a8ee5</td>\n",
              "      <td>lik</td>\n",
              "      <td>1</td>\n",
              "      <td>noon</td>\n",
              "      <td>60-70</td>\n",
              "      <td>Angola</td>\n",
              "      <td>32866272.0</td>\n",
              "      <td>1246700.0</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-1fac013e-7ab4-4807-8b97-614fc9c00313 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-1fac013e-7ab4-4807-8b97-614fc9c00313');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-18bb33f3-b76c-46a4-9746-2ca594aa50c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_test",
              "summary": "{\n  \"name\": \"data_test\",\n  \"rows\": 3534,\n  \"fields\": [\n    {\n      \"column\": \"textID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3534,\n        \"samples\": [\n          \"142108215\",\n          \"fb08563a7b\",\n          \"9a2c6ae21c\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3496,\n        \"samples\": [\n          \"hah fin got hom\",\n          \"nic day look lik going rainjust get excit tak photo get hom\",\n          \"would lik gam system\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time of Tweet\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"morning\",\n          \"noon\",\n          \"night\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age of User\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"0-20\",\n          \"21-30\",\n          \"70-100\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 195,\n        \"samples\": [\n          \"Philippines\",\n          \"Belgium\",\n          \"Sierra Leone\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Population -2020\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 146875664.36244348,\n        \"min\": 801.0,\n        \"max\": 1439323776.0,\n        \"num_unique_values\": 195,\n        \"samples\": [\n          109581078.0,\n          11589623.0,\n          7976983.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Land Area (Km\\u00b2)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1839133.911427382,\n        \"min\": 0.0,\n        \"max\": 16376870.0,\n        \"num_unique_values\": 193,\n        \"samples\": [\n          2267050.0,\n          1280000.0,\n          100250.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Density (P/Km\\u00b2)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1967.012367010644,\n        \"min\": 2.0,\n        \"max\": 26337.0,\n        \"num_unique_values\": 136,\n        \"samples\": [\n          400.0,\n          71.0,\n          331.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "# Displaying the first 5 rows of columns of Testing Dataset\n",
        "data_test.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "O00Sev3KZpQs"
      },
      "outputs": [],
      "source": [
        "# Select features for training and testing datasets\n",
        "x_train = df_train['text']\n",
        "x_test = data_test['text']\n",
        "y_train = df_train['sentiment']\n",
        "y_test = data_test['sentiment']"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h2> Data Augumentation </h2>"
      ],
      "metadata": {
        "id": "LNJHUCkFwrCk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('wordnet')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uE6qZU3Y2ftC",
        "outputId": "64fd5027-ea61-40ee-889f-02d009c19b76"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3> Synonym Replacement"
      ],
      "metadata": {
        "id": "9Y8H6qNNF1FK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Synonyms Replacemnt\n",
        "import random\n",
        "from nltk.corpus import wordnet\n",
        "\n",
        "def synonym_replacement(text, n=1):\n",
        "    words = text.split()\n",
        "    new_words = words.copy()\n",
        "    random_word_list = list(set([word for word in words if wordnet.synsets(word)]))\n",
        "    random.shuffle(random_word_list)\n",
        "\n",
        "    num_replaced = 0\n",
        "    for random_word in random_word_list:\n",
        "        synonyms = wordnet.synsets(random_word)\n",
        "        if synonyms:\n",
        "            synonym = synonyms[0].lemmas()[0].name()\n",
        "            new_words = [synonym if word == random_word else word for word in new_words]\n",
        "            num_replaced += 1\n",
        "        if num_replaced >= n:\n",
        "            break\n",
        "\n",
        "    return ' '.join(new_words)\n",
        "\n",
        "# Apply it on training data\n",
        "X_train_augmented = [synonym_replacement(text) for text in x_train]"
      ],
      "metadata": {
        "id": "HaC87eeyxIQu"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3> Random Insertion"
      ],
      "metadata": {
        "id": "DDBHFMgbFtD4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "from nltk.corpus import wordnet\n",
        "\n",
        "def random_insertion(text, n=1):\n",
        "    words = text.split()\n",
        "\n",
        "    # If the text is empty or has less than 2 words, return the text as is\n",
        "    if len(words) < 2:\n",
        "        return text\n",
        "\n",
        "    new_words = words.copy()\n",
        "\n",
        "    for _ in range(n):\n",
        "        # Ensure words list is not empty\n",
        "        if len(words) > 0:\n",
        "            random_word = random.choice(words)\n",
        "\n",
        "            # Get synonyms for the random word\n",
        "            synonyms = wordnet.synsets(random_word)\n",
        "\n",
        "            # If synonyms are found, insert one synonym randomly\n",
        "            if synonyms:\n",
        "                synonym = synonyms[0].lemmas()[0].name()\n",
        "                random_position = random.randint(0, len(new_words) - 1)\n",
        "                new_words.insert(random_position, synonym)\n",
        "\n",
        "    return ' '.join(new_words)\n",
        "\n",
        "# Apply it on training data\n",
        "X_train_augmented += [random_insertion(text) for text in x_train if len(text.split()) > 0]  # Ensure non-empty text\n"
      ],
      "metadata": {
        "id": "AjT3u1fZ5eeI"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3> Random Deletion"
      ],
      "metadata": {
        "id": "PPZe47NSGL6c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def random_deletion(text, p=0.2):\n",
        "    words = text.split()\n",
        "    if len(words) <= 1:  # Check for both empty and single-word cases\n",
        "        return text\n",
        "\n",
        "    new_words = [word for word in words if random.uniform(0, 1) > p]\n",
        "\n",
        "    if len(new_words) == 0:\n",
        "        # If all words are removed, return the original text instead of choosing from an empty list\n",
        "        return text\n",
        "    return ' '.join(new_words)\n",
        "\n",
        "# Apply it on training data\n",
        "X_train_augmented += [random_deletion(text) for text in x_train]"
      ],
      "metadata": {
        "id": "5QkFF8h6GLdz"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "<h3> Back-Translation"
      ],
      "metadata": {
        "id": "CmATcqbHFqjl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pip install googletrans==4.0.0-rc1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Df6yWWuAqIv",
        "outputId": "4a7c1c75-0247-4903-fee5-9702bcb4b558"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting googletrans==4.0.0-rc1\n",
            "  Downloading googletrans-4.0.0rc1.tar.gz (20 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting httpx==0.13.3 (from googletrans==4.0.0-rc1)\n",
            "  Downloading httpx-0.13.3-py3-none-any.whl.metadata (25 kB)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx==0.13.3->googletrans==4.0.0-rc1) (2024.8.30)\n",
            "Collecting hstspreload (from httpx==0.13.3->googletrans==4.0.0-rc1)\n",
            "  Downloading hstspreload-2024.10.1-py3-none-any.whl.metadata (2.1 kB)\n",
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            "  Downloading chardet-3.0.4-py2.py3-none-any.whl.metadata (3.2 kB)\n",
            "Collecting idna==2.* (from httpx==0.13.3->googletrans==4.0.0-rc1)\n",
            "  Downloading idna-2.10-py2.py3-none-any.whl.metadata (9.1 kB)\n",
            "Collecting rfc3986<2,>=1.3 (from httpx==0.13.3->googletrans==4.0.0-rc1)\n",
            "  Downloading rfc3986-1.5.0-py2.py3-none-any.whl.metadata (6.5 kB)\n",
            "Collecting httpcore==0.9.* (from httpx==0.13.3->googletrans==4.0.0-rc1)\n",
            "  Downloading httpcore-0.9.1-py3-none-any.whl.metadata (4.6 kB)\n",
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            "  Downloading h11-0.9.0-py2.py3-none-any.whl.metadata (8.1 kB)\n",
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            "  Downloading h2-3.2.0-py2.py3-none-any.whl.metadata (32 kB)\n",
            "Collecting hyperframe<6,>=5.2.0 (from h2==3.*->httpcore==0.9.*->httpx==0.13.3->googletrans==4.0.0-rc1)\n",
            "  Downloading hyperframe-5.2.0-py2.py3-none-any.whl.metadata (7.2 kB)\n",
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            "  Downloading hpack-3.0.0-py2.py3-none-any.whl.metadata (7.0 kB)\n",
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            "Downloading hyperframe-5.2.0-py2.py3-none-any.whl (12 kB)\n",
            "Building wheels for collected packages: googletrans\n",
            "  Building wheel for googletrans (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for googletrans: filename=googletrans-4.0.0rc1-py3-none-any.whl size=17397 sha256=d3592ab5ffb980d066ceb8bd1ce12be2435b05c619b0619a8c54418f9c94c3e4\n",
            "  Stored in directory: /root/.cache/pip/wheels/c0/59/9f/7372f0cf70160fe61b528532e1a7c8498c4becd6bcffb022de\n",
            "Successfully built googletrans\n",
            "Installing collected packages: rfc3986, hyperframe, hpack, h11, chardet, idna, hstspreload, h2, httpcore, httpx, googletrans\n",
            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 5.2.0\n",
            "    Uninstalling chardet-5.2.0:\n",
            "      Successfully uninstalled chardet-5.2.0\n",
            "  Attempting uninstall: idna\n",
            "    Found existing installation: idna 3.10\n",
            "    Uninstalling idna-3.10:\n",
            "      Successfully uninstalled idna-3.10\n",
            "Successfully installed chardet-3.0.4 googletrans-4.0.0rc1 h11-0.9.0 h2-3.2.0 hpack-3.0.0 hstspreload-2024.10.1 httpcore-0.9.1 httpx-0.13.3 hyperframe-5.2.0 idna-2.10 rfc3986-1.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from googletrans import Translator\n",
        "\n",
        "translator = Translator()\n",
        "\n",
        "def back_translation(text):\n",
        "    try:\n",
        "        # Translate from English to French and back to English\n",
        "        translation = translator.translate(text, src='en', dest='fr').text\n",
        "        return translator.translate(translation, src='fr', dest='en').text\n",
        "    except Exception as e:\n",
        "        print(f\"Error translating text: {text} - {str(e)}\")\n",
        "        return text  # Return the original text if translation fails"
      ],
      "metadata": {
        "id": "hclgfMX9A3b2"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_augmented = x_train.tolist()  # Assuming X_train is in sparse format, convert to list\n",
        "y_train_augmented = y_train.tolist()\n",
        "\n",
        "# Apply data augmentation on training data\n",
        "for text, label in zip(x_train, y_train):\n",
        "    augmented_text = random_insertion(text)  # Augment text\n",
        "    X_train_augmented.append(augmented_text)  # Add augmented text\n",
        "    y_train_augmented.append(label)  # Add the corresponding label for the augmented text\n",
        "\n",
        "# After augmentation, ensure that both X_train_augmented and y_train_augmented have the same length\n",
        "print(len(X_train_augmented), len(y_train_augmented))  # They should be the same"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5dIuvbLY5hfH",
        "outputId": "c48da319-00b2-4b88-ea32-a9db07d2642c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "54960 54960\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "# Initialize the TF-IDF vectorizer\n",
        "vectorizer = TfidfVectorizer()\n",
        "\n",
        "# Fit and transform the training data\n",
        "X_train_vectorized = vectorizer.fit_transform(X_train_augmented)\n",
        "\n",
        "# Transform the testing data\n",
        "X_test_vectorized = vectorizer.transform(x_test)"
      ],
      "metadata": {
        "id": "Ujv8PyjIxIAw"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1FuCR8j9h5NC"
      },
      "source": [
        "<h1> Machine Learning (ML) Models </h1>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "88WZP6gncEJi"
      },
      "outputs": [],
      "source": [
        "# Analyze the results\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn import metrics\n",
        "\n",
        "def analyze_results(y_test,y_pred):\n",
        "    # Get the metrics\n",
        "    accuracy = metrics.accuracy_score(y_test, y_pred)\n",
        "    precision =  metrics.precision_score(y_test, y_pred, average='weighted')\n",
        "    recall = metrics.recall_score(y_test, y_pred, average='weighted')\n",
        "    f1_score = metrics.f1_score(y_test, y_pred, average='weighted')\n",
        "\n",
        "    # print metrics\n",
        "    print(\"Accuracy:\", \"%.6f\" % accuracy)\n",
        "    print(\"Precision:\", \"%.6f\" % precision)\n",
        "    print(\"Recall:\", \"%.6f\" %  recall)\n",
        "    print(\"F1 Score:\", \"%.6f\" %  f1_score)\n",
        "    print(\"Classification Report:\")\n",
        "    print(metrics.classification_report(y_test, y_pred))\n",
        "\n",
        "    # Produce a confusion matrix\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Neutral', 'Positive', 'Negative'],\n",
        "                yticklabels=['Neutral', 'Positive', 'Negative'])\n",
        "    plt.xlabel('Predicted Labels')\n",
        "    plt.ylabel('True Labels')\n",
        "    plt.title('Confusion Matrix')\n",
        "    plt.show()\n",
        "\n",
        "    return accuracy, precision, recall, f1_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "of6c_75HWG50"
      },
      "source": [
        "<h2> Logistic Regression </h3>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# prompt: apply logistic regression\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Initialize the Logistic Regression model\n",
        "logistic_regression = LogisticRegression(max_iter=1000)\n",
        "\n",
        "# Train the model\n",
        "logistic_regression.fit(X_train_vectorized, y_train_augmented)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred_lr = logistic_regression.predict(X_test_vectorized)\n",
        "\n",
        "# Analyze the results for Logistic Regression\n",
        "accuracy_lr, precision_lr, recall_lr, f1_score_lr = analyze_results(y_test, y_pred_lr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "id": "Wo7_Wdw69BFA",
        "outputId": "ad224a54-bd51-4738-da8f-9b954dd5cadb"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.694963\n",
            "Precision: 0.700346\n",
            "Recall: 0.694963\n",
            "F1 Score: 0.695887\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.64      0.72      0.68      1430\n",
            "           1       0.79      0.71      0.75      1103\n",
            "           2       0.69      0.64      0.66      1001\n",
            "\n",
            "    accuracy                           0.69      3534\n",
            "   macro avg       0.71      0.69      0.70      3534\n",
            "weighted avg       0.70      0.69      0.70      3534\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1b5gDx3gWLjv"
      },
      "source": [
        "<h2> Random Forest Classifier</h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "KFG3XRMyhE66",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "outputId": "6c753548-40c0-4cc8-beb3-432f2d68cbc0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.713922\n",
            "Precision: 0.715729\n",
            "Recall: 0.713922\n",
            "F1 Score: 0.713723\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.67      0.72      0.69      1430\n",
            "           1       0.76      0.77      0.76      1103\n",
            "           2       0.73      0.65      0.69      1001\n",
            "\n",
            "    accuracy                           0.71      3534\n",
            "   macro avg       0.72      0.71      0.72      3534\n",
            "weighted avg       0.72      0.71      0.71      3534\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Initialize the Random Forest Classifier\n",
        "random_forest = RandomForestClassifier(n_estimators=100, random_state=42)\n",
        "\n",
        "# Train the model\n",
        "random_forest.fit(X_train_vectorized, y_train_augmented)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred_rf = random_forest.predict(X_test_vectorized)\n",
        "\n",
        "# Analyze the results for Random Forest\n",
        "accuracy_rf, precision_rf, recall_rf, f1_score_rf = analyze_results(y_test, y_pred_rf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mW8cjXkZiBI5"
      },
      "source": [
        "<h2> Support Vector Machine (SVM) Classifier </h2>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "eyQH_ZMIhAdd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 838
        },
        "outputId": "e39ff198-f43b-4285-8f0e-7f992e8e7d78"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.705716\n",
            "Precision: 0.708943\n",
            "Recall: 0.705716\n",
            "F1 Score: 0.706396\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.66      0.72      0.68      1430\n",
            "           1       0.78      0.73      0.76      1103\n",
            "           2       0.70      0.66      0.68      1001\n",
            "\n",
            "    accuracy                           0.71      3534\n",
            "   macro avg       0.71      0.70      0.71      3534\n",
            "weighted avg       0.71      0.71      0.71      3534\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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GhYWFSZLCwsJ06dIlbd261dZnxYoVSklJUdWqVW191qxZo5s3b9r6LFu2TKVLl1ZgYGC64yGRBAAAcCJxcXHasWOHduzYIenWDTY7duxQTEyMLBaLevXqpREjRmj+/PnavXu32rZtq5CQEDVt2lSSVKZMGdWvX1+dO3fWpk2b9Ntvv6lbt25q2bKlQkJCJEmtWrWSh4eHOnbsqL1792rOnDmaMGFCqin4u2FqGwAAwIm2/9myZYvq1Klje347uWvXrp1mzJih/v3769q1a+rSpYsuXbqkmjVrasmSJfL09LS9Jjo6Wt26dVPdunXl5uam5s2ba+LEibbj/v7+Wrp0qSIjI/XYY48pb968Gjx4cIa2/pEki2EYxj1er9PxqtzN0SEAqexd+qGjQwDsXEtIcnQIgJ3yhXwddm6v6m9l2djX143KsrEdjYokAABAJq9ldBXOU8cFAADAA4WKJAAAgBOtkXyQkEgCAAAwtW0K6TcAAABMoSIJAADA1LYpvGsAAAAwhYokAAAAFUlTeNcAAABgChVJAAAAN+7aNoOKJAAAAEyhIgkAAMAaSVNIJAEAANiQ3BTSbwAAAJhCRRIAAICpbVN41wAAAGAKFUkAAADWSJpCRRIAAACmUJEEAABgjaQpvGsAAAAwhYokAAAAayRNIZEEAABgatsU3jUAAACYQkUSAACAqW1TqEgCAADAFCqSAAAArJE0hXcNAAAAplCRBAAAYI2kKVQkAQAAYAoVSQAAANZImkIiCQAAQCJpCu8aAAAATKEiCQAAwM02plCRBAAAgClUJAEAAFgjaQrvGgAAAEyhIgkAAMAaSVMclkheuXIl3X39/PyyMBIAAACY4bBEMiAgQJa7ZP+GYchisSg5Ofk+RQUAAFwSayRNcVgiuXLlSkedGgAAwB5T26Y4LJGsXbu2o04NAACATOBUN9vEx8crJiZGiYmJdu0VKlRwUEQAAMAV3G25HdLmFInkuXPn1KFDB/30009pHmeNJAAAgPNxipWlvXr10qVLl7Rx40Z5eXlpyZIlmjlzpkqVKqX58+c7OjwAAJDNWSyWLHtkZ05RkVyxYoV+/PFHValSRW5ubgoNDdXTTz8tPz8/RUVFqVGjRo4OEQAAAP/gFBXJa9euKSgoSJIUGBioc+fOSZLKly+vbdu2OTI0AADgCixZ+MjGnCKRLF26tA4ePChJqlixoj7++GOdPHlSU6dOVYECBRwcHQAAANLiFFPbPXv2VGxsrCTp3XffVf369RUdHS0PDw/NmDHDscEBAIBsL7uvZcwqTpFItmnTxvbzY489phMnTujAgQMqUqSI8ubN68DIAACAKyCRNMfhU9s3b95UiRIltH//flubt7e3Hn30UZJIAAAAJ+bwimTOnDl148YNR4cBAABcGBVJcxxekZSkyMhIvf/++0pKSnJ0KAAAAEgnh1ckJWnz5s1avny5li5dqvLly8vHx8fu+A8//OCgyAAAgCugImmOUySSAQEBat68uaPDyJZqPFpCvduG69GyRVQgn79a9P5EC1btsusz6LVG6vBcdQXk8tL6nb+rx6g5Ohpzay/PIgVya2CX+nry8YeUP4+fYs9d1leLN+v9T3/WzaRbX11ZKjRIk95uqYeLB8vf10ux5y5rzk9bNPKTxUpKSrnv14wHz+4dW/Xd7Bk6cnC/Lpw/p0Gjxql6radsxxvUrJjm6zq+3lvPt2ovSWr3fAOdPX3K7niHV3uoxcsdsyxuZE8/zJ6ujWtX6mTMcXlYrSpdtoLadOmhgoWL2vp8PHakdm3bqIvn/5Knl5ceKldRL3furoJFikmSjh89pLlfzdCBPTt09fIl5QsuoHqNm6tR81YOuiogazhFIvn55587OoRsy8fLqt2HTmrWj+s1Z2yXVMffaB+u11+qrc6Dv9Dxk+c1+PXGWjA5UpWbj1BCYpJKF8svN4ubuo34Wkf/OKdyJUM0edBL8vGyauC4uZKkm0nJil64STsO/KHLV+NV/qFCmjzoJbm5WfTuRwvu9yXjAXTj+nUVL1la9Ro11Yi3+6Q6Hv3jcrvnWzas1fj3hqhG7XC79pc7va76z/zvH6Xe3t5ZEzCytX27tqn+sy+o5MPllJycrNmffaTh/SM1fvp38vTykiQVf6iMnghvoLxBwYq7clnfzPpEw9+M1OQvF8jd3V1HD+2Xf0Cgegwcrrz58uvg3l2aOm6E3Nzd1aDpiw6+QqSJgqQpTpFIPvXUU/rhhx8UEBBg137lyhU1bdpUK1ascExg2cDS3/Zp6W/77ng8slUdvT/tZy1ctVuS1GnQLJ34JUrP1qmob3/eqmXr9mvZuv/dUX/85Hk9FBqkzi88YUskj588r+Mnz9v6xMReVK0qpVSjcoksuipkN4+H1dTjYTXveDx3HvsdHDasXaUKjz6uAgUL2bV7efuk6gtk1DvvfWT3PLL/UHVsHq7fD+9X2QqPSpKebtzMdjwoOEQtO7yuvl1a6tyZUwoOKay6DZrYjZE/pJAO7tuljb+uIJFEtuIUN9usWrVKiYmJqdpv3LihX3/91QERuYaiBfOoQD5/rdh4wNZ2Je6GNu85rqoVit7xdX6+XrpwJf6Ox4sXzqunq5fRr1uPZGa4gCTp4oXz2rTuV0U0ei7VsW+/nK4WDWspskMLfTd7hpK5gQ+ZIP5anCTJN5dfmsdvXL+ulT/PV1CBgsqTL/hfx/HN5Z8lMeLeWSyWLHtkZw6tSO7a9b+1evv27dPp06dtz5OTk7VkyRIVLFjQEaG5hOC8t34pnr1w1a797Pmryp8n7V+YxQvn1Wsta9uqkX+3ckYfVXq4sDytOfXpd2s1bMqizA8aLu+Xn+bLy9tbNWrXtWtv8vxLKvlQGeXy89e+PTs0Y+pEXTh/Tl2693NQpMgOUlJS9PnkD/XwIxVVpFhJu2NLfvxGX34yUTduXFdI4VANHj1ZOXPmTHOcA3t3at2qpRo4asL9CBu4bxyaSFaqVMmWrT/11FOpjnt5eWnSpEn/OkZCQoISEhLs2oyUZFnc3DM1Vkgh+fw1/6NI/fDLdn0+d12q4y+/OV2+Pp6q8FBBjerVVL3b1tXYmb84IFJkZ0sXzVOdeg3lYbXatTdr2db2c7GSDylHjpya9MEItX+1pzw8PO53mMgmPp34nv44flQjJnyW6tgTdRuo4mPVdPHCX5r/zRcaO2yARkycLg8P+89mzLEjGj2oj15o20WVqoTdr9CRQdm9cphVHJpIHjt2TIZhqHjx4tq0aZPy5ctnO+bh4aGgoCC5u/97QhgVFaWhQ4fatbnnf1w5C/wnS2LOTk7/dUWSFJQ7l+1nSQrKk0u7Dv5p17dAPn8tmdZTG3b9rsjhX6U53p9nLkmSDvx+Wm5ubpr8zksa/8VypaQYWXMBcDl7dm7TnzHHNXDo6Lv2fbhseSUnJ+ns6VMqVKRo1geHbOfTie9r64a1GjZumvLky5/quI9vLvn45lKBQkVUqkx5tW/6pDatXamaT9W39fnj+O8a2vc1hTdqpufbdLqf4SODSCTNcWgiGRoaKunW1IFZAwcOVJ8+9nd5Bj3x5j3F5SqOnzyv2HOXVadqae06dFKSlMvHU48/UlTTvl1r6xfy/0nk9v0x6vLulzKMuyeGbm4W5czhLjc3C4kkMs3PC+eqVOmyKl6q9F37Hj1yUG5ubvIPyH0fIkN2YhiGPps0WpvWrtTQsZ8of4F0LLEyDBmGoZt/W+//x/GjGvJGVz1Zr7FadYzMwogBx3GKu7ZnzZr1r8fbtm17x2NWq1XWf0xxMa39Pz5eHipR+H+V3qIF86jCQwV18Uq8/jh9UZNnr9SbnerrSMw5HT95Xu++3kix5y5r/sqdkm4lkT9/2lMxsRc0cOxc5Qv0tY115vyttZUtG1TRzaRk7TlySgmJSXqsbBEN7/6svlu6lX0kkS7X4+N16mSM7fmZ2JM6eviAcuXyV1BwAUnStWtx+nXlUnXu9kaq1+/fs1MH9u1WxcqPy8vbR/v37tQnEz9QnXqNlMsv7fW+wJ18OvE9/bp8id4cPlae3t66eOEvSZK3j6+sVk+dOfWnflu1VBWrhMnPP0Dn/zqreV/NkIeHpx6temv3gZhjRzSkb1dVqhKmxi+0to3h5uYu/4BAh10b7oyKpDlOkUj27NnT7vnNmzcVHx8vDw8PeXt7/2siiX/3aNlQLf30f+/v6L639tj7Yv4GdXn3S42Z8Yu8vaz66J2XFJDLS+t2HNWzkf9VQuKtu12fqvawShYJUskiQTq6dKTd2F6Vu0mSkpJT1Kf90yoVGiSLxaKY2AuaMmeNJn3Jtk1In8MH9urNHv+b9vtk0oeSpPAGz+qNt4dLklb/skQypCfDG6R6fc6cHlr9yxJFT5+qm4mJyh9SUM+9+LKee/Hl+3MByFZ+nv+dJOndPvZ770b2e1d16j+rnB5W7d+9Q4u+/0rX4q7IPzCPylSorJGTpss/8FYFfP2a5bpy6aLW/LJYa35ZbBsjX/4CmjJ74f27GCCLWYz0zFM6wOHDh/Xaa6+pX79+ioiIyNBrbyc4gDPZu/RDR4cA2LmWwPZIcC7lC/nevVMWydMu7fX/meH8zJeybGxHc4p9JNNSqlQpvffee6mqlQAAAHAOTjG1fSc5cuTQqVOn7t4RAADgHrBG0hynSCTnz59v99wwDMXGxuqjjz5SjRo1HBQVAAAA/o1TJJJNmza1e26xWJQvXz499dRTGjNmjGOCAgAALoOKpDlOkUjeyz6SAAAA94pE0hynutkmMTFRBw8eVFISdxICAAA4O6dIJOPj4/XKK6/I29tb5cqVU0zMrY2Ju3fvrvfee8/B0QEAgGzPkoWPbMwpEsmBAwdq165dWrVqlTw9PW3t4eHhmjNnjgMjAwAAwJ04xRrJefPmac6cOapWrZrdGoVy5crp6NGjDowMAAC4AtZImuMUFclz584pKCgoVfu1a9f4gwUAAHBSTpFIVqlSRYsWLbI9v508fvrppwoLC3NUWAAAwEVYLJYse2RnTjG1PWrUKDVo0ED79u1TUlKSJkyYoH379mndunVavXq1o8MDAABAGpyiIlmzZk3t2LFDSUlJKl++vJYuXaqgoCCtX79ejz32mKPDAwAA2RwVSXOcoiIpSSVKlNC0adMcHQYAAHBB2T3hyyoOTSTd3Nzu+gdnsVjYoBwAAMAJOTSRnDt37h2PrV+/XhMnTuTrEwEAQNajIGmKQxPJJk2apGo7ePCgBgwYoAULFqh169YaNmyYAyIDAADA3TjFzTaSdOrUKXXu3Fnly5dXUlKSduzYoZkzZyo0NNTRoQEAgGyOm23McXgiefnyZb355psqWbKk9u7dq+XLl2vBggV65JFHHB0aAAAA/oVDp7ZHjx6t999/X8HBwfrqq6/SnOoGAADIatm9cphVHFqRHDBggG7cuKGSJUtq5syZatasWZoPAAAAV5CcnKxBgwapWLFi8vLyUokSJTR8+HAZhmHrYxiGBg8erAIFCsjLy0vh4eE6fPiw3TgXLlxQ69at5efnp4CAAHXs2FFxcXGZHq9DK5Jt27blXwAAAMDhnCUfef/99zVlyhTNnDlT5cqV05YtW9ShQwf5+/urR48ekm7N6E6cOFEzZ85UsWLFNGjQIEVERGjfvn3y9PSUJLVu3VqxsbFatmyZbt68qQ4dOqhLly6aPXt2psZrMf6e4mYTXpW7OToEIJW9Sz90dAiAnWsJ7NEL51K+kK/Dzl24249ZNvaRMfWVkJBg12a1WmW1WlP1bdy4sfLnz6/PPvvM1ta8eXN5eXnpyy+/lGEYCgkJ0RtvvKG+fftKunW/Sf78+TVjxgy1bNlS+/fvV9myZbV582ZVqVJFkrRkyRI1bNhQf/75p0JCQjLt2hx+sw0AAEB2FhUVJX9/f7tHVFRUmn2rV6+u5cuX69ChQ5KknTt3au3atWrQoIEk6dixYzp9+rTCw8Ntr/H391fVqlW1fv16Sbf24g4ICLAlkZIUHh4uNzc3bdy4MVOvzWm+IhEAAMBRsnJqe+DAgerTp49dW1rVSOnW/SNXrlzRww8/LHd3dyUnJ2vkyJFq3bq1JOn06dOSpPz589u9Ln/+/LZjp0+fVlBQkN3xHDlyKHfu3LY+mYVEEgAAIAvdaRo7Ld98842io6M1e/ZslStXTjt27FCvXr0UEhKidu3aZXGkGUciCQAAXJ6z3GzTr18/DRgwQC1btpQklS9fXidOnFBUVJTatWun4OBgSdKZM2dUoEAB2+vOnDmjSpUqSZKCg4N19uxZu3GTkpJ04cIF2+szC2skAQAAnER8fLzc3OzTM3d3d6WkpEiSihUrpuDgYC1fvtx2/MqVK9q4caPCwsIkSWFhYbp06ZK2bt1q67NixQqlpKSoatWqmRovFUkAAODynKUi+cwzz2jkyJEqUqSIypUrp+3bt2vs2LF65ZVXJN2Ks1evXhoxYoRKlSpl2/4nJCRETZs2lSSVKVNG9evXV+fOnTV16lTdvHlT3bp1U8uWLTP1jm2JRBIAAMBpTJo0SYMGDdLrr7+us2fPKiQkRK+++qoGDx5s69O/f39du3ZNXbp00aVLl1SzZk0tWbLEtoekJEVHR6tbt26qW7eu3Nzc1Lx5c02cODHT42UfSeA+YR9JOBv2kYSzceQ+ksV6LcqysY+Nb5RlYzsaFUkAAADnmNl+4HCzDQAAAEyhIgkAAFyes9xs86ChIgkAAABTqEgCAACXR0XSHCqSAAAAMIWKJAAAcHkUJM2hIgkAAABTqEgCAACXxxpJc0gkAQCAyyOPNIepbQAAAJhCRRIAALg8prbNoSIJAAAAU6hIAgAAl0dB0hwqkgAAADCFiiQAAHB5bm6UJM2gIgkAAABTqEgCAACXxxpJc0gkAQCAy2P7H3OY2gYAAIApVCQBAIDLoyBpDhVJAAAAmEJFEgAAuDzWSJpDRRIAAACmUJEEAAAuj4qkOVQkAQAAYAoVSQAA4PIoSJpDIgkAAFweU9vmMLUNAAAAU6hIAgAAl0dB0hwqkgAAADCFiiQAAHB5rJE0h4okAAAATKEiCQAAXB4FSXOoSAIAAMAUKpIAAMDlsUbSHCqSAAAAMIWKJAAAcHkUJM0hkQQAAC6PqW1zmNoGAACAKVQkAQCAy6MgaU62TCQ3/Bjl6BCAVJ4Y/JOjQwDsHJ30nKNDAPCAy5aJJAAAQEawRtIc1kgCAADAFCqSAADA5VGQNIeKJAAAAEyhIgkAAFweayTNIZEEAAAujzzSHKa2AQAAYAoVSQAA4PKY2jaHiiQAAABMoSIJAABcHhVJc6hIAgAAwBQqkgAAwOVRkDSHiiQAAABMoSIJAABcHmskzSGRBAAALo880hymtgEAAGAKFUkAAODymNo2h4okAAAATKEiCQAAXB4FSXOoSAIAAMAUKpIAAMDluVGSNIWKJAAAAEyhIgkAAFweBUlzSCQBAIDLY/sfc5jaBgAAgClUJAEAgMtzoyBpChVJAAAAmEJFEgAAuDzWSJpDRRIAAACmUJEEAAAuj4KkOVQkAQAAYAoVSQAA4PIsoiRpBokkAABweWz/Yw5T2wAAADCFiiQAAHB5bP9jDhVJAAAAmEJFEgAAuDwKkuZQkQQAAIApVCQBAIDLc6MkaUqGK5IzZ87UokWLbM/79++vgIAAVa9eXSdOnMjU4AAAAOC8MpxIjho1Sl5eXpKk9evXa/LkyRo9erTy5s2r3r17Z3qAAAAAWc1iybpHdpbhRPKPP/5QyZIlJUnz5s1T8+bN1aVLF0VFRenXX3/N9AABAACymsViybJHRp08eVJt2rRRnjx55OXlpfLly2vLli2244ZhaPDgwSpQoIC8vLwUHh6uw4cP241x4cIFtW7dWn5+fgoICFDHjh0VFxd3z+/TP2U4kfT19dX58+clSUuXLtXTTz8tSfL09NT169czNzoAAAAXcvHiRdWoUUM5c+bUTz/9pH379mnMmDEKDAy09Rk9erQmTpyoqVOnauPGjfLx8VFERIRu3Lhh69O6dWvt3btXy5Yt08KFC7VmzRp16dIl0+PN8M02Tz/9tDp16qTKlSvr0KFDatiwoSRp7969Klq0aGbHBwAAkOWcZQr6/fffV+HChfX555/b2ooVK2b72TAMjR8/Xu+8846aNGkiSZo1a5by58+vefPmqWXLltq/f7+WLFmizZs3q0qVKpKkSZMmqWHDhvrwww8VEhKSafFmuCI5efJkhYWF6dy5c/r++++VJ08eSdLWrVv10ksvZVpgAAAA2UFCQoKuXLli90hISEiz7/z581WlShW98MILCgoKUuXKlTVt2jTb8WPHjun06dMKDw+3tfn7+6tq1apav369pFv3sAQEBNiSSEkKDw+Xm5ubNm7cmKnXluGKZEBAgD766KNU7UOHDs2UgAAAAO63rNz+JyoqKlWe9O6772rIkCGp+v7++++aMmWK+vTpo7feekubN29Wjx495OHhoXbt2un06dOSpPz589u9Ln/+/LZjp0+fVlBQkN3xHDlyKHfu3LY+mSVdieSuXbvSPWCFChVMBwMAAJDdDBw4UH369LFrs1qtafZNSUlRlSpVNGrUKElS5cqVtWfPHk2dOlXt2rXL8lgzKl2JZKVKlWSxWGQYRprHbx+zWCxKTk7O1AABAACyWlYukbRarXdMHP+pQIECKlu2rF1bmTJl9P3330uSgoODJUlnzpxRgQIFbH3OnDmjSpUq2fqcPXvWboykpCRduHDB9vrMkq5E8tixY5l6UgAAAKRWo0YNHTx40K7t0KFDCg0NlXTrxpvg4GAtX77cljheuXJFGzdu1GuvvSZJCgsL06VLl7R161Y99thjkqQVK1YoJSVFVatWzdR405VI3g4eAAAgOzKz32NW6N27t6pXr65Ro0apRYsW2rRpkz755BN98sknkm7F2atXL40YMUKlSpVSsWLFNGjQIIWEhKhp06aSblUw69evr86dO2vq1Km6efOmunXrppYtW2bqHduSibu2JemLL75QjRo1FBISYvtaxPHjx+vHH3/M1OAAAADuBzdL1j0y4vHHH9fcuXP11Vdf6ZFHHtHw4cM1fvx4tW7d2tanf//+6t69u7p06aLHH39ccXFxWrJkiTw9PW19oqOj9fDDD6tu3bpq2LChatasaUtGM5PFuNPCxzuYMmWKBg8erF69emnkyJHas2ePihcvrhkzZmjmzJlauXJlpgeZUTtjrjo6BCCVhlG/ODoEwM7RSc85OgTAjmeG95LJPK2/2JFlY0e/XCnLxna0DFckJ02apGnTpuntt9+Wu7u7rb1KlSravXt3pgYHAABwPzjTVyQ+SDKcSB47dkyVK1dO1W61WnXt2jXTgfz6669q06aNwsLCdPLkSUm3ptDXrl1rekwAAABknQwnksWKFdOOHTtStS9ZskRlypQxFcT333+viIgIeXl5afv27bbd3i9fvmzbRwkAACCrWCxZ98jOMpxI9unTR5GRkZozZ44Mw9CmTZs0cuRIDRw4UP379zcVxIgRIzR16lRNmzZNOXPmtLXXqFFD27ZtMzUmAAAAslaGl7V26tRJXl5eeueddxQfH69WrVopJCREEyZMUMuWLU0FcfDgQdWqVStVu7+/vy5dumRqTAAAgPTK7msZs4qp+6Nat26t1q1bKz4+XnFxcam+zzGjgoODdeTIERUtWtSufe3atSpevPg9jQ0AAICsYfpG+7Nnz9p2XrdYLMqXL5/pIDp37qyePXtq+vTpslgsOnXqlNavX6++fftq0KBBpscFAABIj4zu94hbMpxIXr16Va+//rq++uorpaSkSJLc3d314osvavLkyfL3989wEAMGDFBKSorq1q2r+Ph41apVS1arVX379lX37t0zPB4AAEBGMLVtToZvtunUqZM2btyoRYsW6dKlS7p06ZIWLlyoLVu26NVXXzUVhMVi0dtvv60LFy5oz5492rBhg86dO6fhw4ebGg8AAABZL8MVyYULF+rnn39WzZo1bW0RERGaNm2a6tevbyqIL7/8Us2aNZO3t7fKli1ragwAAACzqEeak+GKZJ48edKcvvb391dgYKCpIHr37q2goCC1atVKixcvVnJysqlxAAAAcP9kOJF855131KdPH50+fdrWdvr0afXr18/0jTGxsbH6+uuvZbFY1KJFCxUoUECRkZFat26dqfEAAAAyws1iybJHdpauqe3KlSvbLUI9fPiwihQpoiJFikiSYmJiZLVade7cOVPrJHPkyKHGjRurcePGio+P19y5czV79mzVqVNHhQoV0tGjRzM8JgAAALJWuhLJpk2bZnEY/+Pt7a2IiAhdvHhRJ06c0P79++/buQEAgGvK5oXDLJOuRPLdd9/N6jhslcjo6GgtX75chQsX1ksvvaTvvvsuy88NAACAjDO9IXlmatmypRYuXChvb2+1aNFCgwYNUlhYmKPDAgAALoJ9JM3JcCKZnJyscePG6ZtvvlFMTIwSExPtjl+4cCHDQbi7u+ubb75RRESE3N3dM/x6AAAA3H8Zvmt76NChGjt2rF588UVdvnxZffr0UbNmzeTm5qYhQ4aYCiI6OloNGzYkiQQAAA5hsWTdIzvLcEUyOjpa06ZNU6NGjTRkyBC99NJLKlGihCpUqKANGzaoR48e6Rpn4sSJ6tKlizw9PTVx4sR/7ZveMXF3c7/6XJvWrtTJP47Lw2rVQ2UrqE2n7gopXDRVX8MwFPV2T+3YvE59h3yo/9R4MlWfq1cuqd+rrXThr7P6fO5K+fjmyvqLQLbiZpHeaFxGzf5TWPn8PHXm8nV9uz5G4386aOvToFKIXn6iqCoUCVSgr4fqjVyhvX9ethvHmsNNg58vryaPFZJHDjet2n9Gb321U39dTbjfl4Rs7rNpn2ji+DFq3aat+g98W5L0R0yMxnz4vnZs26rExETVqPmEBrw1SHny5nVwtEiv7L5NT1bJcCJ5+vRplS9fXpLk6+ury5dv/TJv3LhxhvaRHDdunFq3bi1PT0+NGzfujv0sFguJZCbat2ubIp59QSVKl1VycrK+mj5ZIwZ009hPv5Wnl5dd30U/zL7rTv9TxgxXaLGSuvDX2awLGtlaZMRDalurmHrN3KqDp66qYmiAxrZ9VFdu3NT0lb9Lkrw93LXp6Hkt2HZSH7Z5NM1xhrxQXnUfCdarn27UletJGvliRX36alU1/XDN/bwcZHN7du/Sd99+rYceKm1ri4+PV9cur+ih0g9r2vSZkqTJkyaoe2RXffnVN3Jzy/DkH/DAyHAiWahQIcXGxqpIkSIqUaKEli5dqkcffVSbN2+W1WpN9zjHjh1L82dkrbejJtk9j+w3RJ1eeFq/H96vshX+9xf08SMHtfC7aL03eZa6vJj2V18uXfCd4uOu6vk2nbV9M5vHw5wqxfPo552xWr7njCTpzwvxavJ4IVUK/d83ZX2/6Q9JUqHc3mmOkcszh1pWL6pu0zfrt4N/SZJ6z9qqNUOe1qPFArXt2MUsvgq4gvhr1zTwzX56d+gITft4iq19x/ZtOnXypOZ8N0++vr6SpOGj3tcTYY9r08YNqhZW3VEhIwMoSJqT4X8mPffcc1q+fLkkqXv37ho0aJBKlSqltm3b6pVXXjEVxLBhwxQfH5+q/fr16xo2bJipMZE+8dfiJEm+ufxsbQk3bmhC1Dvq2L2/AnKnPS3z54nf9d2X09TtzWGyuPF/H8zb8vt51Xw4n4oH3foLuGxBP/2nRB6t3Hsm3WNUCA2QRw43/XrgnK3t6Jk4/Xk+Xo8Vy53pMcM1jRoxTLVq1U6VGCYmJspiscjDw8PWZrVa5ebmpu3btt7vMIH7KsMVyffee8/284svvqjQ0FCtW7dOpUqV0jPPPGMqiKFDh6pr167y9ravNsTHx2vo0KEaPHiwqXHx71JSUjRjyhiVLldRRYqVtLXPnDpGpctW0OPVn0zzdTcTEzVh1Ntq07mn8gYF60zsn/cpYmRHH/18SL6eObX63XAlG4bcLRa9P3+f5m5O/+cqn5+nEm4m68r1m3bt567eUD4/z8wOGS7op8WLtH//Ps2ek3pv4woVK8nLy0vjx3yg7r36yDAMTRg3RsnJyTp37lwao8EZsf2POfe8j2S1atVUrVo1nT17VqNGjdJbb72V4TEMw0jzD3Dnzp3KnfvfqwkJCQlKSLBfTJ+YkCiPDEyzu6rPJr2vP44f1bBxn9ratqxbrT3bt2j01Og7vm729I9UsEhR1QpveD/CRDb3zGMF1ezxQor8fLMOnbqqcoX8NfSFCjpz+Ya+3RDj6PAAnY6N1ej3RurjadPTXMKVO3dufTB2gkYOH6LZ0V/Izc1N9Rs2Upmy5eTGjA2yuUzbkDw2NlaDBg3KUCIZGBgoi8Uii8Wihx56yC6ZTE5OVlxcnLp27fqvY0RFRWno0KF2ba/2GqDXemc8oXUln016X9s2rtXQMZ8oT778tvY9O7boTOyfat+0jl3/McP6q8wjlTRkzCfas32LYo4f0YY1VSVJhgxJUsfm4WrW6hW1aJfx71uH6xr03CP6aOkhzd9yUpJ04NQVFcrjrW4RD6U7kTx35YasOd3l55XTriqZL5enzl25kSVxw3Xs27dXF86fV8sXmtnakpOTtXXLZn39VbQ2b9+t6jVqatGSX3Tx4gW5u+eQn5+fnqpVQ4Ua8A/uBwW3RJnj0G+2GT9+vAzD0CuvvKKhQ4fK39/fdszDw0NFixa96zfcDBw4UH369LFrO3gm8Q69YRiGpn80Wpt+W6UhH36soAIF7Y43bdlOTzVoYtfWt0tLtevaR1WqPSFJeuPd0UpM+N9fzkcP7tOUMcM0bNw05S9QKOsvAtmKl0cOGYZ9W3KKkaGtOHaduKTEpBTVfDifFm8/JUkqkd9XhfJ4a+uxjH9JAvB3VatV03fzFti1vfv2QBUtXlwdOna22wM5MPDWLNrGDet14cJ5PVnnqfsaK3C/OTSRbNeunSSpWLFiql69unLmzJnhMaxWa6qpBo9LVzMlvuzos0nva+2KJeo/dIy8vL116cKtO1y9fXzlYfVUQO68ad5gkzco2JZ0BofYJ4tXr1ySJBUsUox9JJFhy3bHqkf90jp5IV4HT13VI4X91aVuSX297oStT4B3ThXM7a38/rfWO5bIf+vGnLNXbujclQRdvZGkr9cd17vNy+vStURdvZGkES0qaMvR89yxjXvm4+OrUqUesmvz8vZWgH+ArX3e3O9VvHgJBQbm1s6d2zU6apTatG2vosWKOyJkmMAaSXMclkheuXJFfn637hSuXLmyrl+/ruvXr6fZ93Y/3LulC24tFB/S1376+fW+7+rJCHM3SwH34p05u9T/2TIa1bKS8uSy6szl6/py7TGNW3TA1qdehQIa1+4x2/Mpnf4jSRqzcL/G/n+/Id/uVoohfdKlqqw53LRq31m99fWO+3otcF3Hjx3TxHFjdfnyZYUULKhOXbrq5XbtHR0WMoDlrOZYDOOfk0pp++f08T+dO3dOs2fPVnJycrpO7O7urtjYWAUFBcnNzS3NfwncvgknvWPetjOGiiScT8OoXxwdAmDn6KTnHB0CYMfTgfOkvX48cPdOJo1v8nCWje1o6f4j2759+1371KpVK90nXrFihe2O7JUrV6b7dQAAAJmNiqQ56U4kMzvZq127dpo/AwAA4MHgFHe7L1myRGvXrrU9nzx5sipVqqRWrVrp4kUWygMAgKx1ezvCrHhkZ06RSPbr109XrlyRJO3evVt9+vRRw4YNdezYsbuuzQQAAIBjOHT7n9uOHTumsmXLSpK+//57PfPMMxo1apS2bdumhg3ZzBUAAGQt1kia4xQVSQ8PD8XHx0uSfvnlF9WrV0/Sra+dul2pBAAAgHNxiopkzZo11adPH9WoUUObNm3SnDlzJEmHDh1SoUJ8UwoAAMha2XwpY5YxVZH89ddf1aZNG4WFhenkyVvfj/vFF1/Y3TCTER999JFy5Mih7777TlOmTFHBgre+QeWnn35S/fr1TY0JAACQXm4WS5Y9srMMVyS///57vfzyy2rdurW2b9+uhIQESdLly5c1atQoLV68OMNBFClSRAsXLkzVPm7cuAyPBQAAgPsjw4nkiBEjNHXqVLVt21Zff/21rb1GjRoaMWKE6UCSk5M1b9487d+/X5JUrlw5Pfvss3J3dzc9JgAAQHo4xU0jD6AMJ5IHDx5M8xts/P39denSJVNBHDlyRA0bNtTJkydVunRpSVJUVJQKFy6sRYsWqUSJEqbGBQAAQNbJcAIeHBysI0eOpGpfu3atihcvbiqIHj16qESJEvrjjz+0bds2bdu2TTExMSpWrJh69OhhakwAAID0sliy7pGdZbgi2blzZ/Xs2VPTp0+XxWLRqVOntH79evXt21eDBg0yFcTq1au1YcMG23dvS1KePHn03nvvqUaNGqbGBAAAQNbKcCI5YMAApaSkqG7duoqPj1etWrVktVrVt29fde/e3VQQVqtVV69eTdUeFxcnDw8PU2MCAACkV3a/uzqrZHhq22Kx6O2339aFCxe0Z88ebdiwQefOndPw4cNNB9G4cWN16dJFGzdulGEYMgxDGzZsUNeuXfXss8+aHhcAAABZx/SG5B4eHravNbxXEydOVPv27VW9enXlyHErpKSkJD377LOaMGFCppwDAADgTihImpPhRLJOnTqy/Mu7vWLFinSPlZKSog8++EDz589XYmKimjZtqnbt2slisahMmTIqWbJkRsMDAADIML5r25wMJ5KVKlWye37z5k3t2LFDe/bsUbt27TI01siRIzVkyBCFh4fLy8tLixcvlr+/v6ZPn57RsAAAAHCfZTiRvNO3zQwZMkRxcXEZGmvWrFn673//q1dffVWS9Msvv6hRo0b69NNP5ebG1qAAAOD+4GYbczItW2vTpk2GK4kxMTFq2LCh7Xl4eLhtSyEAAAA4N9M32/zT+vXr5enpmaHXJCUlpXpNzpw5dfPmzcwKCwAA4K4oSJqT4USyWbNmds8Nw1BsbKy2bNmS4Q3JDcNQ+/btZbVabW03btxQ165d5ePjY2v74YcfMhomAAAAsliGE0l/f3+7525ubipdurSGDRumevXqZWistG7OadOmTUZDAgAAuCfctW1OhhLJ5ORkdejQQeXLl1dgYOA9n/zzzz+/5zEAAADgGBm62cbd3V316tXTpUuXsigcAACA+8+Shf9lZxm+a/uRRx7R77//nhWxAAAAOISbJese2VmGE8kRI0aob9++WrhwoWJjY3XlyhW7BwAAAFxDutdIDhs2TG+88YZt38dnn33W7qsSDcOQxWJRcnJy5kcJAACQhbJ75TCrpDuRHDp0qLp27aqVK1dmZTwAAAB4QKQ7kTQMQ5JUu3btLAsGAADAESzsSG5KhtZI8iYDAADgtgztI/nQQw/dNZm8cOHCPQUEAABwv7FG0pwMJZJDhw5N9c02AAAAcE0ZSiRbtmypoKCgrIoFAADAIVi9Z066E0nWRwIAgOzKjTzHlHTfbHP7rm0AAABAykBFMiUlJSvjAAAAcBhutjEnw1+RCAAAAEgZvNkGAAAgO2KJpDlUJAEAAGAKFUkAAODy3ERJ0gwqkgAAADCFiiQAAHB5rJE0h0QSAAC4PLb/MYepbQAAAJhCRRIAALg8viLRHCqSAAAAMIWKJAAAcHkUJM2hIgkAAABTqEgCAACXxxpJc6hIAgAAwBQqkgAAwOVRkDSHRBIAALg8pmjN4X0DAACAKVQkAQCAy7Mwt20KFUkAAAAn9d5778lisahXr162ths3bigyMlJ58uSRr6+vmjdvrjNnzti9LiYmRo0aNZK3t7eCgoLUr18/JSUlZXp8JJIAAMDlWbLwYdbmzZv18ccfq0KFCnbtvXv31oIFC/Ttt99q9erVOnXqlJo1a2Y7npycrEaNGikxMVHr1q3TzJkzNWPGDA0ePPgeokkbiSQAAICTiYuLU+vWrTVt2jQFBgba2i9fvqzPPvtMY8eO1VNPPaXHHntMn3/+udatW6cNGzZIkpYuXap9+/bpyy+/VKVKldSgQQMNHz5ckydPVmJiYqbGSSIJAABcnpvFkmWPhIQEXblyxe6RkJDwr/FERkaqUaNGCg8Pt2vfunWrbt68adf+8MMPq0iRIlq/fr0kaf369Spfvrzy589v6xMREaErV65o7969mfiukUgCAABkqaioKPn7+9s9oqKi7tj/66+/1rZt29Lsc/r0aXl4eCggIMCuPX/+/Dp9+rStz9+TyNvHbx/LTNy1DQAAXF5W3rM9cOBA9enTx67NarWm2fePP/5Qz549tWzZMnl6emZhVJmDiiQAAHB5FkvWPaxWq/z8/Owed0okt27dqrNnz+rRRx9Vjhw5lCNHDq1evVoTJ05Ujhw5lD9/fiUmJurSpUt2rztz5oyCg4MlScHBwanu4r79/HafzEIiCQAA4CTq1q2r3bt3a8eOHbZHlSpV1Lp1a9vPOXPm1PLly22vOXjwoGJiYhQWFiZJCgsL0+7du3X27Flbn2XLlsnPz09ly5bN1HiZ2gYAAC7PWTYkz5Urlx555BG7Nh8fH+XJk8fW3rFjR/Xp00e5c+eWn5+funfvrrCwMFWrVk2SVK9ePZUtW1Yvv/yyRo8erdOnT+udd95RZGTkHSuhZpFIAgAAPEDGjRsnNzc3NW/eXAkJCYqIiNB///tf23F3d3ctXLhQr732msLCwuTj46N27dpp2LBhmR6LxTAMI9NHdbCdMVcdHQKQSsOoXxwdAmDn6KTnHB0CYMfTgeWtOdtPZtnYL1YumGVjOxprJAEAAGAKU9sAAMDlOcsayQcNFUkAAACYQkUSAAC4POqR5lCRBAAAgClUJAEAgMtjjaQ52TKRvJ6Y7OgQgFTYagXOpuF/1zs6BMDOih5hDjs3U7Tm8L4BAADAlGxZkQQAAMgIprbNoSIJAAAAU6hIAgAAl0c90hwqkgAAADCFiiQAAHB5LJE0h4okAAAATKEiCQAAXJ4bqyRNIZEEAAAuj6ltc5jaBgAAgClUJAEAgMuzMLVtChVJAAAAmEJFEgAAuDzWSJpDRRIAAACmUJEEAAAuj+1/zKEiCQAAAFOoSAIAAJfHGklzSCQBAIDLI5E0h6ltAAAAmEJFEgAAuDw2JDeHiiQAAABMoSIJAABcnhsFSVOoSAIAAMAUKpIAAMDlsUbSHCqSAAAAMIWKJAAAcHnsI2kOiSQAAHB5TG2bw9Q2AAAATKEiCQAAXB7b/5hDRRIAAACmUJEEAAAujzWS5lCRBAAAgClUJAEAgMtj+x9zqEgCAADAFCqSAADA5VGQNIdEEgAAuDw35rZNYWobAAAAplCRBAAALo96pDlUJAEAAGAKFUkAAABKkqZQkQQAAIApVCQBAIDL4ysSzaEiCQAAAFOoSAIAAJfHNpLmkEgCAACXRx5pDlPbAAAAMIWKJAAAACVJU6hIAgAAwBQqkgAAwOWx/Y85TlOR/PXXX9WmTRuFhYXp5MmTkqQvvvhCa9eudXBkAAAASItTJJLff/+9IiIi5OXlpe3btyshIUGSdPnyZY0aNcrB0QEAgOzOYsm6R3bmFInkiBEjNHXqVE2bNk05c+a0tdeoUUPbtm1zYGQAAAC4E6dYI3nw4EHVqlUrVbu/v78uXbp0/wMCAAAuJZsXDrOMU1Qkg4ODdeTIkVTta9euVfHixR0QEQAAcCmWLHxkY06RSHbu3Fk9e/bUxo0bZbFYdOrUKUVHR6tv37567bXXHB0eAAAA0uAUU9sDBgxQSkqK6tatq/j4eNWqVUtWq1V9+/ZV9+7dHR0eAADI5tj+xxynSCQtFovefvtt9evXT0eOHFFcXJzKli0rX19fR4cGAACAO3CKRPLLL79Us2bN5O3trbJlyzo6HAAA4GKy+zY9WcUp1kj27t1bQUFBatWqlRYvXqzk5GRHhwQAAIC7cIpEMjY2Vl9//bUsFotatGihAgUKKDIyUuvWrXN0aAAAwAVw07Y5TpFI5siRQ40bN1Z0dLTOnj2rcePG6fjx46pTp45KlCjh6PAAAACQBqdYI/l33t7eioiI0MWLF3XixAnt37/f0SEBAIDsLruXDrOI0ySS8fHxmjt3rqKjo7V8+XIVLlxYL730kr777jtHhwYAALI5tv8xxykSyZYtW2rhwoXy9vZWixYtNGjQIIWFhTk6LAAAAPwLp0gk3d3d9c033ygiIkLu7u6ODgcAALgYtv8xxykSyejoaEeHAAAAgAxyWCI5ceJEdenSRZ6enpo4ceK/9u3Ro8d9igoAALgiCpLmOCyRHDdunFq3bi1PT0+NGzfujv0sFguJJAAAgBNyWCJ57NixNH8GAAC47yhJmuIUG5IPGzZM8fHxqdqvX7+uYcOGOSAiAAAA3I3FMAzD0UG4u7srNjZWQUFBdu3nz59XUFBQhr97e8ORS5kYXfay4JsZ2rpulWL/PKGcHlaVKlNeLTp0U4FCobY+Z2L/1NefTdThvTt182aiyj8Wppe7viH/wDy2PvO//lw7N/+mmGOHlCNHTk35ZrkjLueBUqlogKNDeCB9Nu0TTRw/Rq3btFX/gW9Lkv6IidGYD9/Xjm1blZiYqBo1n9CAtwYpT968Do72wdLwv+sdHYLTyuvjoc41iug/oQHyzOmuk5duaPQvR3To7DVbnyKBXupSo4gqFPSTu5tFJy5c15BFB3U2LlGS1KhckOqWzqtSQT7y8cihZ6Zu0rXEjP195mpW9HDc1n97T167eyeTyhX0ybKxHc0pKpKGYciSxn33O3fuVO7cuR0QUfZ1cPd21W30vAaN+Uz9R0xUclKSPninhxJuXJckJdy4rg/e6SGLLHozarLe+XCakpNuatywvkpJSbGNk5R0U4/XrKunGjZ31KXABezZvUvfffu1HnqotK0tPj5eXbu8IovFomnTZ2rml1/p5s2b6h7Z1e4zCpjla3XXxBfKKTnF0MD5B9Thyx2auva44hKSbH1C/K2a8Hw5xVy8rj4/7FXn2Tv15aY/lZj8v8+gZ043bT5xSbM3n3TEZQD3hUO3/wkMDJTFYpHFYtFDDz1kl0wmJycrLi5OXbt2dWCE2U/f4RPsnnfqM1jdW9XXsSMH9PAjlXVo3079dTZWwyfNkpe3rySpc5939fqL4dq/c4vKVf6PJKlZmy6SpF+XLby/FwCXEX/tmga+2U/vDh2haR9PsbXv2L5Np06e1Jzv5snX99ZndPio9/VE2OPatHGDqoVVd1TIyCZeeqygzl5N1OhfjtraTl9JsOvzSlgRbTpxSZ/8FmNrO3XZvs/3O05LkioW9MvCaJFZ2EfSHIcmkuPHj5dhGHrllVc0dOhQ+fv72455eHioaNGifMNNFrt+LU6S5Ot76xdd0s2bssiiHDk9bH1yenjIYnHToX07bYkkkNVGjRimWrVqq1pYdbtEMjExURaLRR4e//uMWq1Wubm5afu2rSSSuGdhxQO15cQlvdvgIVUo6Ke/riVq/q7TWrT3rKRb92RUKxqor7ee1PtNyqhkPh+dvnJDs7ec1G+/X3Rs8DCNPNIchyaS7dq1kyQVK1ZM1atXV86cOR0ZjstJSUlR9CfjVKpsBRUqWkKSVOLhR2T19NQ3n3+k59u+LsnQN59PVkpKsi5d+MuxAcNl/LR4kfbv36fZc75LdaxCxUry8vLS+DEfqHuvPjIMQxPGjVFycrLOnTvngGiR3YT4eerZ8sH6dvspRW/5U6WDfNWtdjHdTDa09MA5BXjnlLeHu16qUlCfr/9Dn/x2Qv8JDdDQRqXV54d92nXyiqMvAbhvnOKbbWrXrm37+caNG0pMTLQ77ud352mBhIQEJSTYTyckJiTIw2rN3CCzoVlTPtDJE7/r7Q8+trX5+QcqcuAozZw8WsvmfyOLxU3Vaj+t0BKlZXFziiW1yOZOx8Zq9Hsj9fG06bKm8f9x7ty59cHYCRo5fIhmR38hNzc31W/YSGXKlpObGzUF3DuLRTp09po+W/+HJOnIuXgVy+OtZ8rn19ID53T7Y7bu94v6bkesJOnoX/EqVyCXnn0kP4nkg4pfH6Y4RSIZHx+v/v3765tvvtH58+dTHf+3u7ajoqI0dOhQu7aO3d9Upx4DMj3O7GTWlA+0c9NavfX+x8qdN7/dsfKPVtOHn/2gq5cvyc3dXT6+udSjdQMFBYc4KFq4kn379urC+fNq+UIzW1tycrK2btmsr7+K1ubtu1W9Rk0tWvKLLl68IHf3HPLz89NTtWqoUIOGDowc2cWFazd1/IL9lnQxF6+rVslbO1dcvp6kpOQUnfhHnxMXrqt8SK77FifgDJyixNSvXz+tWLFCU6ZMkdVq1aeffqqhQ4cqJCREs2bN+tfXDhw4UJcvX7Z7tH21932K/MFjGIZmTflAW9ev1pujJivfvySHufwD5OObS/t2btGVyxdVuWqt+xgpXFXVatX03bwFmvP9PNujXLlH1LDxM5rz/Ty5u7vb+gYG5pafn582blivCxfO68k6TzkwcmQXe2KvqnCAl11boQBPnbl6a/YrKcXQwbPXVDjQvk/hQC+duWo/o4YHhyUL/8uIqKgoPf7448qVK5eCgoLUtGlTHTx40K7PjRs3FBkZqTx58sjX11fNmzfXmTNn7PrExMSoUaNG8vb2VlBQkPr166ekpCRlNqeoSC5YsECzZs3Sk08+qQ4dOuiJJ55QyZIlFRoaqujoaLVu3fqOr7VarammvzysbAFyJ7P++4E2rP5ZPQd9IE8vH126cKsC7O3jIw+rpyRpzbIFCilcVLn8A3Vk/25FfzJWEU1fsttr8vzZ04q7ekXnz51WSkqKThw9JEnKH1JInl7e9//CkG34+PiqVKmH7Nq8vL0V4B9ga58393sVL15CgYG5tXPndo2OGqU2bduraLHijggZ2cx3209p0guPqFWVglp1+Lwezu+rRo/k19gVv9v6zNl6SoMalNKuk1e0/c8r+k9ogMKKBar393ttfQK9cyq3d04VDLj1u7V4Xm/FJybr7NVEXU3I/L/QkT2sXr1akZGRevzxx5WUlKS33npL9erV0759++Tjc2s/yt69e2vRokX69ttv5e/vr27duqlZs2b67bffJN2axWnUqJGCg4O1bt06xcbGqm3btsqZM6dGjRqVqfE6xYbkvr6+2rdvn4oUKaJChQrphx9+0H/+8x8dO3ZM5cuXV1xcXIbGY0PyO2vXqGqa7Z16DdITTzeWJH3z+WSt/WWh4uKuKG9QAT3VsJkimr5ktz3TtLHDtHb5olTjDIj6r8pUeCxrgn/AsSG5eR3bv6zSpR+2bUg+fuyHmj9vri5fvqyQggX1QouWerld+zT3o8WdsSH5nVUrGqBO1UNVKMBTsVdu6Lvtsba7tm+rXzafWlUpqHy+Vv1x8bpmbPxD6/5213a7qoXUrmrhVGO/v+yIft7PjWFpceSG5AdPp/6GvcxSOth8geXcuXMKCgrS6tWrVatWLV2+fFn58uXT7Nmz9fzzz0uSDhw4oDJlymj9+vWqVq2afvrpJzVu3FinTp1S/vy3lq9NnTpVb775ps6dO2e368W9copEskKFCpo0aZJq166t8PBwVapUSR9++KEmTpyo0aNH688//8zQeCSScEYkknA2JJJwNtk1kSwa6J7qxuC0ZlTTcuTIEZUqVUq7d+/WI488ohUrVqhu3bq6ePGiAgICbP1CQ0PVq1cv9e7dW4MHD9b8+fO1Y8cO2/Fjx46pePHi2rZtmypXrpxZl+YcayQ7dOignTt3SpIGDBigyZMny9PTU71791a/fv0cHB0AAMjuLFn4iIqKkr+/v90jKirqrjGlpKSoV69eqlGjhh555BFJ0unTp+Xh4WGXREpS/vz5dfr0aVuf25XIvx+/fSwzOcUayd69/3dzTHh4uA4cOKCtW7eqZMmSqlChggMjAwAALiELV8YMHDhQffr0sWtLTzUyMjJSe/bs0dq1a7MqtHvmFInkP4WGhio0NPTuHQEAAJxceqex/65bt25auHCh1qxZo0KFCtnag4ODlZiYqEuXLtlVJc+cOaPg4GBbn02bNtmNd/uu7tt9MotTJJITJ05Ms91iscjT01MlS5ZUrVq17Lb9AAAAyCwZ3aYnqxiGoe7du2vu3LlatWqVihUrZnf8scceU86cObV8+XI1b95cknTw4EHFxMTYvlY6LCxMI0eO1NmzZxUUFCRJWrZsmfz8/FS2bNlMjdcpEslx48bp3Llzio+PV2BgoCTp4sWL8vb2lq+vr86ePavixYtr5cqVKlw49R1wAAAA2UFkZKRmz56tH3/8Ubly5bKtafT395eXl5f8/f3VsWNH9enTR7lz39pLt3v37goLC1O1atUkSfXq1VPZsmX18ssva/To0Tp9+rTeeecdRUZGZrgyejdOcbPNqFGj9Pjjj+vw4cM6f/68zp8/r0OHDqlq1aqaMGGCYmJiFBwcbLeWEgAAILNYLFn3yIgpU6bo8uXLevLJJ1WgQAHbY86cObY+48aNU+PGjdW8eXPVqlVLwcHB+uGHH2zH3d3dtXDhQrm7uyssLExt2rRR27ZtNWzYsMx6u2ycYvufEiVK6Pvvv1elSpXs2rdv367mzZvr999/17p169S8eXPFxsbedTy2/4EzYvsfOBu2/4GzceT2P0fOXs+ysUsGed290wPKKaa2Y2Nj0/zanqSkJFtJNyQkRFevXr3foQEAABfgHCskHzxOMbVdp04dvfrqq9q+fbutbfv27Xrttdf01FO3vjt39+7dqRacAgAAwHGcIpH87LPPlDt3bj322GO2W+SrVKmi3Llz67PPPpN062sUx4wZ4+BIAQBAtpSVO5JnY04xtR0cHKxly5bpwIEDOnTokCSpdOnSKl26tK1PnTp1HBUeAADI5pxl+58HjVMkkrcVL15cFotFJUqUUI4cThUaAAAA/sEpprbj4+PVsWNHeXt7q1y5coqJiZEkde/eXe+9956DowMAANmds2z/86BxikRy4MCB2rlzp1atWiVPT09be3h4uN2+SQAAAHAeTjF/PG/ePM2ZM0fVqlWT5W+pe7ly5XT06FEHRgYAAFxBNi8cZhmnqEieO3fO9l2Qf3ft2jW7xBIAAADOwykSySpVqmjRokW257eTx08//dT2BeQAAABZhu1/THGKqe1Ro0apQYMG2rdvn5KSkjRhwgTt27dP69at0+rVqx0dHgAAANLgFBXJmjVraseOHUpKSlL58uW1dOlSBQUFaf369XrsscccHR4AAMjmLFn4X3bmFBVJSSpRooSmTZvm6DAAAIAL4pYMcxyaSLq5ud31ZhqLxaKkpKT7FBEAAADSy6GJ5Ny5c+94bP369Zo4caJSUlLuY0QAAMAVUZA0x6GJZJMmTVK1HTx4UAMGDNCCBQvUunVrDRs2zAGRAQAA4G6c4mYbSTp16pQ6d+6s8uXLKykpSTt27NDMmTMVGhrq6NAAAEA2x1ckmuPwRPLy5ct68803VbJkSe3du1fLly/XggUL9Mgjjzg6NAAAAPwLh05tjx49Wu+//76Cg4P11VdfpTnVDQAAkPWyeekwi1gMwzAcdXI3Nzd5eXkpPDxc7u7ud+z3ww8/ZGjcDUcu3WNkQOarVDTA0SEAdhr+d72jQwDsrOjhuG+z+/NiYpaNXSjQI8vGdjSHViTbtm3Ld2kDAACHIx0xx6GJ5IwZMxx5egAAAElMbJvl8JttAAAA8GBymq9IBAAAcBSmts2hIgkAAABTqEgCAACXZ2GVpClUJAEAAGAKFUkAAAAKkqZQkQQAAIApVCQBAIDLoyBpDokkAABweWz/Yw5T2wAAADCFiiQAAHB5bP9jDhVJAAAAmEJFEgAAgIKkKVQkAQAAYAoVSQAA4PIoSJpDRRIAAACmUJEEAAAuj30kzSGRBAAALo/tf8xhahsAAACmUJEEAAAuj6ltc6hIAgAAwBQSSQAAAJhCIgkAAABTWCMJAABcHmskzaEiCQAAAFOoSAIAAJfHPpLmkEgCAACXx9S2OUxtAwAAwBQqkgAAwOVRkDSHiiQAAABMoSIJAABASdIUKpIAAAAwhYokAABweWz/Yw4VSQAAAJhCRRIAALg89pE0h4okAAAATKEiCQAAXB4FSXNIJAEAAMgkTWFqGwAAAKZQkQQAAC6P7X/MoSIJAAAAU6hIAgAAl8f2P+ZQkQQAAIApFsMwDEcHAeeUkJCgqKgoDRw4UFar1dHhAHwm4ZT4XMKVkUjijq5cuSJ/f39dvnxZfn5+jg4H4DMJp8TnEq6MqW0AAACYQiIJAAAAU0gkAQAAYAqJJO7IarXq3XffZfE4nAafSTgjPpdwZdxsAwAAAFOoSAIAAMAUEkkAAACYQiIJAAAAU0gk4RCrVq2SxWLRpUuXHB0KHgDp/bwULVpU48ePvy8xAWbxOUV2QiL5gGvfvr0sFovee+89u/Z58+bJkonfQH/8+HFZLBbt2LEj08ZE9nP782ixWOTh4aGSJUtq2LBhSkpKuqdxq1evrtjYWPn7+0uSZsyYoYCAgFT9Nm/erC5dutzTufBgu1+/E9ODzylcAYlkNuDp6an3339fFy9edHQoSkxMdHQIcLD69esrNjZWhw8f1htvvKEhQ4bogw8+uKcxPTw8FBwcfNdEIF++fPL29r6nc+HB50y/E9PC5xTZCYlkNhAeHq7g4GBFRUXdsc/atWv1xBNPyMvLS4ULF1aPHj107do123GLxaJ58+bZvSYgIEAzZsyQJBUrVkySVLlyZVksFj355JOSbv3rv2nTpho5cqRCQkJUunRpSdIXX3yhKlWqKFeuXAoODlarVq109uzZzLtoOC2r1arg4GCFhobqtddeU3h4uObPn6+LFy+qbdu2CgwMlLe3txo0aKDDhw/bXnfixAk988wzCgwMlI+Pj8qVK6fFixdLsp/aXrVqlTp06KDLly/bqp9DhgyRZD9l2KpVK7344ot2sd28eVN58+bVrFmzJEkpKSmKiopSsWLF5OXlpYoVK+q7777L+jcJWSozfifGxsaqUaNG8vLyUrFixTR79uxUU9Jjx45V+fLl5ePjo8KFC+v1119XXFycJPE5hcsgkcwG3N3dNWrUKE2aNEl//vlnquNHjx5V/fr11bx5c+3atUtz5szR2rVr1a1bt3SfY9OmTZKkX375RbGxsfrhhx9sx5YvX66DBw9q2bJlWrhwoaRbvwiHDx+unTt3at68eTp+/Ljat29/bxeKB5KXl5cSExPVvn17bdmyRfPnz9f69etlGIYaNmyomzdvSpIiIyOVkJCgNWvWaPfu3Xr//ffl6+ubarzq1atr/Pjx8vPzU2xsrGJjY9W3b99U/Vq3bq0FCxbY/mKXpJ9//lnx8fF67rnnJElRUVGaNWuWpk6dqr1796p3795q06aNVq9enUXvBu6HzPid2LZtW506dUqrVq3S999/r08++STVP4bd3Nw0ceJE7d27VzNnztSKFSvUv39/SXxO4UIMPNDatWtnNGnSxDAMw6hWrZrxyiuvGIZhGHPnzjVu//F27NjR6NKli93rfv31V8PNzc24fv26YRiGIcmYO3euXR9/f3/j888/NwzDMI4dO2ZIMrZv357q/Pnz5zcSEhL+Nc7NmzcbkoyrV68ahmEYK1euNCQZFy9ezOAVw5n9/fOYkpJiLFu2zLBarUbTpk0NScZvv/1m6/vXX38ZXl5exjfffGMYhmGUL1/eGDJkSJrj/vPz8vnnnxv+/v6p+oWGhhrjxo0zDMMwbt68aeTNm9eYNWuW7fhLL71kvPjii4ZhGMaNGzcMb29vY926dXZjdOzY0XjppZfMXD6cQGb8Tty/f78hydi8ebPt+OHDhw1Jts9XWr799lsjT548tud8TuEKcjgqgUXme//99/XUU0+l+lfvzp07tWvXLkVHR9vaDMNQSkqKjh07pjJlytzTecuXLy8PDw+7tq1bt2rIkCHauXOnLl68qJSUFElSTEyMypYte0/ng3NbuHChfH19dfPmTaWkpKhVq1Zq1qyZFi5cqKpVq9r65cmTR6VLl9b+/fslST169NBrr72mpUuXKjw8XM2bN1eFChVMx5EjRw61aNFC0dHRevnll3Xt2jX9+OOP+vrrryVJR44cUXx8vJ5++mm71yUmJqpy5cqmzwvnYfZ34qFDh5QjRw49+uijtuMlS5ZUYGCg3Ti//PKLoqKidODAAV25ckVJSUm6ceOG4uPj070Gks8pHnQkktlIrVq1FBERoYEDB9pNI8fFxenVV19Vjx49Ur2mSJEikm6tkTT+8W2Zt6cc78bHx8fu+bVr1xQREaGIiAhFR0crX758iomJUUREBDfjuIA6depoypQp8vDwUEhIiHLkyKH58+ff9XWdOnVSRESEFi1apKVLlyoqKkpjxoxR9+7dTcfSunVr1a5dW2fPntWyZcvk5eWl+vXrS5JtKnHRokUqWLCg3ev4zuTswezvxEOHDt117OPHj6tx48Z67bXXNHLkSOXOnVtr165Vx44dlZiYmKGbafic4kFGIpnNvPfee6pUqZLtphdJevTRR7Vv3z6VLFnyjq/Lly+fYmNjbc8PHz6s+Ph42/PbFcfk5OS7xnDgwAGdP39e7733ngoXLixJ2rJlS4avBQ8mHx+fVJ+1MmXKKCkpSRs3blT16tUlSefPn9fBgwftKtSFCxdW165d1bVrVw0cOFDTpk1LM5H08PBI12exevXqKly4sObMmaOffvpJL7zwgnLmzClJKlu2rKxWq2JiYlS7du17uWQ4MTO/E0uXLq2kpCRt375djz32mKRblcG/3wW+detWpaSkaMyYMXJzu3W7wTfffGM3Dp9TuAISyWymfPnyat26tSZOnGhre/PNN1WtWjV169ZNnTp1ko+Pj/bt26dly5bpo48+kiQ99dRT+uijjxQWFqbk5GS9+eabtl9kkhQUFCQvLy8tWbJEhQoVkqenp21Pv38qUqSIPDw8NGnSJHXt2lV79uzR8OHDs/bC4dRKlSqlJk2aqHPnzvr444+VK1cuDRgwQAULFlSTJk0kSb169VKDBg300EMP6eLFi1q5cuUdl10ULVpUcXFxWr58uSpWrChvb+87VoBatWqlqVOn6tChQ1q5cqWtPVeuXOrbt6969+6tlJQU1axZU5cvX9Zvv/0mPz8/tWvXLvPfCNx3Zn4nPvzwwwoPD1eXLl00ZcoU5cyZU2+88Ya8vLxsW1CVLFlSN2/e1KRJk/TMM8/ot99+09SpU+3OzecULsHBazRxj/6+sPy2Y8eOGR4eHsbf/3g3bdpkPP3004avr6/h4+NjVKhQwRg5cqTt+MmTJ4169eoZPj4+RqlSpYzFixfb3WxjGIYxbdo0o3Dhwoabm5tRu3btO57fMAxj9uzZRtGiRQ2r1WqEhYUZ8+fPt7tZh5ttsqc7fR4MwzAuXLhgvPzyy4a/v7/h5eVlREREGIcOHbId79atm1GiRAnDarUa+fLlM15++WXjr7/+Mgwj7c9L165djTx58hiSjHfffdcwDPubGG7bt2+fIckIDQ01UlJS7I6lpKQY48ePN0qXLm3kzJnTyJcvnxEREWGsXr36nt8LOEZm/U48deqU0aBBA8NqtRqhoaHG7NmzjaCgIGPq1Km2PmPHjjUKFChg+zzPmjWLzylcjsUw/rEwDgAA2Pnzzz9VuHBh/fLLL6pbt66jwwGcBokkAAD/sGLFCsXFxal8+fKKjY1V//79dfLkSR06dMhu2Q/g6lgjCQDAP9y8eVNvvfWWfv/9d+XKlUvVq1dXdHQ0SSTwD1QkAQAAYApfkQgAAABTSCQBAABgCokkAAAATCGRBAAAgCkkkgAAADCFRBKAae3bt1fTpk1tz5988kn16tXrvsexatUqWSwWXbp0KcvO8c9rNeN+xAkA9xOJJJDNtG/fXhaLRRaLRR4eHipZsqSGDRumpKSkLD/3Dz/8kO7vVb/fSVXRokU1fvz4+3IuAHAVbEgOZEP169fX559/roSEBC1evFiRkZHKmTOnBg4cmKpvYmKiPDw8MuW8uXPnzpRxAAAPBiqSQDZktVoVHBys0NBQvfbaawoPD9f8+fMl/W+KduTIkQoJCVHp0qUlSX/88YdatGihgIAA5c6dW02aNNHx48dtYyYnJ6tPnz4KCAhQnjx51L9/f/3z+wz+ObWdkJCgN998U4ULF5bValXJkiX12Wef6fjx46pTp44kKTAwUBaLRe3bt5ckpaSkKCoqSsWKFZOXl5cqVqyo7777zu48ixcv1kMPPSQvLy/VqVPHLk4zkpOT1bFjR9s5S5curQkTJqTZd+jQocqXL5/8/PzUtWtXJSYm2o6lJ/a/O3HihJ555hkFBgbKx8dH5cqV0+LFi+/pWgDgfqIiCbgALy8vnT9/3vZ8+fLl8vPz07JlyyTd+jq4iIgIhYWF6ddff1WOHDk0YsQI1a9fX7t27ZKHh4fGjBmjGTNmaPr06SpTpozGjBmjuXPn6qmnnrrjedu2bav169dr4sSJqlixoo4dO6a//vpLhQsX1vfff6/mzZvr4MGD8vPzk5eXlyQpKipKX375paZOnapSpUppzZo1atOmjfLly6fatWvrjz/+ULNmzRQZGakuXbpoy5YteuONN+7p/UlJSVGhQoX07bffKk+ePFq3bp26dOmiAgUKqEWLFnbvm6enp1atWqXjx4+rQ4cOypMnj0aOHJmu2P8pMjJSiYmJWrNmjXx8fLRv3z75+vre07UAwH1lAMhW2rVrZzRp0sQwDMNISUkxli1bZlitVqNv37624/nz5zcSEhJsr/niiy+M0qVLGykpKba2hIQEw8vLy/j5558NwzCMAgUKGKNHj7Ydv3nzplGoUCHbuQzDMGrXrm307NnTMAzDOHjwoCHJWLZsWZpxrly50pBkXLx40dZ248YNw9vb21i3bp1d344dOxovvfSSYRiGMXDgQKNs2bJ2x998881UY/1TaGioMW7cuDse/6fIyEijefPmtuft2rUzcufObVy7ds3WNmXKFMPX19dITk5OV+z/vOby5csbQ4YMSXdMAOBsqEgC2dDChQvl6+urmzdvKiUlRa1atdKQIUNsx8uXL2+3LnLnzp06cuSIcuXKZTfOjRs3dPToUV2+fFmxsbGqWrWq7ViOHDlUpUqVVNPbt+3YsUPu7u5pVuLu5MiRI4qPj9fTTz9t156YmKjKlStLkvbv328XhySFhYWl+xx3MnnyZE2fPl0xMTG6fv26EhMTValSJbs+FStWlLe3t9154+Li9McffyguLu6usf9Tjx499Nprr2np0qUKDw9X8+bNVaFChXu+FgC4X0gkgWyoTp06mjJlijw8PBQSEqIcOez/V/fx8bF7HhcXp8cee0zR0dGpxsqXL5+pGG5PVWdEXFycJGnRokUqWLCg3TGr1WoqjvT4+uuv1bdvX40ZM0ZhYWHKlSuXPvjgA23cuDHdY5iJvVOnToqIiNCiRYu0dOlSRUVFacyYMerevbv5iwGA+4hEEsiGfHx8VLJkyXT3f/TRRzVnzhwFBQXJz88vzT4FChTQxo0bVatWLUlSUlKStm7dqkcffTTN/uXLl1dKSopWr16t8PDwVMdvV0STk5NtbWXLlpXValVMTMwdK5llypSx3Th024YNG+5+kf/it99+U/Xq1fX666/b2o4ePZqq386dO3X9+nVbkrxhwwb5+vqqcOHCyp07911jT0vhwoXVtWtXde3aVQMHDtS0adNIJAE8MLhrG4Bat26tvHnzqkmTJvr111917NgxrVq1Sj169NCff/4pSerZs6fee+89zZs3TwcOHNDrr7/+r3tAFi1aVO3atdMrr7yiefPm2cb85ptvJEmhoaGyWCxauHChzp07p7i4OOXKlUt9+/ZV7969NXPmTB09elTbtm3TpEmTNHPmTElS165ddfjwYfXr108HDx7U7NmzNWPGjHRd58mTJ7Vjxw67x8WLF1WqVClt2bJFP//8sw4dOqRBgwZp8+bNqV6fmJiojh07at++fVq8eLHeffdddevWTW5ubumK/Z969eqln3/+WceOHdO2bdu0cuVKlSlTJl3XAgBOwdGLNAFkrr/fbJOR47GxsUbbtm2NvHnzGlar1ShevLjRuXNn4/Lly4Zh3Lq5pmfPnoafn58REBBg9OnTx2jbtu0db7YxDMO4fv260bt3b6NAgQKGh4eHUbJkSWP69Om248OGDTOCg4MNi8VitGvXzjCMWzcIjR8/3ihdurSRM2dOI1++fEZERISxevVq2+sWLFhglCxZ0rBarcYTTzxhTJ8+PV0320hK9fjiiy+MGzduGO3btzf8/f2NgIAA47XXXjMGDBhgVKxYMdX7NnjwYCNPnjyGr6+v0blzZ+PGjRu2PneL/Z8323Tr1s0oUaKEYbVajXz58hkvv/yy8ddff93xGgDA2VgM4w4r5QEAAIB/wdQ2AAAATCGRBAAAgCkkkgAAADCFRBIAAACmkEgCAADAFBJJAAAAmEIiCQAAAFNIJAEAAGAKiSQAAABMIZEEAACAKSSSAAAAMOX/AD3LwgrDOtZ7AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "# Initialize the SVM model\n",
        "svm_classifier = SVC(kernel='linear', random_state=42)\n",
        "\n",
        "# Train the model\n",
        "svm_classifier.fit(X_train_vectorized, y_train_augmented)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred_svm = svm_classifier.predict(X_test_vectorized)\n",
        "\n",
        "# Analyze the results for SVM\n",
        "accuracy_svm, precision_svm, recall_svm, f1_score_svm = analyze_results(y_test, y_pred_svm)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-nx7VsP-iLuf"
      },
      "source": [
        "<h2> Ensemble Learning - AdaBoost Algorithm </h2>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "\n",
        "# Initialize the AdaBoost Classifier with a base estimator (e.g., Decision Tree)\n",
        "adaboost_classifier = AdaBoostClassifier(n_estimators=50, random_state=42)\n",
        "\n",
        "# Train the model\n",
        "adaboost_classifier.fit(X_train_vectorized, y_train_augmented)\n",
        "\n",
        "# Make predictions on the testing data\n",
        "y_pred_adaboost = adaboost_classifier.predict(X_test_vectorized)\n",
        "\n",
        "# Analyze the results for AdaBoost\n",
        "accuracy_adaboost, precision_adaboost, recall_adaboost, f1_score_adaboost = analyze_results(y_test, y_pred_adaboost)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "clvW6yOQEUcx",
        "outputId": "aa0c76b9-28c8-4e0c-d35a-320e310313c5"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.670345\n",
            "Precision: 0.692639\n",
            "Recall: 0.670345\n",
            "F1 Score: 0.665568\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.59      0.79      0.68      1430\n",
            "           1       0.76      0.70      0.73      1103\n",
            "           2       0.76      0.47      0.58      1001\n",
            "\n",
            "    accuracy                           0.67      3534\n",
            "   macro avg       0.70      0.65      0.66      3534\n",
            "weighted avg       0.69      0.67      0.67      3534\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a DataFrame to store the accuracy scores of each model\n",
        "model_accuracies = pd.DataFrame({\n",
        "    'Model': ['Logistic Regression', 'Random Forest', 'SVM', 'AdaBoost'],\n",
        "    'Accuracy': [accuracy_lr, accuracy_rf, accuracy_svm, accuracy_adaboost]\n",
        "})\n",
        "\n",
        "# Plotting the accuracies using a bar chart\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x='Model', y='Accuracy', data=model_accuracies)\n",
        "plt.title('Model Accuracies')\n",
        "plt.xlabel('Machine Learning Model')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0, 1)  # Set y-axis limits to 0-1 for accuracy\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "7979RVmEckMW",
        "outputId": "d3b73a0b-f512-4f6d-a69c-94e0d31741e8"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RwO9QQlEil9K"
      },
      "source": [
        "<h1> LLM Models <h1>\n",
        "\n",
        "\n",
        "<h2> Transformers <h2>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from transformers import pipeline\n",
        "\n",
        "# # Load a pre-trained sentiment analysis model\n",
        "# classifier = pipeline(\"sentiment-analysis\")\n",
        "\n",
        "# # Function to predict sentiment using the LLM model\n",
        "# def predict_sentiment_llm(text):\n",
        "#     result = classifier(text)\n",
        "#     if result[0]['label'] == 'POSITIVE':\n",
        "#         return 1\n",
        "#     elif result[0]['label'] == 'NEUTRAL':\n",
        "#         return 0\n",
        "#     else:\n",
        "#         return 2\n",
        "\n",
        "# # Apply the LLM model to the test dataset\n",
        "# y_pred_llm = [predict_sentiment_llm(text) for text in data_test['text']]\n",
        "\n",
        "\n",
        "# # Assuming your y_test corresponds to the cleaned and tokenized x_test\n",
        "# y_test = data_test['sentiment']  # This should be the actual sentiment labels for your test data\n",
        "# x_test = data_test['text']  # This is the original text data for predictions\n",
        "\n",
        "# # Predict using the LLM model\n",
        "# y_pred_llm = [predict_sentiment_llm(text) for text in x_test]\n",
        "\n",
        "# # Ensure lengths are consistent\n",
        "# if len(y_test) != len(y_pred_llm):\n",
        "#     print(f\"Length of y_test: {len(y_test)}, Length of y_pred_llm: {len(y_pred_llm)}\")\n",
        "# else:\n",
        "#     # Analyze the results\n",
        "#     analyze_results(y_test, y_pred_llm)\n",
        "\n",
        "# unique_predictions = set(y_pred_llm)\n",
        "# print(f\"Unique predictions: {unique_predictions}\")\n",
        "\n",
        "# # Check if the model predicts at least one of each sentiment\n",
        "# if 0 in unique_predictions and 1 in unique_predictions and 2 in unique_predictions:\n",
        "#     print(\"The model predicts at least one example of each sentiment (neutral, positive, negative).\")\n",
        "# else:\n",
        "#     print(\"The model does not predict all three sentiments.\")\n"
      ],
      "metadata": {
        "id": "a7pqm6CFKTpL"
      },
      "execution_count": 33,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOCKJ2vOggVAeGWMvveYWEo"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}